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1. Introduction This report provides a comprehensive analysis of the competitive moat surrounding Nvidia's artificial intelligence (AI) hardware and software ecosystem, assessing its trajectory over the past 24 months. The central finding is that Nvidia's integrated moat has demonstrably widened. This expansion is not uniform across all dimensions of its business but is powerfully driven by an accelerating cadence of hardware innovation, a widening performance gap in the most advanced AI workloads, and a deepening, strategic control over the critical nodes of the advanced semiconductor manufacturing supply chain. While the overall breadth and depth of the moat have increased, its composition is undergoing a significant transformation. The software component, centered on the proprietary CUDA platform, was once considered an unassailable fortress. It now faces its most credible and systemic challenges to date. These pressures arise from the maturation of competitive software stacks, most notably AMD's ROCm, and the burgeoning adoption of hardware-agnostic abstraction layers like OpenAI's Triton and open standards such as SYCL. These forces are actively working to commoditize the underlying hardware by reducing software lock-in. However, this narrowing of the software moat has been more than offset by a simultaneous and dramatic widening of the hardware performance gap. Nvidia's latest architectures are not just incrementally better; they are delivering order-of-magnitude improvements in performance and efficiency on the next-generation AI tasks, such as complex reasoning, that will define the market's future. The competitive landscape has evolved from a near-monopoly to a state of dominant market leadership. Competitors, particularly AMD and Intel, have successfully fielded viable hardware alternatives. These products offer compelling price-performance characteristics in specific market segments, thereby eroding the perception of Nvidia as the only choice. They have secured important design wins with major cloud providers and OEMs, establishing a foothold in the market. Nevertheless, they remain, by objective measures, a full architectural generation behind Nvidia in terms of peak performance, system-level integration, and overall ecosystem maturity. The strategic outlook for Nvidia's dominance appears secure for the immediate 24 to 36-month horizon. This position is firmly underpinned by the aggressive Blackwell and Rubin product roadmaps and the company's commanding control over TSMC's advanced CoWoS packaging capacity. The long-term sustainability of its moat will be contingent on its ability to successfully transition its primary software advantage away from the proprietary, low-level CUDA API and toward a higher-level, platform-centric value proposition, exemplified by its AI Enterprise suite and NVIDIA Inference Microservices (NIMs). This strategic shift is necessary to counter the commoditizing influence of open software standards. Finally, significant structural risks persist, with high customer concentration and geopolitical constraints representing the most potent potential disruptors to its continued market supremacy. 2. Anatomy of Nvidia's AI Moat To assess the trajectory of Nvidia's competitive advantage, it is first necessary to dissect its constituent components. The company's moat is not a single wall but a multi-layered defense system, integrating silicon architecture, a pervasive software ecosystem, and system-level engineering into a cohesive and self-reinforcing platform. The efficacy of this platform is most clearly reflected in its extraordinary financial performance. 2a. Architectural Supremacy from Hopper to Rubin The most tangible element of Nvidia's moat is its consistent delivery of market-leading semiconductor hardware. This dominance is not static; it is defined by a relentless pace of innovation that perpetually raises the bar for competitors. The financial manifestation of this hardware supremacy is stark. Nvidia's Data Center business segment has experienced a period of explosive, almost unprecedented, growth. In the second quarter of fiscal year 2025 (Q2 FY25), Data Center revenue reached $26.3 billion, a remarkable 154% increase year-over-year. This momentum continued unabated, with the segment's revenue growing to $35.6 billion in Q4 FY25 and reaching a staggering $41.1 billion by Q2 FY26, representing a 56% year-over-year increase on an already massive base. This financial trajectory serves as the clearest top-line indicator of the moat's effectiveness in capturing the vast majority of the market's AI infrastructure spending. Underpinning this financial success is an aggressive innovation cadence, which CEO Jensen Huang has characterized as a "one-year-rhythm." The transition from the highly successful Hopper architecture to the next-generation Blackwell platform, which commenced production shipments in Q2 FY26, is a testament to this pace. More significantly, the company has already disclosed that the chips for its next architecture, codenamed Rubin, are already "in fab". This strategy of pre-announcing future generations serves a critical competitive function: it signals to customers that any investment in competing hardware risks rapid obsolescence and assures them that the Nvidia platform will remain at the performance frontier. This creates a perpetually moving target for rivals, forcing them to compete not with what Nvidia is selling today, but with what it will be selling in 12 to 24 months. At its core, the hardware moat is built on raw performance and efficiency. The Blackwell platform represents a significant leap over Hopper. The GB300 system, for instance, promises a "10x improvement in token per watt energy efficiency". This is a crucial metric, as power consumption and the associated operational costs have become the primary limiting factor in scaling modern AI data centers. By focusing on performance-per-watt, Nvidia directly addresses the core economic drivers of its largest customers, making its platform not just the fastest but also the most economically viable to operate at scale. This technological leadership grants Nvidia immense pricing power, which is reflected in its consistently high gross margins. Throughout this period of hypergrowth, the company has maintained non-GAAP gross margins in the mid-70% range, a figure almost unheard of for a hardware company. For example, non-GAAP gross margin was 75.7% in Q2 FY25 and 72.7% in Q2 FY26. This pricing power is a direct result of its performance lead and the market's perception that there are no true performance-equivalent alternatives at scale. The immense free cash flow generated by these margins funds a massive and accelerating research and development budget. Nvidia's R&D expenses for FY2025 reached $12.914 billion, a 48.86% increase from the prior year, a sum that significantly outpaces the growth in R&D spending at Intel and dwarfs the absolute R&D budget of AMD. This creates a self-reinforcing cycle: superior products command high margins, which in turn fund the R&D necessary to create the next generation of superior products, thus widening the technological gap and strengthening the moat. 2b. CUDA's Pervasive Ecosystem Parallel to its hardware dominance, Nvidia has cultivated a software ecosystem that is arguably an even more durable competitive advantage. The Compute Unified Device Architecture (CUDA) is more than just a programming model; it is a deeply entrenched platform comprising specialized libraries, developer tools, and decades of accumulated code and expertise. This ecosystem creates powerful switching costs. An AI application is rarely written just using the base CUDA API. Instead, it leverages a rich stack of highly optimized libraries like cuDNN for deep neural network primitives, TensorRT for inference optimization, and NCCL for collective communications. These libraries are finely tuned for Nvidia's hardware architecture. Porting a complex application to a competing platform requires not only rewriting the custom code but also finding functional and performance-equivalent replacements for this entire library stack, a process that is both resource-intensive and fraught with risk. Company leadership consistently highlights this "full stack" advantage. During an earnings call, CFO Colette Kress emphasized that "the power of CUDA libraries and full stack optimizations...continuously enhance the performance and economic value of the platform". This underscores a critical point: the performance of an Nvidia GPU is not derived solely from its silicon. It is a product of the tight co-design and continuous optimization between the hardware and the software stack. This integration means that competitors cannot simply match Nvidia's hardware specifications; they must also replicate the performance delivered by its entire optimized software ecosystem, a far more challenging task. For nearly two decades, CUDA has been the default platform for general-purpose GPU computing, creating a powerful form of lock-in based on human capital. Universities teach CUDA, researchers publish CUDA-based code, and an entire generation of AI engineers has built their careers on this platform. This creates a significant hiring and training advantage for enterprises operating within the Nvidia ecosystem and a steep learning curve for those considering a move to a competing platform. 2c. The Full-Stack Advantage: Integrating Hardware, Software, and Networking Nvidia's moat extends beyond individual GPUs and software libraries to encompass the entire system-level architecture of an "AI Factory." The company has invested heavily in networking and interconnect technologies that are critical for scaling AI workloads, transforming itself from a component supplier into a full-stack computing infrastructure company. Technologies like NVLink and NVSwitch provide proprietary, high-bandwidth, direct GPU-to-GPU communication that far exceeds the capabilities of standard PCIe connections. This is essential for training massive AI models that must be distributed across hundreds or thousands of GPUs. Furthermore, Nvidia has built a formidable networking business around its Spectrum-X Ethernet and Quantum InfiniBand platforms. Networking revenue has become a significant contributor to the Data Center segment, growing 16% sequentially in Q2 FY25 alone. This integrated approach culminates in the sale of complete, rack-scale systems like the DGX SuperPOD and the GB200 NVL72. By offering a pre-validated, fully integrated hardware and software solution, Nvidia abstracts away the immense systems engineering complexity of building a large-scale AI cluster. This strategy not only creates a higher-value product but also ensures that every component - from the GPU to the network interface card to the switch - is an Nvidia product, optimized to work together. This holistic platform is exceedingly difficult for competitors, who typically focus on individual components, to replicate. The scale of this operation is immense, with the company now producing approximately 1,000 GB300 racks per week, indicating a massive industrialization of its system-level solutions. 3. Forces Strengthening Nvidia's Dominion While the foundational elements of Nvidia's moat are well-established, a wealth of recent evidence suggests that its overall competitive dominion is not merely being maintained but is actively widening. This expansion is driven by a quantifiable acceleration in performance leadership, a strategic tightening of its grip on the manufacturing supply chain, and the powerful reinforcing effects of its growing ecosystem. 3a. Blackwell and the Pace of Innovation Objective, industry-standard benchmarks provide the most compelling evidence of Nvidia's widening performance lead. The latest results from the MLCommons consortium's MLPerf benchmarks, which are considered the gold standard for measuring real-world AI performance, showcase a significant leap forward for Nvidia's new architectures. In the MLPerf Inference v5.1 results, the newly introduced Blackwell Ultra architecture (powering the GB300 system) established new performance records across every data center category in which it was submitted. This dominance was particularly pronounced on the new, more challenging benchmarks designed to reflect the state of modern AI. On the DeepSeek-R1 benchmark, which measures a model's reasoning capabilities, and the Llama 3.1 405B benchmark, a massive large language model, Blackwell Ultra set a new high-water mark for the industry. The most critical insight from these results is not just that Nvidia is leading, but the margin by which it is extending its lead in the highest-value, next-generation workloads. On the DeepSeek-R1 reasoning test, the Blackwell Ultra platform demonstrated a 4.7x improvement in offline throughput and a 5.2x improvement in server throughput compared to the already formidable Hopper architecture. This is not an incremental, evolutionary gain; it is a revolutionary, generational leap. It signals that Nvidia is not only winning on today's established workloads but is also defining the performance envelope for the emerging AI tasks that will drive future market demand. Competitors are now faced with the daunting task of catching up to a target that has just accelerated away from them at an extraordinary rate. This dominance extends to AI training. In the MLPerf Training v4.0 benchmark suite, Nvidia demonstrated its platform's ability to scale with near-perfect efficiency. A submission using 11,616 H100 GPUs was able to train the massive GPT-3 175B model in a mere 3.4 minutes. This capability to efficiently harness vast numbers of processors is a complex systems engineering challenge that is as much a part of the moat as the performance of a single chip. It showcases a mastery of the entire stack - from silicon to networking to software - that is currently unmatched in the industry. This relentless pursuit of performance is a deliberate strategy to redefine the economic calculus for its customers. The company is keenly aware that for large-scale AI operators, the total cost of ownership (TCO) is dominated by operational expenditures like power, not the initial capital expenditure on hardware. By delivering massive leaps in performance-per-watt, as seen with Blackwell Ultra's 10x token/watt improvement over Hopper, Nvidia directly slashes the primary operational cost for its customers. The company has begun to frame this advantage in terms of revenue generation, estimating that a $100 million investment in its latest systems could generate $5 billion in token revenue. This powerful framing shifts the customer's focus from the high purchase price of the hardware to the immense and rapid return on investment. It becomes exceptionally difficult for a competitor to compete on a lower chip price if their hardware results in a significantly higher TCO and lower revenue potential for the customer. In this way, Nvidia is weaponizing performance to create an economic moat that complements its technological one. 3b. Manufacturing Lock-In and Symbiosis with TSMC Nvidia has fortified its hardware leadership by establishing a deeply integrated and preferential relationship with the world's leading semiconductor foundry, Taiwan Semiconductor Manufacturing Company (TSMC). This partnership extends far beyond a typical customer-supplier dynamic and constitutes a powerful structural moat. A key element of this strategy is securing a dominant share of TSMC's advanced packaging capacity. Reports indicate that Nvidia has contracted for over 70% of TSMC's Chip-on-Wafer-on-Substrate (CoWoS) capacity for the year 2025. CoWoS is a critical 2.5D packaging technology that is essential for building the large, high-performance, multi-die AI accelerators that define the high end of the market. By locking up the majority of this finite and highly specialized manufacturing capability, Nvidia effectively creates a supply bottleneck for its primary competitors, including AMD, who also rely on TSMC for their most advanced products. This strategic move can limit the ability of rivals to scale production to meet demand, even if they have a competitive chip design, thereby constraining their market share and slowing their growth. Even more strategically significant is the deepening technological partnership between the two companies, exemplified by the production deployment of the NVIDIA cuLitho platform at TSMC. Computational lithography, the process of transferring circuit patterns onto silicon wafers, is the single most compute-intensive workload in the entire semiconductor manufacturing process. By developing a GPU-accelerated software platform that can speed up this critical bottleneck by 40-60x, Nvidia has made its own technology indispensable to TSMC's future. The deployment involves replacing vast farms of 40,000 CPU systems with just 350 NVIDIA H100 systems, demonstrating a massive leap in efficiency. This collaboration creates a powerful, self-reinforcing feedback loop. Nvidia's GPUs are now being used to design and optimize the manufacturing processes and fabs that will build the next generation of Nvidia's GPUs. This gives Nvidia unprecedented early access, insight, and influence over the development of future process nodes, such as 2nm and beyond. It transforms Nvidia from merely being TSMC's largest and "closest" partner into a foundational technology provider for TSMC's own roadmap. This symbiotic relationship is a hidden, secondary manufacturing moat that ensures Nvidia remains at the front of the line for both capacity allocation and access to next-generation manufacturing technology, a structural advantage that is exceptionally difficult for any competitor to replicate. 3c. The Ecosystem Flywheel with Neo-Clouds and Sovereign AI The dominance of Nvidia's platform is creating a powerful ecosystem flywheel effect, where its success begets further adoption, which in turn reinforces its market leadership. The rapid emergence of specialized "neo-cloud" providers and the new market for "Sovereign AI" are prime examples of this dynamic. Coreweave, a specialized AI cloud provider built almost exclusively on Nvidia's full stack, serves as a compelling case study. The company has experienced explosive growth, with its revenue surging over 200% year-over-year to $1.2 billion in Q2 2025. More telling is its massive revenue backlog, which stood at $30.1 billion at the end of that quarter. This backlog represents contractually committed future spending on Coreweave's services, which translates directly into future demand for Nvidia's hardware, networking, and software. The success of companies like Coreweave, which was the first cloud provider to offer Nvidia's Blackwell GB200 systems at scale, validates the market's demand for a purpose-built, highly optimized AI platform and creates a powerful, loyal sales channel for Nvidia's integrated systems. Simultaneously, Nvidia has successfully cultivated an entirely new market segment in Sovereign AI. This involves nations and governments building their own domestic AI infrastructure to ensure technological autonomy and data sovereignty. Nvidia has positioned itself as the default technology partner for these ambitious projects, forecasting that this segment will grow into a "low-double-digit billions" revenue stream in the current fiscal year alone. High-profile deployments, such as Japan's ABCI 3.0 supercomputer which integrates H200 GPUs and Quantum-2 InfiniBand networking, further entrench the Nvidia platform as the global standard for large-scale AI infrastructure. 3d. Deepening the Software Trench: From AI Enterprise to NIMs Recognizing that the long-term threat to its moat lies in the potential commoditization of hardware via open software, Nvidia is proactively moving up the software stack to capture more value and increase customer stickiness. This strategy is most evident in its push with NVIDIA AI Enterprise and, more recently, the introduction of NVIDIA Inference Microservices (NIMs). NIMs represent a brilliant strategic maneuver to reinforce the moat in an era of powerful open-source AI models. NIMs are pre-built, containerized, and highly optimized microservices that allow for the "one-click" deployment of popular AI models like Llama or Mixtral. By providing these NIMs, Nvidia is abstracting away the significant engineering complexity of model optimization, quantization, and deployment. This makes it dramatically easier for enterprises to begin using generative AI, but it does so in a way that guides them directly and seamlessly onto Nvidia's hardware platform. This strategy effectively co-opts the open-source model movement and turns it into a tool for strengthening the Nvidia ecosystem. The proliferation of open-source models threatens to commoditize the model layer of the AI stack, shifting value to the hardware and software that can run them most efficiently. By ensuring that the easiest, fastest, and most performant way to deploy a popular open-source model is via an Nvidia NIM, the company captures value from the open-source trend and uses it to deepen its platform's entrenchment. This is a strategic widening of the software moat, shifting the battleground from the low-level CUDA API to a higher-level, solution-oriented platform that is even more difficult for competitors to displace with a simple "good enough" hardware offering. 4. Competitive and Structural Pressures Despite the formidable and widening nature of its moat, Nvidia's dominance is not absolute. A confluence of credible competitive threats, a maturing open-source software ecosystem, and significant structural risks are creating the first meaningful pressures on its fortress. These forces are actively working to narrow the moat in specific dimensions, primarily by reducing software lock-in and providing viable, cost-effective alternatives. 4a. Credible Alternatives from AMD and Intel For the first time in the AI era, Nvidia faces credible, high-performance hardware competition at scale. Both AMD and Intel have successfully brought competitive AI accelerators to market, securing significant customer adoption and challenging Nvidia's hardware monopoly. AMD has firmly established itself as the primary challenger. Its Instinct MI300X accelerator presents a compelling architectural alternative, particularly with its industry-leading 192 GB of HBM3 memory, a crucial advantage for inferencing large language models that may not fit into the memory of a single Nvidia GPU. The company is maintaining an aggressive roadmap, with the next-generation MI350 series, based on the new CDNA 4 architecture, slated for release in 2025 and promising a massive 35x generational increase in AI inference performance. While Nvidia continues to lead in overall peak performance benchmarks, AMD has demonstrated its ability to win in specific, real-world workloads. In the MLPerf Inference v5.1 benchmarks, an 8-chip AMD system showed a 2.09x performance advantage over an equivalent Nvidia GB200 system in offline testing of the Llama 2 70B model, proving its hardware can be highly competitive. Intel, meanwhile, is pursuing an asymmetric strategy focused on price-performance and enterprise accessibility with its Gaudi 3 accelerator. Intel positions Gaudi 3 as a cost-effective alternative to Nvidia's flagship products, claiming it delivers 50% better inference performance and 40% better power efficiency than the Nvidia H100 at a substantially lower cost. This value proposition is designed to appeal to the large segment of enterprise customers who are more cost-sensitive and are deploying smaller, task-specific models rather than training frontier models. For these customers, a "good enough" accelerator at a fraction of the price is a highly attractive option. Crucially, this hardware is no longer theoretical; it is being deployed by the world's largest infrastructure buyers. AMD's MI300 series has been adopted for large-scale deployments by Microsoft Azure, Meta, and Oracle, with major OEMs like Dell, HPE, and Lenovo also offering MI300-based servers. Similarly, Intel's Gaudi 3 has secured design wins with the same tier-one OEMs and has a significant cloud deployment partnership with IBM Cloud. This broad adoption provides the market with viable alternatives for the first time, transforming the landscape from a monopoly to a competitive, albeit Nvidia-dominated, market. 4b. Maturation of ROCm and the Promise of Open Standards The most significant force working to narrow Nvidia's moat is the systematic assault on its CUDA software lock-in. This attack is proceeding on two fronts: a "bottom-up" effort by AMD to bring its ROCm software stack to parity with CUDA, and a "top-down" movement from the broader AI community to build hardware-agnostic abstraction layers that render the underlying proprietary APIs irrelevant. AMD's Radeon Open Compute platform (ROCm), long considered a significant liability due to instability and a lack of features, has matured into a viable alternative. A pivotal development has been the upstreaming of stable ROCm support into the official repositories of PyTorch and JAX, the two most critical frameworks for AI development. This means that developers can now run their existing PyTorch or JAX code on AMD hardware with minimal to no modification, dramatically lowering the barrier to adoption and experimentation. The software experience, while still lagging CUDA in the breadth of its library support and overall polish, has crossed a critical threshold of usability for mainstream AI workloads. To address the massive existing body of CUDA code, AMD has developed the Heterogeneous-Compute Interface for Portability (HIP). HIP includes automated porting tools, such as hipify-perl and hipify-clang, which can translate CUDA source code to HIP source code with remarkable efficiency. Case studies have shown that these tools can automatically convert over 95% of the code for complex HPC applications, allowing entire codebases to be ported in a matter of days or even hours. This directly attacks the stickiness of the legacy CUDA ecosystem by drastically reducing the cost and effort of migration. Perhaps a more profound long-term threat to the CUDA moat comes from the rise of hardware-agnostic programming models. OpenAI's Triton is a leading example. It is a Python-based language that allows developers to write high-performance custom GPU kernels without needing to write low-level CUDA or HIP code. The Triton compiler then takes this high-level code and generates highly optimized machine code for different hardware backends, including both Nvidia and AMD GPUs. As more performance-critical kernels for new AI models are written in Triton, the underlying hardware becomes an interchangeable implementation detail. A developer can write a single Triton kernel and have it run with high performance on hardware from multiple vendors, effectively neutralizing the CUDA API as a source of lock-in. This trend is mirrored by the push for open standards like SYCL, a C++-based programming model from the Khronos Group. Implementations such as Intel's oneAPI Data Parallel C++ (DPC++) now support compiling a single SYCL source file to run on CPUs and GPUs from all three major vendors. Performance studies have shown that for many workloads, SYCL code running on Nvidia or AMD GPUs can achieve performance that is comparable to native CUDA or HIP code. While SYCL adoption is still in its early stages, it represents a systemic, industry-wide effort to create an open, portable alternative to proprietary, single-vendor programming environments. The combined effect of these trends is a clear narrowing of the software moat. The historical barriers to using non-Nvidia hardware - the difficulty of porting existing code and the lack of a mature ecosystem for writing new code - are being systematically dismantled. The following matrix provides a qualitative assessment of the current maturity of the CUDA and ROCm ecosystems. 4c. Hyperscaler: Competition and Cooperation A significant structural pressure on Nvidia's moat stems from the nature of its customer base. An outsized portion of Nvidia's revenue is derived from a very small number of hyperscale customers - the major cloud service providers (CSPs) like Microsoft, AWS, Meta, and Google. In Q2 FY26, for instance, just two unnamed customers accounted for 39% of the company's total revenue.This high degree of customer concentration creates a dynamic of "coopetition." On one hand, these CSPs are Nvidia's most important partners, spending tens of billions of dollars annually on its GPUs to build out their AI cloud infrastructure. The explosive growth of Microsoft Azure's AI services, which drove a 39% increase in its cloud revenue in Q4 FY25, is largely built on the back of Nvidia hardware. This symbiotic relationship fuels Nvidia's growth and funds its roadmap. On the other hand, these same customers are also Nvidia's most significant long-term competitive threat. Each of the major CSPs is investing heavily in designing its own custom AI silicon (e.g., AWS Trainium and Inferentia, Google's TPU, Microsoft's Maia) with the explicit goal of reducing their long-term dependence on Nvidia, controlling their own technology stack, and lowering their costs. While these custom chips do not yet match the peak performance of Nvidia's flagship GPUs, they are optimized for the specific workloads running in their data centers and can offer superior TCO for those tasks. This creates a fundamental strategic misalignment: the CSPs need Nvidia's best-in-class hardware today to remain competitive in the AI arms race, but their long-term goal is to replace as much of that hardware as possible with their own in-house solutions. 4d. Structural Headwinds: Customer Concentration and Geopolitics Beyond direct competition, Nvidia faces two major structural risks. The first is the aforementioned customer concentration. A strategic decision by even one of the major CSPs to significantly slow its infrastructure build-out or to more aggressively shift to an in-house or alternative solution could have a disproportionately large impact on Nvidia's revenue and growth trajectory. The second is the complex and unpredictable geopolitical landscape. U.S. government export controls aimed at restricting China's access to advanced AI technology have had a direct and tangible financial impact. Nvidia has been forced to design and market lower-performance chips, such as the H20, specifically for the Chinese market, and has acknowledged revenue headwinds as a result. These restrictions have effectively ceded a portion of the vast Chinese market to domestic competitors and created an uncertain regulatory environment. AMD has faced similar challenges with its MI308 products, which were also subject to export controls that resulted in significant inventory charges. This geopolitical factor acts as an artificial but very real narrowing of the moat in one of the world's largest technology markets. 5. Conclusions The analysis of the forces strengthening and narrowing Nvidia's competitive advantage leads to a nuanced and multi-dimensional conclusion. The central question of whether the moat is widening or narrowing cannot be answered with a simple binary; instead, its trajectory must be understood as a dynamic reshaping of its core components. 5a. Strategic Outlook The final assessment of this report is that Nvidia's overall competitive moat is widening, but with significant qualifications. The expansion is being driven overwhelmingly by the dimensions of raw hardware performance, performance-per-watt, and manufacturing supply chain control. The relentless innovation cadence, which has produced a generational leap in performance from the Hopper to the Blackwell architecture, has extended Nvidia's lead in the most computationally demanding and economically valuable AI workloads. This performance advantage, coupled with a strategic lock on the majority of TSMC's advanced CoWoS packaging capacity, creates a formidable barrier to entry for any competitor seeking to challenge Nvidia at the high end of the market. Simultaneously, however, the moat is demonstrably narrowing along the critical dimension of software lock-in. This is the most significant change in the competitive landscape over the past 24 months. The maturation of AMD's ROCm software stack to a point of "good enough" viability for mainstream AI frameworks, combined with the rise of hardware-agnostic abstraction layers like Triton and SYCL, is systematically dismantling the proprietary walls of the CUDA ecosystem. These developments are successfully reducing switching costs and creating a more level playing field where hardware can be evaluated more directly on its price and performance merits, rather than on its adherence to a specific software standard. The net effect is a fundamental transformation of the moat's character. It is evolving from a balanced hardware-software fortress into one that relies more heavily on its sheer hardware performance and manufacturing scale. The overall trajectory remains positive for Nvidia in the near-to-medium term, as its lead in these areas is substantial and growing. However, the competitive attack surface has expanded, and the long-term defensibility of its position is now more dependent on its ability to continue out-innovating competitors on a yearly cadence. 5b. Key Indicators for Future Assessment To provide ongoing counsel, Dr. Teki should monitor a specific dashboard of key indicators that will signal shifts in the moat's trajectory:
5c. Implications for the Client This analysis translates into several actionable strategic insights for various stakeholders in the AI ecosystem:
Disclaimer: The information in the blog is provided for general informational and educational purposes only and does not constitute professional investment advice.
 Introduction As of August 21, 2025, the enterprise landscape is defined by a stark and costly paradox: The GenAI Divide. Despite an estimated $30-40 billion in corporate spending on Generative AI, a landmark 2025 report from MIT's NANDA (State of AI in Business 2025) initiative reveals that 95% of these investments have yielded zero measurable business returns. The primary cause is not a failure of technology but a failure of integration. A fundamental "learning gap" exists where rigid, enterprise-grade AI tools fail to adapt to the dynamic, real-world workflows of employees, leading to widespread pilot failure and abandonment. In stark contrast, the successful 5% of organizations are not merely adopting AI; they are re-architecting their core business processes around it. These leaders demonstrate strong C-suite sponsorship, focus on tangible business outcomes, and are pioneering the shift from passive, prompt-driven tools to proactive, agentic AI systems that can autonomously execute complex tasks. This evolution is powered by a strategic move towards more efficient and agile Small Language Models (SLMs). Meanwhile, a "Shadow AI Economy" thrives, with 90% of employees successfully using personal AI tools, proving value is attainable but is being missed by top-down corporate strategies. For leaders, the path forward is clear but urgent: bridge the learning gap, embrace an agentic future, and transform organizational structure to turn AI potential into P&L impact.  1. The Great GenAI Disconnect: Understanding the 95% Failure Rate 1a. The Scale of the Problem: A Sobering Look at MIT NANDA's Findings The prevailing narrative of a seamless AI revolution has collided with a harsh operational reality. The most definitive analysis of this collision comes from the MIT NANDA initiative's 2025 report, "The GenAI Divide: State of AI in Business 2025." The report's findings are a sobering indictment of the current approach to enterprise AI, quantifying a chasm between investment and impact. Across industries, an estimated $30-40 billion has been invested in enterprise Generative AI, yet approximately 95% of organizations report no measurable impact on their profit and loss statements. This disconnect is most acute at the deployment stage. The research highlights a catastrophic failure to transition from experimentation to operationalization: a staggering 95% of custom enterprise AI pilots fail to reach production. This is not an incremental challenge; it is a systemic breakdown. While adoption of general-purpose tools like ChatGPT and Microsoft Copilot is high - with over 80% of organizations exploring them - this activity primarily boosts individual productivity without translating into enterprise-level transformation. The sentiment from business leaders on the ground confirms this data. As one mid-market manufacturing COO stated in the report, "The hype on LinkedIn says everything has changed, but in our operations, nothing fundamental has shifted". This gap between the promise of AI and its real-world performance defines the GenAI Divide. 1b. Root Cause Analysis: Why Most GenAI Implementations Deliver Zero Business Value The reasons behind this 95% failure rate are not primarily technological. The models themselves are powerful, but their application within the enterprise context is fundamentally flawed. The failure is rooted in strategic, organizational, and operational deficiencies. i. The "Learning Gap": The True Culprit The central thesis of the MIT NANDA report is the existence of a "learning gap". Unlike consumer-grade AI tools that are flexible and adaptive, most enterprise GenAI systems are brittle. They do not retain feedback, adapt to specific workflow contexts, or improve over time through user interaction. This inability to learn makes them unreliable for sensitive or high-stakes work, leading employees to abandon them. The tools fail to bridge the last mile of integration into the complex, nuanced reality of daily business operations. ii. Strategic & Leadership Failures Successful AI initiatives are business transformations, not IT projects. Yet, a majority of failures stem from a lack of strategic alignment and committed executive sponsorship. Studies indicate that as many as 85% of AI projects fail to scale primarily due to these leadership missteps.9 Common failure patterns include:
iii. Data Readiness and Infrastructure Gaps Generative AI is voracious for high-quality, relevant data. However, many organizations are unprepared. Over half (54%) of organizations do not believe they possess the necessary data foundation for the AI era. Key issues include:
iv. Organizational and Cultural Inertia Technology implementation is ultimately a human challenge. Cultural resistance, often stemming from fear of job displacement or a lack of AI literacy, can sabotage adoption.9 Furthermore, poor collaboration between siloed business and technical teams often results in the creation of technically sound models that fail to solve the actual business problem or are too complex for end-users to adopt. If the people who are meant to use the AI system do not trust it, understand it, or feel it helps them, the project is destined to fail. 1c. The Shadow AI Economy: Where Individual Success Masks Enterprise Failure While enterprise-sanctioned AI projects flounder, a vibrant and productive "Shadow AI Economy" has emerged. This is the report's most telling paradox. Research reveals that employees at 90% of companies are regularly using AI tools like ChatGPT for work-related tasks, but the majority are hiding this usage from their IT departments. This clandestine adoption is not trivial. Employees are actively seeking a "secret advantage," using these tools to boost their personal productivity and overcome the shortcomings of official corporate software. A Gusto survey found that two-thirds of these workers are personally paying for the AI tools they use for their jobs. This behavior creates what the report calls a "shadow economy of productivity gains" that is completely invisible to corporate leadership and absent from financial reporting. The disconnect is profound. A McKinsey survey found that C-suite leaders estimate only 4% of their employees use AI for at least 30% of their daily work. The reality, as self-reported by employees, is over three times higher. This shadow economy is the clearest possible signal of unmet user needs. It demonstrates that employees can and will extract value from AI when the tools are flexible, intuitive, and directly applicable to their tasks. The failure of enterprise AI is not that value is impossible to create, but that organizations are failing to provide the right tools and environment to capture it at scale. 1d. Performance Gaps: Why Only Technology and Media/Telecom See Material Impact The GenAI Divide is not uniform across all industries. The MIT NANDA report's disruption index shows that significant, structural change is currently concentrated in just two sectors: Technology and Media & Telecommunications. Seven other major industries show widespread experimentation but no fundamental transformation. The success of these two sectors is intrinsically linked to the nature of their core products. Their primary outputs - software code, text-based content, digital images, and communication streams - are composed of information, the native language of generative models. For a software company, using AI to write and debug code is not an ancillary efficiency gain; it is a direct acceleration of the core manufacturing process. For a media company, using AI to generate marketing copy or summarize content is a fundamental enhancement of its content production pipeline. McKinsey research quantifies this advantage, projecting that GenAI will unleash a disproportionate economic impact of $240 billion to $460 billion in high tech and $80 billion to $130 billion in media. These sectors thrive because they did not have to search for a use case; GenAI directly targets their central value-creation activities. For other industries, from manufacturing to healthcare, the path to value is less direct. It requires a more profound re-imagining of physical or service-based processes as information-centric workflows that AI can optimize. The failure of most industries to do so is not a failure of technology, but a failure of strategic and operational imagination. 2. Decoding the Successful 5%: What Works in GenAI Implementation? While the 95% struggle, the successful 5% offer a clear blueprint for value creation. These organizations are not simply using AI; they are fundamentally rewiring their operations to become AI-native. Their success is built on a foundation of strategic clarity, a forward-looking technology architecture, and a commitment to deep, operational integration. 2a. Success Patterns: Characteristics of High-Performing GenAI Implementations The organizations that have crossed the GenAI Divide share a set of distinct characteristics that separate them from the experimental majority. First, success begins with strong, C-suite-level executive sponsorship. In these firms, AI is not delegated to a siloed innovation department but is championed as a core business transformation priority, often with the CEO directly responsible for governance.6 This top-down mandate provides the necessary authority and resources to drive change across the enterprise. Second, these leaders redesign core business processes to embed AI, rather than simply layering AI on top of existing workflows. This is the critical step that closes the "learning gap." By re-architecting how work gets done, they create an environment where AI is not an add-on but an integral component of operations. This often involves creating dedicated, cross-functional teams that unite business domain experts with AI and data specialists to co-develop solutions. Third, they maintain a relentless focus on measurable business outcomes. The goal is not to deploy AI but to solve a business problem. This is evident in numerous real-world case studies. For example, by targeting specific workflows, companies are achieving remarkable returns:
These successes are not accidental; they are the result of a disciplined, strategic approach that directly links AI implementation to tangible P&L impact. 2b. The Agentic Web Evolution: From Passive Tools to Proactive CollaboratorsThe technological leap that enables the successful 5% to move beyond simple productivity tools is the evolution toward agentic AI systems. The first generation of LLMs, while impressive, suffered from critical limitations for enterprise use: they were fundamentally passive, requiring a human prompt to act; they lacked persistent memory, making it difficult to handle multi-step tasks; and they often struggled with complex reasoning. Agentic AI is the next paradigm, designed specifically to overcome these limitations. An AI agent is a system that can:
This transforms AI from a reactive tool into a proactive, goal-driven virtual collaborator. Instead of asking an LLM to "write an email," a user can task an agent with "manage the entire customer onboarding process," which might involve sending emails, updating the CRM, scheduling meetings, and generating reports. High-impact use cases are already emerging across industries, including streamlining insurance claims processing, optimizing complex logistics and supply chains, accelerating drug discovery, and automating sophisticated financial analysis and risk management. 2c. The Small Language Models (SLM) Revolution: The Engine of Scalable Agentic AIThe economic and technical foundation for this agentic future is the rise of Small Language Models (SLMs). The prevailing assumption has been that "bigger is better" when it comes to AI models. However, for the specialized, repetitive, and high-volume tasks that characterize most enterprise workflows, this assumption is proving to be incorrect and economically unsustainable. The seminal ArXiv paper "Small Language Models are the Future of Agentic AI" argues that SLMs are not a compromise but are, in fact, superior for most agentic applications. The reasoning is compelling for business and technology leaders:
The strategic shift to SLMs is therefore a critical enabler for any organization serious about deploying agentic AI at scale. It transforms AI from a costly, centralized resource into a flexible, cost-effective, and powerful component of modern enterprise architecture. 3. Successful Integration: Overcoming the Pilot-to-Production Chasm The journey from a successful pilot to a production-scale system is where most initiatives fail. The successful 5% navigate this chasm by systematically addressing both technical and organizational hurdles. The primary challenges to scaling include:
To overcome these, high-performing organizations adopt a structured approach. They implement robust MLOps to automate the deployment, monitoring, and maintenance of AI models. They build strong data foundations with clear governance. Crucially, they foster deep, cross-functional collaboration and invest heavily in change management and upskilling to ensure that the human part of the human-machine equation is prepared for new ways of working. The rise of agentic AI, powered by SLMs, represents a fundamental shift in enterprise computing. It signals the "unbundling" of artificial intelligence. The era of relying on a single, monolithic, general-purpose LLM from a handful of providers is giving way to a new paradigm. In this future, enterprise solutions will be composed of heterogeneous systems of many small, specialized AI agents, each an expert in its domain. This creates the conditions for a new kind of digital marketplace - not for software applications, but for discrete, intelligent capabilities. The protocols emerging to govern this "Agentic Web" are the foundational infrastructure for this new economy of skills. For enterprises, the strategic imperative is no longer just to build or buy a single AI tool, but to develop an orchestration capability - a platform to discover, integrate, and manage a diverse team of specialized AI agents to drive business outcomes. 4. Strategic Pathways Across the GenAI Divide Crossing the GenAI Divide requires more than just better technology; it demands a new strategic playbook. Leaders must act with urgency to make foundational architectural decisions, implement robust frameworks for measuring value, transform their organizational structures, and strategically harness the nascent productivity already present in the Shadow AI Economy. 4.1 The 12-18 Month Window: Navigating Vendor Lock-in and Architectural Decisions The MIT NANDA report issues a stark warning: enterprises face a critical 12-18 month window to make foundational decisions about their AI vendors and architecture. The choices made during this period will have long-lasting consequences, creating deep dependencies that could lead to significant vendor lock-in. Relying on proprietary, black-box APIs from a single vendor can stifle innovation and limit an organization's flexibility to adopt new, best-of-breed technologies as they emerge. Navigating this period requires a shift from evaluating vendor demos to conducting rigorous due diligence based on clear business requirements. Leaders must move beyond the hype and assess vendors on their ability to deliver enterprise-grade solutions that are secure, scalable, transparent, and interoperable. 4.2 Emerging Frameworks: Building the Infrastructure for the Agentic Web To avoid being locked into a single vendor's ecosystem, forward-thinking leaders must understand the emerging open standards that will form the foundation of the Agentic Web - an internet of collaborating AI agents. Just as protocols like TCP/IP and HTTP enabled the human-centric web, new protocols are being developed to allow AI agents to discover, communicate, and transact with each other securely and at scale. The three most critical frameworks are:
Understanding these protocols is crucial for future-proofing an organization's AI strategy, enabling the creation of composable, interoperable, and resilient AI ecosystems. 4.3 ROI Measurement: Moving Beyond Vanity Metrics to Business Impact A primary reason for the 95% failure rate is the inability to prove value. Vague objectives and vanity metrics (e.g., number of chatbot interactions) fail to convince budget holders. To secure investment and scale initiatives, leaders must adopt a rigorous, multi-tiered ROI framework that connects AI activity directly to business impact. This framework consists of three interconnected layers:
By tracking metrics across all three tiers, leaders can build a comprehensive business case that demonstrates how AI-driven operational improvements translate directly into tangible financial outcomes. 4.4 From Shadow to Strategy: A Governance Framework for the Shadow AI Economy The Shadow AI Economy should not be viewed as a threat to be eliminated, but as a strategic opportunity to be harnessed. The widespread, unauthorized use of AI tools is the most potent form of user research an organization can get; it reveals precisely where employees see value and what kind of functionality they need. The goal of governance should be to channel this innovative energy into a secure, productive, and enterprise-wide advantage. 4.5 Building AI-Native Organizations: The Human and Structural Transformation Ultimately, crossing the GenAI Divide is a challenge of organizational design. Technology is an enabler, but value is only unlocked through deep structural and cultural change. Drawing on insights from McKinsey, building an AI-native organization requires a holistic transformation:
The most profound competitive advantage in this new era will not be the AI model an organization uses, as SLMs will likely become increasingly powerful and commoditized. Instead, the ultimate, defensible moat will be the proprietary "process data" generated by AI agents as they execute core business workflows. Every action, decision, error, and human correction an agent makes creates a unique data asset. This data captures the intricate, tacit knowledge of how an organization actually operates. When fed back into a continuous MLOps loop, this process data becomes a powerful flywheel, relentlessly fine-tuning the agents to become uniquely effective within that company's specific context. The organization that can deploy agents into its core processes fastest, and build the infrastructure to harness this data flywheel, will create an AI capability that competitors simply cannot replicate. 5. Conclusion: Navigating the GenAI Divide in 2025-2026 The GenAI Divide is the defining strategic challenge for enterprise leaders today. The 95% failure rate is not a statistical anomaly; it is a verdict on an outdated approach that treats AI as a simple technology to be procured rather than a transformative force that must be integrated into the very fabric of the organization. To cross this divide and join the successful 5%, leaders must internalize the lessons from both the failures and the successes. The journey requires a multi-faceted action plan tailored to different leadership roles:
The path forward is clear: move from passive tools to proactive agents; from monolithic models to specialized intelligence; and from isolated experiments to a full-scale, strategic reconfiguration of work itself. The 12-18 month window for making these foundational decisions is closing. The leaders who act decisively now will not only survive the disruption but will define the next era of competitive advantage, charting a course for success from 2025 to 2035. The GenAI Divide represents the defining challenge of our era. To move from the failing 95% to the successful 5% and accelerate your organization's AI transformation, consider exploring personalized strategic guidance through Dr. Sundeep Teki's AI Consulting. If you are interested in reading similar in-depth posts on AI, feel free to subscribe to my upcoming AI Newsletter (form is in the footer or the contact page). Thank you! 6. Resources
Primary Sources
Job Description of a Forward Deployed Engineer at OpenAI 1. The Genesis of a Hybrid Role: From Palantir to the AI Frontier 1a. Deconstructing the FDE Archetype: More Than a Consultant, More Than an Engineer The Forward Deployed Engineer (FDE) represents a fundamental re-imagining of the technical role in high-stakes enterprise environments. At its core, an FDE is a software engineer embedded directly with customers to solve their most complex, often ambiguous, problems. This is not a mere rebranding of professional services; it is a paradigm shift in engineering philosophy. The role is a unique hybrid, blending the deep technical acumen of a senior engineer with the strategic foresight of a product manager and the client-facing finesse of a consultant. This multifaceted nature means FDEs are expected to write production-quality code, understand and influence business objectives, and navigate complex client relationships with equal proficiency. The central mandate of the FDE is captured in the distinction: "one customer, many capabilities," which stands in stark contrast to the traditional software engineer's focus on "one capability, many customers". For a standard engineer, success is often measured by the robustness and reusability of a feature across a broad user base. For an FDE, success is defined by the direct, measurable value delivered to a specific customer's mission. They are tasked not with building a single, perfect tool for everyone, but with orchestrating a suite of powerful capabilities to solve one client's most critical challenges. 1b. Historical Context: Pioneering the Model at Palantir The FDE model was pioneered and popularized by Palantir, a company built to tackle sprawling, mission-critical data challenges for government agencies and large enterprises. Palantir's engineers, often called "Deltas," were deployed to confront "world-changing problems" that defied simple software solutions - combating human trafficking networks, preventing multi-billion dollar financial fraud, or managing global disaster relief efforts. The company recognized early on that the value of its powerful data platforms, Gotham and Foundry, could not be unlocked by a traditional sales or support model. These systems required deep, bespoke configuration and integration into a client's labyrinthine operational and data ecosystems. The FDE was created to be the human API to the platform's power. They were responsible for the entire technical lifecycle on-site, from wrangling petabyte-scale data and designing new workflows to building custom web applications and briefing customer executives. This approach allowed Palantir to deliver transformative solutions in environments where off-the-shelf software would invariably fail. 1c. The Strategic Imperative: The FDE as the Engine of Services-Led Growth The rise of the FDE is intrinsically linked to the business strategy of Services-Led Growth (SLG). This model, which stands in contrast to the self-service, low-touch ethos of Product-Led Growth (PLG), posits that for complex, high-value enterprise software, high-touch expert services are the primary driver of adoption, retention, and long-term revenue. For today's advanced enterprise AI products, this "implementation-heavy" model is not just an option but a necessity. As noted by VC firm Andreessen Horowitz, AI applications are only valuable when deeply and correctly integrated with a company's internal systems. The FDE is the critical enabler of this model, performing the "heavy lifting of securely connecting the AI application to internal databases, APIs, and workflows" to provide the essential context for AI models to function effectively. This reality reveals a deeper strategic layer. The challenge for enterprise AI firms is not merely building a superior model, but ensuring it delivers tangible results within a customer's unique and often chaotic operational environment. This "last mile" of implementation is a formidable barrier, requiring a synthesis of technical expertise, domain knowledge, and client trust that cannot be fully automated. The FDE role is purpose-built to conquer this last mile. Consequently, a company's FDE organization transcends its function as a service delivery arm to become a powerful competitive moat. A rival can replicate a model architecture or a software feature, but replicating a world-class FDE team - with its accumulated institutional knowledge, deep-seated client relationships, and battle-hardened deployment methodologies - is an order of magnitude more difficult. This team makes the product indispensable, or "sticky," in a way the software alone cannot. This dynamic fuels the SLG flywheel: expert services drive initial subscriptions, which generate proprietary data, which yields unique insights, which in turn creates demand for new and expanded services. 2. The FDE Operational Framework 2a. Anatomy of an Engagement: From Scoping to Production A typical FDE engagement is a dynamic, high-velocity process that diverges sharply from traditional, waterfall-style development cycles. It is characterized by rapid iteration, deep customer collaboration, and an unwavering focus on delivering tangible outcomes. Phase 1: Problem Decomposition & Scoping. The process rarely begins with a detailed technical specification. Instead, it starts with a broad, nebulous business problem, such as "How can we more effectively identify instances of money laundering?" or "Why are we losing customers?". The FDE's initial task is to function as a consultant and product manager. They work directly with customer stakeholders to dissect the high-level challenge, identify specific pain points within existing workflows, and define a tractable scope for an initial proof-of-concept. Phase 2: Rapid Prototyping & Iteration. FDEs operate in extremely tight feedback loops, often coding side-by-side with the end-users. They build a minimally viable solution, deploy it for immediate feedback, and iterate in real-time based on user reactions. This phase is defined by a strong "bias toward action," prioritizing speed and value delivery over architectural purity. The goal is to demonstrate tangible progress within days or weeks, not months. Phase 3: Optimization & Hardening for Production. Once a prototype has proven its value, the focus shifts from speed to robustness. The FDE transitions into a rigorous engineering mindset, concentrating on performance, scalability, and reliability. For modern AI FDEs, this is a critical phase involving intensive model optimization - using advanced methods to slash inference latency, implementing request batching to boost throughput, and meticulously benchmarking the system to ensure it meets stringent production SLAs. Phase 4: Deployment & Knowledge Transfer. The final stage involves deploying the hardened solution onto the customer's production infrastructure, whether on-premise or in the cloud. This is followed by a crucial handover process, where the FDE trains the customer's internal teams to operate and maintain the system. The engagement, however, does not end there. The FDE often transitions into a long-term advisory and support role. Critically, they are also responsible for a feedback loop back to their own company, channeling field learnings, reusable code patterns, and customer-driven feature requests to the core product and engineering teams, thereby improving the underlying platform for all customers. 2b. The Technical Toolkit: Core Competencies The FDE role demands a "battle-tested generalist" who is not just comfortable but proficient across the entire technology stack. They must possess a broad and deep set of technical skills to navigate the diverse challenges they encounter. Software Engineering: This is the bedrock. FDEs are expected to write significant amounts of production-grade code. This can range from custom data integration pipelines and full-stack web applications to performance-critical model optimization scripts. Mastery of languages like Python, Java, C++, and TypeScript/JavaScript is fundamental. Data Engineering & Systems: A substantial portion of the FDE's work, particularly in its Palantir-defined origins, involves data integration. This requires expertise in wrangling massive, messy datasets, authoring complex SQL queries, designing and building ETL/ELT pipelines, and working with distributed computing frameworks like Apache Hadoop and Spark. AI/ML Model Optimization: For the modern AI FDE, this skill is paramount and distinguishes them from a generalist. It extends far beyond making a simple API call. It requires a deep, systems-level understanding of model performance characteristics and the ability to apply advanced optimization techniques such as quantization, knowledge distillation, and request batching. Proficiency with specialized inference runtimes and compilers like NVIDIA's TensorRT is often necessary to meet demanding latency and throughput requirements in production. Cloud & DevOps: FDEs deploy solutions directly onto customer infrastructure, which is predominantly cloud-based (AWS, GCP, Azure). This necessitates strong practical skills in core cloud services (compute, storage, networking), containerization technologies (Docker, Kubernetes), and infrastructure-as-code principles to ensure repeatable and maintainable deployments. 2c. The Human Stack: Mastering Client Management and Value Translation For an FDE, technical prowess is merely table stakes. Their success is equally, if not more, dependent on a sophisticated set of non-technical skills - the "human stack." Customer Fluency: This is the ability to "debug the tech and de-escalate the CIO". FDEs must be bilingual, fluent in both the language of code and the language of business value. They must be able to translate complex technical architectures into clear business outcomes for executive stakeholders while simultaneously gathering nuanced requirements from non-technical end-users. Problem Decomposition: A core competency, explicitly valued by companies like Palantir, is the ability to take a high-level, ill-defined business objective and systematically break it down into a series of solvable technical problems. This requires a blend of analytical rigor and creative problem-solving. Ownership & Autonomy: FDEs operate with a degree of autonomy and end-to-end responsibility akin to that of a startup CTO. They are expected to own their projects entirely, from initial conception to final delivery, making critical decisions independently and demonstrating relentless resourcefulness when faced with inevitable obstacles. High EQ & Resilience: The role is characterized by intense context-switching between multiple high-stakes projects, managing tight deadlines, and navigating the pressures of direct customer accountability. A high degree of emotional intelligence is essential for building trust, managing expectations, and maintaining composure under fire. Resilience is non-negotiable. 3. The Modern AI FDE: Operationalizing Intelligence 3a. Shifting Focus: From Big Data to Generative AI The FDE role is undergoing a significant evolution in the era of generative AI. While the foundational philosophy of embedding elite engineers to solve complex customer problems remains constant, the technological landscape and the nature of the problems themselves have been transformed. The center of gravity has shifted from traditional big data integration and analytics to the deployment, customization, and operationalization of frontier AI models such as LLMs. Leading AI companies, from foundational model providers like OpenAI and Anthropic to data infrastructure leaders like Scale AI, are aggressively building FDE teams. Their mission is to "turn research breakthroughs into production systems" and bridge the gap between a model's potential and its real-world application. This new breed of "AI FDE," sometimes termed an "Agent Deployment Engineer," focuses on building sophisticated LLM-powered workflows, designing and implementing advanced Retrieval-Augmented Generation systems, and operationalizing autonomous AI agents within complex enterprise environments. 3b. Case Studies in Practice: FDE Projects at Leading AI Companies OpenAI: At OpenAI, FDEs are tasked with working alongside strategic customers to build novel, scalable solutions that leverage the company's APIs. Their role involves designing new "abstractions to solve customer problems" and deploying these solutions directly on customer infrastructure. This positions them as a critical feedback channel, funneling real-world usage patterns and challenges back to OpenAI's core research and product teams, effectively moving the company from a pure API provider to a comprehensive solutions partner. Scale AI: The FDE role at Scale AI is focused on the foundational layer of the AI ecosystem: data. FDEs there build the "critical data infrastructure that powers the most advanced AI models". They design and deploy systems for large-scale data generation, Reinforcement Learning from Human Feedback (RLHF), and model evaluation, working directly with the world's leading AI research labs and government agencies. This demonstrates the FDE's pivotal role in the very creation and refinement of frontier models. AI Startups: Within the startup ecosystem, the FDE role is even more entrepreneurial and vital. They often act as the "technical co-founders for our customers' AI projects," shouldering direct responsibility for demonstrating product value, securing technical wins to close deals, and generating early revenue. Their work is intensely hands-on, with a heavy emphasis on model performance optimization and building full-stack, end-to-end solutions that solve immediate customer pain points. 3c. Challenges and Frontiers: Navigating the New Landscape The modern AI FDE faces a new set of formidable challenges that require a unique combination of skills. Model Reliability and Safety: A primary challenge is managing the non-deterministic nature of large language models. FDEs must develop sophisticated strategies for testing, evaluation, and monitoring to mitigate issues like hallucinations, ensure factual consistency, and maintain safe and reliable model behavior in production environments. Complex System Integration: The task of integrating powerful AI agents with a company's legacy systems, private data sources, and intricate business workflows remains a significant technical and organizational hurdle. FDEs are the specialists who architect and build these complex integrations. Security and Data Privacy: Deploying AI models that require access to sensitive, proprietary enterprise data necessitates a deep and rigorous approach to security, access control, and data privacy compliance. The very existence of this role in the age of increasingly powerful AI reveals a crucial truth about the nature of technological adoption. The successful deployment of truly transformative AI is not merely a technical integration challenge; it is fundamentally an organizational change management problem. It requires redesigning long-standing business processes, redefining job functions, and overcoming human resistance to change. By being embedded within the customer's organization, the FDE gains a ground-level, ethnographic understanding of existing workflows, internal power dynamics, and the cultural nuances that can make or break a technology deployment. They are not just deploying code; they are acting as change agents. They build trust with end-users through close collaboration, demonstrate the technology's value through rapid, tangible prototypes, and serve as a human guide to navigate the friction that inevitably accompanies disruption. This elevates the FDE from a purely technical role to that of a sociotechnical engineer. Their work is a powerful acknowledgment that you cannot simply "plug in" advanced AI and expect transformation. A human translator, champion, and diplomat is required to bridge the vast gap between the technology's abstract potential and the messy, complex reality of a human organization. 4. A Comparative Analysis of Customer-Facing Technical Roles The term "Forward Deployed Engineer" is often conflated with other customer-facing technical roles. However, key distinctions in responsibility, technical depth, and position in the customer lifecycle set it apart. Understanding these differences is critical for aspiring professionals and hiring managers alike. FDE vs. Solutions Architect (SA): The primary distinction lies in implementation versus design. A Solutions Architect typically operates in the pre-sales or early implementation phase, focusing on high-level architectural design, technical validation, and demonstrating the feasibility of a solution. They design the blueprint. The FDE, conversely, is a post-sales, delivery-centric role that takes that blueprint and builds the final structure, owning the project end-to-end through to production and beyond. The FDE role is significantly more hands-on, with reports of FDEs spending upwards of 75% of their time on direct software engineering and model optimization. FDE vs. Sales Engineer (SE): This is a distinction of pre-sale versus post-sale. The Sales Engineer is a pure pre-sales function, supporting the sales team by delivering technical demonstrations, answering questions during the sales cycle, and building targeted POCs to secure the technical win. Their engagement typically concludes when the contract is signed. The FDE's primary work begins after the sale, focusing on the deep, long-term implementation required to deliver on the promises made during the sales process and ensure lasting customer value. FDE vs. Technical Consultant: The key difference here is being a product-embedded builder versus an external advisor. While both roles involve advising clients on technical strategy, an FDE is an engineer from a product company. Their primary toolkit is their company's own platform, which they leverage, extend, and configure to solve customer problems. A traditional consultant, by contrast, may build a fully bespoke solution from scratch or integrate various third-party tools. FDEs are fundamentally builders empowered to create and deploy software artifacts directly. 5. Palantir: FDE Role & Interview Profile Primary Focus Large-scale data integration, custom application development, and workflow configuration on proprietary platforms (Foundry, Gotham). Typical Projects Building systems for government/enterprise clients to tackle problems like fraud detection, supply chain logistics, or intelligence analysis. Tech Stack Palantir Foundry/Gotham, Java, Python, Spark, TypeScript, various database technologies. Inteview Focus
6. OpenAI: FDE Role & Interview Profile Primary Focus Frontier model deployment, rapid prototyping of novel use cases, and building custom solutions on customer infrastructure using OpenAI models and APIs. Typical Projects Scoping and building proof-of-concept applications with strategic customers to showcase the power of models like GPT-5. Tech Stack OpenAI APIs, Python, React/Next.js, Vector Databases, Cloud Platforms (AWS/Azure/GCP) Inteview Focus
7. Structured Learning Path to Becoming an FDE 1: Technical Foundation Learning Objectives: Achieve production-level proficiency in core software engineering, database technologies, and distributed data systems. Prerequisites: Foundational computer science knowledge (data structures, algorithms, object-oriented programming). Core Lessons:
Practical Project: Build a Real-Time Analytics Pipeline.
2: AI & ML Specialization Learning Objectives: Develop the specialized skills to design, build, optimize, and deploy modern AI and LLM-based applications in a production context. Prerequisites: Completion of Module 1, a solid grasp of machine learning fundamentals (e.g., the bias-variance tradeoff, supervised vs. unsupervised learning, evaluation metrics). Core Lessons:
Practical Project: Build an End-to-End RAG Q&A System for Technical Documentation.
3: The Client Engagement Stack Learning Objectives: Master the non-technical "human stack" skills of communication, strategic problem-solving, and stakeholder management that are critical for FDE success. Core Lessons:
Practical Project: Develop a Full Client-Facing Project Proposal.
1-1 Career Coaching to Break Into Forward-Deployed Engineering Forward-Deployed Engineering represents one of the most impactful and rewarding career paths in tech - combining deep technical expertise with direct customer impact and business influence. As this guide demonstrates, success requires a unique blend of engineering excellence, communication mastery, and strategic thinking that traditional SWE roles don't prepare you for. The FDE Opportunity:
The 80/20 of FDE Interview Success:
Common Mistakes:
Why Specialized Coaching Matters? FDE roles have unique interview formats and evaluation criteria. Generic tech interview prep misses critical elements:
Accelerate Your FDE Journey: With experience spanning customer-facing AI deployments at Amazon Alexa and startup advisory roles requiring constant stakeholder management, I've coached engineers through successful transitions into AI-first roles for both engineers and managers. What You Get?
Next Steps:
Contact: Email me directly at [email protected] with:
Forward-Deployed Engineering isn't for everyone - but for the right engineers, it offers unparalleled growth, impact, and career optionality. If you're curious whether it's your path, I'd be happy to explore it together. 8. Resources Company Tech Blogs: Actively read the engineering blogs of Palantir, OpenAI, Scale AI, Netflix, and other data-intensive companies to understand real-world architectures and problems. Key Whitepapers & Essays: Re-read and internalize foundational pieces like Andreessen Horowitz's "Services-Led Growth" to understand the business context. Data Engineering: DataCamp (Data Engineer with Python Career Track), Coursera (Google Cloud Professional Data Engineer Certification), Udacity (Data Engineer Nanodegree). AI/ML: DeepLearning.AI (specializations on LLMs and MLOps), Hugging Face Courses (for hands-on transformer and diffusion model experience). Communication: Coursera's "Communication Skills for Engineers Specialization" offered by Rice University is highly recommended. Forums: Participate in Reddit's r/dataengineering and r/MachineLearning to stay current. Newsletters: Subscribe to high-signal newsletters like Data Engineering Weekly and The Batch. 9. References
Table of Contents 1. Conceptual Foundation: The Evolution of AI Interaction
2. Technical Architecture: The Anatomy of a Context Window
3. Advanced Topics: The Frontier of Agentic AI
4. Practical Applications and Strategic Implementation
5. Resources - my other articles on context engineering  The Evolution of LLM Interaction Paradigms 1. Conceptual Foundation: The Evolution of AI Interaction 1.1 The Problem Context: Why Good Prompts Are Not EnoughThe advent of powerful LLMs has undeniably shifted the technological landscape. Initial interactions, often characterized by impressive demonstrations, created a perception that these models could perform complex tasks with simple, natural language instructions. However, practitioners moving from these demos to production systems quickly encountered a harsh reality: brittleness. An application that works perfectly in a controlled environment often fails when scaled or exposed to the chaotic variety of real-world inputs.1 This gap between potential and performance is not, as is commonly assumed, a fundamental failure of the underlying model's intelligence. Instead, it represents a failure of the system surrounding the model to provide it with the necessary context to succeed. The most critical realization in modern AI application development is that most LLM failures are context failures, not model failures.2 The model isn't broken; the system simply did not set it up for success. The context provided was insufficient, disorganized, or simply wrong. This understanding reframes the entire engineering challenge. The objective is no longer to simply craft a clever prompt but to architect a robust system that can dynamically assemble and deliver all the information a model needs to reason effectively. The focus shifts from "fixing the model" to meticulously engineering its input stream. 1.2 The Historical Trajectory: From Vibe to SystemThe evolution of how developers interact with LLMs mirrors the maturation curve of many other engineering disciplines, progressing from intuitive art to systematic science. This trajectory can be understood in three distinct phases:
This progression from vibe to system is not merely semantic; it signals the professionalization of AI application development. Much like web development evolved from simple, ad-hoc HTML pages to the structured discipline of full-stack engineering with frameworks like MVC, AI development is moving from artisanal prompting to industrial-scale context architecture. The emergence of specialized tools like LangGraph for orchestration and systematic workflows like the Product Requirements Prompt (PRP) system provide the scaffolding that defines a mature engineering field.2 1.3 The Core Innovation: The LLM as a CPU, Context as RAM The most powerful mental model for understanding this new paradigm comes from Andrej Karpathy: the LLM is a new kind of CPU, and its context window is its RAM.14 This analogy is profound because it fundamentally reframes the engineering task. We are no longer simply "talking to" a model; we are designing a computational system. If the LLM is the processor, then its context window is its volatile, working memory. It can only process the information that is loaded into this memory at any given moment. This implies that the primary job of an engineer building a sophisticated AI application is to become the architect of a rudimentary operating system for this new CPU. This "LLM OS" is responsible for managing the RAM-loading the right data, managing memory, and ensuring the processor has everything it needs for the current computational step. This leads directly to Karpathy's definition of the discipline: "In every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step". 2. Technical Architecture: The Anatomy of a Context Window To move from conceptual understanding to practical implementation, we must dissect the mechanics of managing the context window. The LangChain team has proposed a powerful framework that organizes context engineering operations into four fundamental pillars: Write, Select, Compress, and Isolate.14 These pillars provide a comprehensive blueprint for architecting context-aware systems. 2.1 Fundamental Mechanisms: The Four Pillars of Context Management 1. Write (Persisting State): This involves storing information generated during a task for later use, effectively creating memory that extends beyond a single LLM call. The goal is to persist and build institutional knowledge for the agent.
2. Select (Dynamic Retrieval): This is the process of fetching the right information from external sources and loading it into the context window at the right time. The goal is to ground the model in facts and provide it with necessary, just-in-time information.
3. Compress (Managing Scarcity): The context window is a finite, valuable resource. Compression techniques aim to reduce the token footprint of information, allowing more relevant data to fit while reducing noise.
4. Isolate (Preventing Interference): This involves separating different contexts to prevent them from negatively interfering with each other. The goal is to reduce noise and improve focus.
2.2 Formal Underpinnings and Key Challenges The need for these architectural patterns is driven by fundamental properties and limitations of the Transformer architecture. 1. The "Lost in the Middle" Problem:
2. Context Failure Modes: When context is not properly engineered, systems become vulnerable to a set of predictable failures 11:
2.3 Implementation Blueprint: The Product Requirements Prompt Workflow One of the most concrete and powerful implementations of context engineering in practice is the Product Requirements Prompt (PRP) workflow, designed for AI-driven software development. This system, detailed in the context-engineering-intro repository, serves as an excellent case study in applying these principles end-to-end.2 This workflow provides a compelling demonstration of a "Context-as-a-Compiler" mental model. In traditional software engineering, a compiler requires all necessary declarations, library dependencies, and source files to produce a valid executable; a missing header file results in a compilation error. Similarly, an LLM requires a complete and well-structured context to produce correct and reliable output. A missing piece of context, such as an API schema or a coding pattern, leads to a "hallucination," which is the functional equivalent of a runtime error caused by a faulty compilation process.24 The PRP workflow is a system designed to prevent these "compilation errors." The workflow consists of four main stages: 1. Set Up Global Rules (CLAUDE.md): This file acts as a project-wide configuration, defining global "dependencies" for the AI assistant. It contains rules for code structure, testing requirements (e.g., "use Pytest with fixtures"), style conventions, and documentation standards. This ensures all generated code is consistent with the project's architecture.2 2. Create Initial Feature Request (INITIAL.md): This is the "source code" for the desired feature. It is a highly structured document that provides the initial context, with explicit sections for a detailed FEATURE description, EXAMPLES of existing code patterns to follow, links to all relevant DOCUMENTATION, and a section for OTHER CONSIDERATIONS to capture non-obvious constraints or potential pitfalls.2 3. Generate the PRP (/generate-prp): This is an agentic step where the AI assistant takes the INITIAL.md file as input and performs a "pre-compilation" research phase. It analyzes the existing codebase for relevant patterns, fetches and reads the specified documentation, and synthesizes this information into a comprehensive implementation blueprint-the PRP. This blueprint includes a detailed, step-by-step plan, error handling patterns, and, crucially, validation gates (e.g., specific test commands that must pass) for each step.2 4. Execute the PRP (/execute-prp): This is the "compile and test" phase. The AI assistant loads the entire context from the generated PRP and executes the plan step-by-step. After each step, it runs the associated validation gate. If a test fails, the system enters an iterative loop where the AI attempts to fix the issue and re-run the test until it passes. This closed-loop, test-driven process ensures that the final output is not just generated, but validated and working.2 The following table operationalizes the four pillars of context management, mapping them to the specific techniques and tools used in production systems like the PRP workflow.  Core Patterns of Context Engineering 3. Advanced Topics: The Frontier of Agentic AI As we move beyond single-purpose applications to complex, autonomous agents, the principles of context engineering become even more critical. The frontier of AI research and development is focused on building systems that can not only consume context but also manage, create, and reason about it. 3.1 Variations and Extensions: From Single Agents to Multi-Agent Systems The orchestration of multiple specialized agents is a powerful application of context engineering, particularly the principle of isolation. Frameworks like LangGraph are designed specifically to manage these complex, often cyclical, workflows where state must be passed between different reasoning units.5 The core architectural pattern is "separation of concerns": a complex problem is decomposed into sub-tasks, and each sub-task is assigned to a specialist agent with a context window optimized for that specific job.14 For example, a "master" agent might route a user query to a "data analysis agent" or a "creative writing agent," each equipped with different tools and instructions. However, this approach introduces a significant challenge: context synchronization. While isolation prevents distraction, it can also lead to misalignment if the agents do not share a common understanding of the overarching goal. Research from teams like Cognition AI suggests that unless there is a robust mechanism for sharing context and full agent traces, a single-agent design with a continuous, well-managed context is often more reliable than a fragmented multi-agent system.25 The choice of architecture is a critical trade-off between the benefits of specialization and the overhead of maintaining coherence. 3.2 Current Research Frontiers (Post-2024) The field is advancing rapidly, with several key research areas pushing the boundaries of what is possible with context engineering. Automated Context Engineering:The ultimate evolution of this discipline is to create agents that can engineer their own context. This involves developing meta-cognitive capabilities where an agent can reflect on its own performance, summarize its own interaction logs to distill key learnings, and proactively decide what information to commit to long-term memory or what tools it will need for a future task.11 This is a foundational step towards creating systems with genuine situational awareness. Standardized Protocols: For agents to operate effectively in a wider ecosystem, they need a standardized way to request and receive context from external sources. The development of the Model Context Protocol (MCP) and similar Agent2Agent protocols represents the creation of an "API layer for context".26 This infrastructure allows an agent to, for example, query a user's calendar application or a company's internal database for context in a structured, predictable way, moving beyond bespoke integrations to a more interoperable web of information. Advanced In-Context Control: Recent academic research highlights the sophisticated control that can be achieved through context.
3.3 Limitations, Challenges, and Security Despite its power, context engineering is not a panacea and introduces its own set of challenges. The Scalability Trilemma: There is an inherent trade-off between context richness, latency, and cost. Building a rich context by retrieving documents, summarizing history, and calling tools takes time and computational resources, which increases response latency and API costs.12 Production systems must carefully balance the depth of context with performance requirements. The "Needle in a Haystack" Problem: The advent of million-token context windows does not eliminate the need for context engineering. As the context window grows, the "lost in the middle" problem can become more acute, making it even harder for the model to find the critical piece of information (the "needle") in a massive wall of text (the "haystack").11 Effective selection and structuring of information remain paramount. Security Vulnerabilities: A dynamic context pipeline creates new attack surfaces.
The increasing commoditization of foundation models is shifting the competitive battleground. The strategic moat for AI companies will likely not be the model itself, but the quality, breadth, and efficiency of their proprietary "context supply chain." Companies that build valuable products are doing so not by creating new base models, but by building superior context pipelines around existing ones. Protocols like MCP are the enabling infrastructure for this new ecosystem, creating a potential marketplace where high-quality, curated context can be provided as a service.26 The strategic imperative for businesses is therefore to invest in building and curating these proprietary context assets and the engineering systems to manage them effectively. 4. Practical Applications and Strategic Implementation The theoretical principles of context engineering are already translating into significant, quantifiable business value across multiple industries. The ability to ground LLMs in specific, reliable information transforms them from generic tools into high-performance, domain-specific experts. 4.1 Industry Use Cases and Quantifiable Impact The return on investment for building robust context pipelines is substantial and well-documented in early case studies:
4.2 Performance Characteristics and Benchmarking Evaluating a context-engineered system requires a shift in mindset. Standard model-centric benchmarks like SWE-bench, while useful for measuring a model's raw coding ability, do not capture the performance of the entire application.32 The true metrics of success for a context-engineered system are task success rate, reliability over long-running interactions, and the quality of the final output. This necessitates building application-specific evaluation suites that test the system end-to-end. Observability tools like LangSmith are critical in this process, as they allow developers to trace an agent's reasoning process, inspect the exact context that was assembled for each LLM call, and pinpoint where in the pipeline a failure occurred.3 The impact of the system's architecture can be profound. In one notable experiment, researchers at IBM Zurich found that by providing GPT-4.1 with a set of "cognitive tools"-a form of context engineering-its performance on the challenging AIME2024 math benchmark increased from 26.7% to 43.3%. This elevated the model's performance to a level comparable with more advanced, next-generation models, proving that a superior system can be more impactful than a superior model alone.33 4.3 Best Practices for Production-Grade Context Pipelines Distilling insights from across the practitioner landscape, a clear set of best practices has emerged for building robust and effective context engineering systems.2
This strategic approach, particularly the "RAG first" principle, has significant financial implications for organizations. Fine-tuning a model is a large, upfront Capital Expenditure, requiring immense compute resources and specialized talent. In contrast, building a context engineering pipeline is primarily an Operational Expenditure, involving ongoing costs for data pipelines, vector database hosting, and API inference.24 By favoring the more flexible, scalable, and continuously updatable OpEx model, organizations can lower the barrier to entry for building powerful, knowledge-intensive AI applications. This reframes the strategic "build vs. buy" decision for technical leaders: the question is no longer "should we fine-tune our own model?" but rather "how do we build the most effective context pipeline around a state-of-the-art foundation model?" 5. Resources
Core
Citations
   Here's an engaging audio in the form of a conversation between two people. I. The AI Imperative: COOs Leading the Operational Revolution
A. Introduction: From AI Hype to Operational Reality The rapid evolution of Artificial Intelligence, especially Generative AI (GenAI) and the emerging Agentic AI, presents both a formidable challenge and a significant opportunity for enterprise leaders. The imperative is to translate AI's vast potential into tangible operational impact and sustainable strategic advantage.1 Agentic AI, with systems capable of autonomous action, is poised to become a major trend, potentially integrating AI agents into the workforce.2 For Chief Operating Officers (COOs), the focus must be on practical application and value extraction. Many organizations are still in nascent stages; a McKinsey survey revealed only 17% of organizations derive over 10% of their Earnings Before Interest and Taxes (EBIT) from GenAI, and a mere 1% claim full GenAI maturity.1 This highlights a critical execution gap. COOs, at the nexus of strategy and execution, are pivotal in bridging this gap and moving from AI's theoretical possibilities to operational reality. B. The Evolving COO Mandate & The Execution Gap The COO's traditional role as an operational guardian is evolving into that of an AI-powered value architect. They are now central to driving strategic transformation by embedding intelligence into core processes and identifying new AI-fueled value streams.1 This expanded mandate requires COOs to lead the "GenAI-based rewiring" of their organizations, ensuring AI investments yield tangible returns.1 Midlevel leaders, often reporting to COOs, are instrumental in embedding AI into daily practices and cross-functional processes 3, leveraging the COO's oversight of all operational facets.4 Despite enthusiasm, a significant execution gap persists. Only 19% of US C-suite executives reported GenAI increasing revenue by over 5%, and globally, just 17% of organizations derive over 10% of EBIT from GenAI.1 Many find GenAI development too slow, and only 12% have identified revenue-generating use cases.1 This is echoed by findings that while 73% of companies invest over $1 million annually in GenAI, only a third see tangible payoffs 5, and over 80% of AI projects may fail to meet objectives.6 This gap often stems from immature data foundations, a lack of AI literacy, and ineffective change management—challenges COOs must address holistically. II. Architecting for AI Success: Critical Foundations for COOs A. Designing AI-Ready Operating Structures & Data Governance To harness AI, COOs must champion AI-ready operating structures that move beyond traditional silos to foster synergy and agility. Initially, a Center of Excellence (CoE) or a "factory" model, guided by executive and operational committees, can establish standards and build foundational capabilities.1 Gartner notes organizations often evolve from communities of practice towards target operating models for scaling AI.7 As maturity grows, a federated or hub-and-spoke model, like OCBC Bank’s "internal open-source hub" 8, can empower business units while maintaining central guidance. COOs must architect these structures to balance control with empowerment, ensuring solutions are impactful yet achievable.1 Robust data governance is a non-negotiable strategic imperative. The quality, integrity, and ethical handling of data directly determine AI reliability.1 COOs, with CDOs and CIOs, must champion comprehensive data governance frameworks 1, viewing it not as a cost but as an enabler of value and a risk mitigator.10 Governance must be proactive, business-aligned, and embedded into AI workflows, moving towards automated enforcement to scale effectively.2 B. Effective Change Management: Paving the Way for AI Adoption GenAI and Agentic AI fundamentally alter roles and processes, making effective change management critical.1 COOs must sponsor structured change management from the outset. As Forrester notes, "Whatever communication, enablement, or change management efforts you think you'll need, plan on tripling them".12 Frameworks like Gartner's multistep process (prioritizing outcomes, diverse teams, compelling narratives, "culture hacking," addressing resistance) 13 or Prosci’s ADKAR model (Awareness, Desire, Knowledge, Ability, Reinforcement) 14 offer systematic approaches. High AI project failure rates often trace back to poor adoption, a failure of change management. COOs must ensure the organization is prepared technologically, culturally, and behaviorally. III. Driving Operational Impact: From Strategic Use Cases to Measurable ROI A. Identifying & Prioritizing AI Use Cases for Tangible Value COOs must guide a pragmatic approach to AI use case identification, moving beyond "pilot purgatory" to initiatives delivering tangible value aligned with business objectives.1 Gartner’s AI roadmap emphasizes starting by "prioritizing a set of initial use cases, running pilots, and tracking and demonstrating their business value".7 Focus on opportunities where AI can address "long-standing operational logjams" 1 or create new efficiencies, often starting with "narrowly defined, high-impact use cases".9 AWS highlights numerous GenAI use cases spanning customer experience, employee productivity (e.g., automated reporting, code generation), and process optimization (e.g., intelligent document processing, supply chain optimization).15 COOs should use an "impact vs. feasibility" matrix to select strategically sound and operationally achievable initiatives. Illustrative High-Impact AI Domains:
Agentic AI systems "act autonomously to achieve goals without the need for constant human guidance".2 Unlike GenAI or rules-based RPA, they possess independent reasoning, decision-making, and action execution, learning from interactions (Perceive, Reason, Act, Learn).2 Their potential is immense for automating complex workflows where traditional automation falls short.16 Examples include expediting procure-to-pay approvals, resolving order-to-cash discrepancies, collating customer information in contact centers, streamlining HR onboarding, and providing immediate IT troubleshooting.16 As AI gains such autonomy, the need for robust governance, meticulous oversight, and a new trust paradigm becomes even more critical. COOs must plan for Agentic AI as a catalyst for re-imagining entire operational processes. C. Measuring AI ROI: A Pragmatic Approach Demonstrating AI ROI is a "business mandate" 20, yet nearly half of leaders find proving GenAI's value the biggest hurdle.20 COOs need a pragmatic approach encompassing financial metrics, operational efficiencies, and qualitative benefits.6
IV. The Human-Centric Transformation: Building an AI-First Culture A. Fostering an AI-Literate Workforce & AI-First Mindset Creating an AI-first culture requires broad AI literacy—understanding AI's capabilities, limitations, and ethics—and fostering a mindset of curiosity, experimentation, and human-AI collaboration. Forrester states, "Close The AI Literacy Gap To Unlock Real Impact," as hesitation due to lack of understanding cripples adoption.15 The journey involves "building foundational AI knowledge," "cultivating an AI-first mindset" (AI as an enhancer, not a replacer), honing "AI-specific skills," and "leading with confidence".3 Effective AI systems also need human expertise for training with "clear, labeled examples".13 COOs must champion pervasive AI literacy programs for the entire workforce. B. Dr. Teki's Perspective: Neuroscience for Impactful AI Upskilling Traditional corporate training often fails to align with how adults learn . Dr. Sundeep Teki's expertise in neuroscience 3 offers an advantage. Principles like spaced repetition, active learning, managing cognitive load, and leveraging emotional engagement can make AI training more effective, helping overcome the "forgetting curve" . Testimonials for Dr. Teki's training highlight its clarity and interactivity.6 Neuroscience shows that active processing, reinforcement over time, and positive emotional experiences (like achievement) enhance learning and retention . Understanding the brain's response to change is also vital for fostering psychological adaptability . Great Learning's GenAI academy, with hands-on learning and real-world case studies 4, aligns with these principles. Grounding AI upskilling in how people learn improves skill retention and workforce agility. C. Leading Through Change: Overcoming Resistance & Building Trust Successful AI integration is a human challenge, often met with fear of job loss, lack of trust, and resistance to new work methods.26 COOs must lead with empathy, transparency, involve employees, and build trust.14 Addressing "AI Anxiety" 9 involves visible leadership commitment, comprehensive reskilling, clear communication (AI as a supportive tool), and transparent ethical guidelines.26 Gartner emphasizes listening to understand resistance 27, while Prosci’s ADKAR model highlights building Desire and Reinforcing behaviors . Overcoming inertia may require "frame flexibility"—cognitively and emotionally reframing AI to align with organizational values . Trust is the currency of AI transformation. D. Dr. Teki's Perspective: The Indispensable Human Element & Neuroscience of Change The human element is indispensable. Dr. Teki's neuroscience expertise 3 provides insights into cognitive and emotional responses to change. Resistance to AI often stems from fear, anxiety, or perceived loss of status . The brain's preference for predictability means significant changes like AI adoption can trigger stress if not managed carefully . Emotional framing—aligning change with passions and aspirations—can increase adoption . Workplace transformation impacts rational and emotional selves; applying brain science can help employees thrive . This involves fostering emotional intelligence skills like self-awareness, adaptability, empathy, and constructive interaction . Understanding these underpinnings allows COOs to deploy strategies more attuned to the human experience of change, fostering acceptance and accelerating the AI-first journey. V. The Path Forward: The COO as Catalyst for Sustained AI-Driven Advantage Conclusion The COO's success in harnessing GenAI and Agentic AI hinges on integrating several strategic pillars: embracing an evolved mandate as an AI value architect; establishing AI-ready operating structures and robust data governance; pragmatically driving operational impact through strategic use cases and diligent ROI measurement; and leading a human-centric transformation by fostering AI literacy, leveraging neuroscience for upskilling, and empathetically managing change. AI adoption is an ongoing journey of learning and continuous improvement. As AI capabilities advance, strategies and operational models must be agile.3 The pinnacle of AI maturity involves "anticipating continued disruption" and "harnessing those trends to create value".3 COOs must foster a culture of "progress over perfection" 15, valuing experimentation and institutionalizing learning. The opportunity for COOs to redefine operational excellence with AI is immense. By spearheading these multifaceted efforts, COOs can position their organizations at the industry vanguard. Navigating this transformation requires strategic foresight, technological understanding, and a deep appreciation of human dynamics. Explore how tailored AI strategies and corporate training can empower your organization to unlock the full, sustainable promise of Generative and Agentic AI. VI. References
 1. India's ranking in the Stanford AI Index 2025  2. Analysis of India's relative AI strengths and weaknesses vs. USA and China India ranks 4th globally in the AI Index (figure 1) with a score of 25.54, placing it behind the US (1st, 70.06) and China (2nd, 40.17). However, a comparative analysis of India's AI strengths and weaknesses (figure 2) reveals that there are still major concerns and problems for her to solve to be able to compete with global AI leaders.
Strengths for India
Weaknesses for India
Conclusion India shows potential, particularly in leveraging its diversity, policy focus, and growing educational base for AI. However, critical gaps in infrastructure and responsible AI practices, along with translating R&D into economic gains, are major hurdles compared to global leaders like the US and China. AI Strategy & Training for Executives The gap between India's AI potential and its current infrastructural/ethical maturity requires astute leadership. The winners will be those who can strategically:
Leading effectively in the age of AI, particularly Generative AI, requires specific strategic understanding. If you would like to equip your executive team with the knowledge to make confident decisions, manage risks, and drive successful AI integration, reach out for custom AI training proposals - [email protected]. Related blogs  Introduction: From Buzzword to Bottom Line
Generative AI (GenAI) is no longer a futuristic concept whispered in tech circles; it's a powerful force reshaping industries and fundamentally altering how businesses operate. GenAI has decisively moved "from buzzword to bottom line." Early adopters are reporting significant productivity gains – customer service teams slashing response times, marketing generating months of content in days, engineering accelerating coding, and back offices becoming vastly more efficient. Some top performers even attribute over 10% of their earnings to GenAI implementations. The potential is undeniable. But harnessing this potential requires more than just plugging into the latest Large Language Model (LLM). Building sustainable, trusted, and value-generating AI capabilities within an enterprise is a complex journey. It demands a clear strategy, robust foundations, and crucially, a workforce equipped with the right skills and understanding. Without addressing the human element – the knowledge gap across all levels of the organisation – even the most sophisticated AI tools will fail to deliver on their promise. This guide, drawing insights from strategic reports and real-world experience, outlines the key stages of developing a successful enterprise GenAI strategy, emphasizing why targeted corporate training is not just beneficial, but essential at every step. The Winning Formula: A Methodical, Phased Approach The path to success is methodical: "identify high-impact use cases, build strong foundations, and scale what works." This journey typically unfolds across four key stages, underpinned by an iterative cycle of improvement. Stage 1: Develop Your AI Strategy – Laying the Foundation This initial phase (often the first 1-3 months) is about establishing the fundamental framework. Rushing this stage leads to common failure points: misaligned governance, crippling technical debt, and critical talent gaps. Success requires a three-dimensional focus: People, Process, and Technology. 1. People Executive Alignment & Sponsorship: Getting buy-in isn't enough. Leaders need a strategic vision tying AI to clear business outcomes (productivity, growth). They must understand AI's potential and limitations to provide realistic guidance. Training Need: Executive AI Briefings are crucial here, demystifying GenAI, outlining strategic opportunities/risks, and fostering informed sponsorship. Governance & Oversight: Establishing an AI review board, ethical guidelines, and transparent evaluation processes cannot be an afterthought. Trust is built on responsible foundations. Training Need: Governance teams need specialized training on AI ethics, bias detection, model evaluation principles, and regulatory compliance implications. 2. Process Pilot Selection: Avoid tackling the biggest challenges first. Identify pilots offering demonstrable value quickly, with enthusiastic sponsors, available data, and manageable compliance. Focus on addressing real friction points. Training Need: Business leaders and managers need training to identify high-potential, LLM-suitable use cases within their domains and understand the criteria for a successful pilot. Scaling Framework: Define clear "graduation criteria" (performance thresholds, operational readiness, risk management) for moving pilots to broader deployment. Training Need: Project managers and strategists need skills in defining AI-specific KPIs and operational readiness checks. 3. Technology Technical Foundation: Evaluate existing infrastructure, data architecture maturity, integration capabilities, and tooling through an "AI lens." Training Need: IT and data teams require upskilling to understand the specific infrastructural demands of AI development and deployment (e.g., GPUs, vector databases, MLOps). Data Governance: High-quality, accessible, compliant data is non-negotiable. This requires sophisticated governance and data quality management. Training Need: Data professionals need advanced training on data pipelines, quality checks, and governance frameworks specifically for AI. Stage 2: Create Business Value – Identifying and Proving Potential Once the strategy is outlined (Months 4-6, typically), the focus shifts to identifying specific use cases and demonstrating value through well-chosen pilots. Identifying Pilot Use Cases: The best initial projects leverage core LLM strengths (unstructured data processing, content classification/generation) but carry low security or operational risk. They need abundant, accessible data and measurable success metrics tied to business indicators (reduced processing time, improved accuracy, etc.). Defining Success Criteria: Move beyond vague goals. Success metrics must be Specific, Measurable, Aligned with business objectives, and Time-bound (SMART). You can find excellent examples across use cases like ticket routing, content moderation, chatbots, code generation, and data analysis. Choosing the Right Model: Consider the trade-offs between intelligence, speed, cost, and context window size based on the specific task. Training Need: Teams selecting models need foundational training on understanding these trade-offs and how different models suit different business needs and budgets. Stage 3: Build for Production – From Concept to Reality This stage involves turning the chosen use case and model into a reliable, scalable application. Prompt Engineering: It is strongly advisable to invest in prompt engineering as a key skill. Well-crafted prompts can significantly improve model capabilities, often more quickly and cost-effectively than fine-tuning. This involves structuring prompts effectively (task, role, background data, rules, examples, formatting). Training Need: Dedicated prompt engineering training is crucial for technical teams and even power users to maximize model performance without resorting to costly fine-tuning prematurely. Evaluation: Rigorous evaluation is key to iteration. It is recommended to perform detailed, specific, automatable tests (potentially using LLMs as judges), run frequently. Side-by-side comparisons, quality grading, and prompt versioning are vital. Training Need: Data scientists and ML engineers require training on robust evaluation methodologies, understanding metrics, and potentially leveraging proprietary tools Optimization: Techniques like Few-Shot examples (providing examples in the prompt) and Chain of Thought (CoT) prompting (letting the model "think step-by-step") can significantly improve output quality and accuracy. Training Need: Applying these optimization techniques effectively requires specific training for those building the AI applications. Stage 4: Deploy – Scaling and Operationalizing Once an application runs smoothly end-to-end, it's time for production deployment (Months 13+ for broad adoption). Progressive Rollout: Don't replace old systems immediately. Use progressive rollouts, A/B testing, and design user-friendly human feedback loops. LLMOps (Deploying with LLM Ops): Operationalizing LLMs requires specific practices (LLMOps), a subset of MLOps. There are five best practices: 1. Robust Monitoring & Observability: Track basic metrics (latency, errors) and LLM-specific ones (token usage, output quality). 2. Systematic Prompt Management: Version control, testing, documentation for prompts. 3. Security & Compliance by Design: Access controls, content filtering, data privacy measures from the start. 4. Scalable Infrastructure & Cost Management: Balance scalability with cost efficiency (caching, right-sizing models, token optimisation). 5. Continuous Quality Assurance: Regular testing, hallucination monitoring, user feedback loops. Training Need: Dedicated MLOps / LLMOps training* is essential for DevOps and ML engineering teams responsible for deploying and maintaining these systems reliably and cost-effectively. The Undeniable Need for Corporate AI Training Across All Levels A recurring theme throughout industry reports (like BCG citing talent shortage as the #1 challenge), is the critical need for AI competencies at every level of the organisation: 1. C-Suite Executives: Need strategic vision. They require training focused on understanding AI's potential and risks, identifying strategic opportunities, asking the right questions, and championing responsible AI governance.** Generic AI knowledge isn't enough; they need tailored insights relevant to their industry and business goals. 2. Managers & Team Leads: Need skills to guide transformation. Training should focus on identifying practical use cases within their teams, managing AI implementation projects, interpreting AI performance metrics, leading change management, and fostering collaboration between technical and non-technical staff. 3. Individual Contributors: Need practical tool proficiency. Training should equip them to use specific AI tools effectively and safely, understand basic prompt techniques, provide valuable feedback for model improvement, and be aware of ethical considerations and data privacy. 4. Technical Teams (Engineers, Data Scientists, IT): Need deep, specialized skills. This requires ongoing, in-depth training on advanced prompt engineering, fine-tuning techniques, LLMOps, model evaluation methodologies, AI security best practices, and integrating AI with existing systems. Without this multi-layered training approach, organizations risk:
Partnering for Success: Your AI Training Journey Building a successful Generative AI strategy is a marathon, not a sprint. It requires a clear roadmap, robust technology, strong governance, and, most importantly, empowered people. Generic, off-the-shelf training often falls short for the specific needs of enterprise transformation. As an expert in AI and corporate training, I help organizations navigate this complex landscape. From executive briefings that shape strategic vision to hands-on workshops that build practical skills for technical teams and business users, tailored training programs are designed to accelerate your AI adoption journey responsibly and effectively. Ready to move beyond the buzzword and build real, trusted AI capabilities? Let's discuss how targeted training can become the cornerstone of your enterprise Generative AI strategy. Please feel free to Connect to discuss your organisation's AI Training requirements. Introduction
The AI revolution is no longer a distant future—it’s reshaping industries today. By 2025, the global AI market is projected to reach $190 billion (Statista, 2023), with generative AI tools like ChatGPT and Midjourney contributing an estimated $4.4 trillion annually to the global economy (McKinsey, 2023). For tech professionals and organizations, this rapid evolution presents unparalleled opportunities but also demands strategic navigation. As an AI expert with a decade of experience working at Big Tech companies and scaling AI-first startups, I’ve witnessed firsthand the transformative power of well-executed AI strategies. This blog post distills actionable insights for:
Let’s explore how to turn AI’s potential into measurable results. Breaking into AI – A Blueprint for Early-Career Professionals The Skills That Matter in 2024 The AI job market is evolving beyond traditional coding expertise. While proficiency in Python and TensorFlow remains valuable, employers now prioritize three critical competencies: 1. Prompt Engineering: With generative AI tools like GPT4/o/o1-/o-3, Deepseek-R1, Claude Sonnet 3.5 etc., the ability to craft precise prompts is becoming a baseline skill. For example, a marketing analyst might use prompts like, “Generate 10 customer personas for a fintech app targeting Gen Z, including pain points and preferred channels.” 2. AI Literacy: 85% of hiring managers now require familiarity with responsible AI frameworks ([Deloitte, 2023](https://www2.deloitte.com)). This includes understanding bias mitigation and compliance with regulations like the EU AI Act. 3. Cross-Functional Collaboration: AI projects fail when technical teams operate in silos. Professionals who can translate business goals into technical requirements—and vice versa—are indispensable. Actionable Steps to Launch Your AI Career 1. Develop a "T-shaped" Skill Profile: Deepen expertise in machine learning (the vertical bar of the “T”) while broadening knowledge of business applications. For instance, learn how recommendation systems impact e-commerce conversion rates. 2. Build an AI Portfolio: Document projects that solve real-world problems. A compelling example: fine-tuning Meta’s Llama 2 model to summarize legal contracts, then deploying it via Hugging Face’s Inference API. 3. Leverage Micro-Credentials: Google’s [Generative AI Learning Path](https://cloud.google.com/blog/topics/training-certifications/new-generative-ai-training) and DeepLearning.AI’s short courses provide industry-recognized certifications that demonstrate proactive learning. From Individual Contributor to AI Leader – Strategies for Mid/Senior Professionals The Four Pillars of Effective AI Leadership Transitioning from technical execution to strategic leadership requires mastering these core areas: 1. Strategic Vision Alignment: Successful AI initiatives directly tie to organizational objectives. For example, a retail company might set the OKR: “Reduce supply chain forecasting errors by 40% using time-series AI models by Q3 2024.” 2. Risk Mitigation Frameworks: Generative AI models like GPT-4 can hallucinate inaccurate outputs. Leaders implement guardrails such as IBM’s [AI Ethics Toolkit](https://www.ibm.com), which includes bias detection algorithms and human-in-the-loop validation processes. 3. Stakeholder Buy-In: Use RACI matrices (Responsible, Accountable, Consulted, Informed) to clarify roles. For instance, when deploying a customer service chatbot, legal teams must be “Consulted” on compliance, while CX leads are “Accountable” for user satisfaction metrics. 4. ROI Measurement: Track metrics like inference latency (time to generate predictions) and model drift (performance degradation over time). One fintech client achieved a 41% improvement in fraud detection accuracy by combining XGBoost with transformer models, while reducing false positives by 22%. Building an AI-First Organization – A Playbook for Startups The AI Strategy Canvas 1. Problem Identification: Focus on high-impact “hair-on-fire” pain points. A logistics startup automated customs documentation—a manual 6-hour process—into a 2-minute task using GPT-4 and OCR. 2. Tool Selection Matrix: Compare open-source (e.g., Hugging Face’s LLMs) vs. enterprise solutions (Azure OpenAI). Key factors: data privacy requirements, scalability, and in-house technical maturity. 3. Implementation Phases: - Pilot (1-3 Months): Test viability with an 80/20 prototype. Example: A SaaS company used a low-code platform to build a churn prediction model with 82% accuracy using historical CRM data. - Scale (6-12 Months): Integrate models into CI/CD pipelines. One e-commerce client reduced deployment time from 14 days to 4 hours using AWS SageMaker. - Optimize (Ongoing): Conduct A/B tests between model versions. A/B testing showed that a hybrid CNN/Transformer model improved image recognition accuracy by 19% over pure CNN architectures. Generative AI in Action – Enterprise Case Studies Use Case 1: HR Transformation at a Fortune 500 Company Challenge: 45-day hiring cycles caused top candidates to accept competing offers. Solution: - GPT-4 drafted job descriptions optimized for DEI compliance - LangChain automated interview scoring using rubric-based grading - Custom embeddings matched candidates to team culture profiles Result: 33% faster hiring, 28% improvement in 12-month employee retention. Use Case 2: Supply Chain Optimization for E-Commerce Challenge: $2.3M annual loss from overstocked perishable goods. Solution: - Prophet time-series models forecasted regional demand - Fine-tuned LLMs analyzed social media trends for real-time demand sensing Result: 27% reduction in waste, 15% increase in fulfillment speed. Avoiding Common AI Adoption Pitfalls Mistake 1: Chasing Trends Without Alignment Example: A startup invested $500K in a metaverse AI chatbot despite having no metaverse strategy. Solution: Use a weighted decision matrix to evaluate tools against KPIs. Weight factors like ROI potential (30%), technical feasibility (25%), and strategic alignment (45%). Mistake 2: Ignoring Data Readiness Example: A bank’s customer churn model failed due to incomplete historical data. Solution: Conduct a data audit using frameworks like [O’Reilly’s Data Readiness Assessment](https://www.oreilly.com). Prioritize data labeling and governance. Mistake 3: Overlooking Change Management Example: A manufacturer’s warehouse staff rejected inventory robots. Solution: Apply the ADKAR framework (Awareness, Desire, Knowledge, Ability, Reinforcement). Trained “AI ambassadors” from frontline teams increased adoption by 63%. Conclusion The AI revolution rewards those who blend technical mastery with strategic execution. For professionals, this means evolving from coders to translators of business value. For organizations, success lies in treating AI as a core competency—not a buzzword. Three Principles for Sustained Success: 1. Learn Systematically: Dedicate 5 hours/week to AI upskilling through curated resources. 2. Experiment Fearlessly: Use sandbox environments to test tools like Anthropic’s Claude or Stability AI’s SDXL. 3. Collaborate Across Silos: Bridge the gap between technical teams (“What’s possible?”) and executives (“What’s profitable?”).  This image illustrates a significant trend in OpenAI's innovative work on large language models: the simultaneous reduction in costs and improvement in quality over time. This trend is crucial for AI product and business leaders to understand as it impacts strategic decision-making and competitive positioning. Key Insights:
Generative AI startups can capitalize on the trend of decreasing costs and improving quality to drive significant value for their customers. Here are some strategic approaches 1. Cost-Effective Solutions:
2. Enhanced Product Offerings:
3. Strategic Investment in R&D:
4. Operational Efficiency:
Introduction
Data is the cornerstone of businesses from large enterprises to small D2C brands, and huge amounts of it can be collected from websites, mobile apps, chat messages, call centers, business transactions, surveys, and social media platforms, among other channels. All this data represents a gold mine of information that can offer customer insights and lead to new ideas for features or products. However, making sense of the data is easier said than done. The information originates from various channels and in multiple formats. It can be logged erroneously and contain other errors, including missing values. Because it comes from multiple domains, it can include unstructured data like text, images, audio, and video. That is why data preparation is essential. This involves cleaning, curating, transforming, and storing data sets for downstream applications including data analytics and data visualization, as well as predictive intelligence based on machine learning and deep learning models. Data can only provide value once it has been processed from its raw form, and effective data preparation can maximize that value. This article will explain the process of data preparation, especially in terms of data labeling, and will provide a checklist for data engineers to follow. What Is Data Preparation? Data preparation is not an entirely new process in technology companies. Data-driven operations previously focused on statistical analysis of business data from structured tables. The deep learning model has grown over the past decade along with the global penetration of mobile phones, widely available internet access, and cheaper cloud storage facilities. Today an estimated 2.5 quintillion bytes of data are being generated daily. Every user interaction with online companies is recorded, from someone clicking an ad or adding a product to a shopping cart to sharing a photo on a social media app. User-generated data is generally unstructured data: images, text, audio, or video. Such data can be used to train sophisticated deep learning models to predict what users want to type in a text, which branded products are featured in an image, and what kind of customer service will be provided in a phone conversation. For deep learning models to make sense of this data, all data samples need to be labeled. Data labeling tells the machine learning models what knowledge they need to acquire via supervised learning to power smart applications. This makes labeling critical in preparing data sets for training machine learning models. However, data labeling can also represent the chief source of errors, affecting potential improvement in model performance. Machine learning models can only be as accurate as the labeled data, which represents the models’ entire knowledge for the particular use case. For example, the source image data set in a face recognition program requires a label for every face shown in every image. During the labeling process for this data set, every image is reviewed by human subject matter experts, crowdsourced labelers on platforms like Amazon Mechanical Turk, or algorithms. Labeling helps clean and prepare the data set by removing noisy or unusable data. In this case, images that don’t contain any faces, or that show unreadable faces due to poor lighting or angles, should be removed because they won’t be helpful in training a face recognition model. This step also ensures the inclusion of images that are most helpful for the desired use case. Once the data set is reviewed and annotated, it can be used for all subsequent face recognition applications instead of going back to the raw data set. This saves time and effort for data engineers, as well as data scientists who might build novel models using the same data set. Additionally, multiple labels and metadata can be applied to each image during the labeling process so that they’re available for new use cases. A tag that identifies the face as that of a man, woman, or child can be used for different computer vision applications. This can potentially give the data set more flexibility for the future. The labeling can be built upon in subsequent versions of the data set. Once the face recognition model is live in production, new images can be labeled to help the model overcome data drift and augment its performance in the face of changing data distributions. This continued labeling and organizing keeps the models more robust and consistent. Data Preparation Steps There are certain best practices to follow when preparing data sets for deep learning applications. Following is a checklist for data engineers when working with unstructured data: (1) Check data formats Samples in a data set, especially if collected via web scraping or crowdsourcing, may come in multiple data formats. For example, an image could be a JPEG, PNG, or TIFF, while an audio file could be a WAV, MP3, or FLAC. Check whether the data set samples are in different formats, so that you can standardize the format across all samples. (2) Verify data types Certain deep learning applications are based on multimodal data including text, images, audio, video, and structured metadata. For example, a model that predicts what video a user might watch next is trained using multiple data types. It verifies the type of each data sample, then indexes and stores them separately. Note that an individual data type like numbers might also belong to different types like int, float, or string. (3) Verify data dimensions It’s crucial to check the dimensionality of the samples in a data set. For example, a set of images containing faces may be gathered from different cameras, each associated with different default image dimensions. (4) Identify what data needs to be labeled Once you’ve completed the above steps, you can begin data labeling. It may not be feasible in some situations to label each data sample, because manual labeling can be prohibitively expensive and time-consuming. In this case, choose an appropriate number of data samples for labeling. For common machine learning classification use cases, you need to sample data for labeling from each category. (5) Determine what type of labeling to perform The same data sample can be labeled in multiple ways depending on the use case. For instance, an image containing people and cars may be labeled for faces, for segmenting people or cars, or for the vehicle registration plates. (6) Decide who will label the data Data labeling can be performed manually by domain experts, crowdsourced from non-experts, or done programmatically using rule-based or model-based algorithms. Determine which annotators will define what kind of data, depending on their expertise or level of training. If a data set will be labeled using software, then the required configuration parameters, protocols, and performance metrics need to be established so that labeling is consistent. (7) Review data for errors and mistakes Usually, the first round of data labeling contains errors. To improve the data quality and eradicate errors, more experienced annotators should conduct a second or third level of review. Depending on cost, time, and available resources, each data sample can also be independently labeled by multiple annotators; the most commonly provided label can be assigned as the final label. (8) Split the data set into training and testing segments Once a data set is labeled, split it into separate train and test subsets for training and evaluating the model, respectively. Depending on the use case and the amount of available data, the ratio might be 80:20, 90:10, or even 99:1. To obtain more reliable results, k-fold cross-validation is recommended. Multiple training and test sets are sampled randomly, and the final results are averaged across all the different folds. Conclusion Without the protection of systematic data preparation and labeling checks, you may find that poor quality data damages the accuracy and performance of any analysis or models based on that data. If you follow the above guide, you will be able to ensure your data is good quality and labeled accurately. Related Blogs
Published by Andela Introduction
Data culture refers to an organizational culture of using data to derive insights and make informed business decisions. Companies can build a strong data culture by arming themselves with data and the right set of people, policies, and technologies. A data culture helps companies become more competitive and resourceful by leveraging data. And data-driven companies make better, faster, and more objective business decisions. They promote greater employee engagement and retention, and drive better financial outcomes in terms of revenue, profitability, and operational efficiency. In this article, you'll learn about data culture, what its importance is for modern organizations, and how you can build a strong data culture at your company. Why You Need a Strong Data Culture? Without a solid data culture, organizations will inevitably fail to harness the power of data. As previously stated, data culture refers to a set of beliefs and practices that companies use to cultivate and drive more data-driven decisions. Traditionally, businesses relied on the instinct and gut of a select few leaders to make strategic business decisions. However, with the accumulation and collection of massive volumes of customer and business data, domain expertise and instinct can now be complemented with data-driven insights to make more informed decisions. There are several advantages to building a strong data culture. Some of these include the following:
Every business sector, from product to finance to HR, creates and collects a lot of data from external customers or internal operations. For business heads and decision-makers, it's no longer feasible to stay on top of the ever-increasing volumes of data to better understand and evaluate the current state of their organization. However, with data analysts and scientists embedded across each department, it is possible to tap business insights in real time and respond quickly to changes in business performance. A strong data culture also promotes greater employee engagement and retention. When employees see that decisions are made on the basis of data and not driven just by the highest-paid executives, they feel that they can contribute more insights to influence decision-making. In the long term, this facilitates attracting the best talent in the market who can be incentivized to have a greater say in making key business decisions using data. Moreover, there are also strong financial outcomes associated with building and promoting a data culture. Companies with data-driven cultures benefit from increased revenue, better customer services, and more operational efficiencies leading to improved profitability. How to Build a Strong Data Culture? Building a strong data culture is a long-term endeavor that requires patient support and encouragement from leadership. Companies with strong data-driven cultures have executives who lead by example and establish clear expectations that decisions will be objective and based on data. Data leaders can lead from the front by establishing clear goals and guidelines, investing in technology and training, as well as identifying and rewarding employee behaviors that embody a data-led culture. Beyond leadership setting a tone for the whole organization, let's take a look at a few other components that can help build a strong data culture. 1 Bring Business and Data Science Together One of the first steps in building a data culture is to build a strong data science team consisting of data analysts, data engineers, and data scientists. Having quality in-house data talent is a competitive advantage that reaps multiple benefits, including building a robust culture focused on data. Once a data science team is up and running, it needs to be strategically embedded across various departments of the business. This helps business professionals interact with data professionals more regularly and better understand how the power of data analytics and data science can improve business efficiencies and impact profitability and growth. At the same time, this setting enables data professionals to better understand how the business works and build intuition for developing better data and machine learning–powered tools and products. This creates a positive flywheel where both business and data science teams learn to collaborate better and benefit from their respective skill sets. By bringing business and data science together, everyone in the organization learns to appreciate the value of data and use data-driven insights to improve the quality of their decisions, products, and services. 2 Leverage Data When Creating Goals and Deadlines Driving strategic business goals and metrics by leveraging data is a key aspect of encouraging a data-led culture. When goal-setting exercises are conducted objectively and leaders regularly use data and metrics from previous business quarters or external data about competitors or the overall market, everyone in the organization will start to embrace similar data-driven approaches. Leveraging data for setting new targets also enables every stakeholder in the organization to understand and anticipate their future goals and prioritize their work accordingly. Data-led goal setting is a more democratic and fair-minded process that encourages ownership of respective goals by every employee, as opposed to arbitrary, instinct-led, unilateral decisions made by the leadership. 3 Ensure Everybody Has Access to Data A fundamental step toward attaining a data culture is to democratize access to data across the organization. Data culture is a difficult goal when employees in different parts of a business struggle to obtain data. If you don't give your employees access to your data, they won't be able to utilize it when making decisions. This disenfranchises the data analysts, engineers, and scientists disproportionately, as their day-to-day work is impacted the most. Without a motivated team of data professionals, the downstream benefits of data are unlikely to materialize across various business departments. A strong foundation of data governance and data democratization is a prerequisite to achieving the business goals associated with a robust data culture. 4 Keep Your Data Technology Up-to-Date A critical aspect of building a data culture is employing modern tools and technologies to make it easier for employees to access, analyze, and share data-driven insights. Building a modern data stack with newer components like a metrics layer simplifies data-based operations and analytics for everyone, especially nontechnical business stakeholders. Technology, like data warehouses and metrics layers; data analytics tools, like Tableau or Power BI; and customer relationship management (CRM) tools, like Salesforce, are indispensable for modern businesses. Building the data architecture in a cloud environment like Amazon Web Services further improves access to data and reduces the need for multiple tools with a steep learning curve. The right use of tools for data, collaboration, and customer service goes a long way in fostering the use of technology to drive a strong data-led culture. 5 Provide Training for Employees Having supportive leadership and access to data and technology is of little use if employees are not data literate and able to extract insights from data. This requires further investment in terms of learning and development to empower employees with the necessary skills to explore, understand, and share data-driven insights across the organization. In addition to reducing the skills gap, it also encourages people from nontechnical backgrounds to become more data savvy, collaborate better with data experts, and build more comprehensive data products and solutions to benefit the business. 6 Reward Data-Oriented Decisions and Behavior The primary challenge to becoming a data-driven organization is not technical but cultural. A strong data culture is based on a robust foundation of people, policies, and technology. However, once the initial foundation is in place, data leaders need to maintain and bolster the spirit of data-driven decision-making by incentivizing and rewarding behaviors that embody the culture. At the same time, decisions and behaviors that do not represent a holistic data-led process ought to be called out and questioned until every single employee is on board with the philosophy of using data for every decision. This includes encouraging experimentation to answer key business questions for which data does not exist yet or when the current set of data does not yield compelling evidence. Conclusion In this article, you learned about the importance of a data culture for businesses. It's a formidable task to build a strong data culture and is a top priority for a majority of CEOs. Data-driven companies are in a better position to attract and retain talent, make faster decisions with more conviction, and drive stronger growth and profitability to meet their business goals. According to research by McKinsey & Company, data-driven companies are able to achieve their goals faster and realize at least 20 percent more earnings. Related Blogs  
Web3 is the third generation of the internet based on emerging technologies like blockchains, tokens, DAOs, digital assets, decentralised finance that has the potential to give back control of digital assets back to the users with greater trust and transparency.
Typical web3 applications focus on DAOs, DeFi, Stablecoins, Privacy and digital infrastructure, the creator economy amongst others. The web3 ecosystem represents a promising green space for creators, developers, and various types of tech and non-tech professionals as well. In my talk (video and slides shared above) for Crater's Encrypt 2022 hackathon, I describe how AI can be leveraged to build commercially viable web3 applications for India. I cover a number of relevant AI/ML datasets, models, resources and applications for these domains, recognized by the Ministry of Electronics and Information Technology's National Strategy on Blockchain:
Related Blogs  Source: MLOps Org Machine learning operations (MLOps) refer to the emerging field of delivering machine learning models through repeatable and efficient workflows. The machine learning lifecycle is composed of various elements, as shown in the figure below. Similar to the practice of DevOps for managing the software development lifecycle, MLOps enables organizations to smooth the path to successful AI transformation by providing an engineering and technological backbone to underlying machine learning processes.
MLOps is a relatively new field, as the commercial use of AI at scale is itself a fairly new practice. MLOps is modeled on the existing field of DevOps, but in addition to code, it incorporates additional components, such as data, algorithms, and models. It includes various capabilities that allow the modern machine learning team, comprising data scientists, machine learning engineers, and software engineers, to organize the building blocks of machine learning systems and take models to production in an efficient, reliable, and reproducible fashion. MLOps tools MLOps is carried out using a diverse set of tools, each catering to a distinct component of the machine learning pipeline. Each tool under the MLOps umbrella is focused on automation and enabling repeatable workflows at scale. As the field of machine learning has evolved over the last decade, organizations are increasingly looking for tools and technologies that can help extract the maximum return from their investment in AI. In addition to cloud providers, like AWS, Azure, and GCP, there are a plethora of start-ups that focus on accommodating varied MLOps use cases. In this article, I will cover tools for the following MLOps categories:
In the following section, I will list a selection of MLOps tools from the above categories. It is important to note that although a particular tool might be listed under a specific category, the majority of these tools have evolved from their initial use case into a platform for providing multiple MLOps solutions across the entire ML lifecycle. Metadata Management Building machine learning models involves many parameters associated with code, data, metrics, model hyperparameters, A/B testing, and model artifacts, among others. Reproducing the entire ML workflow requires careful storage and management of the above metadata. Featureform Featureform is a virtual feature store. It can integrate with various data platforms, and it enables the management and governance of the data from which features are built. With a unique, feature-first approach, Featureform has built a product called Embeddinghub, which is a vector database for machine learning embeddings. Embeddings are high-dimensional representations of different kinds of data and their interrelationships, such as user or text embeddings, that quantify the semantic similarity between items. MLflow MLflow is an open-source platform for the machine learning lifecycle that covers experimentation and deployment, and it also includes a central model registry. It has four principal components: Tracking, Projects, Models, and Model Registry. In terms of metadata management, the MLflow Tracking API is used for logging parameters, code, metrics, and model artifacts. Versioning For machine learning systems, versioning is a critical feature. As the pipeline consists of various data sets, labels, experiments, models, and hyperparameters, it is necessary to version control each of these parameters for greater accessibility, reproducibility, and collaboration across teams. Pachyderm Pachyderm provides a data layer for the machine learning lifecycle. It offers a suite of services for data versioning that are organized by data repository, commit, branch, file, and provenance. Data provenance captures the unique relationships between the various artifacts, like commits, branches, and repositories. DVC DVC, or Data Version Control, is an open-source version control system for machine learning projects. It includes version control for machine learning data sets, models, and any intermediate files. It also provides code and data provenance to allow for end-to-end tracking of the evolution of each machine learning model, which promotes better reproducibility and usage during the experimentation phase. Experiment Tracking A typical machine learning system may only be deployed after hundreds of experiments. To optimize the model performance, data scientists perform numerous experiments to identify the most appropriate set of data and model parameters for the success criteria. Managing these experiments is paramount for staying on top of the data science modeling efforts of individual practitioners, as well as the entire data science team. Comet Comet is a machine learning platform for managing and optimizing the entire machine learning lifecycle, from experiment tracking to model monitoring. Comet streamlines the experimentation workflow for data scientists and enables clear tracking and visualization of the results of each experiment. It also allows side-by-side comparisons of experiments so users can easily see how model performance is affected. Weights & Biases Weights & Biases is another popular machine learning platform that provides a host of services, including [experiment tracking](https://wandb.ai/site/experiment-tracking). It facilitates tracking and visualization of every experiment, allows rerunning previous model checkpoints, and can monitor CPU and GPU usage in real time. Model Deployment Once a machine learning model is built and tests have found it to be robust and accurate enough to go to production, the model is deployed. This is an extremely important aspect of the machine learning lifecycle, and if not managed well, it can lead to errors and poor performance in production. AI models are increasingly being deployed across a range of platforms, from on-premises servers to the cloud to edge devices. Balancing the trade-offs for each kind of deployment and scaling the service up or down during critical periods are very difficult tasks to achieve manually. A number of platforms provide model deployment capabilities that automate the entire process of taking a model to production. Seldon Seldon is a model deployment software that helps enterprises manage, serve, and scale machine learning models in any language or framework on Kubernetes. It’s focused on expediting the process to take a model from proof of concept to production, and it’s compatible with a variety of cloud providers. Kubeflow Kubeflow is an open-source system for productionizing models on the Kubernetes platform. It simplifies machine learning workflows on Kubernetes and provides greater portability and scalability. It can run on any hardware and infrastructure on which Kubernetes is running, and it is a very popular choice for machine learning engineers when deploying models. Monitoring Once a model is in production, it is essential to monitor its performance and log any errors or issues that may have caused the model to break in production. Monitoring solutions enable setting thresholds as indicators for robust model performance and are critical in solving for known issues, like data drift. These tools can also monitor the model predictions for bias and explainability. Fiddler Fiddler is a machine learning model performance monitoring software. To ensure expected model performance, it monitors data drift, data integrity, and anomalies in the data. Additionally, it provides model explainability solutions that help identify, troubleshoot, and understand underlying problems and causes of poor performance. Evidently Evidently is an open-source machine learning model monitoring solution. It measures model health, data drift, target drift, data integrity, and feature correlations to provide a holistic view of model performance. Conclusion MLOps is a growing field that focuses on organizing and accelerating the entire machine learning lifecycle through best practices, tools, and frameworks borrowed from the DevOps philosophy of software development lifecycle management. With machine learning, the need for tooling is much greater, as machine learning is built on foundational blocks of data and models, as well as code. To bring reliability, maturity, and scale to machine learning processes, a diverse set of MLOps tools are being increasingly used. These tools are developed for optimizing the nuts and bolts of machine learning operations, including metadata management, versioning, model building and experiment tracking, model deployment, and monitoring in production. Over the past decade, the field of AI and machine learning has grown rapidly, with organizations embracing AI and recognizing its critical importance for transforming their business. The field of MLOps is still young, but the creation and adoption of tools will further empower organizations in their journey of AI transformation and value creation. Related Blogs Published by CloudForecast Introduction
Amazon Redshift is a widely used cloud data warehouse that is used by many businesses, like Nasdaq, GE, and Zynga, to process analytical queries and analyze exabytes of data across databases, data lakes, data warehouses, and third-party data sets. There are multiple use cases for Redshift, including enhancing business intelligence capabilities, increasing developer and analyst productivity, and building machine learning models for predictive insights, like demand forecasting. Amazon Redshift can be leveraged by modern data-driven organizations to vastly improve their data warehousing and analytics capabilities. However, the pricing for Redshift services can be challenging to understand, with multiple criteria that define the total cost. In this article, you’ll learn about Amazon Redshift and its pricing structure, with suggestions for how to optimize costs. What Is Amazon Redshift? Essentially, Amazon Redshift provides analytics over multiple databases and offers high scalability in a secure and compliant fashion. Additionally, there is a serverless option called Amazon Redshift Serverless that makes it even easier to rapidly scale analytics setup without requiring a managed data warehouse infrastructure. It helps with data democratization and assists various data stakeholders to extract data insights by simply loading and querying data in the warehouse. Amazon Redshift Pricing In this section, you’ll learn about Amazon Redshift’s capabilities as it pertains to usage and pricing. Free Tier For new enterprise users, the AWS Free Tier provides a free two-month trial of the DC2.Large node. This free service includes 750 hours per month, which is sufficient to run a single DC2.Large node with 160GB of compressed solid-state drives (SSD). On-Demand Pricing When you launch an Amazon Redshift cluster, you select a number of nodes in a specific region as well as their instance type to run your data warehouse. In on-demand pricing, a simple hourly rate applies based on the previous configuration and is billed as long as the cluster is live. The typical hourly rate for a DC2.Large node is $0.25 USD per hour. Redshift Serverless Pricing With Amazon Redshift Serverless, costs accrue only when the data warehouse is active and is measured in units of Redshift Processing Units (RPUs). You’re charged in terms of RPU-hours on a per-second basis. The serverless configuration also includes concurrency scaling and Amazon Redshift Spectrum, and the cost for these services is already included. Managed Storage Pricing Amazon Redshift charges for the data stored in a managed storage at a specific rate per GB-month. Its usage is calculated on an hourly basis as a function of the total amount of data and starts as low as $0.024 USD per GB with the RA3 node. The cost of a managed storage also varies according to the particular AWS region in which the data is stored. For example, consider the cost of a managed storage pricing where 100TB of data is stored with an RA3 node type for thirty days in the US East region, where the cost is $0.024 USD per GB-month. The total usage for thirty days in GB-hours is as follows: 100TB × 1024GB/TB (converting TB to GB) × 30 days × 24 hours/day = 73,728,000 GB-hours Then you can convert GB-hours to GB-months: 73,728,000 GB-hours / (24 × 30) hours per month = 102,400 GB-months Finally, you can calculate the total cost of 102,400 GB-months at $0.024 USD/GB-month in the US East region: 102,400 GB-months × $0.024 USD = $2,457.60 USD Spectrum Pricing With Amazon Redshift Spectrum, users can run SQL queries directly on the data in the S3 buckets. Here, the cost is based on the number of bytes scanned by the Spectrum utility. The pricing of Redshift Spectrum is $5 USD per terabyte of data scanned. Concurrency Scaling Pricing With Concurrency Scaling, Amazon Redshift can be scaled to multiple concurrent users and queries. For every twenty-four hours that your main cluster is live, you accrue a one-hour credit. Any additional usage is charged on a per-second, on-demand rate that depends on the number of types of nodes in the main cluster. Reserved Instance Pricing Reserved instances are designated for stable production workloads and are less expensive than clusters run on an on-demand basis. Significant cost savings can be achieved through long-term usage and commitment to Amazon Redshift in the span of a few years. Pricing for reserved instances can either be paid all up front, partially up front, or monthly over the course of a year with no up-front charges. Amazon Redshift Cost Optimization Considerations Before you begin using Amazon Redshift, you need to be aware of your current costs. AWS Cost ExplorerThe AWS Pricing Calculator provides a configurable tool to estimate the cost of using Amazon Redshift. For instance, the annual cost of one node of the DC2.8xlarge instance in the US East (Ohio) region on an on-demand basis is as follows: 1 instance × $4.80 USD hourly × 730 hours in a month × 12 months = $42,048 USD The cost for the same Amazon Redshift configuration for a reserved instance for a one-year term paid up front is $27,640 USD. AWS Tags Using AWS cost allocation tags can help you decode and manage your AWS costs. Tagsenable AWS resources to be labeled in the form of key-value pairs and can include various types, like technical, business, security, and automation. Once the tags are activated in the Billing and Cost Management console, a cost allocation report can be generated based on the specific resources tagged. Tags can be user-defined or AWS-generated. Amazon Redshift Cost Optimization Optimizing Amazon Redshift costs comes down to effective planning, prudent usage and allocation of resources, and regular monitoring of the usage and associated costs. Optimizing Queries The analytical queries made on the data stored in Amazon Redshift can be optimized to run more efficiently. Queries can be compute-intensive, can be storage-intensive, or can take a long time to execute. There are a number of query tuning techniques that can be used to optimize your queries. Tables with skewed data or missing statistics, and queries with nested loops and long wait times, typically affect query performance and can be improved as illustrated in this AWS developer guide. Here is a commonly used weak query that selects all the columns in a table: SELECT * FROM USERS The previous query can be very inefficient and slow if the table consists of thousands of columns, especially if only a few columns are relevant for the necessary analysis. This query can be optimized by specifying and retrieving the exact column names like the following: SELECT Firstname, Lastname, DOB FROM USERS Cluster Limits and Quotas Usage limits on Amazon Redshift clusters can be programmed using the AWS Command Line Interface (CLI) tool. Limits can be imposed on concurrency scaling in terms of time and spectrum in terms of data scanned. Daily, weekly, or monthly periods can be used. A number of limits and quotas are defined for Redshift resources that can also be applied to constrain the overall costs associated with Redshift. Data Type Amazon Redshift costs can also be managed by storing data in a compressed, partitioned, and columnar data format, like Apache Parquet, since fewer data is scanned. Conclusion Amazon Redshift is a powerful and cost-effective cloud-native data warehouse that provides scalable and performant data analytics and processing capabilities. It also comes with a serverless configuration that allows any data stakeholder to run data queries without the need to provision and manage the data warehouse infrastructure. Amazon Redshift has multiple aspects affecting its pricing, including on-demand or reserved capabilities, serverless, managed storage pricing, Redshift Spectrum pricing, concurrency scaling pricing, and reserved instance pricing. Keeping on top of the various Amazon Redshift costs is not straightforward but can be made easier by AWS cost monitoring tools, like CloudForecast. CloudForecast helps manage AWS costs through daily cost management reports, monthly financial reports, untagged AWS resources discovery, and idle and underutilized resources visibility for cost-saving opportunities. Related blog Strong engineering talent is the bedrock of modern technology companies. Software engineers, in particular, are in high demand given their expertise and skills. At the same time, there is a much greater supply of software companies and startups, all of which are jostling to hire top engineers. Given this market reality, retention of top engineering talent is imperative for a company to grow and innovate in the short as well as the long term.
Retaining employees is critical for numerous reasons. It helps a company retain experience not only in terms of employees’ domain expertise and skills, but also organizational knowledge of products, processes, people, and culture. Strong employee retention rates (>90%) ensure a long-term foundation for success and enhances team morale as well as trust in the company. A stable engineering team is in a better position to both build and ship innovative products and establish a reputation in the market that helps attract top-quality talent. The corporate incentive of maintaining high standards of employee hiring and retention is also related to the costs of employee churn. Turnover costs companies in the US $1 trillion USD a year with an annual turnover rate of more than twenty-six percent. The cost of replacing talent is often as high as two times their annual salary. This is a tremendous expense that can be averted through better company policies and culture. The onus is typically on the human resources (HR) team to develop more employee-friendly practices and promote higher engagement and work–life balance. However, in practice, most HR teams are deferential to the company leadership and that is where the buck stops. Leaders and managers have a fundamental responsibility to retain the employees on their team, as more often than not, employees do not leave the company per se, but the line manager. I will discuss best practices and strategies to improve retention, which ought to be a consistent effort across the entire employee lifecycle--from recruiting to onboarding through regular milestones during an employee’s tenure. Start at the Start More often than not, managers do not invest in onboarding preparation and processes out of laziness and indifference. Good employee retention practice starts at the very beginning, i.e., at the time of hiring. Hiring talent through a structured, transparent, fair, and meritocratic interviewing process that allows the candidate to understand their particular role and responsibilities, the company’s diversity and inclusion practices, and the larger mission of the company sets an important tone for future employees. Hiring the right people who are a good culture fit increases the likelihood of greater engagement and longer tenure at the company. Hiring managers should not hire for the sake of hiring. They should put considerable thought into each new hire and how that hire might fit in on their team. Apart from hiring, managers have other important considerations, including:
In the first few months, the new hires, the hiring team, and company are in a “dating” phase, evaluating each other and gathering evidence on whether to commit to a longer-term relationship. Most new employees make up their mind to stay or leave within the first six months. A third of new hires who quit said they had barely any onboarding or none at all. The importance of a new employee’s first impressions on the joining date, the first week, the first month, and the first quarter cannot be overemphasized. Great onboarding starts before the new hire’s join date, ensuring all necessary preparation is handled, like paperwork. Orientation programs on the join day are essential to introduce the company and expand on its mission, values, and culture beyond what the employee might have learned during the interviews. Minor things like having the team know in advance about a new team member’s join date, and readying the desk, equipment, access, and logins are tell-tale signs of how much thought and effort the hiring team has invested in onboarding. Fellow teammates also make a significant impact, whether they are welcoming and drop in to say “hi” or stop by for a quick chat to get to know the hire better, or take the new employee out for lunch with the whole team. Onboarding should not end on day one but continue in various forms. Some examples include:
A successful onboarding strategy should enable the employee to know their first project, the expectations, associated milestones, and how performance evaluation works. Keep It Up! Onboarding should be followed up with regular check-ins by the manager and HR at the one-month, three-month, and six-month mark. These meetings should be treated as an opportunity for the company to assess the new employee’s comfort level on the team and provide feedback as needed. An onboarding mentor or buddy, if not assigned already, should be provided to help the employee find their feet and learn the informal culture and practices. The manager should set up the employee for success by providing low-hanging projects that are quick to deliver and help the new hire understand the process of building and deploying a new feature using the company’s internal engineering tools and systems. With quick wins, new hires are able to build trust within the organization and gain more confidence to do excellent work. As time goes on, the role of the hiring manager becomes more prominent in coordinating regular 1-on-1 meetings, providing the new hire clear work guidelines, as well as challenging and stimulating projects. Apart from work, an introduction to the organizational setup and culture, as well as social interaction within and beyond the team is also crucial. As the new employee ramps up, it is important to give constructive feedback so that the employee can improve. Where a new employee delivers positive impact in the early days itself, the manager should highlight their work within the team and organization, and motivate the employee to continue to perform well. In addition to core engineering work, employees feel more connected when a company actively invests in their learning and development. Cross-functional training programs that involve employees across different teams foster deeper collaboration and a stronger sense of connection within the various parts of the company. Investment in employees’ upskilling and education via partnership with external learning platforms or vendors also generates a positive culture of instilling curiosity and learning. Learning new skills energizes the employees and provides them opportunities to grow and develop. They can then apply the newly learned knowledge and skills to pertinent business problems. It creates a virtuous culture that yields overall positive outcomes for the employee and employer alike, and positively influences the long-term retention rates. New employees generally feel the need to be positively engaged. A powerful mission statement can sometimes convert naysayers faster and generate a company-wide sense of being part of something impactful. This fosters deeper engagement, loyalty, and trust in the company and helps employees embrace company values, resulting in better employee retention rates. Frequent town hall meetings from the leadership enable a new hire to understand the organization as a coherent whole and their particular role in furthering the company’s mission. Listen to Feedback The diverse organizational efforts to onboard, engage, and enhance new employees’ perception of the company are bound to fail if the organization does not seek and act on any feedback shared by the new hires. Companies ought to create an internal culture of open communication whereby they seek feedback from employees via surveys, meetings, and town halls, and showcase transparent efforts in implementing employees’ suggestions and feedback. Regular 1-on-1 meetings with managers should be treated as an opportunity to gather feedback and offer the employee insights into whether and how the company is taking action on that feedback. However, in spite of organizational efforts to improve employee satisfaction and wellbeing, some attrition is inevitable. Attrition rates of more than ten percent is a cause for concern, however, especially when top-performing employees leave the company. Exit interviews are typically conducted by HR and hiring managers, but in practice these are largely farcical as the employees hardly share their honest opinions and have lost trust that the company can take care of their career interests and development. Companies can implement processes that bring greater transparency around employee decisions related to hiring, promotion, and exit. These processes will also hold HR and managers to greater accountability with respect to employee churn, and incentivize them to increase the retention rates in their teams. In past generations, job stability was a paramount aspiration for employees which meant they typically spent all their working lives at the same company. In today’s world, with a plethora of enterprises and new startups, high-performing talent is in greater demand and it is possible to accelerate one’s career growth by frequently job hopping and switching companies. Nowadays, feedback about company processes, culture, compensation, interviews, and so on, is available on a plethora of public platforms including Glassdoor and LinkedIn. Companies are now more proactive in managing their online reputation and act on feedback from the anonymous reviews on such platforms. Conclusion Employees in the post-Covid remote-working world are prone to greater degrees of stress, mental health issues, and burnout, all of which have adverse impacts on their work–life balance. In such extraordinary times, companies face the unique challenge—and opportunity—to develop and promote better employee welfare practices. At one end of the spectrum, there are companies like Amazon. In 2015, The New York Times famously portrayed the company as a “bruising workplace.” Then, in 2021, The New York Times again reported on Amazon for poor workplace practices and systems, prompting a public acknowledgment from the CEO that Amazon needs to do a better job. On the other end of the spectrum, there are companies like Atlassian or Spotify that have made proactive changes in their organizational culture and are being lauded for new practices to promote employee welfare during the pandemic. Companies that adapt to the changing times and demonstrate that they genuinely care for their employees will enjoy better retention rates, lower costs due to frequent rehiring, and long-term employee trust that conveys the company as a beacon of progressive workplace culture and employment practices. Related Blogs Data science teams are an integral part of early-stage or growth-stage start-ups as midlevel and enterprise companies. A data science team can include a wide range of roles that take care of the end-to-end machine learning lifecycle from project conceptualization to execution, delivery, and monitoring:
The manager of a data science team in an enterprise organization has multiple responsibilities, including the following:
As the data science manager, it’s critical to have a structured, efficient hiring process, especially in a highly competitive job market where the demand outstrips the supply of data science and machine learning talent. A transparent, thoughtful, and open hiring process sends a strong signal to prospective candidates about the intent and culture of both the data science team and the company, and can make your company a stronger choice when the candidates are selecting an offer. In this blog, you’ll learn about key aspects of the process of hiring a top-class data science team. You’ll dive into the process of recruitment, interviewing, and evaluating candidates to learn how to find the ones who can help your business improve its data science capabilities. Benefits of an Efficient Hiring Process Recent events have accelerated organizations’ focus on digital and AI transformation, resulting in a very tight labor market when you’re looking for data sciencedigital skills, like machinelike data science and machine learning, statistics, and programming. A structured, efficient hiring process enables teams to move faster, make better decisions, and ensure a good experience for the candidates. Even if candidates don’t get an offer, a positive experience interacting with the data science and the recruitment teams makes them more likely to share good feedback on platforms like Glassdoor, which might encourage others to interview at the company. Hiring Data Science Teams A good hiring process is a multistep process, and in this section, you’ll look at every step of the process in detail. Building a Funnel for Talent Depending on the size of the data science team, the hiring manager may have to assume the responsibility of reaching out to candidates and building a pipeline of talent. In larger organizations, managers can work with in-house recruiters or even third-party recruitment agencies to source talent. It’s important for the data science managers to clearly convey the requirements for the recruited candidates, such as the number of candidates desired and the profiles of those candidates. Candidate profiles might include things like previous experience, education or certifications, skill set or tech stack, and experience with specific use cases. Using these details, recruiters can then start their marketing, advertising, and outreach campaigns on platforms, like LinkedIn, Glassdoor, Twitter, HackerRank, and LeetCode. In several cases, recruiters may identify candidates who are a strong fit but who may not be on the job market or are not actively looking for new roles. A database of all such candidates ought to be maintained so that recruiters can proactively reach out to them at a more suitable time and reengage the candidates. Another trusted source of identifying good candidates is through employee referrals. An in-house employee referral program that incentivizes current employees to refer candidates from their network is often an effective way to attract the specific types of talent you’re looking for. The data science leader should also publicize their team’s work through channels, like conferences or workshops, company blogs, podcasts, media, and social media. By investing dedicated time and energy in building up the profile of the data science team, it’s more likely that candidates will reach out to your company seeking data science opportunities. When looking for a diverse set of talent, the search an be difficult as data science is a male dominated field. As a result, traditional recruiting paths will continue to reflect this bias. Reaching out and building relationships with groups such as Women in Data Science, can help broad the pipeline of talent you attract. Defining Roles and Responsibilities Good candidates are more likely to apply for roles that have a clear job description, including a list of potential data science use cases, a list of required skills and tech stack, and a summary of the day-to-day work, as well as insights into the interviewing process and time lines. Crafting specific, accurate job descriptions is a critical—if often overlooked—aspect of attracting candidates. The more information and clarity you provide up front, the more likely it is that candidates have sufficient information to decide if it’s a suitable role for them and if they should go ahead with the application or not. If you’re struggling with creating this, you can start with an existing job description template and then customize it in accordance with the needs of the team and company. It's also critical to not over populate a job description with every possible skill or experience you hope a candidate brings. That will narrow your potential applicant pool. Instead focus on those skills and experiences that are absolutely critical. The right candidate will be able to pick up other skills on the job. It can be useful for the job description to include links to any recent publications, blogs, or interviews by members of the data science team. These links provide additional details about the type of work your team does and also offer candidates a glimpse of other team members. Here are some job description templates for the different roles in a data science team: Interviewing process When compared to software engineering interviews, the interview process for data science roles is still very unstructured, and data science candidates are often uncertain about what the interview process involves. The professional position of data scientist has only existed for a little over a decade, and in that time, the role has evolved and transformed, resulting in even newer, more specialized roles, such as data engineer, machine learning engineer, applied scientist, research scientist, and product data scientist. Because of the diversity of roles that could be considered data science, it’s important for a data science manager to customize the interviewing process depending on the specific profile they’re seeking. Data scientists need to have expertise in multiple domains, and one or more second-round interviews can be tailored around these core skills:
Given how tight the job market is for data science talent, it’s important to not over complicate the process. The more steps in the process, the longer it will take and the higher the likelihood you will lose viable candidates to other offers. So be thoughtful in your approach and evaluate it periodically to align with the market. Types of Data Science Interviews Interviews are often a multistep process and can involve multiple steps of assessments. Screening Interviews To save time, one or more screening rounds can be conducted before inviting candidates for second-round interviews. These screening interviews can take place virtually and involve an assessment of essential skills, like programming and machine learning, along with a deep dive into the candidate’s experience, projects, career trajectory, and motivation to join the company. These screening rounds can be conducted by the data science team itself or outsourced to other companies, like HackerRank, HackerEarth, Triplebyte, or Karat. Onsite Interviews Once candidates have passed the screening interviews, the top candidates will be invited to a second interview, either virtually or in person. The data science manager has to take the lead in terms of coordinating with internal interviewers to confirm the schedule for the series of interviews that will assess the candidate’s skills, as described earlier. On the day of the second-round interviews, the hiring manager needs to help the candidate feel welcome and explain how the day will proceed. Some companies like to invite candidates to lunch with other team members, which breaks the ice by allowing the candidate to interact with potential team members in a social setting. Each interview in the series should start by having the interviewer introduce themself and provide a brief summary of the kind of work they do. Depending on the types of interviews and assessments the candidate has already been through, the rest of the interview could focus on the core skill set to be evaluated or other critical considerations. Wherever possible, interviewers should offer the candidate hints if they get stuck and otherwise try to make them feel comfortable with the process. The last five to ten minutes of each interview should be reserved for the candidate to ask questions to the interviewer. This is a critical component of second-round interviews, as the types of questions a candidate asks offer a great deal of information about how carefully they’ve considered the role. Before the candidate leaves, it’s important for the recruiter and hiring manager to touch base with the candidate again, inquire about their interview experience, and share time lines for the final decision. Technical Assessment It is common for there to be some sort of case study or technical assessment to get a better understanding of a candidate’s approach to problem solving, dealing with ambiguity and practical skills. This provides the company with good information about how the candidate may perform in the role It also is an opportunity to show the candidate what type of data and problems they may work on when working for you. Evaluating candidates After the second-round interviews and technical assessment, the hiring manager needs to coordinate a debrief session. In this meeting, every interviewer shares their views based on their experience with the candidate and offers a recommendation if the candidate should be hired or not. After obtaining the feedback from each member of the interview panel, the hiring manager also shares their opinion. If the candidate unanimously receives a strong hire or a strong no-hire signal, then the hiring manager’s decision is simple. However, there may be candidates who perform well in some interviews but not so well in others, and who elicit mixed feedback from the interview panel. In cases like this, the hiring manager has to make a judgment call on whether that particular candidate should be hired or not. In some cases, an offer may be extended if a candidate didn’t do well in one or more interviews but the panel is confident that the candidate can learn and upskill on the job, and is a good fit for the team and the company. If multiple candidates have interviewed for the same role, then a relative assessment of the different candidates should be considered, and the strongest candidate or candidates, depending on the number of roles to be filled, should be considered. While most of the interviews focus on technical data science skills, it’s also important for interviewers to use their time with the candidate to assess soft skills, like communication, clarity of thought, problem-solving ability, business sense, and leadership values. Many large companies place a very strong emphasis on behavioral interviews, and poor performance in this interview can lead to a rejection, even if the candidate did well on the technical assessments. Job Offer After the debrief session, the data science manager needs to make their final decision and share the outcome, along with a compensation budget, with the recruiter. If there’s no recruiter involved, the manager can move directly to making the candidate an offer. It’s important to move quickly when it comes to making and conveying the decision, especially if candidates are interviewing at multiple companies. Being fast and flexible in the hiring process gives companies an edge that candidates appreciate and take into consideration in their decision-making process. Once the offer and details of compensation have been sent to the candidate, it’s essential to close the offer quickly to prevent candidates from using your offer as leverage at other companies. Including a deadline for the offer can sometimes work to the company’s advantage by incentivizing candidates to make their decision faster. If negotiations stretch and the candidate seems to lose interest in the process, the hiring manager should assess whether the candidate is really motivated to be part of the team. Sometimes, it may move things along if the hiring manager steps in and has another brief call with the candidate to help remove any doubts about the type of work and projects. However, additional pressure on the candidates can often work to your disadvantage and may put off a skilled and motivated candidate in whom the company has already invested a lot of time and money. Conclusion In this article, you’ve looked at an overview of the process of hiring a data science team, including the roles and skills you might be hiring for, the interview process, and how to evaluate and make decisions about candidates. In a highly competitive data science job market, having a robust pipeline of talent, and a fast, fair, and structured hiring process can give companies a competitive edge. Related Blogs Published by Domino Data Lab Reproducibility is a cornerstone of the scientific method and ensures that tests and experiments can be reproduced by different teams using the same method. In the context of data science, reproducibility means that everything needed to recreate the model and its results such as data, tools, libraries, frameworks, programming languages and operating systems, have been captured, so with little effort the identical results are produced regardless of how much time has passed since the original project.
Reproducibility is critical for many aspects of data science including regulatory compliance, auditing, and validation. It also helps data science teams be more productive, collaborate better with nontechnical stakeholders, and promote transparency and trust in machine learning products and services. In this article, you’ll learn about the benefits of reproducible data science and how to ingrain reproducibility in every data science project. You’ll also learn how to cultivate an organizational culture that promotes greater reproducibility, accountability, and scalability. What does it mean to be reproducible? Machine learning systems are complex, incorporating code, data sets, models, hyperparameters, pipelines, third-party packages, model training and development configurations across machines, operating systems, and environments. To put it simply, reproducing a data science experiment is difficult if not impossible if you can’t recreate the exact same conditions used to build the model. To do that, all artifacts have to be captured and versioned in an accessible repository. That way when a model needs to be reproduced, the exact environment, using the exact training data and code, within the exact package combination can be recreated easily. Too often it's an archeological expedition that can take weeks or months (or potentially never) when the artifacts are not captured at the time of creation. While the focus on reproducibility is a phenomenon in data science, it has been a cornerstone of scientific research across all kinds of industries, including clinical and life sciences, healthcare, and finance. If your company is unable to produce consistent experimental results, that can significantly impact your productivity, waste valuable resources, and impair decision-making. Situations Where Reproducibility Matters In data science, reproducibility is especially vital for data scientists to apply the experimental findings to their own work. Regulatory Compliance In highly regulated industries like insurance, finance and life sciences, all aspects of a model have to be documented and captured to provide full transparency, justification and validation on how models are developed and used inside an organization. This includes the type of algorithm being used, why the algorithm has been selected and how the model has been implemented within the business. A big part of complying involves being able to exactly reproduce the results of a model at any time. Without a system for capturing the artifacts, code, data, environment, packages and tools used to build a model this can be a time consuming, difficult task. Model Validation In all industries models should be validated prior to deployment to ensure the results are repeatable, understood and the model will achieve its intended purpose. Too often this is a time intensive process with validation teams having to piece together the environment, tools, data and other artifacts that were used to create the model, which slows down moving a model into production. When an organization is able to reproduce a model instantly, validators can focus on their core function of ensuring the model is robust and accurate. Collaboration Data science innovation happens when teams are able to collaborate and compound knowledge. It doesn’t happen when they have to spend time painstakingly recreating a prior experiment or accidentally duplicate work. When all work is easily reproducible, and easily searched, it's easy to build on prior work to innovate. It also means that as team staffing changes, institutional knowledge doesn’t disappear. Ingraining Reproducibility in Data Science Projects Instilling a culture of reproducibility in data science across an organization requires a long-term strategy, technology investment, and buy-in from data and engineering leadership. In this section, you’ll learn about a few established best practices for conducting and promoting reproducible data science work in your industry. Version Control Version control refers to the process of tracking and managing changes to artifacts, like code, data, labels, models, hyperparameters, experiments, dependencies, documentation, as well as environments for training and inference. The building blocks of version control for data science are more complex than software projects, making reproducibility that much more difficult and challenging. For code, there are multiple platforms, like GitHub, GitLab, and Bitbucket, that can be used to store, update, and track code, like Python scripts, Jupyter Notebooks, and configuration files, in common repositories. However that isn’t sufficient. Datasets need to be captured and versioned as well. So do the environments, tools and packages. This is because code may or may not run the same on a different version of Python or R, for example. Data may have changed even if pulled with the same parameters. Similarly capturing different versions of models and corresponding hyperparameters for each experiment is important to reproduce and replicate the results of a winning model that might be deployed to production. Reproducing end-to-end data science experiments is a complex, technical challenge that can be achieved much more efficiently using platforms like Domino’s Enterprise MLOps platform which eliminates all manual work and ensures reproducibility at scale. Scalable Systems Building accurate and reproducible data science models requires robust and scalable infrastructure for data storage and warehousing, data pipelines, feature stores, model stores, deployment pipelines, and experiment tracking. For machine learning models that serve predictions in real time, the importance of reproducibility is even higher in order to quickly resolve bugs and performance issues. End-to-end machine learning pipelines involve multiple components, and an organizational strategy for reproducible data science work must carefully plan for the tooling and infrastructure to enable it. Engineering reproducible workflows requires sophisticated tooling to encompass code, data, models, dependencies, experiments, pipelines, and runtime environments. For many organizations, it makes sense to buy (vs. build) such scalable workflows focused on reproducible data science. Conclusion Reproducible research is a cornerstone of scientific research. Reproducibility is especially significant for cross-functional disciplines like data science that involve multiple artifacts, like code, data, models, and hyperparameters, as well as a diverse set of practitioners and stakeholders. Reproducing complex experiments and results is, therefore, essential for teams and organizations when making important decisions like which models to deploy, identifying root causes when the models break down, and building trust in data science work. Reproducing data science results requires a complex set of processes and infrastructure that is not easy or necessary for many teams and companies to build in-house. Related Blogs Published by Colabra Introduction
Effective communication skills are pivotal to success in science. From maximizing productivity at work through efficient teamwork and collaboration to preventing the spread of misinformation during global pandemics like Covid19, the importance of strong communication skills cannot be emphasized enough. However, scientists often struggle to communicate their work clearly for various reasons. Firstly, most academic institutes do not prioritize training scientists in essential soft skills like communication. With negligible organizational or departmental training and little to no feedback from professors and peers, scientists fail to fully appreciate the real-world importance and consequences of poor communication skills. The long scientific training period in the academic ivory tower is spent conversing with fellow scientists, with minimal interaction with non-technical professionals and the general public. Thus, the lingua franca among scientists is predominantly interspersed with jargon, leading to poor communication with non-scientists. This article will describe best practices and frameworks for professional scientists and non-scientists in commercial scientific enterprises to communicate effectively. How should scientists speak with non-scientists? IndustryThis section describes how professional scientists in industries like biotech and pharma can communicate better with cross-functional stakeholders from non-technical teams like sales, marketing, legal, business, product, finance, accounting, etc. Cross-functional collaboration In industry, scientists are often embedded in self-contained business or product teams with different roles. Taking a biotech product to market like a new drug, which has a long development cycle, involves extensive collaboration between specialists from multiple domains: research, quality assurance, legal and compliance, project management, risk and safety, vendor and supplier management, sales, marketing, logistics, and distribution, to name a few. Scientists are involved from the beginning of the process. However, scientists are often guilty of focusing solely on R&D without acutely considering how the science and technology underlying the product or business is operationalized by cross-functional teams and delivered to the market. Scientists are often less aware of the practical challenges of taking a drug prototype to the patient, such as long timelines due to multiple steps like risk management, safety reviews, regulatory approvals, coordination with pharmaceutical and logistics companies, and bureaucratic hurdles with governments and international bodies. This is a vital mistake in collaborative industry environments and often leads to poor job experience for scientists and their non-scientist peers and managers. The image below shows several communication challenges at the different stages of the drug development process that hinder successful commercialization. Although the various specialists share a common objective, each domain expert speaks a different “language” influenced by their respective training and fails to translate their opinions and concerns into a common language that all can understand. This comes in the way of optimal decision-making resulting in projects that stall even before demonstrating clinical efficacy. In an industry with a 90% drug development failure rate, poor communication and collaboration can be very expensive, to the tune of USD 1.3 billion per drug. The right culture is crucial to ensure successful outcomes, as advocated by AstraZeneca after a thorough review of their drug development pipeline. A recent real-world example pertains to the development of the AstraZeneca Covid-19 vaccine by multiple teams at the University of Oxford. Although the vaccine was developed within two weeks by February 2020, it was not until 30 December 2020 that the vaccine was finally approved for use in the UK, and it is even to date not authorized for use in the US. In particular, the AstraZeneca vaccine was subject to misinformation, fake news, and fear-mongering, which led to vaccine hesitancy and a lack of public trust. This led Drs. Sarah Gilbert and Catherine Green, co-developers of the vaccine, to author ‘Vaxxers,’ with the primary motivation to allay fears and reassure the general public about its safety and efficacy by explaining the science and process of creating the vaccine. Stakeholder management Another critical aspect of working with cross-functional teams involves managing key stakeholders to ensure a successful outcome for the project. Stakeholders often come from diverse non-scientific backgrounds, making working with them more challenging for scientists. The main challenge in effective stakeholder management is understanding the professional goals, metrics, and KPIs that drive each stakeholder. For instance, a product manager might focus on metrics like cost improvement over time, risk mitigation, or timelines; a finance leader may be focused on revenue; a compliance manager may be focused on metrics that capture safety and legal aspects. Understanding each cross-functional stakeholder’s north star can help scientists navigate the intricacies of stakeholder management. Effective stakeholder management involves numerous aspects: Identifying stakeholders The first step is to identify the stakeholders that are critical to the success of the scientific product and understand their motivations and priorities. Successful stakeholder management starts by mapping your stakeholders across several dimensions, including:
Aligning stakeholders Conflicting priorities among stakeholders are common and need to be resolved delicately. Achieving multi-stakeholder alignment for complex projects requires carefully planned discussions and negotiations to assess the lay of the land with each stakeholder and preempt potential conflicts. Focused group meetings that prioritize key points of disagreement or conflicting priorities can help achieve alignment and avoid conflicts. Engaging stakeholders After getting all the stakeholders aligned, it is useful to build a communication strategy to share project updates regularly. The communication plan must be tailored to each stakeholder. For example, individual contributors might need a high-touch approach, while project coordinators and administrators might just want periodic updates and high-level presentations. During the project's execution phase, continuous engagement and clear communication with the stakeholders are essential to keep everyone on the same page. Stakeholders may be involved in multiple biotech projects in parallel, and your project may not be their sole focus or priority. We have previously written about several modes of communication and project management apart from one-on-one meetings. At a minimum, it is beneficial to maintain a project status board detailing the progress of each milestone, metric, team, and timeline, especially to serve as a single source of truth, especially if some teams are working remotely. Entrepreneurship This section will discuss how aspiring startup founders with a scientific background should communicate and “sell” the company's mission to varied stakeholders from investors, employees, vendors, potential hires, and so on. Scientists with domain expertise and an entrepreneurial mindset are increasingly opting to build deep-tech startups soon after graduating from academia. From Genentech to Moderna and CRISPR Therapeutics to BioNTech, there is no shortage of successful biotech companies founded by scientists. However, building a commercially successful and viable biotech startup requires diverse skills with a much stronger need for excellent communication skills. Scientist founders need to have exceptional communication and sales skills to pitch the company to raise venture capital, write scientific grants, forge business partnerships with other companies, retain customers, attract talented employees with their vision for the company, give media interviews, and shape a mission-oriented organizational culture. Scientist-founders must communicate particularly well to bridge the gap between scientific research and commercialization. How should non-scientists speak with scientists? In this section, we will consider the viewpoint of non-scientists and how they can communicate more effectively with scientists. Non-scientists are typically more focused on product, business, sales, marketing, and related aspects of commercializing scientific research. The stakes for effective communication between scientists and managers are very high. This is best highlighted by NASA’s missions, which involve a diverse set of experts, both scientific and non-scientific, similar to the highly complex and multi-year projects described in the previous section. NASA’s failures on projects like the Columbia mission have been attributed to deficiencies in communication and insular company culture. Namely, management not heeding the scientists' and engineers’ warnings. These communication failures are expertly documented in a post-hoc report by the Columbia Accident Investigation Board – "Over time, a pattern of ineffective communication has resulted, leaving risks improperly defined, problems unreported, and concerns unexpressed," the report said. "The question is, why?" (source) Unfortunately, this state of affairs rings true even today in high-stakes and complex scientific enterprises. Here are some recommended tips that follow from such catastrophic mishaps and failures in workplace communication:
How can non-scientists better engage scientists? Non-scientist stakeholders' work largely focuses on business metrics, product roadmaps, customer research, project management, etc. These are critical focus areas that non-scientists need to update and communicate clearly to their scientist colleagues. In industry, it is common to observe scientist colleagues not actively participating in discussions focused on business topics and switch off until their work is the topic of discussion. It is crucial to engage scientists as they are on the front lines of core product development and in a better position to understand and flag potential roadblocks in manufacturing, commercialization, and logistics based on prior experience. Many product-related issues and bugs that surface later in the development cycle can be caught and addressed if there is more proactive communication between scientific and non-scientific teams. Scientists are generally trained to be conservative, focusing on accuracy and reliability, which can conflict with a manager’s ambitious goals for time-to-market or revenue targets. In these situations, managers should allow scientists to voice their concerns, not be afraid to dive deeper, coordinate with other cross-functional stakeholders, and take a balanced decision integrating every stakeholder’s views. In the long term, cultivating an open and progressive culture that encourages debates and tough discussions reaps enormous benefits whereby no business-critical concern is left unvoiced. A transparent and meritocratic culture promotes greater cooperation and understanding among different teams striving towards the same goals. Conclusion We discussed why scientists often struggle with effective communication with other scientists and non-scientist stakeholders when working in industry or building their own company. We addressed how scientists should approach communication with non-scientist colleagues and how to collaborate with them. We also discussed effective communication strategies from the perspective of non-scientists speaking to scientists. In the long run, having strong communication and soft skills confers greater career durability than simply having scientific and technical skills. Understanding this and upskilling accordingly can empower scientists to transition and perform well in industry. Related Blogs Published by Unbox.ai Introduction
Supervised machine learning models are trained using data and their associated labels. For example, to discriminate between a cat and a dog present in an image, the model is fed images of cats or dogs and a corresponding label of “cat” or “dog” for each image. Assigning a category to each data sample is referred to as data labeling. Data labeling is essential to imparting machines with knowledge of the world that is relevant for the particular machine learning use case. Without labels, models do not have any explicit understanding of the information in a given data set. A popular example that demonstrates the value of data labeling is the ImageNet data set. More than a million images were labeled with hundreds of object categories to create this pioneering data set that heralded the deep-learning era. In this article, you’ll learn more about data labeling and its use cases, processes, and best practices. Why is data labeling important? Labeled data is necessary to build discriminative machine learning models that classify a data sample into one or more categories. Once a machine learning model is trained using data and corresponding labels, it can predict the label of a new unseen data sample. Data labeling is a crucial process as it directly impacts the accuracy of the model. If a significant proportion of the training data set is mislabeled, it will cause the model to make inaccurate predictions. Data labeling of production data is also important to counter data drift. The model can be continuously improved by incorporating the newly labeled samples from the real-world data distribution into the training data set. Poorly labeled data can also introduce bias in the data set, which can cause the models to consistently make inaccurate predictions on a subset of real-world data. Mislabelingcan severely impact the fairness and accuracy of models and warrants additional efforts to detect and eliminate labeling errors. Relabeling helps to address mislabeled samples, improving the data quality and, consequently, the accuracy of the machine learning models. How is data labeling performed? Again, data labeling helps train supervised machine learning models that learn from data and their corresponding labels. For example, the following text, sourced from the Large Movie Review Dataset, can be annotated in a number of ways depending on the use case: I saw this movie in NEW York city. I was waiting for a bus the next morning, so it was 2 or 3 in the morning. It was raining, and did not want to wait at the PORT AUTHORTY. So I went across the street and saw the worst film of my life. It was so bad, that I chose to stay and see the whole movie,I have yet to see anything else that bad since. The year was 69,so call me crazy. I stayed only because I could not belive it.........1. Use case: Sentiment analysis
For the named entity recognition use case, data annotators have to review the entire text and identify and label any mention of places. Typically, data annotation is outsourced to vendors who contract subject matter experts relevant for the specific machine learning use case. The team of annotators are assigned different batches of data to label on a daily basis for the duration of the project, using simple tools like Excel or more sophisticated labeling platforms like Label Studio. Labelers’ performance is evaluated in terms of metrics like overall accuracy and throughput—i.e., the number of samples labeled in a day. If the same set of data samples are assigned to multiple annotators, then the labels given by each annotator can be combined through a majority vote. Inter-annotator agreementhelps to reduce bias and mislabeling errors. For several use cases, data labeling can be extremely painstaking and time-consuming, which may lead to labeling fatigue. To counter this, labels assigned to each annotator undergo one or more rounds of review to catch any systematic errors. Once a batch of data is labeled, reviewed, and validated, it is shared with the data science team, who review select samples for labeling accuracy and verification and then provide feedback to the annotators. This iterative and collaborative process ensures that the final labels are of high quality and accuracy to use for training machine learning models. How is data relabeling performed? The repetitive and manual nature of data labeling is often fraught with errors. This necessitates the need to identify and relabel samples that were erroneously labeled the first time around. Relabeling is an expensive but necessary process as it is imperative to have a training data set of high quality. Unlike labeling, relabeling is usually done on a smaller sample of the entire data set and can be completed much faster if the samples are mislabeled in a unique way or associated with the same annotator. Once a trained model is deployed, its predictions on real-world data can be evaluated. A detailed error-analysis process can sometimes reveal systematic prediction errors. Many times, these characteristic errors may be correlated with a certain type of data sample or feature. In such cases, having another look at similar samples in the training data can help identify mislabeled samples. More often than not, labeling errors on a certain segment of the training data can be captured through such error analysis and corrected with relabeling. Best practices for data labeling Data labeling can be prohibitively expensive and time-consuming for large data sets. As model development is contingent on the availability of good-quality labeled data, poor labeling can affect the timelines and prolong the time to build and deploy machine learning models. A good practice for data scientists is to curate a comprehensive data-annotation framework for each use case before starting the data-labeling process. Clear, structured guidelines with examples and edge cases provide much-needed clarity for annotators to do their job with greater speed and accuracy. In the absence of domain experts within the company, external experts can be sought to discuss and conceptualize guidelines and best practices for labeling specific types of data. As labeling of large data sets by domain experts can be quite expensive, in specific cases, data labeling can be crowdsourced to thousands of users on platforms like Amazon Mechanical Turk. Typically, labeling by crowdsourced users is fast but often noisy and less accurate. Still, crowdsourcing can be a significantly quicker method of collecting the first set of labels before doing one or more rounds of relabeling to eliminate errors. Error analysis is another recommended practice to diagnose model prediction errors and iteratively improve model performance. Error analysis can be done manually by the data scientists or with greater speed and reproducibility using machine learning debugging platforms like Openlayer. Another good practice, in the context of very large data sets for deep learning applications, is to leverage machine learning to obtain a first pass of labels using techniques like the following: Conclusion Machine learning and deep-learning models are typically trained on large data sets. To train such models, a label for each data sample is necessary to teach the model about the information in the data set. Labeling, therefore, is an integral aspect of the machine learning lifecycle and directly influences the quality and performance of models in production. In this article, you’ve seen the importance, process, and best practices for efficient data labeling and relabeling. Mislabeled data samples introduce noise and bias in the data set that adversely impact the performance of the model. Identifying mislabeled examples through error analysis is a proven technique to improve the quality of training data that can be accelerated using machine learning debugging and testing platforms like Openlayer. Related Blogs Published by Transform Introduction
A metric layer is a centralized repository for key business metric. This “layer” sits between an organization’s data storage and compute layer and downstream tools where metric logic lives—like downstream business intelligence tools. A metric layer is a semantic layer where data teams can centrally define and store business metrics (or key performance indicators) in code. It then becomes a source of truth for metric—which means people who analyze data in downstream tools like Hex, Mode, or Tableau will all be working with the same metric logic in their analyses. The metric layer is a relatively new concept in the modern data stack, mainly because until recently, it was only available to companies with large or sophisticated data teams. Now it is more readily available to all organizations with metric platforms like Transform. In this article, you’ll learn what a metric layer is, how to use your data warehouse as a data source for the metric layer, and how to get value from this central metric repository by consuming metrics in downstream tools. How a Metric Layer fits into a Modern Data StackThe modern data stack is composed of a number of elements organized in the order of how data flows:
One central benefit of a metric layer is that it sits between the data warehouse and downstream analytics tools. People can access metrics in business intelligence (BI) tools like Tableau, Mode, and Hex, bringing metrics consistency across all business analysis. Use cases for the Metric Layer The formulation and implementation of metric layers was pioneered by prominent tech companies like Airbnb, Spotify, Slack, and Uber. Airbnb designed a metric layer called Minerva to serve as a single source of truth (SSOT) metric platform. They did this by standardizing the way metrics are created, calculated, served, and used across the organization. Uber built uMetric, a standardized metric platform that underlies the entire lifecycle of a metric from definition, discovery, planning, calculation, quality, and consumption. These pillars not only enable rapid metric computation for business decisions, but also help create useful features for training ML models and promoting data democratization. A new component in the Modern Data StackWith the emergence of big data, predictive analytics, and data science, most companies have access to enormous amounts of valuable data. Many organizations have evolved their data stack to simplify computation, transformation, and access to key business metrics, which can accelerate data-driven decision-making. However, as Benn Stancil noted in his popular Substack blog, there was no central repository for defining metrics. This causes confusion and misalignment across an organization. "The core problem is that there’s no central repository for defining a metric. Without that, metric formulas are scattered across tools, buried in hidden dashboards, and recreated, rewritten, and reused with no oversight or guidance." —Benn Stancil, The missing piece of the modern data stack Another common issue is “dashboard sprawl” where metric logic is spread across different tools and data artifacts. Since this logic is different for every tool, teams often end up with different numbers for the same metrics and no one knows where to find the “correct” metric to answer their most important business questions. This problem led to the metric layer becoming a new artifact in the modern data stack. With a single shared store of metrics definitions and values, the metric layer ensures consistent and accurate analysis and reporting of metrics. A metric layer not only centralizes key business data but also helps improve the efficiency of data teams by removing the need for repeated analytics. This helps data stakeholders become key advocates and enablers of data-driven decision-making and data democratization across the entire organization. Reutilization of metrics in diverse contexts and external tools One of the benefits of having a single metrics repository is that it can be connected to a variety of tools; for example, CRM’s, BI tools, tools developed in-house, as well as data quality and experimentation tools. A centralized architecture ensures that no matter how a tool’s internal logic is configured, the end result will be based on the same metric logic and consistent across tools and applications. For instance, MetricFlow, the metric layer behind Transform, has an API that enables users to express requests for their Transform metrics directly within SQL expressions. Core metrics like Net Promoter Score (NPS), Monthly Recurring Revenue (MRR), Customer Acquisition Cost (CAC), loan-to-value (LTV), and Annual Recurring Revenue (ARR) capture the health of the business and need to be accurate for reporting and decision-making. With a metric layer, it’s possible to see the lineage of each metric, how it’s built, what the data source is, and how it’s consumed. By unifying metrics extraction and data analytics on these metrics, the metric layer provides the much-needed consistency that is lacking in modern data stacks. Enhancing transparency between technical and non-technical teams with a single interface A single interface for metrics information gives data stakeholders across an organization—in development, sales, marketing, and more—to have the same view and understanding of key metrics to track goals. This consistency allows all of these teams to speak the same language regardless of the tools they use to compute the metrics. This is a tremendous benefit of a metric layer and promotes stronger data democratization and governance across the entire organization. Transform is unique in that it has the addition of a metrics catalog on top of MetricFlow, its open source metric layer. The metrics catalog is a central location where both data teams and non-technical users can interact with, build context, collaborate on, and share key metrics. Tracking changes is easier Because businesses are constantly evolving and creating new metrics or changing the definition of existing metrics, each data stakeholder has to manually keep track of changes in a data warehouse to update their metrics definition and logic. However, with the combination of a metric layer and a metrics catalog, tracking changes metrics owners are alerted anytime the lineage or definition of a metric changes. This enables data stakeholders to make better sense of data, especially when a new metric definition leads to anomalous or unexpected results. Dig into the Metric Layer A metric layer reduces the problem of disparate results when the same metric is computed by different teams using a wide variety of BI tools. And it makes data-driven analytics more precise and promotes faster and more accurate decision-making. If you’re looking for a streamlined and centralized metric layer, MetricFlow is now open source. You can explore the project on Github. Find more information about Transform’s metric layer and its benefits in the product documentation. Related Blogs
Published by StatusHero Introduction
Teams are the building blocks of successful organizations. The success of modern technology companies is driven to a large extent by their engineering and product teams. It is crucial for new engineering and product team leaders to maximize the productivity of their respective teams while ensuring a strong sense of team spirit, motivation, and alignment to the larger mission of the company, as well as fostering an inclusive and open culture that is collaborative, meritocratic, and respectful of each team member. Effective team development and management is therefore critical for engineering and product leaders, and ensuring robust team development at scale remains a big challenge in the face of changing work conditions. Despite the importance of team building and development, not many leaders are trained to succeed and hone their leadership skills. In many cases, individual contributors who progress or transition to the managerial track may not have the aptitude for developing teams nor have the necessary experience or training in this vital aspect of their new role. Although team development is more an art than a science, this topic has received significant interest from the industry as well as academia, leading to structured team development theories and strategies. In this article, you’ll explore a list of curated tips for engineering and product leaders to better manage the development of your teams and accelerate your learning journey on the leadership track. This particular set of tips focuses on building team cohesion, facilitating the five stages of team development, and providing structures for effective teamwork and communication that foster an open and collaborative team culture. Regular Check-Ins One of the fundamental responsibilities of a team leader is to have periodic check-ins with team members, both individually and as a group. These meetings serve as an opportunity to assess each team member’s work performance, their attitude and motivation toward their respective projects, and even their sense of belonging and identity within the team and the organization at large. These regular one-on-one meetings with direct reports also help to bring to light any professional or personal concerns that the manager can then try to address, whether on their own or with the support of colleagues from the human resources department. Group meetings are also essential to allow team members to gather and discuss work issues as a group and voice any concerns that may affect the entire team’s output, productivity, efficiency, or morale. Such group meetings also provide a window for colleagues to learn more about the work and progress made by other members in the team, as well as provide a collaborative atmosphere in which they are encouraged to share their opinions or suggestions. Holding regular retrospectives is a great way to foster discussion and collaboration. As you can see, both individual and group meetings serve as a vital opportunity for team leaders to check the pulse of each member and the team as a whole to assess whether any interventions are necessary to uplift productivity and motivation. Sometimes, these kinds of meetings can be conducted as a retreat or simply at an off-site location to enable team members to bond in a fun environment and encourage more open communication about the team’s development and progress. Structured Work Team members benefit immensely from a high-level structure to guide their work and appropriately allocate their time and resources to the various projects they are involved in. Ideally, all employees should be assigned projects that suit their particular skill set and interests and should be empowered to take ownership for the success of their projects. With individual owners for each team project, the role of the manager is to simply serve each colleague in terms of offering strategic guidance, providing additional resources or bandwidth, and removing any technical or organizational blocks that may otherwise impede their progress. In addition to a clear and structured assignment of work projects, teams also benefit from having a structured work cycle. For instance, engineering teams usually employ an Agile methodology and a regular Scrum cycle to plan their work in sprints and evaluate their progress. Using these proven methodologies helps team members plan their work effectively and encourages feedback from colleagues and the managers to weigh into project planning and management. Over time, if these processes are followed diligently, teams become vastly more organized and productive, leading to more successful projects and deliverables. Five Stages of Team Development According to research by renowned psychologist Bruce Tuckman, there are five distinct stages in a team’s development. These include the following: Forming This is the first stage in a team’s development, in which team leaders introduce individual team members, highlight their respective experience and skills, and facilitate interactions among the team. Knowing each other’s core strengths helps team members better understand who to reach out to for help or collaborate with to execute their projects successfully. Ideally, this stage should be revisited each time a new colleague joins the team to ensure that they feel welcome and to stimulate effective onboarding. Storming Storming is the next stage in a team’s development, which involves team members openly sharing their ideas for current work or new projects in front of the entire team. Team leaders can facilitate this by organizing meetings or events such as hackathons. During this brainstorming stage, it is important that each individual is allowed to freely express their opinions even if they are in conflict with others’. This provides leaders an opportunity to provide high-level clarity and showcase their leadership by effectively resolving any conflicts and motivating team members to disagree and commit for the greater good of the team. Norming During this stage, the team has crossed the initial hurdles and resolved differing opinions, allowing them to begin to hit their stride and work more productively as a unit. With a clear roadmap and a better sense of team success, individual employees begin to celebrate each other’s strengths and weaknesses and collaborate more effectively. Team leaders should congratulate themselves for attaining the norming stage but also be aware of the need to maintain the team’s motivation and momentum toward achieving their goals. Performing By this stage, a team benefits from high levels of cohesion and trust in each other. Teams are more efficient and can self-sustain their progress and velocity with little oversight or push from the team leaders. This enables them to take on more challenging and audacious projects and push the team’s limits in a positive manner. During this stage, team leaders can step in to hone individual team members’ strengths and help them develop and strive for the next step in their careers. Sincere team leaders leverage their coaching and mentorship skills to empower individuals to progress toward their peak efficiency and realize their full potential at work. Adjourning By this stage, teams have completed their projects. This is an excellent opportunity to discuss what went well, what did not go so well, and how to improve and implement new strategies for future team projects. This is a good time to celebrate individual and team successes and to congratulate employees in a public forum, motivating them to strive for even greater success in the future. Team leaders should also take the feedback from the team and leverage it to improve their team building and development methods. Conclusion Developing teams of engineers and product managers is a critical responsibility for the leaders and managers of modern technology companies. When teams operate at their best, the organization as a whole benefits from their productivity and positive momentum. In this article, you’ve learned several tips and strategies on how engineering and product team leaders absorb and implement in their respective teams. These include conducting regular check-ins with individual employees as well as the entire team, providing a structured framework for carrying out their work and executing projects successfully, and following the principles from the five stages of team development. Essentially, leaders should strive to build a team where the whole is greater than the sum of its parts. This not only requires substantial care, attention, and efforts from the leaders but also a high level of empathy and understanding of each individual in the team. Teams with strong, empathetic, servant leaders rise above other teams in an organization, attracting better and more strategic projects and opportunities for collaboration, ultimately resulting in a win for every team member as well as the team leader. Introduction
"Data democratization" has become a buzzword for a reason. Modern organizations rely extensively on data to make informed decisions about their customers, products, strategy, and to assess the health of the business. But even with an abundance of data, if your business can’t access or leverage this data to make decisions, it’s not useful. To that end, data democratization, or the process of making data accessible to everyone, is quintessential to data-driven organizations. Providing data access to everyone also implies that there are few if any roadblocks or gatekeepers who control this access. When stakeholders from different departments—like sales, marketing, operations, and finance—are permitted and incentivized to use this data to better understand and improve their business function, the whole organization benefits. Successful data democratization requires constant effort and discipline. It’s founded on an organization-wide cultural shift that embraces a data-first approach and empowers every stakeholder to comfortably use data and make better data-driven decisions. As Transform co-founder James Mayfield put it, organizations should think about "democratizing insights, not data." In this article, I will provide a detailed overview of data democratization, why organizations should invest in it, and how to actually implement it in practice. Why democratize access to data? Historically, data used to be kept in silos, usually under the purview of the IT or Analytics departments. When any stakeholder from outside these departments required data for their work, they had to go through these data gatekeepers to access the necessary assets. This philosophy has been the norm for decades but is no longer relevant for modern data-driven organizations. Removing these types of bottlenecks is a necessary first step toward data democratization. Guidelines for data democratization can be noted in a data governance framework to improve access and provide high-quality data for downstream analytics. Improving access is just the first step of an ongoing process where every individual employee is encouraged and trained to make use of data. The more people who can make decisions based on data, the more the organization stands to benefit from a variety of perspectives and ideas. Companies have been dedicating huge investments in data infrastructure and tooling in order to build an analytics advantage over their competitors. The dream is to “democratize data” and get employees to change their ways of working and start making decisions informed by data, not gut feelings. By investing in data education and helping analysts influence, then building modern tools to support metrics, we will continue making progress toward that goal of truly democratized data" —James Mayfield, co-founder, Transform While data analytics and business intelligence efforts are traditionally the domain of data experts, organizations can empower non-technical stakeholders to perform basic data operations via in-house training programs, workshops, and self-service tools that can simplify their onboarding and learning process. They can also use software that surfaces data in an easy-to-consume format for business stakeholders. Data democratization has multiple downstream benefits. It leads to greater data literacy, which can facilitate not only greater data-driven decision-making but also potentially lead to creation of new products or services based on insights mined from the data. Therefore, greater democratization, usage, and adoption of a data-driven approach can unlock massive commercial value and new growth levers for businesses. How do you actually democratize data? Implementing data democratization is a hard challenge and an ongoing process. To be successful, it needs support, buy-in, and a lot of patience from the leadership. Apart from conceptualizing and implementing curated data governance frameworks and policies, organizations can leverage tools to enable data democratization at scale. Tools to enable data democratization The Data Catalog A data catalog is a collection of metadata that, combined with data management and search tools, helps data stakeholders find and acquire data for downstream analytics. A data catalog provides a managed and scalable data discovery and metadata management capabilities which are fundamental requirements of attaining higher levels of data democratization in an organization. The Data Mart A data mart is a subset of a data warehouse focused on a specific business vertical or data domain. Data marts enable specific users to access specific data that empowers them to quickly access these datasets without wasting time searching for the same in the data warehouse. For instance, individual departments like sales, marketing, operations, and finance can have their respective data marts for accelerating their domain-specific data-driven decision making. The Metrics Catalog A metrics catalog is a new layer in the modern data stack. It is a centralized store for all of your organizations’ most important metrics (or key performance indicators) and it's uniquely positioned between the data warehouse and downstream tools. As a self-service place for business KPIs, every stakeholder in the organization has access to track their own metrics and share context with others. By capturing core business metrics in this fashion and this location in the modern data stack, a metrics catalog provides immense visibility and transparency into an organization's most critical metrics and metric lineage for all stakeholders in an organization. This new concept of a metrics catalog can have a significant role to play in democratizing data to everyone. As a single source of ground truth for business data, a metrics catalog enables diverse stakeholders to base all key decisions on the same foundation. It also allows for disparate teams to use the same metrics, ask questions, and keep everyone aligned and on track. This greatly enhances the level of data democratization within an organization. Challenges for data democratization Although the benefits of data democratization are pretty evident, there are also numerous challenges. Some challenges are common, like data being kept in silos and unclear data ownership. The informational silos problem is antithetical to data democratization, and can adversely impact an organization's ability to leverage data for improvising its business performance and decision making. Different teams have ownership of different types of data, which contributes to the problem of information silos. When a particular team has exclusive access to specific data assets, they not only hinder other teams from accessing the data but also guard their analysis and insights derived from the same data. This often leads to duplication of efforts across teams, causing a massive waste of organizational time and resources. As each individual team or department hoards its own data and analyses, it contributes to the adoption of the same undemocratic processes across other teams further compounding the challenges in promoting data democratization. With greater access to the organizational data assets, there is also a challenge of data security, privacy, and potential misuse of the data. It increases the number of gaps in the organization which might become vulnerable to adversarial attacks and data breachers. This is why it’s important to have a balance between data security and data access—including having stronger safeguards around who can access and analyze personally-identifiable information and customer data. Looking ahead If implemented well, data democratization can provide an immense competitive edge that will only compound over time as organizations mature in their digital transformation journey. Several tools and data artifacts can aid in better implementation and adoption of best practices and policies that help in democratizing data. A metrics catalog is one relatively new tool that provides a centralized store of business critical information accessible to multiple stakeholders. It captures essential business metrics and provides a simplified interface that is agnostic of the separate analytics, CRM, and BI platforms used by various teams in the organization. Learn more about how a metrics store can promote data democratization and governance at Transform.co. Introduction
Data governance is a fundamental pillar of modern digital businesses. It refers to a framework of processes and guidelines that companies use to ensure all enterprise data assets are managed and utilized appropriately. Even if an organization has large investments in data infrastructure and teams, without a structured data governance framework, organizations will struggle to harness the full value of their data. A strong framework provides a clear set of guidelines for all employees who access and consume data in downstream applications. It also contributes to greater trust in the authenticity and quality of data and allows data stakeholders to focus on core data tasks instead of worrying about whether the data was created, processed, stored accurately, and in compliance with national or domain-related legislations like GDPR, HIPAA, CCPA, and data localization laws. Given recent data breaches, the importance of a structured data governance framework cannot be emphasized enough. In this article, you’ll learn how to ensure data quality through better data governance mechanisms, leading to an increase in data informed decision-making. You’ll also learn how a clear data governance framework contributes to improved data quality and value creation across the entire organization. Why do you need data governance? The digital revolution is founded on data and the idea that data can generate insights that are critical for decision-making and long-term planning. With the emergence of cloud technologies, it’s easier for businesses to see the importance of data and store it in a more accessible, scalable, and secure way. A data governance framework is a set of rules and processes for collecting, storing, and using data. This diagram shows a simplified outline for how to think about building a data governance framework for your organization.However, collecting and storing data is just the tip of the iceberg. Without a clear and robust governance framework, you can’t fully understand the value of your data. High-quality data will help you make the best possible decision for your company. A data governance framework consists of several layers, stakeholders, business goals, and structured processes with a focus on information and project management. This accountability means organizations can build high-quality data products with confidence. This is evident in the case of top technology companies like Google and Amazon that have invested early and massively in data and data-driven technologies. They benefited from investing and enforcing a data governance framework that lowers the organizational threshold, velocity, and efficiency with which businesses can adapt to change. So, why is data governance important? Investing in data governance leads to many benefits including:
Ensure data quality through governance A major outcome of a solid data governance framework, if carried out properly, is improved data quality. When organizations follow these guidelines, it leads to a clearer understanding of their data assets and increases accountability. First, think about your data lineage. Record the source of each data set and the date/time that it is accessed. It’s also critical to understand the teams that are accessing the data including the applications they’re using.This ensures compliance and prevents data breaches. You can test data quality by asking different stakeholder teams to provide the value for a common business metric. More often than not, different teams will have conflicting answers for the same metric. This can be the result of a flaw in your data governance strategy, fuzzy guidelines, or scattered metrics logic across downstream tools. Create policies that ensure data accuracy Maintaining accurate data across the organization is difficult but rewarding. Once a new data asset is created, either internal or external, it needs to be systematically logged and entered into the appropriate databases. Consistently using data governance best practices for completeness, relevance, reliability, and lifecycle can lead to better data quality and accuracy. Develop practices to test data completeness Data completeness refers to the wholeness of the data. Data is complete when there are no missing values, records, or duplicates. Basic automated checks to validate the number of rows and columns, dimensionality, missing and null values, and data format mismatch can help identify missing elements. Adopt technologies to check data relevance Data relevance refers to the utility of data in providing critical insights. It’s important to remember that not all data is useful or relevant to particular business problems, and identifying the right set of input data can help focus subsequent analytics and modeling efforts. Track relevance with data reliability Data reliability is an indicator of how useful and relevant it is over time. It builds upon the concepts of completeness and relevance, and is more likely to be used and reused by teams for their work. This lays the foundation for multiple use cases and business insights. Stay compliant with data depreciation and lifecycleData timeliness and lifecycle management provides clear timelines for the validity and deprecation of data, ensuring that it’s used only when relevant and compliant with privacy laws. This regulates the lifecycle before it is depreciated or deleted permanently. Standardizing metrics as part of your data governance strategy Let’s take a look at how you can standardize your metrics through metrics catalogs and policies and build into a data governance strategy that ensures data quality. Catalog metrics in a metrics storeStandard metrics like annual recurring revenue (ARR), gross merchandise value (GMV), customer acquisition cost (CAC), customer lifetime value (LTV), and net promoter score (NPS) are common. Once you've defined your metrics, these metrics can be stored in a metrics catalog for greater ease of access, use, and re-use across the organization. A metrics catalog has several advantages. It reduces valuable organizational time and effort to reproduce the underlying analysis, and it creates a centralized metrics store that facilitates better understanding and decision-making. As depicted in the figure below, a metrics store is a centralized and governed place for organizations to store key metrics, creating a repository for stakeholders to access key metrics in a repeatable way, regardless of where people access their data. Policies and practices for sign-off Before creating a metric, there needs to be a clear policy on the steps that people use to analyze and validate their business metrics. Data quality policies should not be treated as an administrative exercise but regarded as an important milestone in this stage of data transformation. In addition to assigning an owner for each of your critical metrics, you should also think about executive sponsorship for the organization’s most important, “north-star” metrics. A stamp of approval from the C-suite or an executive sponsor conveys the importance of the data policy framework to the entire organization but can also be used to negotiate and expedite resolutions when conflicts arise. Conclusion In this article, you’ve learned about data quality as an index that can be used for many attributes of data in an organization. A data governance framework creates a set of best practices that improve data accuracy and relevance. A data governance framework also makes it possible to distribute high-quality data to your teams in the most efficient way possible. Building a metrics store is a critical part of this process because metrics are the language that you use to express whether you achieved your organizational goals. A metrics store, like the Transform Metrics Store, centralizes all of this knowledge in one place for easy access and collaboration. To learn more about the metrics catalog and other solutions, visit Transform.co. |
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