What are Best Practices for Data Governance?

Introduction
Today, data is at the core of many companies, and it's of the highest importance for running a successful business. Companies process huge amounts of data daily, which they must store, categorize, track, and organize by cataloging, and that's where data governance comes in.

Data governance is a set of processes that promote better management of business data, unlocking the true value of data by ensuring that it's more accessible, reliable, secure, and compliant. For modern data-driven organizations, a strong data governance framework is not only important but essential for the best use of data in business decisions. A strong data governance framework usually encompasses functions such as managing data access and data ownership, tracing data lineage, managing duplicate or false data, and classifying and assuring data quality. All of these are the pillars of a successful data governance process.

However, implementing a robust data governance framework is no small feat. If not done systematically, it can lead to a huge loss of organizational time, resources, and effort. Companies that have made significant progress in building data governance frameworks and cultivated a strong and inclusive data culture have done so incrementally, aligning incentives and creating deep collaboration across cross-functional teams that own the data governance roadmap. Organizations are more likely to be successful if they can bring together people, processes, and technology to build their framework.

In this article, you'll learn about best practices for implementing data governance in an organization. Companies can leverage existing best practices and build on them to fast-track their own data governance efforts.

What Are the Challenges of Implementing Data Governance?
Before you plan your data governance strategy, you need to look out for some common challenges.

One major challenge for organizations is building a strong business use case for investing staff and resources in a data governance framework. Those that haven't yet embraced digital transformation and the better, faster decision-making possible with deeper data analysis might not see the long-term business value of data governance. It's important to unite relevant stakeholders across the organization to take on the challenge.

Even when organizations do launch a governance framework, they may fail to achieve its true potential. Poor data leadership and ownership may be an obstacle, for example. Data governance also requires collaboration and consistent enforcement across departments to succeed. For example, the finance department could collaborate with the accountancy department to create a cross-practice team to communicate and transfer data more transparently. 

So, without the buy-in and blessings of the tech and collaborative data ownership that helps break down the organizational silos, the program is unlikely to come to fruition.

Additionally, a good data governance framework relies on high-quality data. The primary goal of data governance is to make data more accessible, secure, and reliable for stakeholders to consume for their own use cases. However, if the quality of the data at the source is poor, implementing data governance becomes much more difficult. 

Data Governance Best Practices 
The following are best practices that have been adopted successfully by numerous organizations, such as Collibra, IBM, Informatica, Select Star, and more,  in building comprehensive data governance frameworks.

1 Build a Strong Business Use Case
The goal of data governance is to enable every stakeholder to use the data to make business decisions relevant to their department, whether that's sales, marketing, finance, or human resources. This means that you need the support and alignment of all users and departments right from the beginning. Without cross-functional support, building a strong business case for investing in a long-term mission like data governance is less likely to succeed. 

Data governance generates some significant business benefits that can make the advantages of the process clear to the leadership. It saves time and provides improved security and reliable and more accurate data, making it easier to make data-driven decisions. When these business benefits are made clear to the leadership, it's easier to get approval for needed staff, budget, and resources for the project. 

2 Identify Data Stewards and Owners
Clearly defined roles and owners are necessary to build the data governance framework in a structured manner. Knowing which stakeholders own certain responsibilities also helps with clear lines of communication. Exact roles may differ across organizations, but the following are common choices:

• Data governance council/board: A governing body that drives the strategy, planning, and execution of the overall data governance program.
• Data managers: Experts who create database systems and architectures that suit the organization's specific needs for in-house as well as externally sourced data assets.
• Data owners: Team members who are accountable for specific data assets. They are the point of contact when a stakeholder requires a particular type of data.
• Data stewards: Team members who are responsible for maintaining and improving data quality and documentation by using the processes enshrined in the data governance framework.
• Data consumers: Experts who directly interact with data to perform analytics, statistics, or more sophisticated data science work.

3 Start Small
Creating a strong data governance framework requires the right mix of people, processes, and technology to come together. It's crucial to start small and aim for quick incremental wins rather than overpromising and underdelivering. Creating governance guidelines requires specific expertise; you could hire this expertise, but empowering and upskilling people within your existing team might be more successful as they already know your data. 

Those responsible for data governance then need to gradually build trust and seek alignment from various cross-functional departments before the framework policies can be enshrined as organization-wide processes.

For governance-based processes to be adopted and diligently followed, your data stewards need to implement regular checks and audits and guide team members and departments that might not be familiar with good data governance practices. This guidance has two dimensions: cultural guidance and technological guidance concerning the required tools. 

When data stewards implement processes, they should also implement the right tools for advanced actions such as automation. Once every cross-functional team understands when and how to use governance principles in their day-to-day work with the help of the tools, you can automate some of the processes.

4 Define and Measure Metrics
Data governance is a long-term investment. However, it's important to measure progress in smaller time frames to ensure that key milestones are being achieved without any delays or hurdles. Monitoring some metrics, such as the percentage of the data assets per ownership, the number of questions or errors that are reported to the data team, or the number of dashboards that are being used across the organization and their types, might help achieve those key milestones in the long term. 

In other words, a clear roadmap with specified deliverables, timelines, and metrics that are shared among all the owners ensures that progress can be evaluated in achievable, measurable steps. You need to be able to periodically check the progress of your governance framework to ensure that it's still on track.

This image shows a detailed roadmap for establishing a data governance program over a period of two years. Individual tasks can be defined for each business quarter and for different aspects of the framework, such as data insights, data quality, data standards, and data governance and management.

For example, improving data quality can be broken down into multiple milestones per business quarter. The goal for the first quarter may be hiring a data engineering team, while the next quarters may focus on establishing reference data repositories, data cleaning, and building data stores and data warehouses. This structured approach keeps cross-functional teams informed on the overall plan and ensures continued progress.

5 Establish Strong Communication Channels
Frequent and effective communication is the key to aligning stakeholders and collaborating across teams. Everyone should understand the desired goals and keep others informed on their progress in implementing them.

Additionally, your data stewards must be as transparent as possible to earn trust across the organization and emphasize the impact of investment in data governance to the executive leadership as well as to the downstream users of the framework. They can create a single channel for communication, which is like a linked data catalog where you can search data assets or collaborate on them. 

This way of communication is pivotal both during the implementation phase and after the framework is established. A single channel for communication will help drive strong adoption rates, resolve queries, and allow you to share updates to the governance policies as data and compliance requirements evolve.

6 Contextualize Data
Data contextualization involves adding any relevant information to data to make it actionable. Contextualization provides users better interpretation of the data and enables organizations to make smarter decisions.

This helps a data governance process work more efficiently as contextualized data has clearer meanings and allows decision makers to have enriched information regarding the actions they should take. Moreover, it can help improve how the organization handles data in its data governance environment.

7 Build a Long-Term Strategy for Data Governance
Achieving a strong data governance framework can be a moving target. You need to ensure that stakeholders know this is a long-term investment. Data governance is a continuous process that consists of many smaller projects and deliverables. Ramping up speed and complexity over time helps to scale your efforts. While the overall framework may take several years, smaller milestones can be set and achieved over shorter time frames, like a business quarter.

For instance, a useful set of milestones to accomplish in the first quarter of working on a data governance framework may include establishing data management policies and standards, hiring a data engineering team, and drafting a data management strategy together with all relevant stakeholders.

As long as they see incremental progress, stakeholders will learn to trust the process and be invested in the success of the project.

8 Expose the Data through Documentation
Knowing exactly what your data represents is a critical component of data governance. Users should have a single, centralized platform where they can find documentation related to their data. This documentation should be continuously updated, reviewed, and revised and should also be directly tied to the actual data assets. These actions will ensure that your users can trust and rely on your documentation, as it will always be up to date and accurate.

Strong data governance should expose the data through process-oriented documentation that is directly connected to the data.

9 Data Lineage and Usage
Knowing the source of data, where your data is flowing, and who is accessing it is important. With data governance, you have to build trust in your data, ensure the data is used properly in your organization, and troubleshoot issues when they arise. 

Data lineage helps automatically identify sensitive information and propagate some data governance-related policies. Data lineage also informs reports, issue logs, and audit logs, which show that the data governance requirements are met.

As an example, data lineage prevents teams from using a dashboard that was supposed to be deprecated or two different business units from building a metric using different underlying data.

Successful Data Governance Frameworks
Several large global companies have successfully implemented data governance frameworks. The following are some examples.

PwC, a global professional services company, has created a data governance framework consisting of the following components:

• Data governance strategy
• Enterprise data management
• Data lifecycle management
• Data stewardship and core functions
• Data governance enablers

ING, a Dutch multinational banking and financial services corporation, leveraged IBM Cloud Pak to improve data governance for its users in a hybrid cloud environment.

There are also several third-party companies that assist larger organizations with their data governance strategy and implementation, such as Collibra, Informatica, and Alation, and data catalogs that provide tools and insights required for implementing a data governance practice on your own, such as Select Star and Atlan.

Outcomes of a Strong Data Governance
Implementing a strong data governance strategy will inevitably lead to outcomes such as improved data quality, decreased data management costs, and better data analytics, which, in turn, leads to better decision-making throughout the organization. The following list provides an overview of the outcomes of effective data governance:

• Better data analytics
• Better decision making
• Improved operations support
• Data consistency
• Error-free data
• Improved data quality
• Decreased operational or data management costs
• Increased access to data from all stakeholders

For an organization, the time it takes to achieve these outcomes is closely related to the strength of its data governance implementation processes. Over time, these all contribute to one overarching outcome: organizational success.

Conclusion
Data governance is an essential requirement for modern organizations to drive greater adoption of data and empower business decision-making. Organizations can find it difficult to extract the full value of their data assets, especially as the amount of data keeps growing. Data governance frameworks lay down clear policies and guidelines for improving the quality of data and democratizing its usage across a business.

If you can navigate the challenges involved and follow the above best practices in creating and implementing your data governance framework, you can accelerate your organization's understanding and usage of data and deliver data-driven decision-making to your organization.

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