Blog > Data Governance > 5 Data Governance Best Practices

5 Data Governance Best Practices

Authors Photo Precisely Editor | February 1, 2021

The number of connected devices has expanded rapidly in recent years, as mobile phones, telematics devices, IoT sensors, and more have gained widespread adoption. At the same time, big data analytics has come of age. The deluge of new data has combined with an increased need for analyzing and exploiting that information to create an unprecedented opportunity for businesses to better understand their customers, new business opportunities, competition, and more. These trends, in turn, have brought increased attention to the discipline of data governance best practices.

The term “data governance” is often used in concert with “data management.” Nevertheless, it implies a broader perspective that incorporates security and compliance, documentation, integration, data architecture, analysis, and more. In short, it covers any topic pertaining to the people, processes, and technologies required to manage, protect, and utilize data assets.

Here are some best practices for developing a sound data governance discipline within your organization.

1. Start with the “Why”

For some, the need for data governance may appear obvious. Nevertheless, it’s important to educate stakeholders throughout the organization as to the benefits of investing the time and energy required to do it well. Data governance helps to increase the confidence of decision-makers throughout the company in the data they use to drive both strategic and tactical choices. After all, being wrong can be very costly.

Security and compliance, likewise, should be high on the list of priorities for C-level executives in any organization. Fines and penalties, not to mention bad publicity, can exact a significant toll on organizations that have not invested adequately in data governance.

In addition, good governance practices can uncover inefficiencies and technical shortcomings which, if addressed, can save an organization money and reduce risk.

Finally, it helps to ensure that an organization’s data assets are used to their maximum effect. When stakeholders throughout the organization have clear visibility to which data assets are available, it opens the door to more effectively exploiting those assets to improve business results.

As you embark on your data governance initiatives, it is critically important to educate and inform executive management and to establish buy-in for the program, including the necessary budget commitment to make your data governance vision a reality.

Read our eBook

Fueling Enterprise Data Governance with Data Quality

We live in a world of increasing data. It’s a driver of growth and change and it’s changing how we’re approaching business. As a result, it is changing how we need to manage and govern our data. Learn how data quality is strengthening the overall enterprise data governance framework.

2. Get the lay of the land

Any good data governance plan begins with understanding the status quo. That includes an inventory of data domains (ERP or CRM, for example, or data residing in custom applications used for operations and logistics).

This high-level inventory of data assets may also include information that is not necessarily stored in databases. If budget processes are driven largely by Excel spreadsheets that reside on an internal file server (or perhaps even stored on laptop hard drives and exchanged via e-mail), that information should be also noted as part of your high-level inventory of data assets.


In addition to cataloging these data domains, it is important to understand whether and how they relate to one another. That means understanding integration points, as well as the timing and technical means of transferring data, error resolution processes, and so on.

3. Prioritize and create quick wins

As with any new initiative, it pays to identify a few specific outcomes that can be achieved quickly, with relatively little effort and investment, and with a high likelihood of success.

Following the high-level inventory of data assets outlined above, it can be useful to drill down into each domain to identify potential gaps and areas for improvement. If data quality in the CRM system is poor, for example, the company could realize cost savings on direct-mail advertising or improved results from digital campaigns. Inaccurate inventory quantities in the ERP system can result in discrepancies between the general ledger and physical inventories and may lead to out-of-stock scenarios that can erode customer satisfaction and result in lost revenue.

Security and compliance gaps or potential data loss resulting from informal processes can also be targeted for potential quick wins. It pays to involve stakeholders in these discussions, not only to discover key pain points but also to build consensus around data governance initiatives.

4. Measure results and communicate successes

For each of the specific outcomes defined above, establish measures in advance that can be used as a yardstick for success. As with any project or initiative, some outcomes are easier to measure than others, but data governance leaders should not shy away from defining and communicating the key metrics that establish the success or failure of their initiatives.

Data governance video conference image.

Data governance best practices requires a long-term organizational commitment. Defining, measuring, and communicating the resulting benefits helps to ensure that stakeholders throughout your organization understand and appreciate the value of data governance programs over the long haul.

5. Rinse and repeat

Ultimately, data governance is a journey, not a destination. The best practices outlined here should be seen as part of an iterative process to refine, improve, and extend the organization’s use of data assets, to safeguard those assets, and to ensure their integrity over the long term.

Artificial intelligence, machine learning, and big data analytics continue to gain ground as a key competitive differentiator. The organizations that are most capable of mastering and exploiting data as a strategic asset will position themselves for long-term advantage.

As they tap into the value of their data, though, they must continue to heed the age-old warning “garbage in, garbage out.” Data quality is more important than ever. Business leaders must be prepared to adapt to a constantly changing regulatory environment and an endless wave of new security threats. They must strive to optimize the overall value of their data.

Data governance is never a one-and-done project; it needs to be evangelized within the organization and internalized across various functions and departments in a spirit of continuous improvement.

Wherever your organization is on its data governance journey, we would love to talk with you. Precisely helps organizations address the challenges of data governance and build lasting value while ensuring effective compliance with data governance best practices.

To find out more about how Precisely can assist you with your data governance programs, download our eBook Fueling Enterprise Data Governance with Data Quality.