5 Data Governance Best Practices

Data governance is rapidly shifting from a leading-edge practice to a must-have framework for today’s enterprises. Although the term has been around for several decades, it is only now emerging as a widespread practice, as organizations experience the pain and compliance challenges associated with ungoverned data.

Briefly stated, data governance involves the formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset. Data governance provides a structured framework for decision-making and authority for data and data-related subjects.

To be successful, a data governance initiative should:

1. Be conceived with a clear purpose linked to the organization’s strategic objectives. 
2. Zero in on the data that matters most, by prioritizing the data sets that link to high value objectives. 
3. Establish clear performance metrics.
4. Focus initially on “quick wins” to deliver rapid results and demonstrate value.
5. Be designed to build on successes. 

The Need for Data Governance

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
• Data integration
• Data architecture
• Data analysis

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”

You may think the need for data governance is obvious. Nevertheless, take proactive steps 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 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, educate and inform executive management and establish buy-in for the program, including the necessary budget commitment to make your data governance vision a reality.

2. Assess Your Organization’s Data Landscape

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 not necessarily stored in databases. If Excel spreadsheets that reside on an internal file server (or perhaps are even stored on laptop hard drives and exchanged via e-mail) drive budget processes, you should note that information as part of your high-level inventory of data assets.

When cataloging these data domains, strive 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 the Data That Matters Most & Deliver Quick Wins

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

Following the high-level inventory of data assets outlined above, drill down into each domain to identify potential gaps and areas for improvement. For example, if data quality in the CRM system is poor, 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.

You can also target security and compliance gaps or potential data loss resulting from informal processes for potential quick wins. It pays to involve stakeholders in these discussions to discover key pain points and build consensus around data governance initiatives.

4. Measure Data Governance Results and Communicate Successes

For each of the specific outcomes defined above, establish measures in advance as a yardstick for success. 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 best practices require 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. Build Sustainable Data Governance: Rinse and Repeat

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

Artificial intelligence, machine learning, and big data analytics continue to gain ground as key competitive differentiators. 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, 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.