Data Governance Best Practices: Building Teams, Setting Goals, and More

By Divya Bhargava

Published on September 30, 2024

Modern businesses rely on accurate, accessible, and secure data to drive decision-making and maintain a competitive edge. But without a solid data management framework, the sheer volume and complexity of data can lead to inefficiencies, compliance risks, and missed opportunities. This is where data governance comes into play.

Data governance best practices ensure that data is managed consistently, securely, and in alignment with organizational goals. From improving communication between teams to setting measurable goals and regularly auditing progress, effective governance lays the foundation for maximizing data value. In this blog, we’ll explore the value of data governance and offer actionable best practices to help your organization harness the full potential of its data.

What’s the business value of data governance?

Most organizations today need help understanding the benefits and ROI of data governance. Without this clarity, these programs may lack the necessary resources and support from leadership and struggle to gain traction. Data leaders can measure the value of a data governance program by tracking progress metrics including:

What % of tables have a data steward assigned?
What % of glossary terms have been updated or approved in the past year?
What % of tables have a definition?

These metrics enable leaders to calculate business-value ROI, including reduced time to find, understand, and use data.

Let’s illustrate the value of data governance with a story. A local government launched a project called "Go Green" to plant trees along the city’s roadsides.

Three different employees were assigned specific tasks:

Employee 1: Dig the hole
Employee 2: Place the tree in the hole
Employee 3: Fill the hole

Employee 1 worked the morning shift, Employee 2 the afternoon, and Employee 3 the evening.

Everything went smoothly until one day when Employee 2 took a week’s vacation. Despite Employee 2’s absence, the work continued—Employee 1 kept digging holes, and Employee 3 kept filling them in. However, no trees were planted during that time.

This failure could have been avoided in this story if data governance had been established to track progress, ensure quality, and facilitate communication.

Similarly, processes in organizations depend on each other to share data. Effective data governance identifies critical processes, controls the quality of the final product, defines the roles & responsibilities, and helps align the entire organization toward a common goal.

What are data governance best practices?

Audit and evaluate

The first step in seeding data governance is evaluating the current state of data management. Start by conducting Alation’s Data Culture Maturity Assessment (DCMA) across business units to identify focus areas for improvement.

Once the assessment is complete, consider the following questions:

Are critical data elements (CDEs) and key performance indicators (KPIs) established and well-known?
Are data owners identified?
Are data quality rules defined and known to the business?
What data exists within the organization, and what needs to be compliant?
What AI models are in use, and what is their data lineage?

Based on the results of the DCMA and the answers to these questions, define a roadmap that focuses on areas aligned with the organization's vision and prioritize initiatives based on the value proposition.

Present these findings to the leadership team, sharing the roadmap, and aligning on the goals to secure the necessary support for the initiative.

Build a team and establish rules and goals

To implement and drive data governance, form a dedicated team or a centralized data council. Your first steps with your new council include:

Define Data Governance: Clearly articulate the program’s objectives.
Define the RACI: RACI is a project management framework that stands for responsible, accountable, contributing, and informed. Assign ownership of key tasks with this framework.
Align with Organizational Initiatives: Ensure the data governance program supports broader business goals.
Establish Data Quality Rules: Collaborate with the engineering team to implement them.

Finally, the council should consider leveraging tools like Alation. A data intelligence platform helps you put your new people processes into practice, enabling you to:

Assign ownership of key data
Define data classification and categorization
Apply data and general policies
Define, document, and integrate master definitions
Integrate data quality (DQ) rules and their results
Identify Critical Data Elements (CDEs) and Key Performance Indicators (KPIs)
Document AI models and data products and associate them with data elements

Oversee a communication program

Effective communication is the backbone of any successful data governance program. Without clear, consistent messaging, even the most well-designed governance strategies can fail to gain traction. Overseeing a dedicated communication program ensures that all stakeholders—from data stewards to business leaders—understand their roles, responsibilities, and the value of data governance. In this section, we’ll explore how to establish a communication framework that keeps everyone aligned and engaged throughout the data governance journey.

Execution of data governance involves coordination and communication with the following stakeholders:

Data Governance Lead/Director
Executive Sponsors
Governance Implementation Team
Data Architects
Key stakeholders
Corporate communication Team

Tailor communications to the audience, ensuring clarity around program objectives, status, next steps, and blockers. This means customizing the message for different stakeholder groups—executives may need high-level updates on progress and business impact, while data stewards and analysts may require more detailed, hands-on guidance.

Clear communication not only ensures that everyone is aligned on the program’s goals but also helps to surface potential challenges early, making it easier to address blockers and keep the initiative on track. Regularly scheduled updates, combined with transparent reporting, foster trust and engagement across the organization.

You should also discuss key milestones, show progress, and create excitement around momentum to keep the organization's employees engaged. Refer to communication templates to foster effective dialogue.

Set achievable goals

Data governance should be seamlessly integrated into the organization and not seen as an additional overhead. To ensure success, break down goals by priority and complexity.

For example, if your organization wants to classify and manage personally identifiable information (PII), start by:

Prioritizing data sources for classification.
Defining and curating only the PII policy first.
Use productivity tools in Alation to curate the content in bulk.
Set clear targets (e.g., classifying 70% of PII data within two weeks).

Tools like Alation support bulk curation with features like catalog sets (which enable you to identify columns based on naming conventions and add classifications and policies); data dictionary uploads (which allow you to upload classification details in bulk for PII columns of a data source); and stewardship workbench (which enables stewards to define PII and associate a given policy to specific data assets).

This iterative approach ensures momentum, allowing the organization to achieve the highest value in the initial execution cycles.

Record, share, and recognize progress:

To maintain momentum and ensure continued success in data governance, it’s crucial to not only document progress but also actively share and celebrate it. Start by regularly tracking key metrics, such as compliance rates, policy adherence, and risk mitigation, to provide a clear picture of how the organization is advancing toward its governance goals. For example, when a risk is avoided—whether through compliance with a data policy or the proactive identification of potential data threats—capture the return on investment (ROI) by calculating the financial or operational impact of that risk avoidance. This approach demonstrates the tangible value of the data governance program.

Once progress is documented, share these achievements with employees across the organization. Use dashboards or reports to highlight milestones, such as reaching a certain percentage of data curation or resolving key governance challenges. These updates not only build transparency but also reinforce the importance of data governance at every level.

Recognizing and rewarding the teams who contribute to these successes is equally important. Consider implementing formal recognition programs, such as awards or incentives, to acknowledge individuals or departments that have played a significant role in meeting data governance objectives.

By showcasing their efforts, you encourage ongoing participation and foster a culture of accountability and data stewardship. Celebrating these wins also raises awareness and promotes the creation of a data-driven culture, where employees view data governance as integral to their daily workflows rather than a compliance obligation.

Audit data governance activities

Auditing is a critical component of any successful data governance program. Unlike a one-time initiative, data governance requires ongoing evaluation to ensure that policies, classifications, and data curations remain effective and relevant as business needs evolve. Regular audits help identify gaps, assess compliance, and provide insights into how well the governance framework is functioning.

One of the best practices for maintaining strong oversight is to track and document audit-related activities meticulously. In Alation, you can leverage custom fields to record important dates such as the Last Audit Date and Last Review Date, helping to establish a clear audit trail for each data asset or policy. These fields enable auditors to quickly identify when an asset was last audited, reviewed, or updated, ensuring that nothing slips through the cracks and that governance remains proactive.

To ensure transparency while protecting sensitive audit information, restrict access to these custom fields so that only authorized personnel, such as auditors or governance committee members, can view or modify them. Leveraging Custom Field Permission Settings not only secures this information but also strengthens the integrity of the auditing process.

By regularly revisiting and auditing data governance elements—whether it's the classification of data, compliance with policies, or the effectiveness of curation—you can maintain accountability and ensure that your governance strategy evolves with the organization. Documenting this process ensures transparency, builds trust, and provides leadership with a clear, data-backed view of the program’s health and progress.

The impact of poor data governance: a personal anecdote

Effective data governance ensures that data is accurate, appropriately used, and properly safeguarded throughout its lifecycle. Without strong governance, errors in data usage can lead to confusion, miscommunication, or even breaches of trust. Let me share a personal example to illustrate the importance of proper data governance practices.

More than a decade ago, I worked at an insurance company, where the IT team was developing a predictive model to help patients. The model analyzed lab results using ICD-9 codes (international codes that define medical procedures). The model would predict potential health conditions and automatically send educational materials based on the findings. The goal was to provide patients with helpful information based on their health data.

At the same time, I happened to visit the hospital, where the doctor ordered a series of tests, including a sonogram. (I should add that, I was part of their IT as a consultant and they also insured me.) A week later, I received a flyer in the mail congratulating me on my pregnancy and offering tips for welcoming a new baby. The problem? I wasn’t pregnant! The predictive model had misinterpreted the ICD-9 codes from my medical records and triggered the wrong communication.

This incident clearly highlighted a lack of data governance in the organization. Several key best practices were missing that could have prevented this situation:

Data Accuracy and Context: The ICD-9 codes in my record were not enough to draw an accurate conclusion. Data governance policies should ensure that data is interpreted within the correct context. In this case, additional information like the purpose of the sonogram (diagnostic vs. pregnancy-related) should have been factored in.
Data Usage Policies: There were no clear guidelines about how my data could be used. Best practices for data governance require clear policies about which data can be used and for what purpose. Had this been in place, the system might have required a manual review before sending out sensitive communications based on predictive analysis.
Data Sharing Protocols: The timing and method of communication also point to a failure in defining appropriate data-sharing protocols. Best practices would have required a verification step before sharing potentially sensitive information with patients, ensuring that the data being used was accurate and relevant.

If the organization had implemented these governance measures, I would have avoided the confusion and discomfort caused by receiving inaccurate health information. This example underscores why data governance isn’t just about compliance—it’s also essential for delivering a positive experience and building trust with customers.

Conclusion

Data governance programs thrive when they receive strong leadership support and are embedded into everyday processes. Data governance can deliver measurable value and foster a culture of data excellence by focusing on key initiatives and ensuring alignment across the organization.

Curious to learn how you can implement governance best practices with a data catalog? Book a demo with us today.