What Is a Data Governance Policy?

A data governance policy documents how an organization uses and manages its data and information. It covers the people, processes, and technology of an organization’s data program and clarifies who is responsible for managing and using data. The policy also ensures that data is managed, curated, and protected consistently, with specific security and quality standards.

A data governance policy can be a huge factor in the success of an organization’s data management (and data culture) success. Here we’ll address various aspects of the creation, maintenance, and monitoring of data policies. 

Data governance policies typically cover the following:

• Data definition: How does the organization define data?

• Data classification: How is data organized?

• Data roles and stewardship responsibilities: Who is responsible for what aspects of data management? What do they do? How do they communicate progress?

• Data access: Who can access what data? What are they authorized to do?

• Data movement: How does data flow across the modern data stack?

• Data retirement/destruction: How long are certain data types stored and where? What is the process for destroying PII?

• Report development: Are there standardized data reporting practices in place?

• Change management: When changes take place in data management processes, how are they communicated and documented?

• Data tools: What data tools are available? What is their purpose? Who can use them? How?

• Data quality & issue management: How are data quality issues addressed and resolved?

• Knowledge management: How does the data team document tribal knowledge?

What are the key elements of a data governance policy?

A policy document generally has several components that make up the complete policy when combined.

The policy

This entails a high-level statement of intent for something that should be achieved. 

An example for data quality (DQ) may read: All data should be profiled; critical data should have DQ rules executed, threshold breaches investigated and rectified, etc. All critical data must be profiled.

The policy standard

This is a natural language explanation of a requirement that must be fulfilled for policy compliance to be met. A policy may have multiple standards.

An example for data quality may read: Customer First Name must be all text, minimum length 2, maximum length 15. If less than 99% of customers meet this requirement, escalate for root cause analysis.

Another example may define critical data and how to derive it. In this case, each Business Data Attribute (Data Element) would have one standard per data quality dimension.

Controls or technical policy implementation 

The controls are the rules executed against a data object to determine if it meets a specific standard. Each standard will have one-to-many controls.

For example, a script or DQ tool may execute a check against the values in a table column to determine the pass and fail percentage for that column as defined by the policy standard.  

As another example, critical data being profiled will have various rules that must be met (for minimum values, maximum values, null counts, missing counts, minimum lengths, and maximum lengths depending on whether text or numeric data). In this case, each column storing the customer's first name would have the control implemented.

Audit and interlink with a data intelligence platform

This element of the policy collects evidence to prove that the policy is being met.

Examples of this include log records and reports demonstrating when data quality checks were run and the results of the checks. All defined critical data has profiling (which can address audit needs).

The combination of these elements in a policy document provides an end-to-end journey from the inception to the implementation and the monitoring of a policy.

Linking published policies and policy control results directly to data assets with a data intelligence platform allows users to understand the data they use and make informed decisions about its appropriate use. Additionally, by adding policy and compliance information as metadata to the platform's data assets, the governance team can complete comprehensive monitoring and reporting of policy compliance across data sources.

The key benefit is that many of these tasks are necessary for an organization to operate safely. Without centralizing the policies, standards, and controls and without clear responsibilities, audits become a time-consuming burden. 

How to write a data governance policy: People, process, and technology

Writing an effective data governance policy requires a clear framework and the involvement of key people across the organization. A well-structured policy will outline how data is managed, who is responsible for what, and how decisions are made. Here’s how to approach it:

1. Involve executives and key stakeholders

Start by getting buy-in from top leadership. Executives set the tone for the importance of data governance, ensuring that the policy aligns with business goals. They also help secure the necessary resources and enforce compliance across the organization.

Stakeholders from different departments should be involved early on. They provide insights into their data needs and ensure the policy supports various business functions like marketing, finance, or customer service. Their input helps craft a policy that is relevant and practical for day-to-day operations.

2. Establish a governance committee

A governance committee, made up of data stewards, IT leaders, and business stakeholders, should oversee the creation and ongoing management of the policy. This committee is responsible for defining data roles, steward responsibilities, and ensuring adherence to the policy. They also manage updates and handle any issues that arise.

3. Create an inventory and define program goals

Before drafting the policy, it’s crucial to build a comprehensive inventory of your data assets. This involves identifying what data your organization has, where it’s stored, and how it’s used. This step ensures that the governance policy is built on a clear understanding of the organization’s data landscape.

Next, work with key leaders—including executives, department heads, and data stewards—to define clear goals for the data governance program. These goals should align with business objectives, such as improving data quality, ensuring compliance, or enhancing data accessibility.

Once goals are set, create common data standards that all teams will follow. This includes defining how data should be classified, labeled, and formatted across the organization. Consistency in data standards helps ensure accuracy and usability across different departments.

Define the metrics that will be used to measure the success of the data governance program. This might include metrics related to data quality, access, security, and user adoption. Tracking these metrics will help gauge the program’s performance and guide future adjustments.

Finally, draft the policy based on all of the above elements, and involve the governance committee in reviewing and revising it to ensure it meets organizational needs and compliance requirements. Make sure the policy is clear, actionable, and easy to follow.

4. Document and communicate the policy

Once the policy is created, it needs to be documented clearly and shared across the organization. The governance committee should oversee this process, ensuring all employees understand their roles in data governance and how to follow the guidelines.

By following these steps and involving executives, stakeholders, and the governance committee, your organization can create a comprehensive data governance policy that supports responsible data management and ensures compliance with regulations.

Conclusion

Here, we’ve seen how data governance policies can formalize work that needs to be done within data management and, in many cases, create unambiguous processes and responsibilities that can lead to multiple efficiencies beneficial to an organization. Making internal and external audits more streamlined and worry-free is a massive step in risk mitigation that can be crucial to protecting before being able to invest more time enabling and empowering data users.   

Curious to learn how a data catalog can help you draft and implement data governance policies? Book a demo with us to learn more.