Data Quality Policies: Definition, Benefits, and Implementation

Updated March 03rd, 2025

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Research shows that strong data quality improves data trust across an entire organization — and effective data quality policies are how you ensure your teams enjoy top-quality data at all times.
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Effective data quality policies are how you align your data handling and structures with your organization’s goals. Understanding how to design and implement data quality at scale is critical for any organization’s data operations. This guide will explain data quality policies in detail, demonstrate how to design and implement policies, and show you how Atlan supports data quality at scale.

Table of Contents #

  1. What are data quality policies?
  2. Why do you need data quality policies?
  3. Benefits of data quality policies
  4. How to set data quality policies
  5. Scaling Data Quality Policies with Atlan
  6. Data Quality Policy Frequently Asked Questions
  7. Conclusion
  8. Data Quality Policies: Related reads

What are data quality policies? #

A data quality policy is a statement that directs decisions around data quality. Generally, a policy will define data ownership, quality standards, or procedures for resolving a data quality issue.

Data quality policies exist to improve the value of an organization’s data assets. To be effective, policies need to align with organizational goals — typically as part of a more extensive data governance policy program covering security, privacy, ethics, etc.

Why do you need data quality policies? #

In any complex data system, disputes around data quality and accuracy will inevitably arise. When they do, data quality policies are there to set the standard for conflict resolution, ensuring that decision making around quality remains consistent.

Data quality policies also prevent costly delays when quality breaks down. When an error or oversight causes a problem in a data pipeline, data quality policies define a clear path for resolving it. Without a well-defined policy in place, issues can languish while teams debate on appropriate steps. Data quality policies ensure that your teams don’t need to reinvent the wheel every time a data pipeline issue arises.

Benefits of data quality policies #

Data quality policies aren’t just for handling problems. They also improve the value of your data assets in multiple ways.

  • Defining data quality standards. Policies spell out which data quality dimensions matter most to your organization’s goals — so teams can focus their data quality efforts where they matter most.
  • Avoiding conflicts. Policies define data quality terms and standards to keep everyone in your org on the same page. These definitions promote consistency, improve communication, and prevent disagreements.
  • Building trust. Well-defined quality policies give your teams confidence in the data they use. Trusted data enhances communication and collaboration around data assets.
  • Streamlined development. With strong data policies in place, analysts can dive into their projects. Confidence in the quality of their source data gets rid of double checking and second guessing, resulting in fewer delays and more efficient delivery.
  • Advancing goals. When effective data quality policies align with organizational goals, you maximize the impact of data assets on furthering important business initiatives. High-quality data leads to high-quality data-driven development.

How to set data quality policies #

Creating an effective data quality policy in your org requires two stages: design and implementation.

Designing a data quality policy #


Quality standards should drive value for an organization, so start by establishing your target goals. Your policy must address the quality dimensions that matter most in your data assets, such as accuracy, completeness, consistency, timeliness, and uniqueness. For example, data freshness is the most important factor in a fraud detection system and so timeliness is a key quality concern.

Next, think through ways your data quality dimensions could break down — and what happens when they do. Where are there potential quality-related failure points among your data assets? Inaccuracies in the part of your medical data system that tracks patient allergies, for example, can have life-threatening consequences. When a quality issue is detected, what is the response?

Finally, define specific procedures to prevent, identify, or fix data quality issues. Whether it is regular audits or a rapid response protocol, make sure a particular response procedure addresses the nature of the data involved — and that it specifically mitigates any possible consequences that could be caused by that particular faulty data.

Implementing a data quality policy #


Now it’s time to put that data quality policy into effect. Start by communicating the new development to stakeholders across your organization. This includes establishing data ownership and assigning responsibility for aspects of data quality that the new policy covers.

Once the policy is in place and sufficiently communicated to the data owners and users in your org, the final duties become ongoing maintenance and improvement. Scaling data quality policies across large data systems can be a serious challenge, so start small and iterate. Monitor relevant metrics and adjust your data quality policies to address any shortfalls that may arise.

Scaling Data Quality Policies with Atlan #

Atlan is a modern data catalog offering tools to support scalable data governance in cloud systems, including data quality maintenance. Atlan uses the power of AI to enhance user engagement with governance policies, providing intuitive interfaces for non-technical users and powerful automation to scale complex governance systems.

  • Customizable scoring: Atlan’s customizable quality scoring system lets you define quality dimensions that align with your goals and then automatically monitor them in real time — instantly providing a clear picture of the quality health of all your data assets.
  • Seamless integration: Atlan integrates seamlessly with tools like Montecarlo and dbt, letting you keep your existing definitions as you develop your quality even further.
  • Built-in automation: Atlan’s automation features let you build data quality pipelines that scale to handle the huge volumes that modern data systems require. For example, features like auto-approval connect with Atlan’s metadata policy tools to let you build sophisticated quality pipelines that maintain the standards that are most important to you — including asset types, relations, locations, and more.
  • Data visibility: Atlan keeps quality visible at every point in your data handling, whether working within the Atlan catalog itself or one of the many partnered tools that integrate with Atlan.
  • Embedded Information: Atlan’s data quality information embeds intuitively into workflows, making quality a part of your organization’s day-to-day culture.

Data Quality Policy Frequently Asked Questions #

What should my data quality policy cover? #


The scope of your data quality policy depends on your goals. Think about how different data quality dimensions can affect your initiatives: if you need up-to-the-second updates, you should have a quality policy covering timeliness; if the accuracy of every entry is critical, you should have a quality policy establishing regular accuracy audits.

What should I include in my data quality policy? #


As with the scope, the contents of your data quality policy depend on your operations. But generally speaking, a data quality policy covers:

  • Standards for important data quality dimensions
  • Definitions of metrics for tracking data quality
  • Procedures for assessing those standards
  • Processes for resolving data quality problems

How many data quality policies do we need? #


There is no fixed number of data quality policies you need. You may find that new quality dimensions matter as your data operations grow. However, too many quality policies may become cumbersome, causing teams to disengage. Automated policy enforcement tools can help you scale your policies without burdening your teams.

How can I tell my data quality policy is working? #


Defining metrics to assess your policy is as important as the policy itself. When you implement a new policy, track associated metrics — for example, specific data quality targes like accuracy, completeness, consistency or timeliness — to see its impact. If you aren’t seeing the impact you expect, make qualitative investigations to find where things are going wrong. Adjusting your policy to improve impact is a part of the policy process.

Conclusion #

Data quality policies are critical tools for developing the value of your data assets by defining data ownership, quality standards, or procedures for resolving a data quality issue.

Effective data quality policy avoids time-consuming conflicts and keeps data operations smooth and reliable. Defining quality policies that align with your organization’s goals turns your data into a valuable foundation for future initiatives.

Atlan’s suite of customizable quality metrics, intuitive integration, and information surfacing features provides scalable, automated data quality policies that embed into every step of your workflows. See how Atlan can improve your data quality by booking a demo today.

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