Data quality audit is a systematic review of data to ensure it meets your organization’s defined standards for accuracy, completeness, consistency, and compliance.
Data quality includes profiling, testing, monitoring, observability, and validation—each focused on capturing the data correctly, extracting business value, and ensuring its reliability for day-to-day operations.
In contrast, data quality auditing is more of a systematic approach of assessing data quality, especially from a governance, compliance, and legal angle.
In this article, we’ll explore:
- The basics of a data quality audit
- The benefits of data quality auditing
- How to ensure a successful data quality audit
- The central role metadata plays in data quality auditing
Table of contents #
- What is a data quality audit?
- What are the benefits of data quality audits?
- What do you need for a successful data quality audit?
- How can Atlan help you with data quality auditing?
- Data quality audit: Summary
- Data quality audit: Frequently asked questions (FAQs)
What is a data quality audit? #
A data quality audit is a formal process of examining and evaluating data quality across a range of legal, business-related, and technical aspects of an organization. These audits can be internal and external:
- Internal data quality audits: These help an organization maintain a self-committed set of standards for data. They can be more frequent and even real-time, depending on the maturity and capability of the monitoring and metadata management systems in place.
- External data quality audits: These are usually part of broader annual or semi-annual data audits. They’re generally conducted ex post facto, for example, analyzing a large amount of data at the end of the year. External data audits are performed by auditing authorities licensed to certify organizations on various levels of compliance.
For an organization to conduct its own internal auditing and ensure that external data auditing is seamless, it should have systems and processes in place to capture data quality-related metadata.
What are the benefits of data quality audits? #
Data quality audits, especially continuous internal data quality auditing, can be really beneficial in strengthening data-driven operations across the business. Whether it’s improving analytics, reporting accuracy, or the performance of machine learning models, high-quality data leads to better outcomes.
Better data quality gives humans interacting with the data the power to make more accurate decisions. It does the same for machine learning models, providing inference to various recommendation systems, customer segmentation models, and churn prediction models.
Key benefits of data quality audits include:
- Enhancement of trust in data assets for making better data-driven decisions.
- Identification of systematic problems with how the organization handles data.
- Detection of inaccurate and inconsistent data and the systems responsible for it.
- Compliance with and fulfillment of legal and regulatory requirements.
- Reduction of bad data-related risks, both from an operational and a legal point of view.
Moreover, internal audits help teams catch and fix issues early, before they affect business outcomes. Meanwhile, external audits provide assurance to regulators, customers, and partners that your data meets high-quality standards.
Now that you have learned about the benefits, let’s consider what you will need for a successful data quality audit.
What do you need for a successful data quality audit? #
To realize the benefits of a data quality audit—like improved decision-making, reduced risk, and regulatory confidence—you need complete visibility into the state and usage of the data.
A successful data quality audit depends on your ability to observe, trace, and understand your data across systems. While external audits focus on regulation-specific questions, internal teams should be able to answer key foundational questions, such as:
- What data assets exist across your organization?
- How is data quality measured for your organization’s data assets?
- Who owns the production and management of any given data asset?
- What are the various systems, products, and features that use the data asset?
- What level of observability and traceability exists to track changes in data?
These are only a few of the key examples but there are many more. Answering such questions requires effective metadata management – how well you extract, store, manage, and use metadata in your organization.
In practice, however, data quality auditing is difficult. Most organizations struggle with siloed data and fragmented metadata. This leads to:
- Incomplete or broken data lineage
- Inconsistent quality standards across domains
- Gaps in audit trails and accountability
- Heavy reliance on manual processes
In such a situation, what you need is a control plane that brings all the data in your organization in one place. This is a single platform capture, connect, and contextualize metadata across your ecosystem. With such a system in place, you can track data usage, assess lineage, and monitor quality with precision.
Atlan is a metadata management platform providing you with a metadata control plane. It brings together metadata from across your stack to help you manage, govern, and audit data quality at scale.
Next, let’s look at how Atlan supports data quality auditing in practice.
How can Atlan help you with data quality auditing? #
Atlan offers you a metadata lakehouse-type repository, bringing together metadata from all your systems into a unified, searchable, and actionable layer. This foundation helps data teams improve audit readiness and continuously monitor data quality at scale.
Here’s how Atlan enables data quality audits:
- Extensive, granular, and automated data lineage to track how the data got into the system, how it was transformed, and how it was used.
- Data quality compliance policies via the Policy Center to enforce a standard of data quality across your data assets.
- Product scores for organizations using the “data as a product” philosophy to manage and govern data assets.
- Integration with major open-source and proprietary data quality tools and platforms like Anomalo, Monte Carlo, and Soda.
- Custom metadata to define custom data quality-related metrics that are super specific to your organization’s processes.
Atlan offers more features to build a 360-degree view of your data assets, a large part of which relates to data quality.
Whether you’re conducting routine internal audits or preparing for an external compliance review, Atlan helps you build a repeatable, scalable, and transparent data quality auditing framework with these features.
Data quality audit: Summary #
This article took you through some of the basics of data quality auditing and its importance for an organization, both from an operational and legal point of view. We looked at the two key types of audits—internal and external—and how both rely on your organization’s ability to trace, monitor, and manage metadata across the data ecosystem.
Effective metadata management is at the core of data quality auditing, which is why we also covered how a platform like Atlan can help by providing a unified metadata control plane.
With features like granular lineage, quality policy enforcement, custom metrics, and integrations with leading data quality tools, Atlan acts as a unified control plane for audit-ready data.
Read more about Atlan’s data quality capabilities on the official blog.
Data quality audit: Frequently asked questions (FAQs) #
1. What is a data quality audit? #
Data quality auditing is the formal process of assessing data across accuracy, consistency, completeness, and compliance dimensions.
2. Why is a data quality audit important? #
Data quality auditing builds trust in your data, ensures compliance, detects systemic issues, and supports better decision-making across the business.
3. What’s the difference between internal and external audits? #
Internal audits are ongoing and help maintain internal data standards. External audits are periodic and conducted by independent bodies for compliance.
4. How often should data quality audits be conducted? #
Internal audits can be continuous or periodic based on system maturity. External audits are typically annual or semi-annual.
5. What makes data quality auditing difficult? #
Data silos, fragmented metadata, inconsistent standards, and lack of lineage make auditing challenging in most organizations.
6. What should a data quality audit checklist include? #
A data quality audit checklist should cover:
- Data inventory (what assets exist)
- Quality metrics (how quality is measured)
- Ownership (who manages each asset)
- Usage (where and how data is used)
- Traceability (lineage and change history)
These checkpoints ensure that your audit captures completeness, accuracy, and accountability across your data landscape.
7. How does Atlan support data quality auditing? #
Atlan offers automated lineage, policy enforcement, custom metrics, and integrations with major data quality tools—all from one metadata control plane.