Automated data quality refers to the use of tools, frameworks, and metadata-driven workflows to continuously monitor, detect, and fix data issues, reducing manual intervention.
As data environments grow more complex, automation has become critical for ensuring data reliability at scale, especially across modern, distributed pipelines.
In this article, we’ll explore:
- The basics and benefits of automated data quality
- Key challenges and capability gaps in automation
- What’s needed to automate data quality at scale
- The central role of metadata in enabling these automations
- How platforms like Atlan act as a metadata control plane to unify and streamline data quality workflows
Table of contents #
- What is automated data quality?
- Why is automated data quality important?
- What are some of the benefits of automating data quality?
- What do you need to implement automated data quality?
- How can Atlan help automate data quality?
- Final thoughts on automating data quality
- Automated data quality: Frequently asked questions (FAQs)
What is automated data quality? #
Automated data quality refers to the automation of key activities involved in measuring, monitoring, and managing data quality, such as profiling, validation, and certification. Rather than relying on manual queries and ad hoc checks, automation ensures these processes run continuously and consistently across data pipelines.
There are two broad approaches to data quality automation:
- Rule-based automation
- AI-based automation
Rule-based automation for data quality #
Rule-based automation is where you can define many predetermined data quality rules against which the data can be constantly checked.
Rule-based automation can also be adaptive, where expected values or thresholds are changed continuously based on the changing nature of the data sources or business requirements.
While rule-based automation streamlines much of the monitoring process, it still requires manual setup of the rules, at least initially.
AI-based automation for data quality #
AI-based automation is where, instead of defining data quality rules yourself, you let an AI-based tool assess the data and suggest and implement data quality tests for automation automagically.
This approach can significantly reduce human effort, but it also introduces challenges around governance and compliance, since AI-based decisions may need to be audited under various legal or regulatory frameworks.
Also, read → Compliance metadata management 101
AI-based automation can now also be seen as data quality automation requests defined in natural language that are automatically implemented by a backend system using, say, data quality agents.
An organization should use a healthy combination of both these types of automation to ensure it provides the highest-quality data to its customers and internal users.
Why is automated data quality important? #
Data quality, similar to many other aspects of data engineering, has been traditionally very manual. Data quality of data assets was ensured by manually running queries or scripts on an ad-hoc and case-by-case basis. While this worked for immediate fixes, it was not sustainable or consistent over time.
This challenge led to the development of data quality frameworks focused on profiling, cleansing, monitoring, and governance.
Tools like Great Expectations, deequ, Monte Carlo, Anomalo, dbt, and Soda have made it easier to automate these tasks and bring data quality checks earlier in the pipeline using a shift-left approach. Moreover, cloud platforms like Snowflake and Databricks also started providing built-in features to automate data quality to a certain extent.
Even with these advances, many organizations still face gaps in automating data quality at scale. Limitations in integration, standardization, and governance often result in manual workarounds or fragmented quality checks.
Let’s look at some key benefits of automating data quality.
What are some of the benefits of automating data quality? #
The obvious advantages of automating data quality include less manual effort and fewer human errors in defining and executing tests. But there are also deeper, long-term benefits that make a strong case for automation:
- Building trust across the business by ensuring all data assets have a baseline of data quality metrics available at all times.
- Facilitating the integration of quality and lineage to understand where data quality degraded in the data pipeline.
- Removing bottlenecks from engineering and infrastructure teams to fix data quality issues manually.
- Enhancing governance and compliance by ensuring a baseline for data quality across the organization.
- Improving SLAs for data reliability and availability by providing actionable insights into things that need to be fixed.
All these benefits make it attractive for organizations to adopt an automated data quality strategy, but they often fall short. In the next section, we’ll look at what’s holding them back and how to bridge those gaps.
What do you need to implement automated data quality? #
The value of automating data quality is seldom contested, but many organizations have various issues that prevent data quality automation.
Some of those issues are listed below:
- Siloed data in disparate data sources with no single pane of visibility
- Lack of consistent data quality rules, definitions, and expectations
- Inability to integrate native tools and libraries for handling data quality
- Not having a clear visibility of the data’s journey within the organization
- Over-reliance on manually defined tests instead of automated and AI-driven tests
To build a strong foundation for automated data quality, using both rule-based and AI-driven methods, organizations need the following:
- Access to the structure and schema of all the data assets from various data sources in the organization.
- A clear understanding of the business and technical data quality rules you need to implement.
- The right data quality tools and frameworks to define and automate data quality tests.
- The ability to integrate data quality tests in data pipelines and CI/CD pipelines for data engineering.
- AI-based tools for natural language-based data quality test generation and agents to implement them automatically.
All of these requirements point to the need for a unified source of metadata that brings together information about your data assets.
This central metadata layer acts as a control plane for managing everything related to data quality, profiling, lineage, and governance. Atlan is one such tool that provides you with this control plane.
In the next section, we will look at how Atlan supports automated data quality at scale.
How can Atlan help automate data quality? #
Atlan provides a unified metadata control plane that brings together metadata from across your organization. It integrates with a wide range of data sources, technical catalogs, lineage tools, and profiling engines to give you full visibility into your data landscape.
For data quality automation, Atlan offers native capabilities while also supporting deep integrations with several data quality tools, such as Soda, Anomalo, and Monte Carlo. This hybrid approach helps teams choose the right tools for the job and centralize monitoring and remediation in one place.
Here are some of Atlan’s key features that help automate and improve data quality:
- Data contracts can help you enforce data structures and schemas and automatically take actions when the contracts are violated.
- Automated data profiling can help you leverage the native data profiling capabilities of many of the major connectors, such as Databricks, Snowflake, BigQuery, Microsoft SQL Server, MySQL, and PostgreSQL.
- Product score can help you build a trustworthiness and maturity metric based on Atlan’s product scoring rubric and scoring method.
- Automated policy compliance helps you create guardrails for data production and usage across your organization by giving you an option to define policies in the Policy Center.
Atlan’s approach of integrating with data source connectors like Databricks and Snowflake, full-fledged data quality tools like Soda, and a range of native data quality-related features offers you an easy way to shift data quality left in the data engineering process, which will be of great benefit to any organization.
For instance, Atlan has a native integration with Snowflake that allows you to tap into Snowflake’s Data Quality and Data Metric Functions. You can also connect to Anomalo’s automated AI data quality features that use unsupervised ML algorithms — all within Atlan’s single governance layer.
This metadata-driven foundation gives data teams the tools they need to scale automated data quality across the modern data stack.
Final thoughts on automating data quality #
Automating data quality can unlock several operational efficiency and business value-related benefits for your organization. However, many organizations struggle to scale these efforts due to fragmented metadata and siloed systems.
That’s where a control plane for metadata like Atlan becomes critical. By connecting data quality tools, pipelines, and policies through a unified layer of metadata, Atlan helps teams implement automation that is both scalable and reliable.
To learn more about Atlan’s data quality capabilities, head over to the official documentation.
Automated data quality: Frequently asked questions (FAQs) #
1. What is automated data quality? #
Automated data quality is the use of tools, frameworks, and metadata-driven workflows to continuously monitor, detect, and resolve data issues with minimal manual effort. It helps ensure consistent, scalable data quality across complex pipelines.
2. What are examples of automated data quality tools? #
Some widely used tools include Great Expectations, Soda, Monte Carlo, Anomalo, deequ, and dbt. Platforms like Snowflake and Databricks also offer built-in features to automate data quality checks.
3. What’s the difference between rule-based and AI-based automation? #
Rule-based automation uses predefined rules to validate data, while AI-based automation leverages machine learning to detect anomalies or generate data quality checks without explicit rules. Most organizations use a mix of both for broader coverage.
4. What are the benefits of automating data quality? #
Benefits include fewer manual errors, faster issue detection, improved data trust, easier compliance, and better reliability SLAs. Automation also frees up engineering time and enables shift-left testing in the data lifecycle.
5. What challenges prevent data quality automation? #
Common blockers include fragmented metadata, inconsistent rule definitions, lack of tool integration, limited lineage visibility, and overreliance on manual testing practices.
6. What do you need to implement automated data quality? #
You’ll need access to metadata across all your systems, well-defined data quality rules, integration-ready tools, and support for CI/CD integration. A metadata control plane is key to orchestrating these components.
7. Which platform is best for automated data quality? #
Atlan is a metadata control plane that supports automated data quality through native capabilities and integrations with tools like Soda, Monte Carlo, and Anomalo. It centralizes data profiling, lineage, and contract enforcement in a single control plane, making it ideal for automating data quality.