AI-ready data must be quality-assured. Quality is the difference between a great agent and a useless one. That is exactly what AI data quality measures for the data used to train and operate AI systems. AI data quality refers to the accuracy, completeness, and reliability of that data. Since manual processes don’t scale and are error-prone, organizations have shifted to automated testing with tools such as dbt, Soda, Monte Carlo, and Anomalo, though automation alone doesn’t solve it because you still have to write the tests.
- Various methods of improving data quality with AI
- Challenges that you face while using AI for data quality
- The need for a metadata control plane to leverage AI for improving data quality
How can AI help improve data quality?
Permalink to “How can AI help improve data quality?”AI, especially generative AI, has been extremely useful in opening up several new avenues of automation that not only improve data quality but also save engineers time and make it easier for business users to build trust in the data.
Some of these improvements are listed below:
- Improved data consistency: While deterministic field-based joins between tables are important, it is very hard to find issues with duplication, inconsistencies, etc. That is where AI saves the day, as it is really good with matching data without hard join conditions and clear mappings by leveraging natural language and generative AI capabilities.
- Adaptive data quality rules: Rather than a fixed set of data quality rules, users can use advanced machine learning models to adjust data quality thresholds dynamically, if and when required. This allows you to take an adaptive approach to data quality.
- Better understanding of data: With LLM-based language understanding, data quality can significantly improve because, rather than just testing data based on field values, you can now run higher-order tests based on meaning by making the LLM take lineage, cataloging, glossary, etc., data into consideration.
- Guided root cause analysis: AI can also help you link new data quality issues with previously raised issues, along with the data lineage, to figure out where the error might have occurred. This type of root cause analysis can save a significant amount of developer time that is typically spent on manually debugging issues and identifying their root cause.
These are some of the ways AI can benefit data quality, and there are more areas where it can help in monitoring, observing, and proactively addressing data and data quality issues.
The applications of AI in data quality are numerous, but they also come with challenges. Let’s find out what the challenges are in the next section.
What are some challenges in using AI for data quality?
Permalink to “What are some challenges in using AI for data quality?”There are many common data quality challenges, some of which stem from the organization’s incorrect operating model. Many data quality challenges exist due to the incorrect tooling, frameworks, and measures for addressing data quality across the board, inconsistent business definitions, a lack of data ownership, poor lineage, and missing validation rules, among other issues.
AI promises to solve some of these challenges, but there is one key foundational challenge that needs to be addressed before AI can effectively help with data quality – the lack of a metadata foundation.
Other challenges include:
- Lack of a single place where metadata is stored for all systems to provide an organization-wide context to systems, processes, and tools.
- Broken lineage or lineage that is not granular enough to support data quality use cases that work on a row, column, or field level.
- Missing semantic context and organizational relevance that helps you (or in this case, the AI) understand the purpose of any given data asset in your data ecosystem.
- Lack of centralized quality and governance that can leverage the structural and contextual metadata, along with documentation, to write and improve data quality tests.
- Lack of any data contract definition or management tooling that can help address a major chunk of the data quality issues, especially with the help of AI.
- Lack of understanding of what data quality means in terms of data, especially concerning data quality metrics, scores, and service levels.
As mentioned earlier, all of these challenges are directly related to the lack of a solid metadata foundation. The garbage-in, garbage-out rule also applies to metadata, which is why having a reliable and trustworthy store of metadata is very important. While it is foundational and a trustworthy store of metadata, it alone isn’t enough.
You need a control plane for metadata, which stores, tracks, manages, and governs all of your organization’s data assets, and also provides you with capabilities to address some of the challenges mentioned above directly. Atlan offers such a metadata control plane.
Let’s look at some of Atlan’s AI data quality-specific capabilities.
How can Atlan help with data quality using AI?
Permalink to “How can Atlan help with data quality using AI?”Atlan is a metadata activation platform that leverages AI for various core use cases, including automating data quality, lineage analysis, and documentation, among other applications. It provides a foundation of all metadata in your organization, a metadata control plane, which is crucial for data quality monitoring and automation.
Atlan’s features, including personalization and curation, a business glossary, and embedded collaboration, all provide various ways to improve data quality. With Atlan AI, you can enrich metadata by adding descriptions to data assets, write documentation, perform lineage analysis, and even write and fix SQL queries.
These features of Atlan enable you to continuously improve the context around data assets, which is ultimately very helpful in tracking data quality, especially when utilizing the new generative AI capabilities. Learn more about Atlan AI in the official documentation.
Summary
Permalink to “Summary”Data quality is one of the most crucial aspects of data, as it determines whether the use cases centered around it are successful or not. Bad data quality trickles down into bad business decisions, so it is important to have visibility and insight into an organization’s state of data quality. Moreover, recognizing the importance of AI in managing data quality is crucial.
With that in mind, this article took you through the key challenges in data quality and how AI can help you solve some of those challenges. The article also described the capabilities of Atlan, whose metadata control plane enables you to bring all your data in one place and helps you streamline data quality, among other things. You can find more about Atlan’s data quality capabilities in the official documentation.
AI for data quality: Frequently asked questions (FAQs)
Permalink to “AI for data quality: Frequently asked questions (FAQs)”1. What is AI data quality and why does it matter?
Permalink to “1. What is AI data quality and why does it matter?”AI data quality refers to how accurate, complete, and reliable your data is for training and operating AI systems. Poor quality leads to faulty predictions, compliance risks, and loss of trust in analytics outcomes.
2. How can AI improve data quality in modern data stacks?
Permalink to “2. How can AI improve data quality in modern data stacks?”AI helps by auto-detecting anomalies, suggesting quality rules, enabling semantic validation using lineage and glossary metadata, and accelerating root cause analysis through historical pattern matching.
3. How can you use generative AI for data quality?
Permalink to “3. How can you use generative AI for data quality?”You can leverage generative AI for data quality by first enriching the metadata context to build the tests upon and, second, based on the metadata context, automatically generating data quality tests that can be run as part of your data pipelines and workflows.
4. What are the main challenges in using AI for data quality?
Permalink to “4. What are the main challenges in using AI for data quality?”AI can’t function well without a reliable metadata foundation. Key challenges include broken lineage, missing context, decentralized governance, and lack of standardized quality rules or metrics.
5. Why is metadata essential for AI-led data quality efforts?
Permalink to “5. Why is metadata essential for AI-led data quality efforts?”AI needs context to be useful. Metadata provides the structure, semantics, and lineage AI models rely on to detect issues, suggest fixes, and improve quality insights across pipelines.
6. Which tools does Atlan integrate with for data quality?
Permalink to “6. Which tools does Atlan integrate with for data quality?”In addition to leveraging the native data quality capabilities of data platforms like Snowflake and Databricks, Atlan also integrates with data quality tools such as Anomalo, Soda, and Monte Carlo. Atlan also has a range of data quality and profiling features that you can leverage.
