Using AI to Deliver Faster Data Quality at the Speed of Business

[DPinto]

Every data team understands the pressure—delivering clean, trusted data under tight deadlines, whether for a quarterly business review, a new AI project, or an upcoming compliance audit. The business needs answers quickly. However, the data quality processes are still catching up with many data engineers manually coding quality checks, stewards debating completeness rules, and analysts often waiting for teams to implement basic data validation. By the time data quality rules are written, tested, and deployed, the data is outdated—and the time to act is missed. So, how can we address this challenge? 

[DPinto]

Today, we’re excited to announce a new Generative AI Data Quality Rule Assistant in Qlik Talend Cloud to close the data quality productivity gap.  Our new feature works by profiling your datasets and uses AI to suggest applicable data quality rules. In fact, the 2025 Gartner Magic Quadrant for Augmented Data Quality solutions calls out that “By 2027, the application of generative AI (GenAI) will accelerate the time to value of data and analytics governance and master data management (MDM) programs by 40%”. Less time spent defining, testing, and deploying rules means faster delivery of trusted data for analytics and AI—with zero guesswork.

A smarter way to define data quality rules

To define data quality rules more intelligently, we first need to understand what a data quality validation rule actually does. It checks whether your data aligns with your business’s expectations — ensuring it follows real-world logic, not just technical constraints. For example, an insurance claim can’t exceed policy coverage, a shipment can’t leave before its order date, or a patient’s discharge date can’t be earlier than admission.

For organizations with large, diverse data estates, manually building every validation rule doesn’t scale and slows down data teams. Instead of starting every data quality validation rule from scratch, the new Generative AI Data Quality Rule Assistant accelerates this process by offering rule suggestions tailored to your data.

For example, let’s say you’re managing a streaming movie catalog stored in a Snowflake Data Cloud. The dataset has fields like TITLE, CONTENT_TYPE, KIDS_SUITABLE, and DURATION_MINUTES, ensuring the quality of this data would mean hand-coding dozens of data quality checks.

Our Generative AI Data Quality Rule Assistant profiles the Snowflake table, scans the metadata, and automatically suggests relevant validation rules. For non-Snowflake data assets, this works similarly.

Automatic data quality validation rule generation

In case of the above example,  here are a few sample rules generated :

• Not empty check: Ensure that the TITLE is not empty
• Allowed values check: Ensure that HAS_SUBTITLES equals a valid value (0 or 1)
• Positive value check: Validate that DURATION_MINUTES is always greater than 0
• Consistency check: Enforce that if KIDS_SUITABLE is true, then AGE_RATING must be “G” and must not be “PG” and vice versa

Each rule comes with a logic preview so that business domain experts can easily see the logic each rule will apply to your dataset. You don’t need to reinvent the wheel — you simply select the relevant rules and proceed. In the data preview, you can select the column of the Snowflake table and see the data quality rules applied with a visual quality indicator.

Data validation rules associated with a particular column and quality bar

The result is a faster setup of trusted data quality checks, fewer bottlenecks waiting for data engineering teams, and confidence that your dataset is ready for analytics and AI.

Who doesn’t love productivity?

Data is moving faster than ever. Analytics pipelines run in real time. AI models retrain daily. Business teams need insights on demand. Manual and tardy rule creation doesn’t fit anymore — it creates bottlenecks, lost opportunities, and growing risks.

However, Generative AI Data Quality Rule Assistant in Qlik Talend Cloud adds intelligence to rule creation and gives organizations the following abilities -

• Faster delivery of trusted data – What once took days of manual effort can now be done in minutes. Stewards and engineers can review, edit, and approve rules before they’re put into place, ensuring oversight without slowing the process
  
  
• Broader coverage – Leveraging the data profile, uncover missing rules that were not previously defined
  
  
• Simplified collaboration– Empower anyone on the data and analytics team (including line-of-business users) to define data quality rules with ease
  

Conclusion

Data quality has always been essential — but in today’s world of real-time analytics and AI, speed is just as critical as accuracy. The Generative AI Data Quality Rule Assistant removes the bottlenecks of manual rule creation. The result: faster delivery of trusted data, broader rule coverage, and simpler collaboration across teams. It’s a leap toward our vision of making AI-ready, trusted data the foundation for every initiative. Want to dive in – check out our documentation to get started or sign-up for a free trial today.