A Guide for Data and IT Leaders

5 Steps to Better Data Quality

Get More Out of Generative AI

According to a 2024 survey of 200 senior analytics and IT leaders, nearly 50% still cite data quality and usability as their main challenge. This flipbook has best practices for controlling data quality at scale to ensure that data efforts don't put AI (and Generative AI) ambitions at risk.

Ensure Effective Data Quality Today

Data Quality Still Ranks as the #1 Challenge to Getting ROI From AI

In a survey from Dataiku and Databricks of 400 global data leaders, we found that data quality is the #1 challenge to getting a return on AI.

There is a massive corner of the data and AI software and services world dedicated to data quality. Hundreds of tools and companies promise to address it, and organizations are spending millions to “fix” it. Spoiler alert: These solutions only address part of the problem. Dataiku can help across the entire analytics and AI lifecycle.

 

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5 Steps to Transform Data Quality

Data and IT Leaders Can Take in the Age of Generative AI 

The Catch-22 of Solving
Data Quality

Data quality can’t be fully solved with current solutions as they only address parts of the problem, so a different approach is needed.

See p. 5

Democratize Data Quality

Data quality must be put into the hands of those building data products in order to be operationalized across the analytics lifecycle.

See p. 6

Embed Data Quality Across Operations

In 2024, IT teams should not be the only owners of data quality. A robust organizational strategy is required.

See p. 7

Understand Data Quality for Generative AI

The approach to data quality for Generative AI projects should differ from traditional methods, and it’s important to understand the differences.

See p. 9

Make Data Quality Part of Larger Governance Efforts

The final step to getting ahead with data quality is widening the thinking around data quality from a narrow problem to part of larger governance efforts around data and AI projects.

See p. 10

“The most important thing we do every day is ensure the accuracy of the input. If you are not investing in a data quality/data governance infrastructure, you're going to fail.”

Jeff McMillan
Chief Data and Analytics Officer, Morgan Stanley Wealth Management

"Through Dataiku, our mutual customer was able to leverage data quality features to save 300 hours per month."

Ben Gardner-Moss
Principal Analytics Consultant, Aimpoint Digital

“A lack of quality data is probably the single biggest reason that organizations fail in their data efforts.”

Jeff McMillan
Chief Data and Analytics Officer, Morgan Stanley Wealth Management
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Access This Flipbook to Tackle Data Quality