Your 5-Step Data Quality Playbook

Get Data Quality Right for GenAI & AI Agents

Outdated records, inconsistent sources, and missing context: the problem with your AI isn’t the models, it’s the data behind them. In fact, only 1% of organizations say they’re mature in AI deployment, and 56% of leaders don’t trust the results from GenAI. 

In this playbook, data and IT executives will learn five clear steps to close data quality gaps, reduce risk, and ensure that analytics, GenAI, and AI agents deliver accurate, explainable, and compliant outcomes.

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*Top 500 companies of the 2024 Forbes Global 2000, excluding China

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Follow the 5 Steps to Data Quality for GenAI
and AI Agents

Strengthen Data Foundations for GenAI and AI Agents

Reduce bias and hallucinations in GenAI and AI agents with techniques like RAG, fine-tuning, and context engineering.

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Don’t Fall Into the Catch-22 of “Solving” Data Quality

Stop chasing “perfect” data. Quality improves when it’s shaped through real analytics, GenAI, and AI agent projects.

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Democratize Data Quality Across Teams

Make data quality a shared responsibility by giving business teams the visibility and context to spot issues and improve trust.

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Embed Quality Into Everyday Operations

Build checks and alerts into daily workflows to catch issues early, as the Catella data team did, automating data prep and cutting data transformation time from five days to five minutes.

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Make Data Quality Part of Larger Governance Efforts

Embed data quality into governance and compliance to reduce risk and align standards across fragmented tools.

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Ensure Data Quality With The Universal AI Platform™

Dataiku puts data quality at the heart of every analytics, GenAI, and AI agent workflow. From built-in validation and lineage to shared governance, Dataiku helps teams catch issues early, standardize quality across systems, and deliver AI that’s accurate, explainable, and compliant at scale.

 

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