Dataiku Ebook

Get Data Quality Right

Don't Let AI Efforts Fall Flat

According to MIT, poor data quality is estimated to cost companies a staggering 20% of revenue. 

So, how can these stakeholders prevent data quality issues from hindering and, worse, fully halting data science initiatives? This ebook has best practices for controlling data quality at scale to ensure that data efforts — from executive decision making to analyst reporting — don’t put AI ambitions at risk.

Data Quality - 3D-Cover-Mockup @2x

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Key Considerations for Controlling Data Quality

What's in the Ebook?

  • The gravity of poor data quality, including how it impacts different lines of business
  • Tips for controlling data quality, from stakeholder buy in to establishing metrics
  • Key data quality attributes to consider and monitor