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.
Getting Data Quality Right: A Guide for CDOs and Data Executives
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