Lebowski - The Monday Morning Memo

“The way we talk about data quality is misleading. We speak of ‘clean’ data as if there is one state where data is both accurate (and bias-free) and reusable. Clean is not the same thing as accurate, and accurate is not the same thing as actionable. Problems on any one of these vectors could impede an A.I. model’s development or interfere with the quality of its results. There are lots of reasons data coming into a model may be problematic. Some are obvious: The data is factually incorrect, corrupted, or in an unexpected format. Other problems are more nuanced: The data was captured in a particular context and is being reused inappropriately; the data is at the wrong level of granularity for the model’s purpose; or the data isn’t standardized, and the same facts are represented or described in different ways.”

- Maryanne Bellotti, OneZero, May 26, 2021

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