4 Data Trends to Watch in 2020

An O’Reilly survey found that those with mature AI practices (as measured by how long they’ve had models in production) cited a “lack of data or data quality issues” as the main bottleneck holding back further ML adoption.

Data quality management ensures data is fit for consumption and meets the needs of data consumers. To be of high quality, data must be consistent and unambiguous. You can measure data quality through dimensions including accuracy, completeness, consistency, integrity, reasonability, timelines, uniqueness, validity, and accessibility.