Everyone knows it’s important to keep data clean, accurate, and updated. While good data quality is always the goal, it’s important to keep bad data, too.
Ventana Research advocates using data lakes to store and retain original raw data—the good and the bad—to identify and solve data quality problems over time.
- Why information architectures should include plans to capture and retain original data—good or bad
- How data quality monitoring should track the frequency of data issues over time
- What creating data quality scorecards and setting goals for improving or maintaining data quality does for analysis