As a data scientist, I found that a drop in metrics was just as often due to a data pipeline issue as it was an actual business problem. This unfortunately causes business users to lose trust in the metrics quickly. How do you plan to differentiate between those two root causes of metric changes?
As a data scientist, I found that a drop in metrics was just as often due to a data pipeline issue as it was an actual business problem. This unfortunately causes business users to lose trust in the metrics quickly. How do you plan to differentiate between those two root causes of metric changes?