Aggregated data is extremely valuable in a world with ever-increasing regulations on data sharing and security. That’s because aggregate data is anonymized and doesn’t carry the same restrictions or consent obligations as personal identifiable information (PII).

This makes it easier to share, which can be vital in fields such as healthcare. However, a common data aggregation mistake is falling for the illusion of less risk, leading to oversharing and data breaches. To overcome this, data administrators need to have greater access control over all data sharing to ensure even aggregated data never leaves their control. 

This includes summary insight derived from the aggregation of multiple data points. However, gathering the required data for querying may be difficult or require lengthy ETL processes, leading to incomplete datasets. With data aggregation taking such a broad view in its outputs, this incomplete data may not be initially noticed but nevertheless has an impact on downstream usage and decisions made.