Five common data aggregation mistakes and how to fix them

Data aggregation can provide insights into key metrics such as revenue growth, unit production, or earnings per customer. Internally, and especially with the improvements in analytics, data aggregation provides a steady stream of insight for teams of all sizes. As such, it’s become an essential tool across many verticals, such as finance, energy and utilities, and healthcare. Below, we’ll look at the most common data aggregation mistakes and how they can be fixed.

Data can only be as useful as the questions asked of it. This becomes apparent through poor query formation leading to discrepancies between what decision-makers think they’re seeing and what the collected data actually says.

For example, a “running daily average” of energy consumption per customer would vary significantly depending on whether it was a weekly, monthly, or quarterly dataset. For effective data aggregation results, data scientists need to be consistent and clear about queries and metrics. This way, outputs such as % change are always from a relevant comparison.

By creating an interoperable virtual layer between data storage and processing, data will always be available for query without the need for time-consuming data migrations. Plus, since data analysis and sharing takes place in secure execution environments, the chance of data leakages during these processes is minimized.