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 duplication already contributes to unmanageable and costly data swamps, and it can also have major negative impacts on data aggregation processes. The double-counting of data can significantly skew results leading to false outputs and decision-making based on erroneous data. Data duplication occurs for a number of reasons, including problems during data integration and lack of metadata usage. Avoiding the impacts of duplicate data on data aggregation is an ongoing governance process that can be assisted through the deployment of custom data architecture.
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 at intertrust.com.
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