Data aggregation is the process where raw data is gathered and summarized in a quantitative format, and is typically used to gain macro insights from large amounts of data. A commonly known example of data aggregation is the Consumer Price Index (CPI) from the Department of Labor which aggregates price changes in a wide variety of goods and services to track the fluctuation of the cost of living in the U.S. Unfortunately, despite the importance of data aggregation and its potential to improve decision-making, organizations still make major data aggregation mistakes.
For businesses, 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.
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