Data aggregation is a useful tool for data operations. Here’s five common data aggregation mistakes and how to solve them.
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.
To extract data from multiple sources and curate datasets that deliver useful, actionable information to downstream users is a key role of data operations. 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.
