What is Data Aggregation?
Any process in which data is acquired and summarized in a summary form is referred to as data aggregation. When data is aggregated, atomic data rows are replaced with totals or summary statistics, which are often acquired from many sources. Summary statistics based on those observations replace groups of observed aggregates. Aggregate data is commonly found in a data warehouse since it can answer analytical queries while also drastically reducing the time it takes to query big volumes of data.
Analysts can use data aggregation to obtain and analyse vast volumes of data in a reasonable length of time. Millions of atomic data entries can be represented by a single row of aggregate data. When data is aggregated, instead of requiring all of the processing cycles to acquire each underlying atomic data row and aggregate it in real-time when queried or accessed, it can be queried rapidly.
What are the most common data aggregation mistakes?
Despite the importance of data aggregation and its ability to improve decision-making, companies continue to make significant data aggregation mistakes.
Here are mentioned 4 most common data aggregation mistakes and how they can be resolved
1. Insecure data sharing
In a world where data sharing and security are becoming increasingly regulated, aggregated data is incredibly beneficial. Because aggregate data is anonymized, it is not subject to the same limits or consent requirements as personally identifiable data (PII). However, a common data aggregation mistake is to believe that there is less risk, which leads to oversharing and data breaches. To address this, data managers must have better access control over all data sharing in order to ensure that even aggregated data is never lost.
2. Duplicate & Incomplete data
Data duplication leads to unmanageable and expensive data swamps, and it can wreak havoc on data aggregation operations. Data double-counting can drastically skew outcomes, resulting in inaccurate outputs and decisions based on incorrect data. Data duplication can occur for a variety of reasons, including data integration issues and a lack of metadata utilisation. A continuous governance procedure is in place to avoid the effects of duplicate data on data aggregation. On the other hand, gathering the necessary data for querying might be challenging or time-consuming, resulting in incomplete datasets. Because data aggregation outputs are so vast, this missing data may go unnoticed at first, but it still has an impact on downstream usage and decisions.
3. Poor process methodology
Data is just as helpful as the questions that are posed to it. Poor query construction reveals this, resulting in disparities between what decision-makers believe they’re seeing and what the collected data truly says. Data scientists must be consistent and transparent about queries and measurements in order to achieve effective data aggregation results. As a result, outputs like percent change are always derived from a relevant comparison.
4. Data moving at different speeds
Data aggregation errors might happen even if you have access to clean and comprehensive datasets. One of the main causes for this is the speed with which data flows are processed, which varies greatly based on storage, access to siloed data, and how data is processed into data lakes. Data aggregation points, particularly those used in real-time dashboards, may face difficulties as a result of this. Consider a network of IoT sensors that monitor transformer throughput and report to a centralised dashboard that is used by the entire utility. The aggregate statistics will be consistently off if one segment of the network is even 30 minutes behind.
How to solve data aggregation mistakes
Data aggregation is a valuable tool for DataOps since it gives meaningful and interesting information. Overcoming data aggregation errors, on the other hand, necessitates overcoming considerable obstacles in terms of data consistency, preventing unnecessary migrations that result in duplication, and allowing administrators more control over how data is used and datasets are prepared for analysis.
Many of the aforementioned data aggregation mistakes can be solved or reduced by deploying a virtualized data platform. Data will always be available for a query without the need for time-consuming data migrations thanks to the creation of an interoperable virtual layer between data storage and processing. The risk of data leakage during data analysis and exchange is reduced because these operations take place in safe execution environments.
Conclusion
In this article, we learned that data aggregation can help firms understand critical parameters like revenue growth, unit output, and earnings per client. Internally, data aggregation provides a continual supply of insight for teams of all sizes, especially with the advancements in analytics. We also looked at the most common data aggregation mistakes that can take place while data processing and how we can overcome them.
