There is an undeniable fact that data surrounds us on every corner. However, extracting meaningful data for multiple purposes is still a problem, which makes data aggregation important. Knowing all about data aggregation is certainly important for the people serving in these fields, and this is what this article seeks to achieve. Read on to explore the various facets of data aggregation and have a better understanding of it.
The data aggregation meaning has evolved over time due to many reasons. To understand what is data aggregation, one must keep in mind the simple phrase ‘summary’.
Data aggregation is a process where data is collected and expressed briefly in a summarised format. Here, observed aggregated groups are simply replaced by the summarised statistics. Aggregate data are found in a data warehouse, as they can provide answers to analytical questions and also reduce the time to query big data sets. Data aggregation is used to form summarised data for business analysis and to provide statistical analysis for groups of people. By using software tools known as data aggregators, aggregation is usually done on a large scale.
These aggregators involve aspects of the collection, procession, and presentation of data. Data aggregation is a critical part of effective data management. The most significant and frequently accessed data can benefit from aggregation, making it feasible to access efficiently as the amount of data storage by organizations continues to expand. Data aggregation generally works on big data that do not provide much information value as a whole. Data aggregation may be performed manually or through specialized software.
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Data aggregation simply summarises data from various sources. They layout capabilities for various aggregate measurements such as counting, sum, and average. Some data aggregation examples include the following-:
Data aggregators usually provide the ability to track data lineage and can trace back to the underlying atomic data that was aggregated. Aggregate data does not need to be numeric. For instance, you can count the number of any non-numeric data element. Data aggregation can also result in a similar effect to individual data elements where personally identifiable details are combined and replaced with a summary representing a group as a whole. Before aggregating any data, it is significant that the atomic data is analyzed for accuracy and that there is enough data for the aggregation to be useful.
Data aggregators are able to work by collecting data from various sources, then processing the data for the latest insights, and lastly, presenting the data in a summarised form. This is explained by the following-:
Thus, it is concluded that nowadays, business decisions are based on huge amounts of data, making quick access to data important for making the right decisions at the right time. The advent of big data and data sources explosion provide organizations and data scientists with a wealth of information. However, extracting meaningful data is still a problem, which makes data aggregation important.
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