Here in Nigeria, one of the popular slogans by a network service provider after an ad is run on national television is: data is life. While I may not have an interest in using their services, I agree that data is life.
As an entrepreneur who runs a fashion store that daily reels out content to the digital space, the absence of data directly ruins the process of getting into people’s subconscious and, more worrisome, limits my chances of closing a sale that day.
So, yes, we understand the sensitivity of data, but what is data aggregation? It almost sounds like some techy and complicated word fused with a popular one to make it look cool. Well, in this post I will be sharing with you some information on data aggregation for your knowledge bank.
What is Data Aggregation?
Data aggregation is simply the gathering of data from multiple sources and bringing it together in one place. This act could either be done manually or through automated means. It also finds great use in cleaning and organizing data, finding trends and patterns, and making data more meaningful.
As we proceed, you must bear in mind that this article has been put together by The Watchtower, a web design agency in Dubai and a leading name in the business of web design and development in Dubai.
Why do we need data aggregation?
to help make a better decision with the pool of information at hand. People can always access the channel on which this information is placed, and they can decide to make the best out of it. This could be just for information; it could also be for research and development.
With the understanding of data aggregation, one tends to understand trends, likewise patterns in history.
Judging by the avalanche of information out there, data aggregation would always help researchers make better decisions.
How can I aggregate data?
- Aggregating data can be done in a variety of ways. The use of a database where the data is kept in tables is one such strategy. A single view of the data can then be created by joining the tables. When working with relational data, this strategy is frequently applied.
- Utilizing a map-reduce framework, like Hadoop or Spark, is an additional prevalent strategy. By breaking up massive volumes of data into smaller, parallelizable bits, these frameworks enable you to process large amounts of data. A single result set is created by combining the results once more.
- Another well-liked tool for data aggregation is Tableau, which is more flexible than Excel and Google Sheets. With Tableau, you can make dynamic dashboards and data visualizations. A tableau is a fantastic tool for data analysis since it lets you connect to numerous data sources.
What are the procedures for data aggregation?
- To begin with, one must collect data from the many sources that will be included in the data set before you can begin the data aggregation process. This data would range from internal business systems, external websites, databases, and other offline sources.
- Once you have all the necessary data, you can start putting everything together to produce a complete data set.
- Depending on the type of data and the resources at your disposal, there are various ways to put your data together. A database or spreadsheet can frequently be used to speed up the procedure. Manual approaches, however, can be required if your data is more complicated or if there are precise guidelines for how it must be structured.
- It is also crucial to carry out quality checks after the data set has been put together to guarantee accuracy and comprehensiveness. This may entail comparing each entry to its source or looking for discrepancies across several data sets. The final data set can be published and used for analysis or reporting when any necessary modifications have been made.

In conclusion, data aggregation was initiated with the sole aim of keeping a large portion of information in readily available memory locations. However, people who intend to start aggregating data need to ensure that the cost does not exceed the solution, and they must factor in size when considering storage.
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