Data aggregation is the process of summarizing or grouping the records in a dataset. If you have, say, a list of 10,000 students in a university, data aggregation entails that you group them according to specific categories. Depending on what’s required, you may aggregate the list of students according to their programs and courses, for instance. You may also group them according to year level, gender, or age.

Beyond the university example, data aggregation is useful across all industries, including finance and banking, technology, production, marketing, and advertising.

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Data Aggregation in Action

In a world where data fuels every organizational decision, data aggregation is very crucial. Have you ever wondered why you keep seeing advertisements for skincare products after once searching for “skincare routines” on Google? Or maybe you curated your summer getaway over the Internet, booking hotels and searching for tourist attractions and other information about the city you’re planning to visit. After that, travel-related ads start showing up on the news feeds of your social media accounts.

That’s data aggregation in action. Advertisers can customize their strategies and ad targeting because they can segment their target markets better with the help of data aggregation. Done right, data aggregation revolutionizes the way companies make decisions, even outside the realm of advertising.

Big Data Aggregation

Data aggregation deals with big data. And since it often happens on a large-scale, companies use tools or software that can combine datasets from multiple sources. These are called “data aggregators.” And they are intelligent enough to check for patterns and relationships between the data they gathered. These tools are usually all-in-one and are capable of:

  • Data extraction: They pull information from many different sources.
  • Data processing: The tools can get insights from all the data gathered by discovering patterns and relationships.
  • Data presentation: Users can present statistical results in a comprehensive and high-quality summarized format.

What Are the Differences between Manual and Automated Data Aggregation?

Manual Data Aggregation

Data aggregation is mostly a manual process, particularly in organizations in their early years. Users need to export data, sort through it on a spreadsheet application, and reformat it to match other data sources for easier comparison.

In some cases, users need to create charts to compare collated data manually. That is a tedious and time-consuming process.

Another downside is that manual data aggregation is prone to mistakes. One wrong entry can significantly change the whole outcome.

As a result, companies that do manual data aggregation run the risk of leaving some stones unturned. What if there is a crucial data source that they unintentionally ignored? What if there is a vital data pattern that is invisible to the naked eye? As such, more and more organizations employ data aggregation tools to stay on top of their industries.

Automated Data Aggregation

When it comes to automated data aggregation, the process is, as the name implies, automatic. Users can use third-party software to pull data, sort it accordingly, and use it for whatever purpose.

Pulling data follows a preprogrammed format. As such, users no longer have to enter data manually. Since the process is automated, information is sorted as it gets pulled from the source. That way, it is easier for organizations to analyze and use it for their strategic needs.

What Mathematical Functions are Often Used in Data Aggregation?

Data aggregation is faster when done using mathematical functions. Some of the most commonly used functions in data aggregation include:

  • Average: This computes the average value of a given set of data.
  • Count: The function gets the total number of datasets in a given category.
  • Max: This function obtains the highest value in a given category.
  • Min: The function displays the lowest value in a category.
  • Sum: This adds all the specified data to identify the total.

It is also possible to perform data aggregation by date to identify trends over a specific period.

Possibilities of Using Data Aggregation in Various Industries

Retail Industry

Retail companies thrive by regularly keeping tabs on competitors’ prices, promotions, and products. Data aggregation makes competitor monitoring more efficient as all necessary information about competitors is stored in one place.

Financial Investment Sector

Financial and investment analysts and planners look at data from different sources to come up with recommendations for marketing specific products. Business news headlines, for instance, provide crucial information that helps institutions detect trends and predict stock prices. But with thousands of websites on the Internet, manually collecting news headlines is impossible and ineffective. Data aggregation helps organizations in this sector gather data from multiple sources promptly.


Even political decisions can depend on available data. A political party would only endorse a trendy candidate. Often, political campaigns only come together after all the data is gathered and analyzed. In a column, Carie Dann and Chuck Todd of the National Broadcasting Company (NBC) went as far as claiming that “Big data revolutionized the way American politicians win elections.”

Once automated, data aggregation can be a necessary process that allows users to make use of information according to their needs. With it, monitoring finances, crafting marketing strategies, and determining product trends over time become possible.

Key Takeaways

  • Data aggregation is the process of summarizing or grouping records in a dataset.
  • Data aggregation deals with big data. And companies use tools that can combine datasets from multiple sources called “data aggregators” to check for patterns and relationships between the data. These tools are usually all-in-one solutions capable of data extraction, data processing, and data presentation.
  • Two major types of data aggregation exist—manual and automated.
  • Data aggregation won’t be possible without using certain mathematical functions including average, count, max, min, and sum.
  • Various industries, such as retail, financial investment, and politics, can benefit from data aggregation.