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What is Data Aggregation | Examples of Data Aggregation

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Data aggregation is a process where raw data is gathered and expressed in the form of a summary for statistical analysis. Data aggregation may be done manually or through specialized software called automated data aggregation. For example, new data can be aggregated over a given period to provide statistics such as sum, count, average, minimum, maximum. After the data is aggregated and written to view or report, you can analyze the aggregated data to gain useful insights about particular resources or resource groups.

What is Data Aggregation

Data aggregation is any process that includes gathering of data and expressed in the summary form for purposes such as statistical analysis. It is essential to collect high-quality, accurate data and a large enough amount to create consistent results. Data aggregation is beneficial for everything from finance or business strategy decisions to product, pricing, services, and marketing strategies.

Data aggregation is an element of business intelligence (BI) solutions. Data aggregation personnel or software search databases find relevant search query data and present data findings in the summarized format that is meaningful and useful for the end-user or application.

Data aggregation generally works on the big data or data marts that do not provide enough information value as a whole.

There are two types of data aggregation.

Time aggregation

It is data points for a single resource over a specified period.

Spatial aggregation 

It is data points for a group of resources over a specified period.

Examples of Data Aggregation

Companies often collect data from their online customers and website visitors. For example, I am using Google analytics to see where my users are from? What kind of content they like etc.

For example, Google collects data in the form of cookies to show targeted advertisements to its users.

Facebook is doing the same thing by collecting and analyzing the information and show ads to its users.

The aggregate data would combine statistics on customer demographics and behavior metrics, such as average age or number of transactions.

The consumer uses a single master personal identification number (PIN) to give them access to their various accounts (such as those for financial organizations, airlines, book and music clubs, and so on). Performing that type of data aggregation is sometimes referred to as “screen scraping.”

The aggregated data can be handled by the marketing team to personalize messaging, offers, and more in the user’s digital experience with the brand. It overall improves the user experience.

It can also be used by the product team to learn which products are successful and which are struggling. Moreover, the data can also be used by company executives and finance teams to help them decide how to allocate budget towards marketing or product development strategies.

Manual Data Aggregation vs. Automated Data Aggregation

Aggregating data can be a remarkably manual process, especially if your company is in the early stages.

Click on the export button. Go through an excel sheet. Reformat it so it looks like other data sources.

Then create charts to compare the performance/budget/progress of your multiple marketing campaigns.

If you want to go for the automated process, then It looks like the implementation of third-party software, sometimes called Middleware, that can pull data automatically from your marketing tools.

So, Manual and automated data aggregation is possible based on your company’s requirements.

Data aggregation in marketing

In marketing, the data aggregation usually comes from your marketing campaigns and the different channels you use to market to your customers.

For example, if you are running the campaign on Google Ads, you can see that there are lots of factors you need to pay attention to boost your sales.

You can aggregate your data from a particular campaign, looking at how it performed over time and with specific cohorts.

Ideally, though, you are aggregating the data from each particular campaign to compare them to each other one grand data aggregation that tells you how your product is being received across channels, populations, and cohorts.

In short, we refer to them as ETV (Extract, Transform, Visualize). Together, this is a workflow of extracting and preparing data from SaaS applications for analysis.

For each of these three steps, which are the following

1. Extract – Data extraction layer

2. Transform – Data preparation layer

3. Visualize/Analyze – Visualization and analytics layer

So, you need to follow each steps in order to better understand the data.

Data aggregation in the retail industry

The retail and e-commerce industries have many possible uses for data aggregation. One is competitive price monitoring. Competitive research is necessary to be successful in e-commerce and retail space.

Companies have to know what they’re up against. So, they must always be gathering the fresh information about their competitors’ product offerings, promotions, and prices.

The data can be pulled from competitor’s websites or from other sites their products are listed on. To get exact information, the data needs to be aggregated from every single relevant source. That’s a tall order for manual web data analysis.

Data aggregation in the travel industry

Data aggregation can be used for a wide array of purposes in the travel industry. These include competitive price monitoring, competitor research, gaining market intelligence, customer sentiment analysis, and capturing images and descriptions for the services on their online travel sites.

Competition in the online travel industry is fierce, so data aggregation or the lack there of can make or break the travel company.

Data Aggregation with Web Data Integration

Web Data Integration (WDI) is a solution to the time-consuming nature of web data mining. WDI can extract data from any website your organization needs to reach. Applied to the use cases previously discussed or to any field, Web Data Integration can cut the time it takes to aggregate data down to minutes and increases accuracy by eradicating human error in the data aggregation process.

Data aggregation allows companies to get the data they need, when they need it, from wherever they need it — all with built-in quality control to ensure accuracy.

Conclusion

Data aggregation is any process in which data is brought together and conveyed in a summary form. It is typically used before the performance of the statistical analysis. The information drawn from the data aggregation and statistical analysis can then be used to tell you all kinds of information about the data you are looking at.

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