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What is Data Aggregation? Examples of Data Aggregation by Industry

What is Data Aggregation? Examples of Data Aggregation by Industry

When it comes to extracting data and preparing a report for analysis, it is always complex and requires a significant period of time to perform such a task manually. For that purpose, in order to optimize the method and capture correct and appropriate data as effectively as possible, a successful approach is required. With that in mind, it can be stated that the best approach is data aggregation, as it aims to do all the above for various industries. What's more, data aggregation is currently an important component of the Internet of Things devices today. Springer, the academic publisher, demonstrated the relevance of aggregated data in the modern-day. So, why is it especially beneficial for a company seeking to stay competitive to provide aggregate data?

We will clarify what data aggregation is in this article, provide an example of data aggregation, and include use cases for the sectors of finance, retail and travel. We'll also explain to you how online data resources can be used by companies as a more successful approach. Connect with our experts to learn about our data science courses.

What Is Data Aggregation

Things can get a little complex when collecting data from different sources, particularly if a large volume of data is required. That's where data aggregation comes in, as it facilitates the processing of data that in its raw format will be far too hard to interpret and consume.

Data aggregation is a process in which raw data is collected and expressed for statistical analysis in the form of a summary. The aggregation of data may be performed manually or through advanced tools called automated data aggregation. For instance, over a given time, new data may be aggregated to include statistics such as total count, sum, average, maximum, minimum. You will analyze the aggregated data after the data is aggregated and written for viewing or reporting in order to obtain valuable insights into specific resources or resource groups.

Collecting high-quality, reliable data and a sufficient enough quantity to deliver consistent findings is important. For anything from financial or business planning decisions to pricing, product, marketing and services campaigns, data aggregation is useful. An aspect of business intelligence (BI) solutions is data aggregation. In a summarized format, data aggregation workers or software search libraries find appropriate search query data and present data results that are meaningful and usable for the application or end-user.

A perfect example of this is where search engines display users focused on ads that are built on saved information in the web browser of the users. Companies trying to collect as much data as possible on their online customers and website visitors operate in a similar manner, enabling them to access information and certain other things, such as consumer demographics and behavior.

So, what is aggregate data, exactly? In essence, it can be described as consumer information that enables companies to effectively target customers with customized messaging, promotions, etc. Around the same time, it helps firms to fine-tune their selling strategies so that they can promote some services or goods that sell well and if appropriate, even exclude those from sales. In a reasonable time frame, data aggregation will allow analysts to view and analyze vast volumes of data. Hundreds, thousands, or even more atomic data records may be represented by a row of aggregate data. Instead of having all of the processing cycles to reach each corresponding atomic data row and aggregate it in real-time as it is queried or read, it can be easily queried when the data is aggregated.

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As the volume of data stored by organizations continues to increase, the aggregation will benefit from the most relevant and commonly accessed data, making it possible for productive access. Two forms of aggregation of data are:

  • Aggregation of Time - They are data points over a given time for a single resource.
  • Aggregation of Spatial - They are data points over a specified period for a group of resources.

What Does Data Aggregation Do?

Aggregators of data summarize information from various sources. For several aggregate measurements, such as sum, average and counting, they provide capabilities. The following examples of aggregate data are included:

  • Average client age by product. Each individual consumer is not known, but the age range of the consumer is saved for each product.
  • The turnout of electors by state or county. Personal voting histories are not provided, only the voting totals for the entire area by the nominee.
  • Number of clients by country. A count of the customers in each country is provided instead of analyzing each customer.

When individual data items with personally identifiable information are merged and replaced with a description reflecting a group as a whole, data aggregation may also result in a good effect on data anonymization. An explanation of this is to produce a summary showing the overall average salary per department for workers, rather than scrolling through records of individual workers with salary information.

There is no need for aggregate data to be statistical. For instance, you can count the number of any aspect of non-numeric data. It is important that the atomic data is evaluated for precision before aggregating and that there is adequate detail to be useful for the aggregation. For instance, where only 5% of results are available, counting votes is not likely to create a valid aggregate for prediction.

How Do Data Aggregators Work?

Data aggregators operate by integrating multi-source atomic data, analyzing the data for new ideas and displaying the aggregate data in a summarized view. In addition, data aggregators typically have the ability to map data lineage and may make reference to the aggregated underlying atomic data.

Collection. Firstly, data aggregation software can capture data from various sources, preserving it as atomic data in massive databases. Data from the sources of the Internet of Things (IoT) can be extracted, such as the following:

  • news headlines;
  • social media communications;
  • browsing history and personal data from IoT devices; and
  • podcasts, call centers, etc. (via recognition of speech).

Processing. It is processed once the data is collected. The atomic data to be aggregated would be defined by the data aggregator. For new insights, the data aggregator may apply artificial intelligence (AI), predictive analytics or machine learning algorithms to the data collected. To aggregate the results, the aggregator then applies the required statistical functions.

Presentation. The aggregated data can be presented by users in a summarized format that includes new information itself. The methodological findings are detailed and of high quality.

The aggregation of data can be done manually or by the use of data aggregators. Data aggregation, though, is mostly conducted on a large-scale basis, making manual aggregation less feasible. Manual aggregation, in comparison, risks accidental omission of main sources and trends of data.

Uses for Data Aggregation

For several disciplines, data aggregation may be useful, such as decisions on financing and corporate management, product planning, pricing of goods and services, optimization of operations, and design of marketing strategies. Data scientists, data analysts, administrators of data warehouses, and subject matter specialists can be users. Based on particular demographic or behavioral characteristics such as age, occupation, education level or income, aggregated data is widely used for statistical analyses to collect knowledge about specific groups.

Data may be aggregated into summaries for business analysis purposes that support leaders in making well-informed decisions. To offer businesses critical insights into customers, user data can be aggregated from various sources, like IoT device browsing history, social media communications and other personal data.

Examples of Data Aggregation

Companies frequently gather information from their online users and visitors to the website. For example, to see where my users are from, can I use Google Analytics? What type of material do they like? etc. For instance, Google gathers information in the form of cookies to show its customers focused ads. Through gathering and processing the data and displaying advertisements to its customers, Facebook does the same thing.

The aggregate results will incorporate consumer demographic statistics and measures of behavior, such as the number of transactions or average age. In order to allow them access to their different accounts (like those for airlines, books, financial companies, and music clubs, the customer uses a single master’s personal identification number (PIN). Screen scraping is often referred to as performing that type of data aggregation. The aggregated data would be managed by the sales team to personalize the digital experience of the customer with the brand's messaging, deals and more. It increases customer experience overall.

The product team can also use it to learn which items are popular and which are failing. In addition, business management and finance departments may also use the data to help them determine how to assign budgets for marketing or product growth plans.

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Automated Data Aggregation vs. Manual Data Aggregation

Aggregating data, especially if your business is in the early stages, can be a surprisingly manual operation. Click on the button for export. Go over a sheet of Excel. Reformat it so that it can appear like other sources of data. Then create maps to measure the various marketing campaigns' performance/budget/progress. If you'd like to go with the automatic system, then it appears like installing third-party applications that can automatically pull data from your marketing tool, often called middleware. So, depending on the specifications of the organization, manual and automatic data aggregation is feasible.

Data Aggregation with Web Data Integration

A solution to the time-consuming aspect of online data mining is Web Data Integration (WDI). WDI may collect data from any website you use to enter your company. Web Data Integration will cut the time it takes to aggregate data down to minutes and maximizes reliability by eradicating human errors that may occur aggregation process, applicable to the use cases previously addressed or to any area. This helps organizations to access the information they need whenever they need it, anytime they need it. To ensure accuracy, all with built-in quality control.

Not only does WDI extract and compile the data you want, but it also processes and cleans the information and gives it for incorporation, analysis, and overview in a consumable format. So, Online Data Integration is perfect for you if your business needs reliable, up-to-date information from the web. To explore how Web Service Integration will integrate into the process of your enterprise, contact a data specialist today. Taking data science training from a reputable provider is also very helpful for this.

Data Aggregation in the Travel Industry

It is clear that organizations in the travel industry will greatly benefit from the use of data aggregation processes, whether it is by helping you to obtain business insight, accurately study your competitors or monitoring the product prices of your rivals. It is extremely important to take care of any of the above aspects in the present day since not doing so could mean failure for travel companies who would ultimately lag well behind their competitors in a highly competitive market.

In the travel industry, data aggregation can be used for a broad variety of purposes. This includes comparative pricing monitoring, research of competitors, acquiring business insight, assessing user sentiment, and collecting images and details on their travel website platforms for the services. In the online travel industry, competition is fierce, so data aggregation or the failure of it will seriously affect a travel business.

Travel firms need to gain proficiency with the ever-changing rates of travel and the availability of the property. They will need to recognize which destinations are trending, and for their travel deals, which markets they can reach. The knowledge needed to obtain this information is available across several sites on the internet, making it hard to acquire manually. That's where web Data Incorporation, our data retrieval, and aggregation service, comes in. This enables data aggregation software extremely effective in the modern-day and age for travel firms who must keep up with competitors' deals. Around the same time, in order to retain clients and discourage them from booking with an alternate provider, organizations must be willing to have appropriate deals for trending destinations.

Without aggregated data, it would be very difficult to achieve the required insights regarding any of the aforementioned objectives, since it will require an enormous amount of time and energy to conduct such a process manually, with data having to be obtained from several different sources.

Data Aggregation in Finance and Investment

Companies must continually keep on top of all the recent updates within these various sectors in order to explore all the latest developments and respond accordingly. The optimal way to do all that is to aggregate data, as it helps investment and finance companies change their perceptions to better accommodate the success of a business or product they are investing in. Increasingly, finance and investment companies base their decisions on alternative information. A large chunk of the information comes from the press when stakeholders need to keep up-to-date on financial trends in industry and business. In order to capture headlines and article copies, financial institutions may use data aggregation to use the analysis for predictive analytics, to identify trends, incidents and changing perceptions that may impact the budgets of the businesses and goods they are monitoring.

This business research is accessible free of charge on news outlets, but it is spread through hundreds of websites. Scanning on every individual page manually is time-consuming and can produce inaccurate databases due to missing data. At the end of this article, we can discuss more how investment and financial institutions will accelerate the mechanism in this case of usage. Of course, it is possible to access such details merely by accessing one of the news blogs reporting information on developments in finance and investment. But with so many websites accessible to select from when conducting the task manually, it can also be difficult to access all the necessary data required. What's more, if not enough data is collected, the durability of data sets can become a concern.

Thankfully, data aggregation helps tackle this problem by helping you collect a vast volume of data in a much more effective and reliable way from across hundreds of websites. This is achieved by using advanced methods for data aggregation, which makes it much easier to collect reliable and meaningful data.

Retail Industry Data Aggregation

The retail market is one that is extremely challenging now, much like the travel business. It is for this same reason that corporations need to stay as efficient and effective as possible to consumers who would simply buy elsewhere if their criteria were not met. Therefore, it is evident that data aggregation has a major role to play in more than one way within the retail sector.

There are several practical benefits for data aggregation in the retail and e-commerce sectors. One is price monitoring that is competitive. To be efficient in the e-commerce and retail market, competitive analysis is important. Companies need to realize what they're up against. So, they must always obtain new details about the product deals, discounts and pricing of their competitors. This data can be derived from the websites of competitors or from other pages on which their services are described. The data needs to be aggregated from every single relevant source in order to get accurate information. This is a big ask for manual analysis of web data.

Some other way that retail and e-commerce businesses utilize data aggregation is to collect images and descriptions of products to be used on their site. This may come from producers, and the already existing images and descriptions from them are much easier to reuse than to craft your own. It takes time to manually collect product listings or competition-based pricing and makes it more difficult to ensure that it is regularly updated. After looking at the travel industry, we have informed you how retail and e-commerce businesses can more efficiently aggregate and integrate information.

Competitive price monitoring is the most prominent use of data aggregation within this specific industry. This is something that is essential in modern times if a business is to be profitable. It is also fair to conclude that through competitive research, e-commerce and retail firms need to keep track of the deals and pricing provided by competitors. In addition, firms across the retail and e-commerce industries need to concentrate on obtaining as much accurate data as possible from competitors' websites throughout the process of data aggregation. In doing so this would allow retailers to gain reliable statistics that will greatly assist in improving pricing and target marketing strategy.

Data Aggregation in Marketing

The aggregation of data in marketing typically comes from the marketing strategies and the various platforms that you use to sell to your clients. For example,, if you're running a Google Advertising campaign, you can see that you have to pay attention to several variables to improve your revenue. Your results from a single initiative should be aggregated, looking at how it progressed over the period and for particular cohorts.

Ideally, however, to correlate the data from each specific campaign with each other, you combine one broad data aggregation that shows you how the product is viewed through platforms, demographics and cohorts. In short, as ETV (Extract, Convert, Visualize), we apply to them. Together, this is a workflow to extract and prepare data for analysis from SaaS applications.

The following are for each of these three steps, which are

  1. Extract-Layer of data extraction
  2. Transform-Layer of Data preparation
  3. Visualize/Analyze-Layer of visualization and analytics

But in order to understand the data better, you need to follow each step.


So, if you decide to own a firm within one of the above sectors, in terms of data aggregation, it is well worth taking the above points into account. Your travel, shopping or e-commerce company would stand a far greater chance of remaining an appealing option for consumers if you do exactly that. Or, it would become much easier for firms within the investment and finance industries to increase the efficiency of generating both precise and reliable data sets.

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