Data Aggregation: Examples and Best Practices

Data can be the difference between a business turning a profit or enduring a period of losses. While that might sound dramatic, business leaders need data to make critical decisions that drive growth and help their company remain competitive. It’s the reason many organizations have turned to Big Data aggregation tools that help them centralize the way they collect, store, and analyze data.

Even with the arrival of new technologies designed to transform the way we do business, the fundamentals always remain the same. Leaders need relevant information that helps them understand customers, operate more efficiently, and make smarter decisions.

Data aggregation benefits users at all company levels by providing them with an endless resource of pertinent information. Having summarized information at their fingertips helps business leaders make the most innovative, intelligent choices regarding their company.


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What is data aggregation in data analytics?

What is data aggregation in data analytics?

A common definition of data aggregation is the process of pulling information together to make it more understandable. Many companies accomplish this by using a web scraper to draw out information from different web sources. From there, they store the information in a way that makes it possible for business users and company leaders to locate the kind of insights that drive the direction of a business.

Summarizing information through data aggregation requires a lot less time and effort from analysts tasked with accessing and examining the data. One row of aggregated data can hold hundreds, even thousands of information blocks.

Typically, a leader who requests specific insights from the information collected by the company might have to wait for a bunch of processing cycles to complete each data row before getting what they need. Thanks to data aggregation, it’s possible to retrieve information in real time when it’s accessed.

Let’s say a company has a lot of social media channels from which they want to pull data around customer interactions, like the pages they browse or which action calls generated the most interest. Data aggregation makes it easier to execute data analysis that provides leaders with insights into the demographics of the current customer base, like their education level, age, and how much they earn.

That information can guide leaders who need to decide on new product lines or expand company operations into a new location. From there, they can direct those who report to them about marketing new products, refining sales pitches, or optimizing customer service responses.

How does data aggregation work?

How does data aggregation work?

As mentioned, one row of aggregated data collected from a tool like a web scraper might contain records in millions. Collecting information from each record to set up a report can take a lot of time, especially if an analyst must do it manually. Ideally, it’s best to have tools that make it easier to pull data in an organized manner and capture and store it effectively.

Many organizations, like financial companies that use data aggregation platforms, leverage their software to review information around industry trends, study data they collected from a competitor’s website, or look at information about who the top users are of a specific product line or business service.

Collecting data

Data aggregation starts with collecting data using a web scraper or other technology. In addition to web scraping HTML files, you can collect data stored in public databases, spreadsheets, and other data sources. First, you’ll need to decide upon a data aggregation technique, which can include one of the following:

  • In-network— Relies upon using a multi-hop system to route and process data.
  • Tree-based — This technique constructs an aggregation tree that maps information from roots out to leaves, or from source nodes to link nodes.
  • Cluster-based — Here, you pull together large quantities of data from an entire network.
  • Multi-path — Partially aggregated data gets sent to a parent node, which sends the information out to a branch.

You’ll also have to decide the periods during which you want to collect data for aggregation.

  • Reporting period — Defines the periods when your web scraping software or other technology goes out and collects information. You may want your automation to collect data from a frequently updated site every hour to ensure you have the most up-to-date information.
  • Granularity — Defines the period during which you want to collect data points from a specific resource or group of resources for aggregation. Here, you might decide to get information about the average number of users who frequent a specific social media channel over thirty minutes.
  • Polling — Defines how often you want to use specific resources for data samples. For example, you might decide to go to one source every 10 minutes, while another might only be tapped into once every three days.

Below are examples of mathematic functions used in data aggregation.

  • Average — Computes the average of all the data points in a given set.
  • Max — Provides you with the highest value from the data points in a given set.
  • Min — Provides you with the lowest value from the data points in a given set.
  • Sum — Provides the total of all data points in a given set.
  • Count — Provides a count of the data points in a given set.

Presenting data

Once your data aggregator collects the desired information, it filters and preprocesses it to remove errors, invalid data, or other inconsistencies. That way, you have a consistent and accurate data set prepared and ready to move on to the next data aggregation step.

All the processed data gets merged into a meaningful form for analysts and other business users. The format will depend on your company’s needs and your desired outcome. From there, the aggregated information gets stored in a data warehouse, where those at the executive level can tap into it when they need to make a data-driven business decision.

Types of data aggregation

Types of data aggregation

Even with the advancement of tools on the market for data aggregation, many companies still believe in the value of carrying out the process manually. Unfortunately, that means you must tap into a lot more human capital to structure the thousands of unique records coming in from numerous sources.

The complexity of this task and the amount of time it takes can quickly become overwhelming for analysts. It’s also less efficient and not at all cost-effective. You also have to consider the other drawbacks to manual data aggregation, including the following:

  • Slower product and service rollout — The time it takes to set up manual data sets required to make decisions around market releases will make your company less agile, meaning competitors can beat you to the punch.
  • More expensive — Analysts with years of experience working with data can still make mistakes that affect the quality of company information. In addition, manually running data aggregation processes adds to an individual employee’s workload, hindering their ability to focus on other important business initiatives. That means your company would have to invest in more resources to ensure that essential business functions aren’t overlooked or neglected.
  • Harder to scale — You’ll need more data to support future growth plans. It’s harder to do that when you’re relying on human resources.

The best way to accommodate your data aggregation needs is through automation. Whether you end up with a single platform or multiple solutions, you’ll need a robust data proxy capable of supporting your data scraping robots or other web data collection processes. That makes it possible for you to remove the need for manual oversight, freeing up your employees for different projects and enabling your organization to function more agilely.

What are some use cases for data aggregation?

What are some use cases for data aggregation?

Today’s successful enterprises continue to thrive because they have learned to use data to benefit their organization. It’s helpful to corporate management as they make decisions around product planning, operational efficiency, and designing more appealing marketing strategies.

Data aggregation summaries can be obtained from places like an Internet of Things (IoT) device’s browsing history, communications made via social media, and user information collected from a company website. Standard use cases that can benefit from data aggregation include the following:

  • Business intelligence — Collecting information from multiple sources like sales and marketing makes it easier for business leaders to develop more strategic solutions. They can determine which products are most profitable or which customer segments display the most brand loyalty.
  • Network monitoring — Data aggregation can help organizations stay on top of issues that could lead to security breaches. For example, a network administrator can aggregate information from switches, firewalls, and other systems to figure out what’s causing a network outage. They can also generate reports that help them identify top security threats.
  • Financial analytics — Collecting and summarizing information from different financial systems, like an enterprise resource planning (ERP) solution, can support risk and portfolio management. You can establish performance reports that help you spot market trends or understand the potential consequences of investing in different initiatives based on their portfolio.

Aggregated results can tell you information that applies to various industries, like the number of transactions completed during a given period. Below are examples of how data aggregation can be used in real-life business scenarios.


Every interaction within a healthcare system, whether it’s at a doctor’s office or outpatient surgery center, generates data on individuals. Healthcare providers typically collect information from patients that includes their names, ages, medical history, and current lab results. Anything that could potentially affect decisions about patient treatment gets captured.

Data aggregation allows physicians to look at patient information from different angles using other supporting information. As a result, it enables them to get a clearer picture of a patient’s condition and develop effective treatment strategies.

Doctors can also learn to identify a possible diagnosis depending on the trends they see in the data around other patients who have demonstrated similar symptoms. Data aggregation also makes it easier for physicians to keep up with trends in healthcare and track patients more effectively and provide them with better outcomes.

Having more knowledge collected about patients helps product developers come up with better solutions from patients that can apply to a large population. They can also look at the market demand for different solutions and come up with ways to ensure there’s enough of a product to meet current patient needs.

Data aggregation makes it easier to set up reliable data records and maintain transparency between healthcare providers, suppliers, and patients. Thus, medical professionals can track healthcare data more efficiently and use the insights to improve their facilities, which benefits the entire healthcare industry.


Marketing analytics is founded upon data aggregation. It’s what makes it possible for marketers to glean advanced insights from massive amounts of data. They need data aggregation to accomplish functions like predictive modeling and marketing attribution. Data aggregation helps marketers obtain timely insights from marketing data that can be used to transform campaigns and appeal to different audiences.

Data aggregation combines all your marketing information into a standardized analysis format. For example, marketers can use it to perform cost aggregation, where they reconcile all the money they’ve spent advertising on different platforms. It’s also possible to use web scraping tools to collect information around engagement, mentions, and hashtag usage.

Because marketers typically must pull information from multiple solutions and campaigns, the right data aggregation tools can reduce the manual challenges of working with large data sets pulled in from data scraping automation and other technology.

From there, marketers can pull together a complete picture of what they’re spending on advertising. That way, they can produce relevant results that help them determine new strategies to improve the return on interest (ROI) on their marketing spend. Marketers can also use data aggregation to look at sales and customer data when deciding how to approach upcoming marketing campaigns.


With many people returning to their regular travel patterns after the pandemic, many travel companies have started leveraging data aggregation to assess risk. These can come in the form of political upheaval in one part of the world or the approach of storm season in another. In the past, many companies have had issues assessing risk accurately using travel data because so much of it needs to be compiled.

For example, business travelers can have information stored in channels like online booking tools or credit card sites. Unfortunately, those gaps in data prevent travel companies from getting a complete picture of travel risks.

Thanks to data aggregation technology, bridging those gaps is now possible. In addition, businesses can use web scraping technology to track rate changes and travel offerings from competitors. They can, then, use data aggregation to come up with better alternatives for travelers.

Suppose an employee is traveling and ends up in an emergency. Companies can use their data aggregation tool to determine the worker’s location and establish a communication channel. In addition, using web tools to collect a continuous flow of travel information helps eliminate security gaps often resulting from an overreliance on outdated information.

In addition, travel companies can use predictive analytics to target customers with specific travel promotions. For example, companies can ensure that visitors expressing interest in a beach vacation receive offers for tour packages in those types of locations. Businesses can also present potential customers with upsell opportunities, like adding on a rental car or discounts on popular visitor spots.


With so much competition in retail, businesses must pull out all the stops to make themselves more attractive to customers. Otherwise, they’ll move on to another company offering similar products. Data aggregation plays a significant role in helping retail businesses separate themselves from others in the marketplace in the eyes of consumers.

The biggest thing customers want is a deal. Thanks to data scraping tools and other web collection software, retailers can continually pull price information from a competitor’s site, put it through an automated data aggregation process, then come up with ways to offer more attractive discounts to customers.

Retail and eCommerce benefits need technology that supports large-scale web data collection, like a web proxy. That way, they can bring in a continuous stream of competitor data that helps leaders adjust prices and strategies for marketing themselves to consumers.

Retailers can also use data aggregation to collect product descriptions and images to use on their business sites. Examples can be found from producers who already have existing images and descriptions. It’s much easier for retailers to capture and reimagine the content versus coming up with something from scratch.


Finance companies must stay on top of changes within the section and respond to those developments. Aggregating data helps finance and investment organizations come up with strategies to ensure the success of a product or business investment. A lot of information can be found in press releases, stock market trading data, and other public material.

Data aggregators can capture the information from the copy and put the data into a format to use with predictive analytics. For example, finance companies can use a combination of public and private financial information to spot trends or incidents that could impact the budgets of any business or product under consideration.

What are the benefits of data aggregation?

What are the benefits of data aggregation?

The most significant benefit of working with data aggregation tools is the ability to transform raw bits of information into valuable business assets. While it’s true that all data doesn’t require an in-depth review, having the information in a summary format can still have an impact on overall business operations. Leaders can pick and choose what information to focus on when making organizational decisions.

Improved data quality

Automated data aggregations clean, collect, and summarize information without human intervention. That cuts down on manual errors that could impact the reliability of your data. From there, you can make the information available to different teams to improve collaboration and ensure that everyone taps into the same source of truth.

Better decisions

Data aggregation makes it easier for executives and other business leaders to understand what’s happening within a department or the entire company. That makes it easier to have confidence in decisions about the future direction of their company.

More efficient channel tracking

Data aggregation helps with tracking the performance of sales channels used to reach a wider audience. You can analyze the information to determine how much demand there actually is for every channel from customers. That way, you can decide if you should increase your usage of a specific channel or redirect resources provided to an underperforming online outlet in a different direction.

Development of effective customer strategies

Data aggregation helps you spot crucial information like the percentage of new and returning customers using various sales channels. From there, you can decide where to focus your efforts on retaining customers. In addition, companies can work out ways to attract fresh customers to help generate additional revenue.

What are some data aggregation best practices?

What are some data aggregation best practices?

The first thing companies must do when setting up data aggregation processes is decide what information to import. You must also draw up metrics to determine what you want to see regarding your information.

When it comes to metrics, think about what’s going to have an impact on your business decisions. For example, if you’re trying to come up with the demographics of who’s using your company’s most popular product, it helps to have a breakdown of demographics by age, location, and other pertinent factors.

One of the most popular import methods for web information is the use of web scrapers supported by proxy servers. In addition, you can use your data aggregation tools to transform the information into a clean, readable format for storage and further analysis.

Finally, you must decide on what format to use to present the data visually. Make sure you have the resources to scale your data aggregation processes as needed. That means finding storage solutions that allow you to store vast amounts of historical data to assist you in spotting past, present, and future trends in your industry.


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Build your optimal data aggregation solution

Build your optimal data aggregation solution

Rayobyte provides companies of all sizes with enterprise-grade solutions to support their data aggregation needs. You can build the kind of flexible architecture that’s responsive to users and able to adapt to changing business needs. Our collection of proxies for business use can help you achieve the speed necessary to make your web scraping technology work faster and more efficiently.

Our web proxies adapt to your need to increase resources to support your web scraping tools. Learn more about how Rayobyte can help you find the right web proxies for your data collection technology by contacting one of our team members.

The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.

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