What is Data Aggregation
Daniel Tarasov
Daniel Tarasov Published: 2023/12/28

Most e-commerce activities nowadays are powered by different data. Companies constantly need to collect and analyze as much data as they can to remain competitive in the market.

However, big and complex projects are not suitable for smaller-scale businesses. In this case, even private entrepreneurs may benefit from information analysis and data aggregation. This process is easily available and can be useful for almost any modern business. In this article, we will see what does aggregation means and how to properly aggregate data.

Why is Data Aggregation Important?

Nowadays, the Internet has an increasing amount of information on any subject imaginable. For a long time now, data has emerged as the primary and most desirable resource for lots of spheres of human activity. However, without proper structure and organization, any data will be irrelevant and useless.

The extraction of the core and main parts makes any bit of data worthless and useless. The process by which data is structured and systematized is called data aggregation. Aggregate data definition also include searching, presenting, and gathering needed information. Data aggregation can also benefit from the use of residential proxies or other similar tools. Just like with web scraping, data aggregation is a process during which you may encounter blocks from sites. Proxies for web scraping can help you change your IP and hide from any tracking in the process.

Examples of Data Aggregation

Data aggregation in different forms has been used throughout the whole history of human interactions. However, modern technologies allow people to use AI, machine learning, and other software to power up and improve data aggregator work. Possible scales for data aggregators work nowadays constantly grow.

Data collection examples can be found in simple tasks like counting steps that you make every week to reach your job. Or it can be a complex system that a taxi app uses for creating prices depending on the current weather, traffic, your location, and other parameters. In this case, the app needs to use and compute colossal amounts of data in a short period of time. With the growing role of technology in our lives, the role of data aggregation is also constantly growing. Nowadays, businesses, corporations, governments, and even regular users use data aggregation for lots of basic and professional tasks.

Types of Data Aggregation

To better understand what is data aggregation, we need to talk about the main types of this process. Modern aggregation has two main types: time and spatial aggregation. Time aggregation indicates the collection of one type of data over a certain time frame. Spatial aggregation represents gathering all data for the necessary amount of time.

Other types of aggregation are called manual and automated. Manual aggregation is carried out directly by humans, as the name suggests. This could be a more thorough and structured approach; however, any manual tasks will involve large time losses. Regularly, this kind of aggregation involves working with data directly in Excel spreadsheets. This leads to hours or even days of work on even fairly simple tasks.

Special software allows for the use of automated data aggregation. This software can automatically export and systematize needed information in minutes. In the end, with simple commands, you can get fully aggregated data ready for further analysis. However, in some cases, you might need to use datacenter proxies or other similar tools to avoid blocks from sites while gathering information from various sources.

Top 3 Data Aggregation Tools

Data aggregation can be performed with many different instruments, and the market can provide you with a variety of tools designed specifically for this kind of task. In this article, we will look at three main tools that you will probably face while working on any kind of data aggregation task.

  1. The first tool, Microsoft Excel, is probably well known to you. Spreadsheets can be useful for both small and large-scale projects. Excel spreadsheets can be powered with complicated formulas or exported to machine learning software. This way, you can use Excel for both your pet projects and full-scale operations.
  2. The next tool is called Google Analytics and is probably and most likely also widely known. Analytics can be used for combining and aggregating different types of information from one source. The gathered data can be visualized and organized for further statistical analysis. This service also allows you to track your site’s changes and collect information on predefined tasks. Also, you can manage comfortable group work with this service.
  3. The last service is called Salesforce. This highly popular tool is meant to help you collect and organize existing customer data from your sources. You can collect large amounts of data in minutes and use them to create statistics and analytical reports in an app.

Data Aggregation in Action

Overall, data aggregation and data harvesting are similar processes that, on a basic level, can be performed similarly. However, in the case of large data mining projects, every process will be far more intricate and require a lot more software. For example, you can use static residential proxies for both small and large scale projects to stay protected and secure all the way.

Usually, data harvesting is used by large companies to track market, marketing, or financial changes in real time. When you crawl a website, you can monitor trends in many different markets and locations at the same time. While data aggregation is a simpler process, companies can still find benefits in using this method for marketing and other tasks.

To see what is aggregation, we will look at the process in detail. Basic data aggregation can be performed manually by almost any user. First, you need to extract information from the needed sites into the sheet format. Some of the sites have that functionality built-in, but in other cases, you will need to use special software that will help you collect raw data from the page. This software usually depends on the performance of your datacenter rotating proxies or other IP-changing tools. Proxy is needed here because of the possible blocks from sites when you make lots of requests at the same time. 

If possible, you need to try to collect as much information as you can. A large-scale data set will help your analysis be more representative and solid. If you need to just get acquainted with the exemplary results, you can focus on the basics and key metrics for collection. In both cases, you can use proxies for scraping to be sure that all of your tasks will be performed safely and quickly.

In the case of, for example, sales data, you need to focus on customer ID, data and sum of purchase, customer location, and platform for purchase. Based on these methods, you can calculate the total sales or analyze every location’s sales potential.The last step in this process is visualization. This can be done directly in Excel or other apps for the sheets that you use. In this manner, you can use timeline charts or just simple diagrams to show the needed mathematical progression. Visualization can be done for any type of metric or for a metrics comparison. You can practice skills of this kind on special websites to practice web scraping.

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Frequently Asked Questions

Please read our Documentation if you have questions that are not listed below.

  • What is data aggregation?

    Data aggregation can be described as the process of collecting, systematizing, and visualizing the data. This can be done both manually and automatically.

  • What kind of proxy is best for data aggregation tasks?

    You can use universal residential proxies and get almost all the needed results. Most of the tasks in this field will require regular and simple proxies for best performance.

  • What is the difference between data aggregation and data harvesting?

    Data aggregation is a fairly simple process that can be done in manual mode by one human. Data harvesting assumes much greater volumes of information that you need to work on. A process like this is almost impossible without proper tools and software.

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