What is Data Aggregation and Why do Businesses Need it?

Data aggregation is the gathering, compiling, and summarizing data from multiple sources into a single view in a simple form. It is the basis of data analysis, taking organizations from disjointed datasets to actionable insights.

In this blog, you will dive deep into the core features, processes, and factors that make data aggregation. You will also find out about real-world applications and how they enhance decision-making. Do you operate on big data or want to streamline your workflow regarding your data? This detailed guide will walk you through everything you know about data aggregation.

What is Data Aggregation?

Data Aggregation is a process of gathering data from multiple sources and compiling, formatting, and processing the data further in a summarized form. It is used to analyze data statistically.

Data Aggregation can include processes such as collecting data about a particular product based on age, profession, location(etc), collecting data from a company/competitor’s website to analyze their trends, and gathering image and product descriptions to be used on the company’s website.

Once the information is collected, it is then converted into documents and reports and then sold to companies in a simplified manner. Different versions of the reports are available and there are unique reports that are prepared for every department and individual. These records can also be requested as per the individual’s preference.

Data Aggregation also deals with the quality of the data being collected. By doing this, they ensure that all the analysis happens in real-time and the quality of data is not hindered during any stage of the aggregation procedure.

Prerequisites Required

• Working knowledge of SaaS applications.
• Working knowledge of Databases.
• Knowledge of the various industries in a country.

Example of Data Aggregation

An E-Commerce company would want to track the number of users purchasing a particular product on their website. Hence, in order to collect this data, the marketing team would need to perform a Data Aggregation on customer data. The aggregate data would include statistics on the demographic and behavioral metrics of various customers, the average age of customers, and the number of transactions of every customer (etc). This information is very important for the marketing team, product team, and finance team. 

Based on the aggregate data, the marketing team is able to personalize messaging and add discounts and offers for each customer. It also helps to make better advertising strategies that appeal to the customers. The product team is able to find out which products are doing well in the market as well. Similarly, this data can also be used by the finance team to allocate resources for the marketing and product team respectively. Hence, it can be seen that the aggregated data is valuable for all departments of an organization.

Why is Data Aggregation Important for Businesses?

Data aggregation is essential for businesses because it helps transform large, complex datasets into actionable insights, leading to better decision-making. Here’s why it’s important:

• Improved Decision-Making: By summarizing data from various sources, businesses can quickly identify trends, patterns, and key metrics.
• Efficiency: Aggregated data streamlines reporting and analysis, saving time and resources.
• Data Accuracy: Aggregation reduces data noise, ensuring that businesses work with accurate, relevant information.
• Personalization: Helps businesses tailor marketing strategies based on customer data, enhancing customer experience.

What are the Types of Data Aggregation?

There are mainly 2 types of Data Aggregation:

• Manual
• Automated

Manual Data Aggregation:

In a Manual Data Aggregation approach, employees aggregate the data manually. A Data Aggregation Tool exports the data from multiple sources, and then all the data is manually sorted through an Excel sheet. Employees format all the data manually into a common format and create charts to compare the performance of the aggregated data based on the metrics considered.

All of these tasks can become very cumbersome and there is a high chance of error. In order to prevent these errors, the whole process is automated.

Automated Data Aggregation:

In the Automated Data Aggregation process, a third-party device, also called Middleware, is used to automatically gather data from the marketing, product, SaaS, and numerous other platforms. When the process is automated, the region of interest for the data is expanded, freeing up time to focus on other parts of the analytical process.

How Does Automated Data Aggregation Work?

Data Aggregation combines the data from various sources to provide a summary view. It works in the following steps:

1. Collection: The aggregation solution gathers data from several sources and combines it into a single format. The platform gathers data from Ad Platforms, Website Analytics Software, Social Media, and other sources for Marketing purposes.
2. Processing: The system then uses harmonization techniques to normalize the data. These techniques assist in removing duplicates, aligning distinct indicators with one another, and eliminating data discrepancies. Analysts gain analysis-ready insights from these activities, which can be used for further investigation.
3. Storage: The data aggregation system keeps data in a separate warehouse. With centralized data storage, it’s considerably easier to get insights. However, the warehouse should be built to handle massive data sets. Analytical databases are the ideal alternative for such data activities.
4. Presentation: Users can present the aggregated data in a summarized format that itself provides new data. The statistical results are comprehensive and high quality.

What are the Data Aggregation Levels?

A Level of Data Aggregation is a classification of the various users who know how to aggregate the data. In general, there are 3 Levels of Data Aggregation:

• Beginner
• Intermediate
• Master

At the Beginner level, a person is not aggregating the data, instead, he is using various platforms such as Google Analytics to make informed decisions about the data. It is more of a comparison being made across multiple channels by the person. Although it is not the best practice for Data Aggregation, it does lay a foundation for anyone who wants to get into analytics.

At the Intermediate level, a dashboard using Excel or Google Spreadsheets is set up by the company and a person is able to update and visualize the data. This can be easy if the dashboard is already created but creating a dashboard is both time-consuming and error-prone. Maintaining one in real-time is also a challenge and so this method is also not very effective.

At the Master level, an automated funnel is set up through which an Automated Data Aggregation approach is chosen. This way insights from the data are seen constantly and can be analyzed within a few minutes. This funnel also helps to send the data to different Data Warehouses, Spreadsheets, or even Business Intelligence Tools/ Data Visualizer Tools.

Steps Involved in Setting Up Data Aggregation

Every Data Aggregation process has some general components and a process that works by the interaction of these components with one another. In general, all the components must interact in such a way that they can make sense of the data gathered for analysis.

Components Involved in Data Aggregation

There are mainly 3 components that help aggregate data:

• Raw Data: The data that needs to be aggregated.
• Aggregator: The system that leverages an aggregation function to aggregate the data.
• Aggregated Data: The data that has been aggregated.

There are 3 main steps involved:

• Preparing Raw Data: In this step, data is collected from multiple sources. This is called raw data. It is then transported to an aggregator, which is an aggregation unit. Loading, Transportation, and Extraction are performed on the raw data to convert the data into a common format.
• Aggregating Raw Data: In this step, the data is ready to be aggregated and an aggregation function is implemented on the raw data that transforms the data into aggregated data.
• Handling the Aggregated Data: In this step, the aggregated data is handled in different ways. It can either be stored in a Data Warehouse, on the Cloud, on a Spreadsheet (etc.) or it can be sent to different Business Intelligence Tools/ Data Visualizer Tools for further analysis.

7 Key Criteria to Set Up an Effective Aggregation Solution

There are 7 key criteria that help ensure that an effective aggregate solution is set up so that the Data Aggregation processes will occur in an efficient and consistent manner. These are given below:

• Enterprise-Grade Solution: Any company that wants to have a proper Data Aggregation procedure setup must have an Enterprise-Grade Solution. These solutions share a number of characteristics that support dynamic business environments. They are also easily maintainable, allow multi-server environments, have a good UI, and have a good backup and recovery strategy.
• Flexible Architecture: In order to have proper Data Aggregation, flexible architecture is a must. A flexible environment is one that is very responsive to the users and constantly changes according to the user’s requirements. It should also optimize the aggregation process and generate visually appealing reports for analysis.
• Performance: Performance refers to the speed and response time of the aggregation process. Performance is a key criterion because it represents the quality of the processes involved in Data Aggregation. The performance must be predictable and up to market standards in order to make a careful analysis of the data.
• Scalability: Scalability refers to the increase of resources of a particular application/process as the data needed for analysis grows. It is important to have a highly scalable environment because it will help users bring in complex data in large volumes for easy and fast analysis.
• Fast Implementation: Implementation time is also important in Data Aggregation. This is the time used to evaluate the speed it takes the software to perform Data Aggregation. Nowadays, Implementation costs are twice or, in some cases, thrice that of the software. Hence, the system must have a methodological and efficient implementation procedure put in place.
• Hardware and Software Resources: Hardware and Software are vital to any process as they provide the resources through which that process can operate. Having proper Hardware and Software set up will ensure that the processes are synchronized in such a way as to reduce both time and storage.
• Price: The Data Aggregation process must be well within the budget of the organization. This is because making financial decisions is no longer a goal but a responsibility in a competing market today.

What are the Applications of Data Aggregation across Various Industries?

Now that the whole process of how Data Aggregation occurs is understood, you can now decide how these processes are done on an industry level. As you already saw, Data Aggregation is applicable anywhere no matter which industry is chosen. The procedures of Data Aggregation processes for all the popular industries are given below:

• Investment and Finance
• Travel
• Retail and E-Commerce
• Banking
• Healthcare
• Education
• Digital Marketing and Advertising

1) Investment and Finance

Investment and Finance firms rely a lot on Data Aggregation. Most of their data come from the news because investors need to keep track of the stocks and financial trends daily. For these industries, Data Aggregation includes gathering headlines of articles that highlight the trends, events, and differing opinions of people on the finances of the products being tracked by them.

This news and articles are available on various marketing websites for free but are spread unevenly. Hence, a proper Data Aggregation strategy must be used in these industries.

2) Travel

Data Aggregation can also be used in the travel industry. Some of the methods include Competitive Price Monitoring, Competitive Research, Understanding the Marketing Trends in Travel, and also a Customer Sentiment Analysis which is important to figure out which travel destinations are popular and how many customers would like to travel to.

Similarly, competition in the travel industry is very high and every travel agency researches its competitors in order to gain a majority share of the market. Other than the competition, Data Aggregation is used by travel agencies to keep track of the trends in travel costs and property availability. This helps them to also keep track of the trending destinations. As there are multiple websites that have this valuable information having an automated Data Aggregation technique is critical.

3) Retail and E-Commerce

The Retail and E-Commerce industry are industries that use Data Aggregations on a daily basis. Similar to the travel industry, the Retail, and E-Commerce industry also uses Data Aggregation for Competitive Research. Companies have to know what their competitors are offering in terms of products, promotions, and prices for each product. This data can be accessed from the competitor’s websites and hence there is no lack of data.

In all cases, an automated Data Aggregation model will best suit these industries. Another technique is that these industries need to gather images and product details to use on their website. This is because it is much easier to use existing images and details than to craft your own.

4) Banking

Banking industries have leveraged the power of Data Aggregation to invent a technique known as Screen Scraping. In this method, all the user’s usernames and passwords are all generalized into PINs. This ensures that users can access multiple websites without remembering their usernames and passwords for the websites but by using PINs to access the websites.

The system can be designed in such a way that there is only 1 PIN, called a Master PIN, that can be used to access multiple websites. The system authenticates the user when the user makes a request and the data aggregators can validate the information using the account holder’s PIN. These systems can be offered as standalone services or they can be merged with other systems that can perform further authentication. Currently, Screen Scraping is used in bill payment and product tracking.

5) Healthcare

Data Aggregation techniques are used in the Healthcare industry to help to monitor patient habits and the way in which they consume various medicines and drugs. It also helps to track the doctor’s and nurse’s interactions with various patients and maintain patient records and reports. Transactions are also easily monitored.

Data Aggregation is also used to maintain transparency and trust between doctors and patients.

6) Education

Data Aggregation can be used in the Education industry by maintaining a database of student and teacher records. This helps correlate and identify which teachers teach which students, and which students are alumni of that school/college to maintain a central repository of both student and teacher data.

By following all these methods, the system’s performance and utility will increase for both students and teachers. In the future, Data Aggregation can be applied to provide online learning as well by collating different pieces of information about a certain topic from multiple sources onto a central database that can be accessed by both the teachers and the students.

7) Digital Marketing and Advertising

Digital Marketing and Advertising also use Data Aggregation in their line of business. Some of the techniques include aggregating news from multiple sources, predicting trends in the market, and also analyzing the competition. This way aggregation techniques ensure that recommended content is constantly provided to the advertising company. Data aggregation also helps to analyze customer data by providing personalized advertisements and efficient marketing efforts to enhance customer experiences.

Data Aggregation with Web Data Integration

As you saw how Data Aggregations are important in various industries, it is important to automate the Data Aggregation process for both performance and efficiency. In order to ensure performance and efficiency at all times, Web Data Integration (WDI) is a solution that is used. It is an extension of web mining that can be used to extract data from any data source for the organization’s needs.

It can remove errors involved in Data Aggregation and reduce the time required to aggregate data. This ensures that companies can get their data aggregated whenever they want, wherever they are, and in minimal time with the highest level of accuracy. Along with extraction and aggregation, WDI also cleans the data and delivers it to the destination in a common format for analysis. It also sends it to various BI tools for visualization.

It simplifies the ETL process (Extract, Transform, Load) process. In this process, data is extracted from multiple sources and transformed into a common format, and loaded onto a destination namely a Data Warehouse, or transported to another source for visualization and analysis. In all ways, WDI plays an important part in Data Aggregation and is crucial to companies.

To understand Web Data Integration (WDI) in further detail, click this link.

Conclusion

This article gave a simplified but systematic study of Data Aggregation along with a detailed explanation of its types, levels, criteria, and processes. It also explained how Data Aggregation is important in many industries and how each industry uses them.

If you are going to set up an effective Data Aggregation solution, then Hevo Data is the right choice for you! It will simplify the Data Aggregation, ETL, and management process of both the data sources and the data destinations.

Data Aggregation FAQ