Data Aggregation 101: Definition, Uses & Benefits

By 2025, more than 180 zettabytes of data will have been created and captured around the globe. With the influx of data in this digitized world, the process of data aggregation has become an essential tool for businesses. It’s a way to take large amounts of separate data from different sources and combine them into one collective body.

Data aggregation can help companies better understand their operations — from cost savings to customer satisfaction— and gain insights about their customers, products, and services.

In this blog post, we'll explain what data aggregation is, discuss some key uses and benefits, and outline how to use it most effectively.

What is data aggregation?

Data aggregation is the process of combining, compiling and organizing large volumes of data from multiple sources into one unified body. This can be done manually or using tools and software designed to collect, store and analyze data.

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The importance of data aggregation

Raw data does not provide much insight without proper aggregation and context from multiple sources. Data aggregation can help businesses gain valuable insights into their operations and customers using data across their company.

Companies can also use data aggregation to identify trends within their industry or generate new insights that lead to:

• Cost savings
• Improved customer satisfaction

Types of data aggregation

Data aggregation can be done in several ways, depending on the type of data and the goals of the company. Here are some common types of data aggregation:

• Time aggregation is the process of grouping together data points based on time intervals, such as hourly, daily, weekly, monthly or yearly. Time aggregation is useful for tracking trends over a period of time and identifying seasonal fluctuations in customer behavior or revenue.
• Spatial aggregation involves the grouping of data points over a specified time period.

Data aggregation use cases

Businesses use data aggregation for a variety of reasons, from tracking customer behavior to analyzing operational performance. Here are some common uses for data aggregation:

Healthcare analytics

Healthcare organizations like hospitals and clinics use aggregated data from health records like patient information, blood test results, inpatient history, and vital signs in order to:

• Identify health trends in the population.
• Develop better treatments and care plans.

With data that are aggregated, healthcare providers can identify risk factors and develop strategies to reduce the risks for their patients.

Customer segmentation

Companies use data aggregation for customer segmentation to gain insights about their customers and create targeted marketing campaigns. For example, data aggregation would allow key demographics to be identified.

By aggregating demographic data from various sources, businesses can identify common characteristics among their customers, such as age, gender, income level, and location. This information can be used to create a common dashboard for customer segments based on these factors.

Sales performance tracking

Data aggregation can help businesses track sales performance over time and identify areas of improvement or cost savings. For example, having aggregated data would help in centralizing sales data.

With data aggregation, businesses can collect and centralize sales data from various sources, such as CRM systems and sales reports. This makes tracking and analyzing key metrics in a single location easier.

Benefits of data aggregation

Data aggregation can provide a variety of benefits to companies, including:

Improved decision making

By aggregating data from multiple sources and analyzing it in real time, businesses can make better decisions faster. This improved decision-making can lead to increased efficiency and improved customer satisfaction.

(See how data streaming enables real-time analysis.)

Simplified data analysis process

Data aggregation simplifies the data analysis process by making it easier to collect and analyze data from multiple sources.

For example, businesses can use a single dashboard to track key metrics like sales performance or customer segmentation. This makes it easier for businesses to spot trends quickly and efficiently.

Better data quality

Through the process of data cleaning during data aggregation, you'll be able to achieve high levels of data quality. This helps businesses maintain a higher data quality by eliminating errors and inconsistencies.

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With aggregated data, businesses can ensure that all the data points are up-to-date, accurate and complete. This makes generating insights from the data and making better decisions easier.

FAQs: Data Aggregation

What are data aggregators?

Data aggregators are software platforms that allow businesses to collect, process, and analyze data from multiple sources. They typically use machine learning algorithms to help organize the data in a meaningful way.

What is manual data aggregation?

Manual data aggregation is the process of manually collecting, organizing and analyzing data from multiple sources. This can be time-consuming and tedious, but it can also provide businesses with valuable insights into customer behavior or operational performance.

Aggregating data manually can be done through the use of spreadsheets, database systems, or software programs that are specifically designed for manual data aggregation.

Here are some examples:

• Spreadsheets like Microsoft Excel and Google Sheets
• Database programs like Oracle SQL and MySQL
• Software tools like Tableau and Sisense
• Business intelligence (BI) platforms such as IBM Cognos, SAP BusinessObjects, and SAS BI

What is automated data aggregation?

Automated data aggregation is the process of collecting, organizing and analyzing data automatically using software programs. Automation simplifies the data aggregation process by taking on manual data cleaning tasks as well as importing and combining data.

Automated data aggregation can be done through various tools such as:

• ETL (extract-transform-load) systems
• Data aggregators
• Machine learning algorithms

What is the difference between data aggregation vs. data mining?

Data aggregation involves collecting and processing data from multiple sources into a single source for data analysis. 

Data mining involves uncovering patterns, trends and insights from large datasets to aid decision-making. The two processes are related, but they have different goals and approaches. 

Who is involved in data aggregation?

Data aggregation involves a variety of stakeholders, including but certainly not limited to these parties:

• Data analysts and data engineers are responsible for collecting and organizing the raw data.
• Software developers and data scientists are responsible for analyzing the aggregated data.
• Data warehouse administrators and database administrators (DBAs) would also be involved in, as they maintain and update the data storage databases.

(Get certified in data analytics or data science.)

Wrapping up

To sum things up, data aggregation is the process of collecting and analyzing data from multiple sources in a single location. This can provide businesses with improved decision-making, simplified data analysis processes, and better data quality.

Data aggregation is an essential step in the data analysis process and can benefit businesses in many ways. With the right data aggregation approach, businesses can gain valuable insights from their data to help them make more informed decisions.

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