What Is Data Aggregation?

Statistical analysis lets you derive meaningful insights from quantitative data by identifying patterns and trends. Using this information, you can make improved, data-driven decisions leading to better outcomes. 

Data aggregation is essential to this process, allowing you to take raw data and reduce it to key statistics for further analysis. Beyond statistical and data analysis, data aggregation is also gaining popularity as artificial intelligence and machine learning become more prevalent. It will continue to become more commonplace with technological advancements.

For a better understanding of the process, discover the critical role data aggregation plays in handling data and its various uses across multiple industries.

What is data aggregation?

Data aggregation takes large amounts of raw data and combines it into a singular form. The process makes the raw data more usable, ultimately enabling you to draw conclusions from it. Data aggregation can apply to data sets of any size. For example, you can take raw data and produce basic metrics, including standard deviation, averages, and the mean, or you can use the process to compile information from data lakes.

The data aggregation process has three stages. First, you must collect the data you are aggregating from various sources, which you will then store in a data warehouse or database. Step two involves processing the data within its database with the help of data aggregation software. Lastly, you can present the now aggregated data using charts and visualizations or in statistical form.

Data aggregation can happen automatically or manually. Automatic data aggregation utilizes tools that automatically perform aggregation processes at set intervals. This is an optimal way to aggregate data when dealing with large amounts. You might use manual data aggregation instead if you’re working with smaller data sets. However, it’s notable that manual data aggregation is slower and has a heightened potential for errors.

Types of data aggregation

You can classify data aggregation into one of two types: time or spatial aggregation. Time aggregation involves taking all the data points belonging to a resource throughout a specific period. Spatial aggregation uses the data points belonging to a group of resources over a particular timeframe. For example, if a marketer wanted to assess metrics like click-thru rates or conversions for a campaign distributed across multiple channels, they would use spatial aggregation. If that same marketer wished to identify the number of purchases linked to ads on a specific app, they would choose time aggregation.

What is data aggregation used for?

Data aggregation can be utilized in many ways. It helps facilitate different kinds of analysis, allowing you to aggregate data for specific periods, ranging from minutes to years. For example, with data aggregation, you can summarize data to highlight key points from large data sets, find information such as the median, mode, or overall frequency of data points, and identify outliers.

You can also use aggregated data to build reports. Presenting data in reports is vital as it helps summarize information in a way that’s less challenging to understand, making the insights more accessible so that essential information stands out.

Who uses data aggregation?

Many industries implement data aggregation to extract information from their data, develop insights, and optimize performance. The following offers a look at how different industries use data aggregation.

Sales

Implementing data aggregation within the sales industry enables organizations to collect and summarize sales data from many sources. In turn, you can use this information to find ways to reduce expenses, monitor performance, and improve processes.

Health care

Health care services become more personalized when implementing data aggregation. The process improves access to patient data, such as health records containing patient history and previous test results, empowering providers with the insights necessary to create better treatment plans and identify potential risk factors.

E-commerce

Data aggregation allows e-commerce websites to gather valuable information about their competitors and their offerings. It also helps them learn more about customers, such as whether or not product recommendations are successful and demographic statistics.

Marketing

Marketing campaigns benefit from data aggregation. Data aggregation provides access to metrics that allow you to direct your efforts more intentionally to reach customers better and judge the performance of marketing campaigns.

Financial services

Data aggregation benefits financial services by making it easier to build customer profiles with data from multiple sources. This allows for developing personalized product offerings and marketing campaigns and access to data for making stock market predictions.

Pros and cons of data aggregation

Data aggregation improves access to high-quality data and empowers improved decision-making.

It offers several benefits; however, some challenges exist as well. It’s helpful to consider both.

Pros

• Real-time data aggregation helps businesses efficiently make informed decisions.

• Data aggregation improves data quality, as the process helps eliminate any errors and inconsistencies.

• Data aggregation allows you to collect data from multiple sources, making it possible to analyze and process data from a single location.

Cons

• When selecting data aggregation software, you must take extra precautions to ensure that it can integrate with your other data management tools.

• Cybersecurity challenges can present themselves with data aggregation tools if you don’t use proper security protocols.

How to get started in data aggregation

As you start learning this process, having prior experience working with databases, software-as-a-service (SaaS) applications, and popular data aggregation tools is helpful. You may already have some experience with some options, including Microsoft Excel and Google Analytics, both widely used to process, analyze, and visualize data. Other options include MongoDB, IBM Cloud Park for Data, Cloudera Distribution for Hadoop, and Zoho Analytics.

Other important processes and systems you should have proficiency using for effective data aggregation include extracting, transforming, and loading (ETL) tools and data warehousing. ETL tools ensure you have up-to-date data by automatically importing data, while data warehouses are a common location for storing data.

Depending on your ultimate goals, you may consider enrolling in a course or a degree program. For example, data analysts and engineers often use data aggregation as part of their everyday job functions. If you aspire to a similar role, you may need to build foundational knowledge or get a degree in an area of study such as information systems or computer science. 

Getting started with Coursera

Data aggregation can help businesses of all types make sounder data-driven decisions. Continue building your knowledge and skills, and learn more about data aggregation with learning opportunities available on Coursera.

For example, Performing Data Aggregation using SQL Aggregate Functions is a project-based course where you can practice using SQL to summarize data. Before enrolling in this course, you should have experience querying databases with SQL. If you’re new to SQL, consider enrolling in SQL for Data Science from UC Davis first. This course will help you learn the basics of SQL for querying databases and how to use SQL to modify and analyze data.