Daily interactions between businesses and customers generate tons of data. It can help companies shift from intuitive to data-driven decision-making when collected and handled right. However, vast raw data datasets are uneasy and barely possible to analyze.

Luckily, many techniques help summarize them into manageable insights. They seem pretty straightforward at first sight, but effective data aggregation demands sophisticated understanding and strategic application. Today, we’ll consider major strategies tailored for robust data analysis and look at some examples to better understand how they work.

What Is Data Aggregation?

Data aggregation embraces many activities that help collect and summarize data from various sources to facilitate statistical analysis and improve decision-making. It involves multiple approaches to help you gain insights into your business performance, customer behavior, market trends, and more. You can classify data aggregation techniques in many ways, but they can be divided into the following major types.

Data aggregation types by dimension:

• Time aggregation involves gathering all data points for a single resource over a specific period. For example, grouping data points based on time intervals, such as yearly, monthly, weekly, daily, or hourly. This type of data aggregation can help you track significant trends and identify seasonal fluctuations;
• Spatial aggregation implies collecting all data points for a group of resources over a given period, for example, calculating the total number of visitors or leads across all marketing channels. Spatial data aggregation can help consolidate data, eliminate data silos, and enhance analysis.

Data aggregation types by process:

• Manual aggregation involves collecting data from different sources, like spreadsheets, databases, or applications, and then summarizing it yourself. It’s a pretty straightforward approach suitable for small datasets. The obvious disadvantage is that making things this way can be time-consuming and error-prone if you have to deal with a large number of data points;
• Automated aggregation, on the other hand, leverages dedicated software tools and platforms to collect, clean, and aggregate data automatically. It’s efficient, accurate, and scalable, making it ideal for larger datasets and real-time analysis.

How to Collect and Store Data Properly?

You need a strong foundation before you can build your house of knowledge. In data aggregation, you can rely on Extract, Transform, and Load (ETL) processes, Data Preparation, and Data Warehousing.

ETL allows extracting data from disparate sources. It’s helpful when you need to gather information from sales records, customer databases, and social media platforms. Making it manually or building a suitable software solution from scratch may be labor-intensive. Therefore, you can rely on AWS Glue, Azure Data Factory, and other solutions.

Even if you have such tools in your possession and can retrieve any information you need from multiple sources, this raw data might be inconsistent, formatted differently, or even contain errors. That’s where data preparation steps in. It helps to clean, standardize, and transform the data into a uniform and usable format. Tools like Datameer, Tableau Prep, and many others can be helpful here.

Finally, the data warehouse serves as a central repository designed to store and manage the aggregated data efficiently. It provides a single source of info, allowing for efficient analysis and exploration of insights.

Want to see examples of data aggregation applications in different industries and know some of the data aggregation techniques? Check our full article: https://xbsoftware.com/blog/data-aggregation-techniques/