Data is one of the most valuable assets a company can have. It can help you make informed decisions, identify new opportunities, and provide better customer experiences.
However, one challenge that companies face isn’t the lack of data. It’s organizing volumes of data and turning them into actionable insights.
This is where data aggregation comes in.
In this article, we’ll explain what data aggregation is and why it’s important. We’ll also provide some examples and cover practical use cases.
What Is Data Aggregation?
Data aggregation is the process of collecting data from different sources and presenting it in a summarized format. It can provide answers to analytical questions and offer valuable insights.
Aggregated data is typically stored in a data warehouse — a large repository of structured and semi-structured data — where it can be accessed and queried for further analysis.
Aggregating data can be performed manually. For example, you can create simple pivot tables with tools like Excel using sales figures to view trends over time.
However, manually collecting and processing your data at scale isn’t practical. Data aggregation is typically done through tools called data aggregators, which allow you to connect your data sources and discover new patterns through data visualization.
Examples of Data Aggregation
To better understand how data aggregation works, it helps to look at a few examples. Here’s how different industries use data aggregation to inform decision-making.
Data aggregation example in retail.
Retailers lose profits when inventory becomes unavailable. This can happen due to poor demand planning.
To forecast demand (and prevent stockouts), retailers can aggregate their sales data and see which products are sold throughout the year and historical volumes. They can use these insights to order the right amount of inventory at the right time.
Data aggregation example in construction.
As a contractor or construction firm, you want to have a clear forecast of how much a building project will cost before taking it on.
Aggregating historical data based on previous projects can improve the accuracy of bidding and estimates. Contractors can also see a detailed cost breakdown and relay that information to their clients.
Data aggregation example in financial services.
Investment firms must pore over volumes of data to identify investment opportunities and develop recommendations for their clients.
They can use aggregation tools to gather data from sources like financial records, industry research, and even various news sources. By analyzing the data, firms can make forecasts on how a company or stock will perform in the future.
Data aggregation example in healthcare.
Data aggregation can help healthcare organizations improve patient care. It can also help maintain transparency and build trust between providers.
With electronic medical records (EMRs), healthcare organizations can aggregate patient data and determine the best course of action when treating patients with similar conditions.
Why Is Data Aggregation Important?
Companies are collecting more data than ever. But data is practically useless unless you can draw actionable insights from it.
Here are just a few reasons why data aggregation is important.
Data aggregation eliminates data silos.
Data silos are just what they sound like — repositories of data that are held by one department and inaccessible to others.
Organizations can end up with data silos due to poor data management practices. And this can hinder operations and prevent company leaders from making informed decisions.
Data aggregators help eliminate data silos by consolidating multiple data sources into a single data source that updates in real-time. This makes data more easily accessible across an organization.
Data aggregation improves data quality.
As many as 95% of companies have experienced issues because of poor data quality. Strategic decisions rely on having access to quality data.
For example, a marketing manager will have a hard time creating a budget if campaign data is missing or inaccurate. In this case, they’d have to manually sift through the numbers and validate the data themselves.
One step in the data aggregation process involves identifying and fixing any irregularities, which greatly improves data quality.
Data aggregation informs decision-making.
Finally, raw data is extremely difficult to work with.
Data aggregation leads to better decision-making, as it facilitates data transformation — the process of converting data from one format or structure to another.
With data aggregators, analysts can transform their data in various ways and test different scenarios to extract more insights.
The Data Aggregation Process Explained
Data aggregation is a three-step process: collection, processing, and presentation.
Let’s take a closer look at each of these steps.
1. Data collection.
The first step of the data aggregation process involves collecting data from different sources and storing it in the cloud or on-premise.
Data sources can include:
- Customer records
- Data from Internet of Things (IoT) devices
- Software-as-a-Service (SaaS) services
The data is then stored as atomic data — data that can’t be broken down further. The number of items sold would be an example of atomic data.
2. Data processing.
Once the data is collected, it’s then processed and “cleansed.” This is where any errors and other irregularities in your data are fixed.
Data processing can be done using machine learning algorithms. The type of processing also depends on how the data will be used (e.g., forecasting sales demand, examining customer patterns, etc.). The easiest way to handle this process is to use a data management platform to create (or adapt pre-made) data apps.
3. Data presentation.
The final step of the data aggregation process involves turning the processed data into more readable formats for further analysis. These can include graphs, charts, and tables.
Here’s an example of a chart that presents data about Olympic medals in a summarized form:
These types of visualizations help analysts and executives uncover patterns and gather useful insights.
In 2022, data is among a company’s most important assets. But data is only valuable if it’s collected, processed, and presented in the right way — the core goals of the data aggregation process.
While you can aggregate data manually or try to develop a custom solution from scratch, using a data platform like Toric is far more efficient. The platform lets you consolidate your data into one workspace where you can transform it and create real-time visualizations.
Get a customized demo today to see how Toric can help you aggregate your data and deliver actionable insights at scale.