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Data Aggregation for Businesses: Benefits, Use Cases, and Examples

Modern ecommerce is ruled by data. The companies that have a high command of data excel in their niche, whatever that niche may be, and ones that do not or cannot gather appropriate data about their company’s operations lag behind.

Data analysis isn’t only for the behemoths of the tech industry like Facebook or Google, even small ecommerce companies can benefit greatly from analyzing data on sales, customers, and other performance metrics. However, if you want to perform data analysis the right way, you should start with proper data aggregation.

Aggregating data in an easily available form will ensure you have the correct data to analyze. This guide will walk you through what data aggregation is, how it works, what are the use cases of data aggregation, and how you can implement it at your organization. The best part is, you don’t need to have ten years of experience in programming to understand any of it!

What is data aggregation?

Simply put, data aggregation is the process of gathering and organizing raw data into a form that is easy to analyze and visualize. Often it means pulling data from various sources and presenting tens of thousands of data points in a single statistic.

Data aggregation can be done on any scale from a small business to a large corporation that has terabytes of data to analyze. No matter the size of a business, data aggregation helps to see the big picture – for example, monthly sales across different platforms.

Why is data aggregation important?

Even a small business or a solopreneur running an ecommerce website can benefit greatly from a couple of simple data aggregation techniques. If you are using a data aggregation solution for your business, you can do the following:

  • Transform large sets of data into averages
  • See the larger picture of sales, marketing, and other areas of business
  • Compare these data points over time
  • Compare different traffic or lead generation channels
  • Track KPIs across all platforms
  • Make business decisions based on that analysis

In fact, if you’re using a CRM with a sales or marketing dashboard or Google Analytics, you’re already using data aggregation. There are also many tools on the market that specialize in aggregating data. Be sure to check the article on data aggregation tools for our top selections.

Types of data aggregation

While data aggregation is mostly a straightforward process of combining data values across large sets, there are several types.

Time vs spatial data aggregation

The two most common types of aggregating time is spatial aggregation and time aggregation. Spatial aggregation takes data points across different sources within a given time frame. For example, calculating the total number of leads across all marketing channels is considered spatial aggregation.

Time aggregation involves gathering data from a single source across a longer period of time, for instance, counting all leads from a single marketing channel per month or per year.

Manual vs automated data aggregation

The distinction between the two is rather obvious. Data aggregation can be done manually if the organization doesn’t have many data sources or doesn’t have much data at all, but it’s best to automate the process right away.

You or an employee can download reports in CSV from most platforms and upload them to your dashboard, but it’s time consuming. If you automate the process, you’re saving a lot of time and making sure there will be no place for human error.

Real time data aggregation

As opposed to weekly or monthly reports, real-time data aggregation provides instant updates to the aggregates that you wish to see in your dashboard or report. However, it’s not always practical to use real-time data aggregation for all of your analytics. In most cases, platforms offering real-time data aggregation will provide hourly updates as it saves a lot of resources. as a solution for the aggregation of data 

With any solution for data aggregation, you will need an importing tool to bring the data from different sources into one place for analysis. With, you can do that quite easily and set up an automated upload schedule to make sure you’re not wasting your time manually exporting data. allows you to not only import raw data, but also import already aggregated data into your preferred system or perform data stitching on the data from the same or different apps. You can schedule automatic imports of P&L reports from a billing service, such as Xero or QuickBooks, summary reports from Clockify, and time reports from Harvest to later use in more comprehensive reporting. supports 60+ data sources that you can export to three destinations: BigQuery, Google Sheets, or Excel. The list of currently supported integrations includes Clockify, Jira, Slack, Shopify and several others. It also provides a JSON as a source, so that you can connect to JSON APIs and get data without any coding skills. 

This way, you’ll have a data aggregation solution that does not require SQL skills or hosting a database.

Data aggregation examples

Business data aggregation can serve any company from a small ecommerce store to a large corporation. Let’s look at two aggregation examples that are probably the most common. The odds are you can use both to benefit your business.

Sales data aggregation

Monitoring sales data is one of the most important areas of any business. Creating a basic sales dashboard with Google Sheets, BigQuery, or data visualization tools like Data Studio is a fairly simple process that only takes you a little time to set up and will work without fail, provided you don’t need rapid scalability.

For example, here’s the sales overview dashboard from the team that aggregated all sales data into a common view and makes it easier to understand the big picture.

Sales dashboard Overview 2023 04 12 13 21 01

The first step to building such a dashboard is importing sales data from the platform you’re using. With, you can automate that import and have it upload data from Shopify or Pipedrive into a spreadsheet.

That document can have dozens of columns with data about your leads including the country of origin, lead ID, sales sum, etc.

Now, this endless data can be aggregated into easy to understand statistics, the most useful being a breakdown of sales numbers by country and providing gross revenue numbers. Here’s how a simple data aggregation with Google Sheets will look.

Those numbers can be easily visualized for reporting with multiple charts offered by Google Sheets. Check out the details of how we’ve built this sales dashboard in Google Sheets.

The beauty of using Google Sheets for making such a dashboard is that you can easily add another sales source to bring all of your sales data together. For instance, you can be running an Etsy shot along your Shopify website. You can import Etsy sales data to another worksheet in your dashboard and add aggregate numbers from that sales source to the dashboard to compare sales across platforms and see total sales numbers.

Marketing data aggregation

Marketing is another sphere of business operation that works with large data sets and needs your constant attention. The situation is made even harder to track if you’re using multiple ad channels, which many companies do.

Uploading data from all of your marketing channels into Google Sheets for further analysis is a great solution for small to medium companies. If your marketing channel doesn’t have a native integration with Google Sheets, you can use to import it via CSV or JSON.

With all the data in one place, you can create a dashboard that aggregates key metrics from all sources for comparison. Here’s an example of how HubSpot data can look in a marketing dashboard.

And if you’re looking for a more visual dashboard, head over to Looker Studio and connect your Google Sheets files or a number of other sources. Here’s for example another dashboard from the team that aggregates data from Google Ads and LinkedIn Ads, and displays them side by side.

Paid Ads Performance by Campaign 2023 04 12 13 31 34

If you could use such a dashboard but lack the expertise or time to build one, get in touch with data experts. We can prepare any custom data project for you, be it visualizations, automations, data aggregations, and much more.

How to set up a data aggregation process

Now that you know what data aggregation can look like, let’s see how you can implement data aggregation for your business.

Basic data aggregation methods

To set up a basic data aggregation system, you need to know three things:

  • What data to import
  • What metrics you want to see
  • How to visualize data effectively

What data to import

The first step is the easiest one. If you’re using Google services for data aggregation, you may find that many platforms already have a native integration. For others, you can use an importing solution that can access data by API and upload it to Google Sheets or a BigQuery database.

If you’re shooting for scalability and have the resources to allow large storage, import as much data as you can. When you have more resources available for analysis, you will already have gigabytes of historical data to comb through. If you’re only going for basic data aggregation, you can import just the key metrics that you want to see in a report.

What metrics you want to see

Knowing what metrics you need in a dashboard is a bit more complicated. With most platforms, there is an overabundance of data. However, analyzing every bit of it is impractical for smaller businesses. For a basic sales report, you’ll need at least these metrics:

  • Customer ID
  • Date and sum of the sale
  • Country of origin
  • Platform
  • Lost/won indicator

How to visualize data effectively

On the basis of those metrics, you can calculate the sum total of sales over any given period of time or across regions, the comparative effectiveness of sales channels, and other basic metrics. You can use pivot tables or Google Sheets formulas to do that.

The last step is to visualize that data for easy reporting. This can be done with any chart provided by Google Sheets. Timeline charts are great for historical data, and you can use simple scorecards for displaying total aggregate values. You can also make visualizations that check whether your sales numbers are in line with KPIs.

Data aggregation vs data mining

The main difference between data aggregation and data mining is that data mining is a much more complex and technically involved process. Typically, data mining is used by larger businesses to discover trends in large data sets, sometimes involving machine learning. Data mining is also more focused on making predictions or educated guesses about user or system behavior based on that data.

Meanwhile, data aggregation is a much simpler way of analyzing data and can be used by smaller organisations to make monthly reports based on data from different sources.

Advanced data aggregation techniques

When you progress to working with more complex data analysis in BigQuery or any other SQL database, the possibilities are endless. One of the first things you may want to implement is real-time reporting. This can be done by connecting APIs of your data sources to the database, but this tends to be resource heavy.

Running advanced aggregations can produce better insights than just looking at aggregate sales numbers. For instance, if your data source does not mark customers as new or existing, you can easily gauge that by checking for customer IDs that only appear once in the database. This can generate a report on how much of your revenue comes from one-time buyers versus repeat customers.

The best use for advanced data aggregation, however, is looking for trends in data across different platforms and sources. This can be comparing monthly sales with a sales department budget to calculate ROI more effectively, or comparing sales numbers with ongoing marketing campaigns to gauge their effectiveness.

How to aggregate data – recap

Data aggregation may sound intimidating for a smaller business, but you don’t need to be a large corporation with millions of dollars to spare on this. You can start really small to look at the most basic aggregate data. This does not require any coding skills or knowledge of databases and costs little to nothing.

Whether you’re looking for a reporting solution or want to find insights in customer data, data aggregation is the way to go. You can explore this topic further and learn in more detail how to combine data from different sources.

  • Elvira Nassirova

    Lead Analytics Engineer at Railsware and I make data accessible and useful for everyone in a team. During the last 7 years, I built dashboards, analyzed user journeys, and even caught criminals 🕵🏿‍♀️ Besides data, I also like doing yoga, cooking, and hiking 🤗

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