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. Data aggregation can be done on any scale from a small business to a large corporation that has terabytes of data to analyze.
Data aggregation meaning
Data aggregation in business involves pulling data from various sources and presenting tens of thousands of data points in a single statistic. Essentially, your business needs data aggregation to be able to see the larger picture — for example, monthly sales across different platforms.
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.
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.
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.
Data aggregation tools
Excel with its Power Query
The simplest data aggregation tool out there is Excel with Power Query. You can do a simple type of data aggregation by creating a column of aggregated data — read this guide on Excel Power Query to learn about it in-depth. The basic aggregations you can start with are sum, average, minimum and maximum values, and counting the rows of data.
You can also group data by several identifiers and aggregate specific data. For instance, you can group order data by the location or date and get columns of those aggregates.
Power Query can do a lot more than that — if you connect the apps and services you’re using to Microsoft Power BI, you can work with a consolidated Azure database. From there, you can aggregate data from multiple sources.
Google Sheets with its QUERY function
The prominent Excel alternative, Google Sheets, can also be used for data aggregation. You can use simple functions like =SUM or =AVG to see sums and averages, or more advanced functions to group these aggregates by specific columns of data. Check out this advanced guide on the Google Sheets Query Function to explore more possibilities.
This can be used to then run data visualisation with multiple tools available in Google Sheets. If you want to make your data aggregation a little bit more advanced, you can use Google Apps Script to automate most of the reporting. If not, you can just develop and use a couple of reporting templates to save time.
The best part of using Google Sheets for basic data aggregation is that you don’t need to pay a dime to test it, nor do you need advanced programming skills to set up the basics.
Another tool offered by Google is BigQuery, a data warehouse that supports both standard and legacy SQL. It’s a good solution for those who need to store huge amounts of data and use machine learning in analytics.
BigQuery allows for much more than standard data aggregation with Google offering AI and ML solutions for this tool as well as handling all data warehousing itself. If you’re operating with multiple terabytes of data, it can save significant resources for your organization as you won’t have to worry about the logistics of storing that data. On the flipside, BigQuery tends to be rather costly. However, you can pay by usage instead of a flat rate monthly payment. For details, check out our blog post on BigQuery Cost.
The good news is, you can use it for free as long as you’re working with under 10 GB of data in storage. A small business can use all of the functions of BigQuery without paying anything for data storage or processing.
You can even take advantage of its function without coding by connecting a BigQuery database to a spreadsheet and creating pivot tables or data visualizations. When you do use code, keep in mind that Google’s version of SQL and the SQL every other database is using may be different so importing code without modifying it isn’t a good solution. Learn more about the BigQuery SQL.
Any SQL database
Alternatively, you can use any type of SQL database or data warehouse for data aggregation. Normally, these are used by large corporations that operate with petabytes of data.
The upside of using a database is freedom as you are in total control of all your data and can even host your databases on company servers. However, with freedom comes responsibility. Maintaining servers, either in-house or a rented VPS, and managing all that data by yourself can become a large financial burden.
Coupler.io as a solution for data aggregation reports
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 Coupler.io, 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.
Coupler.io 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.
Coupler.io supports 15+ 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.
Python Pandas or R dplyr
If you have an IT department and have the resources to spare on data aggregation, you can use other programming languages instead of SQL for this purpose. The best solutions would be Python with Pandas library or R with dplyr.
These can allow for more flexibility with the backend that you’re using and can be used with most SQL databases. However, it’s a solution best used for larger companies or those that have a need for their own data storage and have the resources to support it.
For most small to medium businesses starting out with data aggregation, the easiest solution would be to start with Google Sheets and progress to BigQuery. This path will take the least resources and provide an initial understanding of how data aggregation can serve your business needs.
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 or BigQuery is a simple process that only takes you a little time to set up and will work without fail, provided you don’t need rapid scalability.
The first step is to import sales data from the platform you’re using. With Coupler.io, 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 Coupler.io 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.
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
- 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.
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.
Data aggregation is necessary for any business
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.Back to Blog