Data Aggregation 101: What It Is and What It Can Do for Your Team
In today’s digital economy, data is invaluable. For a company to make informed, strategic decisions that spur growth, they need to analyze and understand their business, their customers, and how they work together. This is where data aggregation comes in.
Data aggregation isn’t always an overly complex process reserved for database architects or software engineers. If you’re a member of a sales team that relies on quarterly reports of sales data to guide prospecting, guess what? You’re using data aggregation!
In this guide, we’re breaking down all the basics to help make this topic more accessible.
What is data aggregation?
Simply put, data aggregation is the process of condensing large amounts of data into an easy-to-understand format.
Different types of businesses record different types of data from multiple sources – social media, newsletters, user devices, website traffic, and more – and store it in data warehouses. All this information is overwhelming to sift through, which is why it needs to be aggregated.
If you need to know how many sales were made in a specific country over the past 14 days, your company likely has the tools to easily pull a report displaying this information for you. This is data aggregation at work!
Different programs and tools can easily automate the process and pull information from countless data sets to give you information quickly and succinctly.
Why is data aggregation important?
Imagine trying to figure out how many sales your team made in the past two weeks without any data aggregation tools – that’s nightmare fuel.
Data aggregation is essential for any business that wants to scale. A lack of informed decisions will result in a lack of growth. You need to be able to analyze large amounts of data and apply this information and knowledge in a way that benefits your business.
Knowing what audiences to target and where to allocate marketing resources, for example, is easier when you have the proper data to back decisions. This is why data aggregation is so important.
A good data aggregation process will allow a company to do the following with ease:
- Track KPIs and set realistic future targets.
- Easily assess and visualize large data sets.
- Compare lead generation channels.
- Compare sales results over different periods of time.
- Plan targeted marketing campaigns.
- Measure a team’s sales performance.
- Uncover patterns of success or periods of underperformance.
- Make informed business decisions based on measured historical data.
Important metrics for data aggregation
Before you begin asking for aggregated data, you need to know exactly what you’re looking for.
An e-commerce business, for example, would record each sale it makes. This means potentially tens of thousands of purchase records, all including the date and time the sale was made, the total amount the customer spent, where the customer lives, the type of device they made their purchase on, how long their items sat in their cart before they were purchased, and more.
You wouldn’t need (or want to see) all these metrics at once – it’d be incredibly overwhelming – so it’s important to know what needs to be sectioned out in your report before the data is aggregated.
Before you set up or request any reports, you need to determine the metrics you want to analyze. These can include the following:
- Geographic locations.
- Purchase totals.
- A customer’s age or gender.
- Device types.
- Email open rates and clickthrough rates.
There are endless metrics to choose from, and you can run various reports depending on your needs.
Types of data aggregation
There are four main types of data aggregation: time, spatial, manual, and automatic.
- Time aggregation: This method collects data for one resource over a specific period.
- Spatial aggregation: This method collects data for multiple resources over a given period.
- Manual aggregation: As the name suggests, this method is done manually by employees and requires a lot of time and resources. It’s considered to be an outdated method and runs the risk of human error.
- Automatic aggregation: Different tools can be set up to run data aggregation automatically, which is the preferred method for real-time results and companies that deal with massive amounts of data.
Companies will use different types of data aggregation depending on their unique needs. However, automatic aggregation is becoming commonplace.
Data aggregation tools
The tools used by companies for data aggregation also vary. A small, independent retailer owned by an individual would conduct data aggregation quite differently than a large B2B or B2C company.
Let’s look at some common tools.
Microsoft Excel (or Google Sheets)
Spreadsheets are often used for manual aggregation and do simple functions such as sums, totals, averages, and breaking out data into charts and graphs for visual representations. This would be a good option for a small business to use.
Salesforce
Many sales teams use Salesforce as their primary customer management relationship (CRM) platform. In it, sales reps can run reports to aggregate customer data and keep track not only of sales, but active clients, renewals, and more granular information across accounts.
MySQL
MySQL is an open-source database management system, often used by larger companies to manage and store data. It requires specific knowledge to utilize and has many aggregate functions available that make dealing with large amounts of data quick and easy.
Amazon Web Services (AWS)
If you don’t want to host your own database, AWS is a good option to consider. This cloud-based platform has hundreds of features, including multiple options for database management and aggregation.
Aggregate away
Regardless of the type of work you do, data aggregation can help simplify important information.
Whether you’re looking at regional sales data, customer demographics, the average rate of subscription renewals, or measuring the reach of Facebook marketing campaigns, using these tools will create an accurate overview of your company’s performance. From there, you can set realistic goals and focus on future growth.
Remember, data doesn’t have to be scary.