Data Aggregation: Definition, Benefits, and Examples

Data aggregation is the process of collecting data to present it in summary form. This information is then used to conduct statistical analysis and can also help company executives make more informed decisions about marketing strategies, price settings, and structuring operations, among other things.

Data aggregation is typically performed on a large scale via software programs known as data aggregators. These tools spread a company’s data to several different publishing websites such as social media platforms, search engines, and review sites.

Data scientists and analysts are the most common users of data aggregation tools and data aggregation software.

Benefits of raw data aggregation

Working with vast amounts of raw data without a summarized format is quite difficult. It doesn't provide actionable insights and, therefore, doesn't help your business. Aggregated data, on the other hand, tells you a story in which you are the narrator.

Here is a list of benefits a data aggregator can offer you.

Extracts data that provides insights

Data collected and data analyzed are two very different processes. Not all your data requires analysis. Presenting aggregate data of the information that can impact your business operations is the thing you should go for. That's what data aggregators typically do—let you choose the data that you need to see.

Enables statistical analysis

There are three different time intervals during which data can be gathered and aggregated for statistical analysis. Here are these three intervals:

• Granularity

Granularity is the period of time over which data points for one or multiple resources are gathered for aggregation. This period can range anywhere from a few minutes to one month.

• Reporting period

The reporting period is the interval over which data is gathered to be presented. This period can include either raw data or aggregated data and can last anywhere between one day and a year. The reporting period also generally determines the granularity for data collection.

• Polling period

The polling period is the stretch that dictates the frequency with which data samples are collected. For instance, if a polling period for a given dataset is 5 minutes and the granularity is 10 minutes, then each data point is sampled twice over this period. In order to obtain the aggregated result, you simply have to calculate the average of all collected data points.

Improves marketing

Digital marketing relies on data. There's no doubt about it. Whether you're trying to get your website up in Google search or struggling to keep up with the conversion rates. 

Aggregating data that shows your rankings, user behavior, bounce rate, time spent, and a bunch of other metrics enables data-driven decisions and allows you to focus on what's really important. 

Google Analytics is likely the best data aggregator in terms of website metrics. Simply select the results that you want to see and its data aggregation process will do the rest for you.

Improves sales

Sales data is ripe with atomic data records. Atomic data represents the lowest level of detail, i.e., an item sold instead of monthly or quarterly sales summaries. 

For a sales team of a large-scale business, thousands of such sales data records definitely wouldn't help gather insights. 

Aggregated data, however, that shows a sum of total items sold in a quarter is another story. In other words, data aggregation is crucial to improving business intelligence.

There are various other benefits that can enhance your data analysis and provide new insights, but the key takeaway is this: data aggregation is crucial for data analysts.

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Types of data aggregation

There are a few types of data aggregation: time, spatial, manual, and automated being the most common.

Time and spatial aggregation

There are two primary types of data aggregation: time aggregation and spatial aggregation. The former method involves gathering all data points for one resource over a specific period of time. The latter technique consists of collecting all data points for a group of resources over a given time period.

Manual and automated data aggregation

As you can probably imagine, manual aggregation is generally much more time-consuming than its automated counterpart. 

Manual aggregation typically involves clicking an “export data” button, reviewing information in an Excel spreadsheet, and reformatting this file until it resembles other data sources. Manual aggregation can sometimes take up to several hours or even days.

Fortunately, companies today can use third-party software - occasionally known as “Middleware” - to automatically export and aggregate data in just minutes. DataView360® is an example of such a software tool that is also utilized for risk management purposes.

Top 3 data aggregation tools

There are countless aggregation tools and you most likely use at least one of them even if you're not aware of it. Here are 3 most popular aggregation tools:

• Microsoft Excel. Data stored in Excel can provide statistical analysis for entry-level analysts. You can enter different formulas and let machine learning algorithms aggregate the data for you.
• Google Analytics. Data from multiple sources can be combined, visualized, and analyzed. You can even add additional dimensions and metrics for further analysis. It allows you to gain insights into the performance of your websites and predefined goals.
• Salesforce. You can aggregate your existing customer data with various filters on a large-scale basis to keep track of as much data as you need. You can use the aggregation service to change the granularity of the data and create convenient summary statistics.

Examples of data aggregation

Businesses frequently gather large amounts of data about their online customers and other website visitors. In the case of any company that sells a product or service online, aggregated data might include statistics on customer demographics (e.g., gender, average age, location, etc.) as well as behavior indicators (e.g., average number of purchases or subscriptions).

An organization’s marketing department can use this aggregate data to optimize customers’ digital experiences through personalized messages and other similar strategies.

Data aggregation in the financial and investment sectors

Today, many finance and investment companies utilize public web data to advise their clients on important decisions and make predictions on market trends. Much of this information is derived from news articles on stock market variations and other relevant industry trends.

Financial services firms use data aggregators to issue daily, quarterly, and annual reports that contain detailed analyses of industry events. Data aggregation thus saves investment executives a significant amount of time they would otherwise spend browsing each individual news outlet manually.

Manual data aggregation can also sometimes be ineffective because missing information may lead to unreliable datasets.

Data aggregation in the travel industry

There are several objectives to utilizing data aggregation processes in the travel industry. These include acquiring market knowledge, competitor research, price monitoring, and customer sentiment analysis.

Travel companies can also use data aggregation to select images for the services listed on their websites or to view and analyze trends and information on property availability and transportation costs.

Firms in this industry also typically need to remain informed about which destinations are the most popular each season (these can change from one year to the next) and which demographic groups to target in travel ads.

Automated data aggregation can help simplify the process of collecting all of this information.

Data aggregation in marketing

Why is data aggregation important in marketing? It allows to combine data and create a marketing dashboard that's a lot more comfortable to use and analyze. It also enables data-driven predictive analytics as to what content unit's optimization would bring the most value.

Data aggregators work to provide marketing teams with critical insights that would probably be missed with aggregation that is performed manually. You are responsible for representing the entire organization online. Therefore, automated aggregation is the process that will help you keep the representation at higher levels.

Levels of data aggregation proficiency

Many data experts agree that there are three distinct levels of data aggregation: beginner, intermediate, and master. Here is a close look at each of these levels.

Beginner at data aggregation

Beginner-level data aggregation typically involves observing your marketing platforms to observe their traffic rates. Specifically, you can calculate and assess key metrics such as lead conversion rates, bounce rates, exit rates, click-through rates, and average cost per lead.

You can then utilize these values to devise strategies to help improve your customers’ online experience with your brand.

However, you will likely still be missing a significant amount of relevant data, which means your business decisions will only be mildly informed.

Intermediate at data aggregation

This level of data aggregation can include the use of a spreadsheet to record and monitor data. This type of document is typically updated daily, weekly, or monthly.

A spreadsheet can help you gain valuable insights into how your marketing campaigns are faring.

Of course, creating and updating this document takes time, so be prepared to devote ample resources to this project.

Master at data aggregation

Once you have fully understood how to use spreadsheets, dashboards, APIs, and other marketing tools, you can become a “master” at data aggregation by automating this process.

Many third-party software programs designed for this purpose allow you to view insights into your data in real time. These tools can effectively “funnel” your data into any location you want (e.g., data warehouse, storage devices, other spreadsheets, visualization tools, etc.).

They can also help you save time and devote more energy toward reducing costs and increasing return on investment (ROI).

Final thoughts

Data aggregation is one of the most powerful methods of compiling data for statistical analysis. This process can help you extract data that provides valuable insights into the effectiveness of your products, services, or marketing campaigns.

Depending on your objectives and the industry your organization is in, different reporting and polling periods may be chosen. However, automated data aggregation is generally considered to be more efficient than its manual counterpart.

Data aggregation can ultimately impact nearly every significant aspect of your business operations. Therefore, it’s important to choose your data collection and aggregation methods wisely.