What is Data Aggregation?
December 20, 2020
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 data aggregation 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 softwares.
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.
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, aggregate 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.
Manual data aggregation vs. automated data aggregation
As you can probably imagine, manual data aggregation is generally much more time-consuming than its automated counterpart. Manual data 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.
Data aggregation in the financial and investment sectors
Today, many finance and investment companies utilize alternative 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.
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 on your data in real-time. These tools can effectively “funnel” your data into any location you want (e.g., 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).
Time intervals for data collection and aggregation
There are three different time intervals during which data can be gathered and aggregated for statistical analysis. Here are these three intervals:
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.
Data aggregation is one of the most powerful methods of compiling data for statistical analysis. This process can help you, and your company gain 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.
Frequently asked questions
What are the two types of data aggregation?
There are two primary types of data aggregation: time aggregation and spatial aggregation. Time aggregation gathers data points for one resource over a period of time. Spatial aggregation gathers all data points for a group of resources over a period of time.
What is data aggregation used for?
Data aggregation gathers data to conduct statistical analysis and present data in summary form. Data aggregation enhances business operations and helps companies make better-informed business decisions.
Why is data aggregation important?
Data aggregation is important because it is an essential step in conducting high-quality and useful data analysis.
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