Data Aggregation in 2023: Definition, Benefits, and Examples
Data aggregation involves gathering raw data from individual sources and expressing it in a simplified form for statistical analysis. Many companies use automated data aggregation to achieve specific business goals or conduct human and process analyses.
Table of Contents
Below we will define data aggregation and review some benefits and examples of data aggregation. We will also discuss how to use scraping tools and application programming interfaces (APIs) to perform data aggregation.
What Is the Definition of Data Aggregation?
In data analytics, what is data aggregation? It’s when raw data is gathered and expressed in a summary data set, spreadsheet, or view for statistical analysis. There are several types of data aggregation:
- Spatial aggregation is aggregating all data points for a group of resources.
- Time aggregation involves aggregating all data points for a single resource over a specific period.
- Manual data aggregation requires users to click an “export data” button, review data in an Excel spreadsheet, and reformat the file until it resembles other data sources. Manual data aggregation can take several days or weeks, depending on how much information a company has.
- Automated data aggregation is much less time-consuming than manual data aggregation. Automated data aggregation tools automatically export and aggregate data in minutes.
To save time, companies typically use automated spatial and time data aggregation tools. Here’s how data analysts and scientists perform data aggregation:
- Choose a suitable automated data aggregation software.
- Collect raw data from different websites, spreadsheets, or databases.
- Filter the collected data to remove errors, inconsistencies, and invalid values.
- Convert the filtered data into a format that makes aggregation easier.
- Merge the processed data into a single data set. This usually involves producing streamlined views, creating pivot tables, or calculating summary statistics.
Once data aggregation has been completed, companies can use the data to make business decisions or store the data in a data warehouse for future analysis.
Why Is Data Aggregation Important?
Data aggregation provides several advantages for businesses.
Improved marketing decisions
Marketing departments can use data aggregation’s actionable insights to better understand customers’ needs. This allows them to create better marketing campaigns and improve communications, products, and services.
Better business decisions
By gathering and summarizing data from different sources, data aggregation lets the C-suite see the big picture. It also allows them to identify patterns and trends in raw data sets, leading to better decision-making and more efficient operations.
Suppose an eCommerce company wants to improve its website user experience. By aggregating data from customer reviews, website analytics, Google Business analytics, and purchase histories, the company can identify which products are the most popular and which website features are causing customers to “bounce” or leave.
Website designers and marketers can use this information to optimize the website’s design, search functionality, and navigation. They can also use this data to personalize marketing campaigns and product recommendations based on customers’ behavior and interests.
Improved data quality and efficiency
By performing data aggregation, organizations can gather, clean, and condense data automatically. They can then distribute this aggregated data to various teams and departments. This minimizes manual labor and communication overhead, saving time, energy, and money.
Data aggregation also helps you comply with regulatory requirements like the European Union’s General Data Protection Regulation (GDPR) or California’s California Consumer Privacy Act (CPRA).
For instance, the GDPR requires companies to access multiple sources of data regarding a certain data subject. This requirement gives data subjects a wide range of rights, including the right to access information and the right to rectification and erasure. Data aggregation can help company staff quickly locate this information.
Examples of Data Aggregation
Data aggregation can be used in many industries, including:
- Big Data: Big data aggregation tools make it easier for data analysts and others to analyze large data sets and store them in a data warehouse for future projects.
- Healthcare: Medical facilities and practitioners use data aggregated from health tests, health records, and lab results to provide top-notch patient experiences and make treatment decisions.
- Finance: Banks, credit unions, and other financial data aggregation companies aggregate data from different sources to determine their customers’ creditworthiness. For instance, they can use data aggregation to assess whether or not to approve a credit card or loan. Financial institutions also use aggregated data to identify and analyze stock market trends.
- Marketing: Businesses use aggregated data from social media platforms, review websites, and company websites to monitor engagement, mentions, and hashtags. This gives them a clear understanding of how customers perceive their brand and what they can do to attract more leads. Businesses also aggregate customer and sales data to derive actionable insights for upcoming marketing campaigns.
- Travel: Travel agencies can use data aggregation for price monitoring, acquire market knowledge, research competitors, and analyze customer sentiment. They can also use data aggregation to view and analyze trends and information on transportation costs and property availability.
- Application monitoring: Software and applications regularly aggregate network and application data to monitor application performance, fix issues, and discover new bugs.
How To Perform Data Aggregation With Data Aggregation Tools and Analytics
If you are interested in reaping the benefits of data aggregation, follow these steps to master data aggregation.
Beginner-level data aggregation
Beginner-level data aggregation usually involves analyzing website analytics and marketing platforms to observe traffic rates. Pay close attention to the following key metrics:
- Lead conversion rates
- Exit rates
- Bounce rates
- Average cost per lead
- Click-through rates
You can then use these metrics to develop better marketing strategies and online experiences for leads. However, you are still missing a large amount of relevant data, so your marketing and business decisions are only mildly informed.
Intermediate-level data aggregation
Intermediate-level data aggregation involves using a spreadsheet to record and monitor data. Teams typically update the spreadsheet daily, weekly, or monthly, depending on their goals and industry.
Besides spreadsheets, you must learn to use dashboards and other marketing tools to master data aggregation. You should also start learning about data aggregation best practices.
Master-level data aggregation
After mastering intermediate-level data aggregation, you can learn automated third-party data aggregation software. This will empower you to gather and analyze large amounts of data quickly. Using automated software will also give you the following advantages:
- Fewer errors: Human error can be a major problem in beginner-level and intermediate-level aggregation if done manually. Automated programs can reduce inconsistencies and errors by following standardized procedures and rules.
- More accurate insights: Automated data aggregation apps can perform complex calculations and analyses that may be time-consuming or difficult for staff. This can lead to more reliable and accurate insights.
- Improved scalability: Manually aggregating data becomes more difficult as a company grows and collects more data. Automated programs can scale up to handle large amounts of data, helping companies gain insights and make informed decisions as they expand.
Use Scrapers and Scraping APIs for Data Aggregation
If you’re interested in mastering data aggregation, consider using web scraping APIs and web scraper bots.
A web scraping API creates an automated data pipeline between you and a specific part of a target site, letting you gather data manually or on an automated schedule. It does not extract data — it only gives you access to data that website owners want you to have. In contrast, a web scraping bot or web scraper extracts all of the content from a target website and stores it as a data file.
Each tool has slightly different use cases. Use a web scraping API when:
- You need to aggregate specific data types, such as weather or social media data, and you have access to a web scraping API that can provide that data.
- You need to aggregate data regularly and want to schedule the data aggregation process.
- You need to aggregate data from a large number of target sites and want to automate the process.
You should use a web scraper bot when:
- You need to aggregate data from target sites that do not allow automated access or provide APIs.
- You need to extract data that requires interaction with the website, such as CAPTCHA or clicking buttons.
- You want to avoid restrictions or limitations that a web scraping API may impose.
- You want full control over the web scraping and data aggregation process.
- You want to access data on sites in real time.
Scraping Robot API and Web Scraper
You can plug and play Scraping Robot’s API and use our scraper to complete your data aggregation projects in no time. Our scraper handles metadata parsing and proxies so you can focus on getting valuable insights and improving your business. It also provides the following features:
- Graphs of how many scrapes you’ve performed in the last month, week, or day
- Records of most recently used projects and modules so previous results are always at your fingertips
- CAPTCHA solving
- The ability to look for new anti-scraping updates from target websites
- 24/7/365 customer support
- New modules added monthly
- Frequent improvement updates
- 7-day storage
Create a Scraping Robot account today to receive 5,000 free scrapes. You can check out our Business and Enterprise tiers if you want more scrapes. Our Business tier gives you up to 500,000 scrapes at only $0.0018 per scrape, and our Enterprise tier provides over 500,000 scrapes at rates as low as $0.00045 per scrape. Enterprise customers also have the ability to make custom API requests.
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