What is Data Aggregation?

This article explains the benefits and different types of data aggregation and looks at some of the top tools available to help businesses with data aggregation. Data aggregation is the process of gathering raw data from one or more sources and presenting it in a summarized format for high-level statistical analysis.

For example, a business might aggregate a pool of 1,000 customers by their locations, by their purchases, or by their demographics—gender and age—to better understand customer behavior and buying decisions.

Aggregated data can be analyzed and reported on to provide insights about particular resources or resource groups, and data aggregation plays a role in both data analytics and Business Intelligence (BI).

Table of Contents

• The Process Of Data Aggregation
• Benefits Of Data Aggregation
• Who Uses Data Aggregation?
• Manual Vs. Automated Data Aggregation
• Top Data Aggregation Tools
• Bottom Line: Data Aggregation

The Process Of Data Aggregation

Data aggregation is done using data aggregation software tools that typically include features for collecting, processing, and presenting aggregate data.  Some perform highly specialized single tasks to meet specific use cases.

Aggregation is done on varying scales, and data can be aggregated over different time frames—for example, a business might gather data from a few hours of website traffic to monitor customer behavior, or from five years to show long-term trends.

Data aggregation is a three-step process:

1. Collection: Data aggregation tools extract data from one or multiple sources, storing it in large databases or data warehouses as atomic data. Atomic data is the smallest unit into which data can be broken down.
2. Processing: Extracted data is processed by the database, aggregation software, or middleware. In this step, the data is “cleaned”—errors are corrected, formatting rules are applied, and garbage data is discarded.
3. Presentation: The aggregate is then presented in a readable form, such as charts or statistics, to be presented to non-technical users.

Benefits Of Data Aggregation

Data aggregation can benefit businesses in multiple ways. Here are a few of them:

Provides Insights Through Extracted Data

Data collection and data analysis are separate processes, and data does not always need analysis. Sometimes aggregate data can reveal information that can lead a business to make a change.

Enables Statistical Analysis

Aggregation facilitates the statistical analysis of large amounts of data which can lead to the discovery of patterns and trends.

Improves Data Quality

Data aggregators automatically collect, clean, and summarize data that can be shared for collaboration. Data aggregators can reduce labor, communication overhead, save time, energy, and money.

For more tools for data quality: Best Data Quality Tools & Software

Leads to Better Decision-Making

Data aggregation collects and summarizes information across the entire company, allowing a business to see the big picture.

Who Uses Data Aggregation?

Data is a vital source of information for all industries and at all levels. Businesses rely on data analysis for everything from price-setting and product development to planning, hiring, and marketing. Here are some of the ways data aggregation can be used across different industries.

• Marketing teams can aggregate data from campaigns and the different channels they use to reach customers. They might aggregate data from a specific campaign to see how it performed over time, and then aggregate the results of that campaign with others to see how it compares. Or they might aggregate data from each specific campaign and compare them to each other to see how they are being received across channels, populations, and cohorts.
• Websites, particularly content-driven sites, can aggregate visitors by location, time of visit, time spent, and which content was popular and which was not to understand the behavior of site visitors over time and improve engagement and retention.
• E-commerce sites can aggregate data from a time of day to understand peak and low visits, visitor demographics, the number of transactions, and whether customers made purchases based on recommendations. They can also aggregate data about competitors—like product offerings, promotions, and prices—to learn what they’re up against.
• Because the news can drive stock market activity, financial and investment firms are becoming more dependent on news-related data. Aggregating headlines and article copy can provide data for predictive analytics.
• Despite the tight burdens of such regulatory compliance as HIPAA, even the healthcare industry can benefit from aggregation—especially case analysis. By aggregating the data of numerous similar cases, medical experts can come up with more effective treatment methods to accelerate overall healthcare treatment.

Manual Vs. Automated Data Aggregation

Manual aggregation is, as the name implies, the process of gathering and extracting data and aggregating it manually—in a spreadsheet, for example. This can be a time-consuming process with a high risk of errors, which means it is better-suited to small amounts of data. It also has the potential to overlook some data.

Automated data aggregation is the process of using tools to extract and aggregate data from sources automatically. This can be done on a recurring basis at regular intervals.

Given the enormous amounts of data that businesses are capable of gathering, manual aggregation is increasingly less feasible than automation through data aggregators.

Top Data Aggregation Tools

Here are some of the most widely used data aggregation tools:

1. Microsoft Excel

Excel is a hidden gem of analytics with remarkable power and capability to load from data stores, clean and process data, and generate reports. It is often the entry-level tool for people new to analytics, and there are plenty of web resources on the subject.

2. Cloudera Distribution for Hadoop (CDH)

CDH is aimed at enterprise-class businesses with an emphasis on big data. The open source tool has a free platform distribution that encompasses Apache Hadoop, Apache Spark, Apache Impala, and more technologies, all for collecting and processing huge amounts of data. There is a commercial version as well.

3. MongoDB

MongoDB is a NoSQL, document-oriented database that comes in three “flavors”: free-to-use, small-to-medium business, and enterprise. The open source tool supports multiple operating systems, and its main features include aggregation, ad hoc queries, indexing, replication, and sharding—a means of splitting large datasets into manageable chunks.

4. Sisense

Sisense is a popular software package for preparing, analyzing, visualizing, and organizing data for a business, and is designed to address inquiries directly through a single channel as well as gather and present data as relevant insights through interactive dashboards.

5. Zoho Analytics

Zoho Analytics is a popular BI, data analytics, and online reporting tool for creating data visualizations and generating actionable business insights. Zoho Analytics is designed to be used by anyone regardless of their technical skills.

6. DbVisualizer

DbVisualizer is a feature-rich database management tool for consolidating and visualizing information from across multiple database applications. Developers and analysts can manage multiple databases and configure tables with the software’s drag-and-drop interface, and it also comes with an advanced SQL editor to write SQL queries.

7. Google Looker

Looker, which was acquired by Google, is a cloud-based data-discovery platform that provides companies with real-time access to relevant data to make better business decisions. Primarily a BI platform, it allows users to explore and transform data, but also to create reports and make them accessible to everyone.

8. Stata

Stata is a data analysis and statistical software solution designed and developed specifically for researchers from different disciplines, ranging from epidemiology to political science. It offers a point-and-click graphical user interface, comprehensive statistical tools, command-line features, complete data management capabilities, and publication-quality graphs.

9. Alteryx

Alteryx is focused on what it calls analytic process automation (APA), which unifies analytics, data science and machine learning (ML), and business process automation into one end-to-end platform to accelerate digital transformation, and is designed to be used by non-technical staff.

10. IBM Cloud Pak for Data

IBM Cloud Pak for Data is a fully integrated data and AI platform that modernizes how businesses collect, organize, and analyze data, forming the foundation to apply AI across their organizations. Built on Red Hat OpenShift, and available on any cloud, it is designed to help companies accelerate and manage the end-to-end AI lifecycle.

11. GoSpotCheck

GoSpotCheck is one of the top data collection tools for businesses that depend on gathering field data. It collects data in real-time and analyzes it instantly to help users complete tasks right then and there. It is mobile-based with built-in content collection and distribution that makes sharing information with the rest of the team easy.

12. Repsly Mobile CRM

Repsly Mobile CRM is a premier all-in-one field management CRM tool. It is a cloud-based field service software for mobile sales teams and fits SMB needs and covers a range of different industries. It provides a detailed customer history, data collection with fully customizable mobile forms, and real-time visibility into how a company’s brand and their competitors are presented at retail.

For data analysis software: Top Data Analytics Tools

Bottom Line: Data Aggregation

Data aggregation is a means of gathering raw data from multiple sources and simplifying it for statistical analysis and reporting. Aggregated data can be analyzed and reported on to provide insights about particular resources or resource groups, making it a vital source of information for all industries and at all levels.

For more information on big data: Big Data Trends and The Future of Big Data

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