Data Aggregation

Data aggregation is the process where raw data is gathered and presented in a summarized format for statistical analysis. The data may be gathered from multiple data sources with the intent of combining these sources to produce a summary of data for analysis.

Aggregation is the actual functional part of the more widely used terms, data analytics and business intelligence (BI). Analytics and BI are the task, and aggregation is the actual process or function. You can’t have analytics or intelligence without aggregation first.

Hence, there is no market for data aggregation, per se. The market for data analytics, though, is huge: Market Research Future (MRFR) predicts the global data analytics market will reach USD $132 billion by 2026.

In aggregation, raw data can be gathered and aggregated over a given time period to provide statistics, such as a high and low, an average, total sum and more. After the data is aggregated and written to a view or report, you can analyze the aggregated data to gain insights about particular resources or resource groups.

Who Uses Data Aggregation?

Anyone doing any form of analytics uses aggregation since the two go together. Different industries have different interests and outcomes, but they all have to process, present and analyze data.

In general marketing campaigns, data aggregation usually comes from your campaign and the different channels you use to market to your customers. You can aggregate your data from one specific campaign and look at how it performed over time and then aggregate the results of that campaign with others to see how it compares.

Ideally, though, you are aggregating the data from each specific campaign to compare them to each other — one grand data aggregation that tells you how your product is being received across channels, populations and cohorts.

* Websites, particularly content-driven sites, would aggregate visitors by location, time of visit, time spent, which content was popular and which was not.

* E-commerce sites would like a time of day for peak and low visits, age and gender of the visitor, number of transactions and whether customers made purchases based on recommendation. They would also gather competitive research, so they know what they’re up against. That means gathering information about their competitors’ product offerings, promotions and prices.

* Financial and investment firms are becoming more dependent on other sources of data, like the news. People may buy and sell stocks based on what is happening in the news, so financial firms can use data aggregation to gather headlines and article copy and use that data for predictive analytics.

* Even health care can benefit from aggregation, despite the tight burdens of regulatory compliance, such as HIPAA. Much of that comes from case analysis. By aggregating the data of numerous similar cases, medical experts can come up with more effective treatment methods to accelerate the overall health care treatment.

See more: What is Raw Data?

The Process of Data Aggregation

Aggregation is done on varying scales, from a few hours or minutes to much larger scales of days, weeks or months. The aggregation is done through software tools known as data aggregators. Data aggregators typically include features for collecting, processing and presenting aggregate data, and some perform a highly specialized single task.

When data is aggregated it is placed into what is known as atomic data, which means the lowest level of detail. For the longest time, this was a number, but it might be a price, numeric count, inventory, time of day, day of the week or any other data entry point, and the database was your typical row-and-column relational database.

However, things have changed in recent years, with the advent of non-SQL databases and non-traditional sources, such as social media, news feeds, personal data, browsing history, IoT devices, call centers, podcasts and so on.

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.

2) Processing: Once the data is extracted, it is processed by the database, aggregation software or middleware. This is where data is “cleaned,” where errors are corrected, formatting rules applied and garbage data is discarded.

3) Presentation: The aggregate is then presented in a readable form, such as charts and statistics, customized by the research team and made presentable to non-technical users.

Manual vs. Automated Data Aggregation

Aggregating data can be a decidedly manual process, especially if your company is in the early stages of accumulating data and learning the process of aggregation and automation. It also is a reflection of the control you have over the data collection. Manual aggregation means your data collection is done on your terms, whereas automation means you collect the data when it is scheduled.

Of course you can do both. Manual and automated data aggregation are possible based on your company’s requirements.

Given that terabytes and even petabytes of data can be involved, manual aggregation may less feasible than automation through data aggregators. Not to mention the possibility of human error.

See more: What is Data Segmentation?

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 aims at enterprise-class deployments with an emphasis on big data. CDH is totally open source and 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 written that comes in free-to-use, SMB and enterprise flavors and is open source, supporting a number of operating systems. Its main features include aggregation, ad hoc queries, sharding, indexing, replication and more.

4) Sisense

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

5) Zoho Analytics

Zoho Analytics is a popular business intelligence, 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 your own SQL queries.

7) Google Looker

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

8) Stata

A data analysis and statistical software solution designed and developed for 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 and business process automation into one, end-to-end platform to accelerate digital transformation and is usable 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 organization. It is built on Red Hat OpenShift and available on any cloud, and 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 your 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 your brand and your competitors are presented at retail.

See more: Best Data Quality Tools & Software 2021