What Is Aggregate Data? (In-Depth Guide With Examples)

By Indeed Editorial Team

Published 23 May 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data is a crucial commodity for many organisations as they look to utilise it to direct business decisions and conduct optimal strategies. There are many methods to combine data and different industries use data for many reasons. If you're looking to develop your technical and business knowledge, learning about aggregating data in a business environment can help bolster your resume and skill set. In this article, we discuss what aggregate data is and how it's useful, list some aggregation tools and provide examples of data collection use cases.

What is aggregate data?

Aggregate data is a broad term that refers to the compilation of data from multiple sources that companies interpret into summaries or reports. While data is usually numerical, it's possible to attach qualitative data from research, such as surveys, interviews or focus groups, into any report. Data aggregation helps organisations or departments create valuable analyses and insights to direct critical business operations or make necessary changes to a business model.

All levels of business can use data aggregation to their benefit to make use of any raw data they've collected. Once researchers and data analysts collect the relevant data, it's important they organise, segment and understand it to extract key trends, points or results. When collecting data, it's crucial to store as much information as possible that represents a diverse and varied data set that more closely reflects reality so that analysts can extract more accurate conclusions.

Data aggregation vs. data mining

The two terms data aggregation and data mining sound synonymous, but have a few key differences that separate them. Data mining is a highly technical and complex process that aims to extract information and data from user activities and other primary forms of research to create individual customer profiles for an organisation. Data mining is mostly used by large organisations due to its complexity and resource-intense nature.

Data aggregation focuses more on the analysis aspect and less on the data collection methodology. Many smaller organisations can utilise data aggregation by compiling it from secondary or public resources, eliminating the need for large-scale primary data collection through mass surveys, focus groups or clinical studies. Once a business aggregates all the data, they can focus on creating useful documents, such as monthly reports and statements or formulate actionable insights.

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How is data aggregation useful?

As technology evolves, so does the complexity of data. Data aggregation allows analysts, researchers and decision-makers to devise and execute more informed strategic decisions with numerical evidence and information. Here are a few other benefits of collecting and aggregating data for businesses of all sizes:

  • obtain a quantitative view of business operations, sales and trends

  • transform large amounts of data into more easily understandable information

  • compare different data points

  • create actionable business insights based on quantitative analysis

  • track key performance indicators across multiple platforms and channels

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Data aggregation tools

Organisations use a few tools to combine data depending on their needs. Some large corporations have specially built software or applications tailored to their needs, but many businesses use these basic tools:

Excel's power query function

Excel's power query function is a widely used tool for aggregating data as it's easily accessible and most organisations use Excel daily, so it's easier to learn. The power query function in Excel provides users with in-depth usability, but is also excellent for basic use cases. The basic aggregations you can perform on power query are sum, average, minimum and maximum values and counting rows of data. What's beneficial about using Excel's power query is that you can implement it into any spreadsheet document and customise its presentation.

Other benefits of power query include grouping data by filtering different identifiers and choosing specific data you want to aggregate. For example, if you wanted to group data according to date or location, you can format a new column to aggregate data into it. You can also connect power query to Microsoft Power BI, a data visualisation software, and work with Azure databases for ease of access to data.

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Cloudera Distribution for Hadoop

Cloudera Distribution for Hadoop (CDH) is an open-source program with free platform distribution that includes Apache Hadoop, Apache Spark and Apache Impala and other applications. This tool is ideal for big data uses and the aggregation of large amounts of data within an organisation, as it's designed specifically for enterprises with its cloud storage and integration with multiple applications. Many industries can utilise CDH, such as insurance, retail, telecommunications, healthcare and any sector that performs large amounts of data analytics.

MongoDB

MongoDB is a multifunctional platform that enterprises can employ as one of their primary tools for all processes related to data. Its products include a multi-cloud database platform, enterprise support software and data services for mobile and web applications. MongoDB is a common choice for any sized organisation looking to utilise data more and aggregate it into meaningful reports.

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3 examples of aggregate data

To better understand data aggregation and how it's used, here are three examples of how industries use data aggregation:

1. Travel industry

The travel industry has many elements that fluctuate frequently and change based on market and customer behaviour trends, such as flight pricing and availability. Many companies in this industry utilise this data aggregation to keep track of competitor pricing, offers and demographics or to gain market insight through studying trends and reports. Providing new and exciting offers backed by numerical data can help companies stay relevant to customers in an industry with a variety of choices. Here's an example of a travel company using data aggregation:

After analysing customer sentiment and seasonal purchasing behaviour, combined with growing trends on social media, a travel and accommodation company offers a special package to beach destinations shortly before summer. To capture their audience's attention, they film an enticing video advertisement on a famous beach and sponsor specific influencers to make content on beaches or near the ocean to encourage people to go.

2. Clinical trials

Pharmaceutical trials utilise this type of data frequently as companies and labs aim to create new medicines and treatments. Each trial undergoes a rigid scientific process, where each step carefully utilises research skills and numerical evidence to support claims and areas of further study. When researchers conduct their trials on human subjects, they look to collect specific metrics that may influence the trial, such as subject background, dosages, time or behaviour. Here's an example of how a pharmaceutical company uses data aggregation.

A pharmaceuticals company is conducting a trial to understand the effectiveness of their new drug in treating hair loss. They took results from 121 people in various stages of hair recession, ranging from 21 to 65 years old. When they aggregated their findings, they determined that their new drug increased blood flow to the scalp and stimulated keratin production in the hair. Because of these positive results, the pharmaceuticals company increases funding for further development of this new drug.

3. Retail industry

Many retail companies utilise data aggregation to analyse consumer trends and behaviours. They also commonly use data aggregation to monitor competitor pricing and market trends. It's important for companies to monitor their competitors' marketing and product choices to ensure they don't develop a competitive advantage. Here's an example of a retail company using data aggregation:

An e-commerce store that specialises in selling candles plans to expand into diffusers and incense. To justify their decision to expand into other product lines, they aggregated data from competitors in the field to determine their chosen price points for certain products. They then conducted research into how many of each product their competitors sold to determine customer sentiment towards these products. Upon aggregating this data, they concluded that the diffusers and incense markets weren't profitable enough to be worth the required investment and focusing on candles can help them maintain their profit margins.

Please note that none of the companies, institutions or organisations mentioned in this article are affiliated with Indeed.

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