What Are Analytics in Business? (Definition and Examples)

Updated July 9, 2022

There are a number of ways to measure a business' success, such as analyzing its customer engagement, employee satisfaction, or revenue. These measurements all fall under business analytics. Knowing more about business analytics and the ways you can use them may help you analyze any business. In this article, we explain what analytics in business is, give you examples of business analytics, discuss the benefits and challenges of it, and answer frequently asked questions you may have.

What are analytics in business?

Analytics in business is the process of analyzing how well an organization is doing. Every company may have a different process, but business analytics include any technology, skills, or practises they use to analyze their business. Businesses use the results from their analyses to predict future trends or outcomes and make informed business decisions.

Related: Analytical Skills Defined and Explained

How do business analytics work?

Before a business starts an analysis, it typically determines what the objective is as it influences what method of analysis it uses. For example, if a business wants to decide whether to expand, it can use forecasting methods to assess how high its revenue may be in the future. Once they determine the objective, they choose an appropriate analysis method and move on to collecting relevant data.

Businesses collect data from a number of sources and typically start the analysis with a smaller sample. This allows analysts to look for patterns and relationships in the data, then refine their questions before moving on to more data. This process typically repeats until analysts achieve the business goal, then create a report with their results. A business then uses these results to make decisions.

Business analytics examples

There are four types of business analytics: descriptive, diagnostic predictive, and prescriptive. Descriptive analysis looks at historical data and key performance indicators (KPIs) to identify trends and patterns. Diagnostic analysis determines uses the same data to determine why something has happened.

Predictive analysis also looks at historical data and aims to make predictions about future outcomes. Prescriptive analysis is a combination of descriptive and predictive analysis. It aims to find a solution for past challenges using data from past and current performance. Within these four categories, there are many types of analytical methods. Here's an example of the components a business analytics dashboard may include:

Data aggregation

Data aggregation is when companies gather data from different sources and summarize them. They then perform data analysis using this summary. It's important that they collect high-quality data to achieve accurate results with their analysis. An example of data aggregation is when companies send surveys to past customers asking them about their experience.

Data mining

Data mining is the process of assessing data to find trends and establish relationships. Analysts use machine learning, databases, artificial intelligence, and statistics to find the necessary information. Many businesses use data mining, such as banks, insurance providers, retailers, or telecommunications providers.

Association analysis

This analysis involves finding relationships between items in large data sets. Analysts are usually looking for frequent item sets or association rules. Frequent item sets are a collection of items that usually occur together. Association rules are a strong relationship between two items. For example, a grocery store looking at the items people are purchasing may notice that many customers who frequently purchase diapers also purchase formula. They may use this information to put the diapers and formula together and increase sales.

Text mining

Also known as text data mining, text mining transforms unstructured text into structured data. Businesses use text mining to help machines understand the unstructured data, automating the process of analyzing the information. Automating the process allows businesses to analyze and understand large, complex data sets more efficiently.


Forecasting involves analyzing historical data from a certain time period, such as the last quarter or year. Analysts collect this data and create reports with it to predict certain future events. For example, if analysts notice that a company's sales have been low for the last three winters, they may advise the company to order less inventory this winter to minimize waste and save money.

Predictive analytics

Predictive analytics is a statistical technique that analyzes current and historical data to make predictions about the future. Predictive analytics look for patterns in the data to make informed predictions. Any industry can use predictive analytics, but meteorology, finance, security, insurance, logistics, economics, and marketing tend to use it the most.

Related: What Is Sports Analytics? (Definition, Importance, and Tips)

Data visualization

To make data clear to everyone, analysts use data visualization techniques. Data visualization presents information in an image or graph to allow for quick data analysis. This makes data easy for anyone, even those who don't have the necessary technical knowledge, to understand.


Once a company identifies trends and patterns and makes predictions, it can make informed decisions. Some businesses may use simulation techniques to test their ideas before implementing them. Others may just use the data and results they received to optimize their processes and policies.

Benefits of business analytics

To determine whether you want to start looking at business analytics at work, consider the following benefits of doing so:

  • Informed decision-making: The main benefit of business analytics is that it allows companies to make informed decisions based on their history and the current economy.

  • Increased revenue: When companies make informed decisions, they can typically increase their revenue. For example, if a business notices that customer engagement is low, they can launch a marketing campaign to increase it and increase sales.

  • Improved efficiency: Business analytics help companies learn from their past mistakes. This allows them to implement procedures and policies that are more likely to be successful, improving efficiency.

Related: How to Become a Business Analyst in 3 Easy Steps

Challenges of business analytics

It's also important to consider the challenges of business analytics, such as the following:

  • Too much data: There is a lot of data available on every platform, making it hard for analysts to find the exact information they're looking for. This can make business analysis time-consuming and expensive.

  • Not enough analysts: The need for business analysts is constantly increasing, but there may not be enough people to fill the roles. Larger businesses, especially, may have trouble finding the right candidates as they may require a team of analysts.

Frequently asked questions about business analytics

To better understand business analytics, consider the answers to the following frequently asked questions you may have:

What jobs are available that work with business analytics?

If you're interested in analytics, here are some of the roles and descriptions for you to consider:

  • Business analyst: Business analysts look at an organization's procedures and conduct extensive research and analysis to find ways to improve them.

  • Data analyst: Data analysts collect and interpret data for companies. They typically work with the company's stakeholders, helping them understand the data so they can use it to make informed decisions.

  • Research analyst: Research analysts collect and examine data to ensure it's accurate and meaningful. They typically work in marketing, finance, accounting, customer service, or economics.

Related: 11 Business Analyst Interview Questions (With Example Answers)

What's the difference between business analytics and data analytics?

Business analytics and data analytics are similar, as both analyze data to reveal certain trends and metrics. The main difference is that data analytics is a general term for any type of data, while business analytics focuses on information that can help businesses improve their processes. Data scientists, data analysts, and data engineers typically complete data analytics.

What's the difference between business analytics and data science?

Data science is a branch of science that studies data using technology, algorithms, and statistics. It involves more technical skills, such as coding. Data science is a superset of business analytics. This means a data scientist or someone with the necessary skills can perform business analytics, but a business analyst can't work in data science.

What's the difference between business analytics and business intelligence?

Business analytics and business intelligence also share similarities. Business intelligence focuses on descriptive analysis, while business analytics focuses on prescriptive analysis. Business intelligence aims to find out what has and hasn't worked for a business in the past to determine what areas they can improve. Business analytics focuses on predicting future trends to create solutions for any problems the business is facing. Businesses typically start with business intelligence to identify any issues before moving on to business analytics to solve them.

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