Beginner’s Guide To 4 Types Of Business Analytics


The optimum utilization of data with analytics is helping organizations scale their business to the next level. With data being the new currency, more and more companies are becoming data-driven. Data analytics help organizations understand their consumers, enhance their advertising campaigns, personalize their content, and improve their products to meet the desired goal. While raw data have immense potential, you cannot leverage data’s advantages without the proper data analytics tools and types of analytics processes. As a Business or Data Analyst, you need data analytics to maximize your efforts to grow a business and achieve its goals.

In this article, we’ll break down the four types of business analytics for you. Before going into the details of the different types of analytics, let us understand what data analytics is.

What Is Data Analytics?

Data Analytics refers to the process of analyzing datasets to draw out the insights they contain. Data Analytics empowers Business Analysts to take raw data and reveal patterns to extract significant knowledge. Business Analysts use Data Analytics techniques in their work to make smart business decisions. Using Data Analytics in Business Analysis can help organizations better understand their consumers’ patterns and needs. Ultimately, organizations can use various types of data analytics to boost business performance and improve their products. 

There are mainly 4 broad categories of analytics. These different types of analytics used by Business Analysts empower them with insights that can help them improve business performance. Let’s take a detailed look at the four types of analytics.

  1. Descriptive Analytics
  2. Diagnostic Analytics
  3. Predictive Analytics
  4. Prescriptive Analytics

1. Descriptive Analytics

It is the most straightforward one in the top categories of analytics. Descriptive analytics shuffles raw data from various data sources to give meaningful insights into the past, i.e., it helps you understand the impact of past actions. However, these discoveries can only signal whether something is right or not without any clarification. Therefore, Business Analysts don’t prescribe exceptionally data-driven organizations to agree to descriptive analytics only; they’d preferably combine it with other types of analytics.

It is a significant step to make raw data justifiable to stockholders, investors, and leaders. This way, it becomes simple to recognize and address shortcomings that require attention. Data aggregation and mining are the two fundamental procedures in descriptive analytics. It is to be noted that this technique is beneficial for understanding the underlying behavior and not making any estimations.

Example of Descriptive Analytics

  • Traffic and Engagement Reports – to analyze and understand website traffic and other engagement metrics.
  • Financial Statement Analysis – Used to obtain a holistic view of the company’s financial health. 

2. Diagnostic Analytics

Diagnostic Analytics is one of the 4 broad categories of analytics utilized to decide why something occurred in the past. It is characterized by techniques like drill-down, data discovery, data mining, and correlations. Diagnostic Analytics investigates data to comprehend the main drivers of the events. It is useful in figuring out what elements and events led to a specific outcome. It generally utilizes probabilities, likelihoods, and the distribution of results for the analysis.

It gives comprehensive insights into a particular problem. Simultaneously, an organization must have detailed data available to them.

Examples Of Diagnostic Analytics

  • Examining Market Demand – Used to analyze market demands beforehand and meet the supply accordingly.
  • Explaining Customer Behavior – Very helpful in understanding customer needs and necessities and planning business operations accordingly
  • Identifying Technology Issues – Utilized to run tests and identify technological issues
  • Improving Company Culture – Ideally done by the HR department, the necessary employee data is collected to observe employee behavior.

3. Predictive Analytics

Predictive analytics is one of the four types of data analytics used by Business Analysts that determine what will probably occur. It utilizes the discoveries of descriptive and diagnostic analytics to distinguish groups and exceptional cases and anticipate future patterns, making it an essential tool for forecasting.

One of the primary applications of predictive analytics is sentiment analysis. All the opinions posted via online media are gathered and analyzed (existing text data) to forecast the individual’s opinion on a specific subject as positive, negative, or neutral (future prediction). Hence, predictive analytics comprises designing and validating models that render precise predictions.

Examples Of Predictive Analytics

  • Finance: Forecasting Future Cash Flow – Used to predict and maintain the financial need and health of the organization
  • Entertainment & Hospitality: Determining Staffing Needs – Used to fulfill the staffing needs based on the influx and outflux of the customers.
  • Marketing: Behavioral Targeting – Leveraging the data obtained from consumer behaviors for creating stronger marketing strategies.
  • Manufacturing: Preventing Malfunction – Used to predict a probable malfunction or breakdown and avoid the same to save time and money.

4. Prescriptive Analytics

Predictive analytics is the basis of these types of data analytics used in Business Analytics. Still, it goes past the other three categories of analytics mentioned above to recommend future solutions. It can recommend all favorable outcomes per a predefined game plan and propose a different course of action to achieve a specific result. Therefore, it utilizes a robust feedback system that continually learns and updates the connection between actions and outcomes.

Prescriptive analytics utilizes emerging technologies and tools, such as Machine Learning, Deep Learning, and Artificial Intelligence algorithms, making it modern to execute and oversee. Furthermore, this cutting-edge data analytics type requires internal as well as external past data to provide users with favorable outcomes. That is why Business Analysts suggest considering the needed efforts against a demanded added value before implementing prescriptive analytics to any business system.

Examples Of Prescriptive Analytics

  • Venture Capital- Investment Decisions – Often taken by gut feeling, these decisions sometimes can also be supported with necessary algorithms.
  • Sales: Lead Scoring – Used to analyze and predict the probability of a lead converting to a successful conversion
    Content Curation: Algorithmic Recommendations – Used to predict the creation of necessary content to keep consumers engaged and interested.
  • Banking: Fraud Detection – It is used to detect and flag fraudulent actions that might have occurred in banking transactions.
  • Product Management- Development and Improvement – Here, the necessary data can be collected and collated to derive necessary inputs regarding a product and its develop


Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics are the 4 types of analytics used by Business Analysts to unlock raw data’s potential in order to improve business performance. If you’re someone who loves to play with data and wants to build a successful career in Business Analytics, check our Integrated Program In Business Analytics (IPBA) in collaboration with IIM Indore. It is a 10-month-long online Future Leaders Program aimed at senior executives and mid-career professionals to help them give their careers a significant boost. 

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