Data analytics types

The term data analytics refers to conversion of any form of raw data into useful or actionable insights. This has a wide tool range, including various technologies, as well as that are used for solving problems and finding trends using data. It can reshape businesses and its processes, help in improving decision-making, and also rapidly increase business growth.There are a total of 4 categories of data analytics that exist. They are predictive, descriptive, prescriptive and diagnostic analytics. In this article, we will discuss more about data analytics, its types, why we need data analytics and its understanding in depth.

What is data analytics?

Analyzing data to find answers, spot trends, and draw conclusions is known as data analytics. Business analytics is the term used frequently to describe data analytics applied in business.
To analyze data, you may utilize software, frameworks, and applications like Google Charts, Data Wrapper, Infogram, Tableau, and Zoho Analytics. They can assist you in exploring data from many perspectives and producing visuals that shed light on the narrative you're attempting to convey.
Data analytics also includes the use of algorithms and machine learning, which can acquire, sift, and analyze data more quickly and in greater volume than people. Although writing algorithms is a more complex data analytics ability, you may profit from data-driven decision-making without having a strong background in coding and statistical modeling.

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Why Data Analytics?

Data analytics are needed for applications in the business-to-consumer sector (B2C). The information that businesses get comes from their contacts with customers, other businesses, the economy, and real-world experience. Data is gathered, processed, and then classed as necessary before being analyzed to look at things like purchase trends.

Understanding the data analytics

The understanding of what data analytics is, is to get familiar with how data is evaluated in firms. The lifecycle of data analytics comprises a number of phases. Let's look at it using an analogy.
Imagine you own an online web store having a clientele of approximate 1M people. Your objective is to pinpoint particular business-related problems and then provide data-driven answers to support business growth.
The measures you may take to resolve the problems are listed below.
1. Understand the problem:
Understanding business difficulties, determining corporate objectives, and developing a lucrative solution are all part of the first step of the analytics. The challenges e-commerce companies face include predicting product returns, making helpful product recommendations, canceling purchases, identifying fraud, optimizing routing, etc.

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2. Data Collection:
You must then compile business data as well as client-related information for the last few years in order to address the challenges your firm is experiencing. The information may contain specifics like the quantity of a product that was sold overall, the revenue and profit margins, and the date. Historical data has a significant impact on a company's future.
3. Cleansing of Data:
Your entire data set will frequently be disorganized, have unwanted missing values, and be chaotic overall. Such data are neither pertinent nor suitable for data analysis. Therefore, you must clean the data to remove pointless, redundant, and lacking variables before you can analyze it.
4. Investigating and analyzing data
Doing data analysis is another important step after data collection. Use business intelligence tools, data mining techniques, and predictive modeling for analyzing, visualizing, and estimating the future consequences of this data. These methods allow you to ascertain the relationship and effect between a specific trait and other variables.
5. Interpret the results:
The outcomes are interpreted for determining whether they live up to the expectations or not. You could learn about future trends and occult patterns. You may learn from this to make knowledge-based, data-driven judgments.

Types of Data Analytics

1. Predictive Analytics:

Using predictive analytics, the data are transformed into useful knowledge. Predictive analytics utilizes data to estimate the chance of a condition arising or the likely course of an occurrence.
Predictive analytics combines a variety of statistical techniques from modeling, data mining, machine learning, as well as game theory for forecasting future occurrences. These methods assess both historical and present-day data. The techniques used in predictive analytics include:

  • Linear Regression
  • Analysis and forecasting of time series
  • Data Mining

The three main foundations of predictive analytics are as follows:

  • Predictive modeling
  • Optimization and decision-making
  • Profiling transactions

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2. Descriptive Analytics:

Descriptive analytics studies data and analyzes past events to discover how to approach future occurrences. By analyzing historical data, it examines prior performance in order to determine what caused past successes or failures. This type of analysis is employed in practically all management reporting, including those for sales, marketing, operations, and finance.

The descriptive model quantifies links in data to put consumers or prospects into categories. Unlike predictive models, which focus on predicting a specific customer's behavior, descriptive analytics reveals a range of interactions between the consumer and the product.

3. Prescriptive Analytics:

In order to produce a forecast, prescriptive analytics automatically combine large data, mathematical science, business rules, and machine learning. They then provide a choice alternative to capitalize on the prediction.
Prescriptive analytics not only recommends actions that will benefit from the forecasts but also discusses the implications of each alternative choice for the decision-maker. Prescriptive analytics considers why something will happen in addition to what will happen when and how. Prescriptive analytics may also explain the ramifications of each selection while offering alternatives for how to take advantage of a potential future opportunity or decrease a potential future threat.

4. Diagnostic Analytics:

In this study, historical data is typically preferred above other data when attempting to provide an answer or resolve a query. We look for any dependencies and patterns in the past data related to the specific issue.
For instance, businesses employ this analysis since it provides excellent insight into a problem. They also keep thorough records of their disposal since gathering data would otherwise be laborious and specific to each problem. Below are some typical techniques for diagnostic analytics:
  • Data discovery
  • Data mining
  • Correlations

5. Cognitive analytics :

Cognitive analytics combines several cognitive technologies, such as semantics, artificial intelligence algorithms, deep learning, and machine learning, to do some jobs with intelligence like that of a person.

Importance of Data Analytics:

Understanding trends and patterns from the vast volumes of data being gathered requires the use of data analytics. It aids in cost savings, audience understanding, future outcomes forecasting, and company performance optimization.

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Types of data analytics for companies

The most fundamental type of analytics, descriptive analytics, is used by 90% of firms today. Descriptive analytics may be summed up by saying that they provide an explanation for what has occurred. This sort of analytics analyzes historical and real-time data for ideas on how to approach the future. The main objective of descriptive analytics is to identify the factors that led to substantial historical success or failure. The word "Past" in this context denotes any particular point in time when an event occurred; it may have happened a month ago or even just now. The vast majority of big data analytics used by enterprises are descriptive.


Large volumes of data must be made sense of in order to identify patterns and insights that will boost corporate success. Unprocessed data is transformed into insightful information by data analytics. It includes a range of approaches, technologies, and instruments for using data to spot trends and solve problems. Data analytics may influence corporate procedures, enhance decision-making, and promote company expansion.

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As a content writer at HKR trainings, I deliver content on various technologies. I hold my graduation degree in Information technology. I am passionate about helping people understand technology-related content through my easily digestible content. My writings include Data Science, Machine Learning, Artificial Intelligence, Python, Salesforce, Servicenow and etc.

The following are the four main types of Data Analytics, often known as the four pillars of Data analytics. These are:-

  • Descriptive
  • Predictive
  • Diagnostic
  • Prescriptive

The Data Analytics tools are the software that helps collect and analyze business data to help make decisions. Multiple data analytics tools are available such as Tableau, Microsoft Excel, Power BI, KNIME, Apache Spark, Qlikview, Splunk, Rapid Miner, etc.

The primary purpose of using Data Analytics is to collect and analyze large sets of raw data to derive meaningful insights that help businesses make decisions.

The best example of Data Analytics includes the decisions we make in our daily life based on past and future happenings. We think and analyze what we did in the past and what we can do in the coming days, which helps us to make perfect decisions.

There is no need for coding skills to work with Data Analysis, but some data analysts use coding for some purposes.