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Data Analytics and its type

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  • Difficulty Level : Medium
  • Last Updated : 30 Sep, 2022
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Analytics is the discovery and communication of meaningful patterns in data. Especially, valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming, and operation research to qualify performance. Analytics often favors data visualization to communicate insight. 

Firms may commonly apply analytics to business data, to describe, predict, and improve business performance. Especially, areas within include predictive analytics, enterprise decision management, etc. Since analytics can require extensive computation(because of big data), the algorithms and software used to analytics harness the most current methods in computer science. 

In a nutshell, analytics is the scientific process of transforming data into insight for making better decisions. The goal of Data Analytics is to get actionable insights resulting in smarter decisions and better business outcomes. 

It is critical to design and built a data warehouse or Business Intelligence(BI) architecture that provides a flexible, multi-faceted analytical ecosystem, optimized for efficient ingestion and analysis of large and diverse data sets. 

There are four types of data analytics: 

  1. Predictive (forecasting)
  2. Descriptive (business intelligence and data mining)
  3. Prescriptive (optimization and simulation)
  4. Diagnostic analytics

Predictive Analytics: Predictive analytics turn the data into valuable, actionable information. predictive analytics uses data to determine the probable outcome of an event or a likelihood of a situation occurring. 

Predictive analytics holds a variety of statistical techniques from modeling, machine, learning, data mining, and game theory that analyze current and historical facts to make predictions about a future event.  Techniques that are used for predictive analytics are: 

  • Linear Regression
  • Time series analysis and forecasting
  • Data Mining

There are three basic cornerstones of predictive analytics:

  • Predictive modeling
  • Decision Analysis and optimization
  • Transaction profiling

Descriptive Analytics: Descriptive analytics looks at data and analyze past event for insight as to how to approach future events. It looks at the past performance and understands the performance by mining historical data to understand the cause of success or failure in the past. Almost all management reporting such as sales, marketing, operations, and finance uses this type of analysis. 

The descriptive model quantifies relationships in data in a way that is often used to classify customers or prospects into groups. Unlike a predictive model that focuses on predicting the behavior of a single customer, Descriptive analytics identifies many different relationships between customer and product. 

Common examples of Descriptive analytics are company reports that provide historic reviews like: 

  • Data Queries
  • Reports
  • Descriptive Statistics
  • Data dashboard

Prescriptive Analytics: Prescriptive Analytics automatically synthesize big data, mathematical science, business rule, and machine learning to make a prediction and then suggests a decision option to take advantage of the prediction. 
Prescriptive analytics goes beyond predicting future outcomes by also suggesting action benefit from the predictions and showing the decision maker the implication of each decision option. Prescriptive Analytics not only anticipates what will happen and when to happen but also why it will happen. Further, Prescriptive Analytics can suggest decision options on how to take advantage of a future opportunity or mitigate a future risk and illustrate the implication of each decision option. 
For example, Prescriptive Analytics can benefit healthcare strategic planning by using analytics to leverage operational and usage data combined with data of external factors such as economic data, population demography, etc. 

Diagnostic Analytics: In this analysis, we generally use historical data over other data to answer any question or for the solution of any problem. We try to find any dependency and pattern in the historical data of the particular problem. 

For example, companies go for this analysis because it gives a great insight into a problem, and they also keep detailed information about their disposal otherwise data collection may turn out individual for every problem and it will be very time-consuming.  Common techniques used for Diagnostic Analytics are: 

  • Data discovery
  • Data mining
  • Correlations
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