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Difference Between Business Analytics and Predictive Analytics
  • Last Updated : 30 Apr, 2020

Business Analytics: Business Analytics is a branch of Business Intelligence which primarily focuses on the capacity to gain an accurate and deep understanding of business performance based on data and statistical methods. It makes substantial and comprehensive use of analytical modeling and numerical analysis to urge accurate decision making. It takes advantages of vast variety of tools and involves data analytics, big data processes and data mining to help managers make appropriate data-driven decisions.

Predictive Analytics: Predictive Analytics is a subset of data analytics which leverages a variety of statistical techniques and methods which include machine learning and data mining. It uses past and current data, processes it, and then gives predictions for the future and unknown events. It identifies various similarities and patterns, finds relationships among different dimensions or factors to assess the potential opportunities and risks.

Below is a table of differences between Business Analytics and Predictive Analytics:

S.No.Business AnalyticsPredictive Analytics
1.It processes raw data into valuable information so that people can use it directly.Raw data is first processed into structured data and then patterns are identified to predict future events.
2.It helps people to make decisions based on insights.It helps businesses in various fields to make decisions based on data
3.It is the interpretation of historical data to understand changes that occurred in a business and draws comparisons.It is the interpretation of historical data to predict unknown events by finding hidden patters by using complex algorithms.
4.It is used to know to know what had happened in the past of organisation’s operations.It is used to know to know what will happen in the future or unknown events.
5.Type of data used is traditional and structured data along with manageable data sets.It can use unstructured as well as complex data, or data which is either internal or external to the company.
6.Technologies include data mining, reporting, OLAP, dashboards, descriptive analysis, alerts, surveys etc.Technologies include predictive modeling, forecasting, statistical analysis and machine learning.
7.It helps to solve a business issue, which includes some assumption-making and being dependent on individual experience.It helps to detect complex patterns and creates a model that gives the better insights of what will happen in the future.
8.Using BA, people make better decisions with their insight, without the use of strong verification that they are correct.Using PA, the model itself tells you the optimal decision to make using the insights from the data.

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