“Predictive analytics is a branch of business analytics that has to do with data mining, predictive modelling, statistics and forecasting market trends based on business operation and market history. Predictive analytics tools employ artificial intelligence, machine learning, process automation and built-in pre-defined algorithms to locate patterns and make informed predictions.”

To provide some background, let us discuss what ‘ANALYTICS’ means.

Analytics can be defined as “the methodical mathematical and statistical examination of business operations and data leading to pattern recognition and strategic knowledge creation.”
Business Analytics is the use of analytical methodologies to polish business operations and cultivate business intelligence.
The reach of business analytics extends to many departments of an organization. Every business operation conducted can be analyzed and hence, have scope for analytics.

Be that as it may, business analytics can be broadly categorized into four branches –

1. Decision Analytics – This branch of analytics supports human decision making with provision of mathematical and statistical facts.
2. Descriptive Analytics – Based on historical data, this type of analytics focuses on using comparative interpretation to draw conclusions.
3. Predictive Analytics – This branch of analytics deals with preempting problematic situations that are likely to arise in the future. Predictions related to the operations of an organization and competitive comparisons can help in sustaining performance quality and productivity. Additionally, it can also foresee marketing and purchase trends to help with sales and marketing strategy.
4. Prescriptive Analytics – These analytical models are built with the capability to recommend paths of action to enhance business operations.

But let us re-focus on predictive analytics tools – How do they work?

Modern predictive analytics tools are easy to use with interactive user interface and built-in shortcuts for analyzing, predicting and presentation. They are completely automated and rely on machine learning, quantifiable and contextual data, and semantic and syntactic recognition to find behavioral patterns and foretell the pattern likely to occur. They depend on pre-defined algorithms and logical models to measure and compare variable data fetched from a data warehouse or similar internal and external sources.

To simplify, the functioning of a predictive analytics tool can be broken down into six steps –
Project Definition

The initial decision to commence analysis has to be made by the organization. When this is done, analysts and data scientists can collaborate to set parameters for the project. These parameters could include final aims, deliverables, data extraction sources and level of details required. The parameters create an outline for the predictive analytics software, based on which, it can automate the remaining steps.

Data Extraction

From the accessible data sources, relevant data is fetched and organized systematically. If needed, predictive analytics tools can fetch data from structured, semi-structured, unstructured, or logically structured sources. The organization of all the information is done in a way that is suitable for analysis.

Data Analysis

Once the data is organized, it is segmented, examined, and sifted through to find valuable information. Patterns, correlations, and causations are found and highlighted to reach a conclusive end. This step is helpful to know why a certain prediction is being made.
Predictions are based upon trends that the software has picked up on during analysis. These trends could be behavioral, operational, or textual. If a certain action is recurring and directly causing another action, the predictive analytics tool is likely to mark it as important.
Cause and effect chains, clusters of activity, pain points, bottlenecks and deterrents are all marked by the software and used to investigate the future.

Predictive Model Creation

Utilizing the analyzed data, multiple models are created. Each of these models presents an alternative future and solutions that an organization could choose.
Predictive modelling is based on the mathematical concept that repetitive actions follow a pattern. Based on mathematical and graphical calculations, and historical data, the pattern that is likely to occur can be decided before time.


Once the analysis and model creation are complete, the predictive analytics tool outlines solutions to improve or avoid certain situations that are likely to occur. The organization can choose the most suitable solutions and adopt them in day to day operations as well as for strategy creation.

It goes without saying, that these models require periodic rectification. At any given time, there are numerous factors that could alter the results of the predictive model. While some of the factors can be controlled, there are also natural, economic, and social factors that cannot. Therefore, predictive analytics must become a continuous practice for organizations. Automated predictive analytics reduces the effort and time invested by analysts and makes their work much easier and more efficient.