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Predictive Analytics Tools and its Benefits | Complete Guide

Jagreet Kaur | 01 June 2023

What is meant by Predictive Analytics?

Predictive Analytics is the sub-branch of Advanced Analytics, the key area of Data Science that uses Statistical concepts to output an event's maximum likelihood. Mathematics and Statistics are the building blocks of Predictive Analytics. It looks into the historical data and finds the trends & patterns from it by using the statistics and building the strategy to apply new data to compute and predict the best possible outcomes.

It is based not only on statistical terms but also on data mining that provides Knowledge Discovery and Data-Driven features related to the prediction. You may also learn more about Cognitive Analytics in this insight.

Determine what will happen—how well a product will sell, who is likely to buy it, which marketing to use for the greatest impact. Source - The Future of Data Analytics

Predictive vs Prescriptive Analytics

Prescriptive Analytics is the next step to Predictive analytics. Prescriptive analytics is the branch of Data Analytics that uses Predictive Modelling to suggest the actions to get the optimal outcome. It relies on Optimization and Rule-based Techniques for Decision-Making. E.g., Forecasting the load on the energy grid over the next 24 hours is predictive analytics, while how to operate the power plant based on the forecast of energy load is Prescriptive Analytics.

Predictive analytics

  • Models specific features of a business.
  • Predictions of likely outcomes
  • Determine when it will occur.
  • The results are not actionable; they only point out the need for a decision to be made.
  • Tends to prioritize one function over others when optimizing it
  • The majority of the time, based on hypotheses using predetermined scenarios with limited options

Prescriptive Analytics

  • models the whole company
  • It Is entirely data-driven
  • Makes specific business recommendations
  • Reflects on interdependencies
  • It is not constrained by fixed rules.
  • brings about real, measurable advantages
  • supports hypothetical situations
  • Takes into account all the inputs, variables, and outputs
  • Uses calibrated, verified models that accurately depict how the business is run.

How does Predictive Analytics work?

It incorporates and performs the Knowledge Data Discovery steps and forecasts the maximum likelihood outcome as the prediction. It combines Data mining, Machine Learning to result in qualitative and quantitative predictions for the future. Each one of these is as important as others for estimating the outcome.

These are four necessary steps to be taken for reach Prediction -

  • Access and Explore Data- The necessary and relevant data acquisition is made from various data sources like sensors, databases, data lakes, etc.
  • Pre-processing and Aggregating Data- Here, the data results in the removal of messy data and is transformed into the required format, along with the selected features extracted.
  • Predictive Models Development- Statistical and Computational approaches are used to create the models and their experiments to optimize parameters used while training and monitoring the performance.
  • Integration with Real-Time Systems- Now, the integration of Predictive and Prescriptive analytics comes into the picture to generalize the Intelligent system that functionally works on the Predictive Building and behaves in the way as prescriptive implements.
Predictive Modeling helps to improve the claim process and detect fraud and provider payment abuse. Source: Insurance Analytics, Fraud Detection

What are the best techniques of Predictive Analytics?

It combines various data analysis techniques; likewise, data mining, machine learning, and many more. Listed below are the methods involved in analytics:

Decision Trees

A decision tree is an analytics technique based on Machine Learning that uses algorithms from data mining to predict the possible risks and rewards of pursuing several different actions. It is a visual chart that resembles an upside-down tree, which illustrates a decision's potential outcome. It can solve all classification problems and answer tricky questions when deployed for analytics.

Text Analytics

Text Analytics comes into use when an organization is willing to predict a numerical value. It is based on Statistical, Machine Learning, and linguistics techniques. It helps to predict the topics of a document, and also it examines words used in the given form.

Neural Networks

A process that describes task description, time requirements, Deliverables, and pitfalls. Download to explore the potential of Data Analytics. 

Benefits of Enabling Predictive Analytics

Tools are more reliable. Predictive analysis tools increase the efficiency of prediction. Instead of performing predictive analysis manually, predictive analysis tools are made more accessible. A few benefits of predictive analysis tools are the following:


Performing predictive analysis manually is time-consuming. Whereas tools will take less time. Just need to perform a few actions, and accordingly, the model will predict the result.

Essay to use

All users may not be familiar with the different techniques' models and algorithms. Such users can also use the tools to know about the business's future and make changes in the business.

Reduce error chances

Predictive analysis manually increases the chance of error at the analysis time, whereas everything is predefined in the tools that decrease the error chances.

One can only be aware of some techniques, but as all predictive analysis techniques are predefined with tools, the use of tools is easier for the user.

Predictive maintenance is critical in diverse application areas, such as manufacturing industry, information, and technology. Source: Predictive Maintenance Services.

Why it Matters?

The role of Analytics in Data Science is leveraging various new opportunities, mainly. 

  • Decision-Making and Intelligence
  • Fraud Detection
  • Optimizing the Market Campaigns
  • Optimize operations and results
  • Risks Analysis and Reduction

Where to use Predictive Analytics?

  • Segmentation
  • Customer Retention
  • Dynamic Pricing
  • B2B analytics
  • Customer Lifetime Values

Business use cases of Predictive Analytics

Advanced Analytics has become such a common word to be used by applying it in many fields and creating some specific domains -

  • Supply-Chain Analytics
  • Financial Analytics
  • Risk Analytics
  • Semantic and Sentiment Analytics
  • Workforce Analytics
  • Healthcare Diagnosis
  • Customer Analytics
  • Fraud Detection
  • Equipment Maintenance
  • Virtual Assistants
Enterprises reducing the need of manual decision making, make operational analytics actionable in real-time. Improve cross-department collaboration. Source: Enterprise Operational Analytics Services

What are the best tools for Predictive Analytics

Software Tools have advanced analytical capabilities like Text Analysis, Real-Time Analysis, Statistical Analysis, Data Mining, Machine Learning modeling and Optimization, and many more to add.

Libraries for Statistical Modeling and Analysis

  • Scikit-learn
  • Pandas
  • Stats model
  • NLTK (Natural Language Processing Tool Kit)
  • GraphLab
  • Neural Designer

Open-Source Analytical Tools

  • SAP Business Objects

  • Halo Business Intelligence
  • Daiku-DSS
  • Weka
  • R-Studio(R-Programming used)- the most demanding Statistical tool for Machine Learning
  • Apache Mahout (easy integration with Hadoop)
  • RapidMiner Studio
  • H2O.ai
  • Knime AnalyticsStatistica
  • Orange
  • Anaconda

Many tools are available in the market for predictive analysis. Some of them are discussed below-

IBM SPSS Statistics

It is one of the most reliable and most used predictive analysis tools. It has been around for a long time and offers a robust list of capabilities, including the Statistical Package for Social Sciences(SPSS) modeler. The cost pricing of IBM SPSS is also easily understandable. For the first-time user, the platform is complicated. That’s why the user needs to be experienced. A few key features are data preprocessing, advanced statistics, extract, forecasting, decision tree, neural network, direct marketing, etc.

SAS Advance Analytics

SAS is another useful analytics tool. It offers different incredible predictive analysis tools. These analytics tools could be used according to the purpose. A few key features are descriptive analysis, predictive modeling, code-based model development, dynamic group processing, and many more.

SAP Predictive Analytics

SAP Solution is good for predictive analytics. It is a product of SAP Software. The company provides different options available when it comes to features like SAS Advanced Analytics tools. It provides advanced machine learning and security features for analytics models. A few key features are Python API, data security, compliance features, automated data preprocessing, predictive modeling, native pyspark modeling, and many more. It supports datasets with more than 10000 columns.


It is one of the best open-source tools. It also provides the data mining feature that puts this tool on the top analytics tools list. Its high performance, affordability, and flexibility make it a renowned predictive analytics tool for users. H2O is used for expert data science and citizen data scientists. A few key features are automatic feature engineering, machine learning interpretability, natural language processing, automatic scoring pipelines, time series analysis, and many more.

Oracle DataScience

Oracle also entered the market of data analysis. They purchased a renowned startup company known as DataScience and expanded the tool DataScience. The DataScience tool is mainly used for organizations with the Oracle database and cloud service.


This tool is mainly developed to perform analysis in the market research field. The QResearch tool provides all the required facilities for better market analysis. It is a highly automated tool and needs less time for performing analysis. This tool can be integrated with R, the Microsoft Office product.

Java vs Kotlin
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The best practices of Predictive Analytics

The best practices for predictive analytics are listed below:

  • Validate the database set
  • Clearly define objectives
  • Identify high-quality data streams for its interface
  • Discover the best visual representation
  • Monitor the deployment and Performance

A Holistic Approach

It uses many techniques from Artificial Intelligence, Data mining but analyzes real-time data for future predictions and understanding of predictive analytics. You should review each of the following steps for better understanding: