What is Predictive Analysis? Why is it Important?

The applicability of Data Analytics has transformed how businesses conduct operations and increased profitability across sectors. Analyzing stored data helps better understand how a business will perform and positively affects the decision-making capabilities of any company. Different data analytics fields aim to increase efficiency by eliminating unwanted human labor, which can otherwise be channeled towards novel research-oriented work. 

You can study data analytics under four detailed branches:

  • Descriptive Analysis
  • Diagnostic Analysis
  • Predictive Analysis
  • Prescriptive Analysis

This article focuses mainly on predictive analytics. We will cover the working of predictive analytics and explore its applications in different industries. 

What is Predictive Analytics?

Predictive analytics is a branch of data analytics that predicts future outcomes of events based on past data and information. The results are calculated by using a broad spectrum of modern-day technologies that include various mathematical processes, statistical modeling, machine learning, data mining, big data, and a lot more.

Using predictive analytics, highly accurate predictions are made through multiple cycles of trial and error. The technique is used by businesses to get deep insight into future events to improve decision-making and facilitate maximized sales. 

Gaining detailed knowledge of predictive analytics is only possible with a solid descriptive, diagnostic, and prescriptive research background.

Understanding the Working of Predictive Analytics 

Predictive analytics works on the blueprint of leveraging historical data for uncovering real-time insights. It relies on the repetition of several steps in a cyclic order to increase the accuracy and viability of every predictive model.

Here are the steps involved in predictive analytics:

  1. Understanding a business 
  2. Analyzing business data 
  3. Preparing data for model 
  4. Preparing the model 
  5. Evaluation 
  6. Working on the flaws and repeating the entire cycle for accuracy 
  7. Deploying the final product

1. Understanding a Business

Understanding the demand before providing a solution for its supply is essential. Therefore, the first step involves gathering relative knowledge and information to chalk out a course of action. Next, you need to collect sufficient data for proper training of the predictive model and identification of predictive patterns.

2. Data Analysis and Preparation

You must analyze the data required to train the model. This means eliminating all unwanted information or noise and ensuring sufficient information for the flawless functioning of the model. 

3. Preparing the Model

This is the most crucial step. Here, you need to prepare the product according to the results of your research. The modeling is carried out using predictive analytic techniques like machine learning, big data, data mining, statistical analysis, etc. At the end of the training, the model will learn from the historical data and identify trends accordingly. 

4. Evaluation of the Model

By working with business analysts and executing trial runs, you can understand whether the model makes sense and delivers according to the needs of the business. This step is a must because complicated algorithms can lead to false predictions, negatively affecting the business. 

5. Checking Accuracy

You can evaluate the accuracy by retraining the model with data sets. This is a continuous process that will progressively increase the model’s efficiency based on the feedback received. 

6. Deployment

After a while, when the model reaches a specific efficiency level, it can be deployed for practical use in real-world situations to solve real-time problems.

Predictive Analytics: Prototype Models

Predictive analytics models form the base of data analytics. In addition, template and prototype models make it easier for users to convert current and past data into mathematically proven predictions that provide future insights. The different types of models used in predictive analytics include:

  • Predictive Maintenance Model: it predicts the chances of business equipment reaching its breaking points.
  • Quality Assurance Model: This model is skilled at predicting and preventing possible defects in a product, thus maintaining quality and providing customer satisfaction. 
  • Customer Lifetime Value Model: It shortlists the customers who are most likely to reinvest in the services and products offered by a company. 
  • Customer Segmentation Model: This model aims to segregate customers into segments based on similar purchasing characteristics and behavior. 

Techniques of Predictive Analytics

  • Decision trees: In simplest terms, this model places data in different segments known as ‘branches’, based on other variable parameters. Decision trees can be handled effortlessly and readily understood as data is extracted as per user requirements in a short period. 
  • Regression: This statistical system facilitates the determination of patterns in data sets and establishes formula-based relationships between the variables. 
  • Neural Networks: Developed by imitating the working of the human neural system, this model uses Artificial Intelligence to recognize patterns. It is highly effective when you have too much data to handle but cannot formulate a relationship between the inputs and outputs. 

Difference between Forecasting Data and Prediction of Data

Prediction and forecasting of data may sound similar, but there is a minute difference between the two. Data forecasting can be projected as a subset of predictive modeling. Prediction is more inclusive of statistical theories, whereas forecasting considers probabilities and time series analysis. To explain in a sentence, we can say that: “all predictions are not forecasts, but all forecasts are predictions.”

Predictive Analytics and Machine Learning

You might be wondering if machine learning and predictive analytics follow the same procedure to develop near-human precision models. Although the original idea behind these technologies is similar, a significant difference exists between them.

Machine learning is aimed at the complete independent working of a system and the elimination of any reliance on human interaction. It seeks to establish an autonomously operating ecosystem without the need for human intervention. 

However, predictive analytics is designed to be operated and modified with human experts in the loop, according to the needs of a company. Without human input, predictive analytics is a stagnant technology and cannot prosper. 

Predictive Analytics and Big Data

Big data has played a revolutionizing role in providing a structure and shape to predictive analytics. Analyzing gigantic volumes of data to leverage strategic decisions wouldn’t have been possible without the introduction of big data.

Applications of Predictive Analytics 

Predictive analytics has made its way into various industries across multiple disciplines. From marketing and insurance companies to restaurant chains, every sector has accepted this emerging technology with open hands. 

Some sectors where predictive analytics has facilities major development are: 

  • Investment: Investors leverage this method to predict the upcoming market movements to enhance their portfolios and save themselves from potential losses. 
  • Banking and Finance: Financial bodies like banks use predictive analytics to ensure client satisfaction and security. Banking-specific models are used for the identification of cross-selling opportunities and the detection of frauds. 
  • Energy and Utilities: Predictive analytics plays an essential role in the energy sector. Complex models are used for predicting the impact of weather change patterns. This helps in undertaking preventive measures to minimize catastrophic losses related to electrical energy through power grid damages. 
  • Manufacturing Industry: Production of any unit can be increased or decreased based on the results of product-specific working models. These models are created as a combination of both predictive analytics and business analytics techniques. 
  • Healthcare: Healthcare organizations have shown zealous enthusiasm to adopt predictive analytics to improve their operations over the last couple of years. According to surveys, healthcare organizations that leverage predictive analytics are likely to save (at least) a whopping 15% of the total budget over the next five years, if not more. In addition, 93% of healthcare executives agreed unanimously that predictive analytics would play a crucial role in shaping their future. The fields in which healthcare organizations implement predictive analytics are: 
  • Optimizing staff schedules
  • Supply acquisition and management
  • Allocating resources during saturated periods


Predictive analytics is an emerging field that is creating widespread demand for itself. In fact, data analytics as a whole will be shaping industries in the future. Not only is it revolutionizing businesses and companies, but it has also played an integral role in generating mass employment. 

With the potential of an exponential boom imminent, data analytics and its related fields of study like Machine Learning and Artificial Intelligence will be impacting human lives marginally in the next five to ten years. 

As it turns out, now’s an excellent time to kick-start your journey in Data Science & Machine Learning. upGrad’s Advanced Certificate Programme in Data Science 8-month program from IIIT-B covers relevant world-class technologies and concepts like Statistics, Python Programming, Predictive Analytics using Python, Basic & Advanced SQL, Visualization using Python, EDA, Basic & Advanced Machine Learning Algorithms. 

The course is taught by renowned data science experts who rely on relevant industry projects and a cutting-edge curriculum to help students develop the necessary skills to succeed in the field. The course also includes 360° career support, industry mentorship & peer to peer networking for sharper outcomes. 

What are some examples of predictive analytics?

Some examples of uses of predictive analytics in practical, real-world scenarios are:

1. Cyber security fraud detection.
2. Forecasting weather patterns.
3. Predicting purchase behavior of customers.
4. Predicting the performance of a team or its players of any sport.
5. Predicting the future of the working and profitability of a company.
Predicting sales of a restaurant chain.

What are some of the tools used for predictive analysis?

Predictive analytics tools are used to satisfy the demands of a specific department or company. The predictive analytic models can be designed by using software available on the market. Some of the leading predictive analytics service and software providers are:

1. IBM
2. SAP
3. TIBCO software
4. Microsoft
5. Acxiom
6. SAS institute

What are some of the prominent open-source tools used for predictive analytics?

Predictive analytics used techniques like regression, neural network systems, gradient boosting, incremental response, support vector machine, etc. The software for designing the models is costly. However, some free predictive analytics software tools are also available. Some of the most used ones are:
1. Orange data mining
2. Anaconda
3. Microsoft R
4. Apache Spark
5. Graphlab Create

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