Emergence of Predictive Analytics

Data, Data, everywhere!

Over the years, there has been growing interest towards understanding, analyzing and translating the impact of data that is generated in various fields. Data analysis is essential to understand the patterns, trends, and how it supports data driven decisionsthereby improving businesses and scientific research. This article sheds light on the Emergence of Predictive Analytics. The evolution of Predictive Analytics will be explained, followed by the implications of Predictive Analytics towards business strategy formulation and industrial applications.

Predictive Analytics explained

Predictive Analytics utilizes data, statistical algorithms and ML techniques to predict and identify the possibilities of future outcome based on the compiled data. In simpler terms, it is actually a part of data mining that extracts information from data, in turn the retrieved data is used for predicting meaningful behavior patterns and trends. For instance, Predictive analytics helps in understanding why and how businesses fail, how to engage better with the users and what can be done to prevent such failures. The outcome of Predictive analytics (an unknown end result) is obtained by understanding the relationships between descriptive and predictive variables from past incidents. It all boils down to level of data dissection and presumption qualities which reflects on accuracy and dependability of the outcome.

Figure 1 : Predictive Analytics Explained

(Source :“Reactive to proactive to predictive — Change the paradigm”, DXC. Technology)

How did it happen?

In the quest to study data back in the early 1980’s first step was taken in the form of static reporting, followed by Data Analysis in Excel, OLAP in 1990’s, monitoring using dashboards in 2000’s to Data Mining and Optimization, Predictive Analytics in 2010’s. It has come a long way!

Presently, Predictive Analytics is used by 87% of B2B market leaders inorder to improve their market share and revenue growth. Besides, one should be aware of the fact that employing predictive models to identify future scenarios alone doesn’t ensure a competitive edge. The truth issmart businesses employ predictive analytics as tool to take operational decisions in real time.This helps in predicting the next ideal possibility and alsofacilitates decision making without any human intervention.

Insight on Predictive Analytics Models

The integral component in Predictive analytics is predictive model. The prediction is performed by considering the individual’s characteristic as an input and providing a predictive score as an output. For instance, higher the predictive score, the more possibility the individual will exhibit the predicted behavior. Currently, predictive models are widely derived using Machine Learning (ML) approach as shown in Figure 2. ML grinds down the data to build the predictive model. It is designed to mechanically form a new capacity for the data. Further, there are five essential components in Predictive Analytics which are Prediction, Data, Induction, Ensemble and Persuasion effects (Source: Eric Seigel, “Predictive Analytics : The power to predict who will click, buy , lie or die”)

PredictionEffect: Deals on how the future outcome would be determined with only 50% accuracy by means of behavior trends and learning patterns.
Data Effect :Describes about the causative relationship that is present, and also features predictive insights within the data.
Induction Effect: Refers to Machine Learning part where data is processed to create predictive models.
Ensemble Effect : Known as Meta- Learning, where two or more predictive models are ensembled together.
Persuasion Effect : Predicts individual’s influence by means of two distinctive data (Uplift model)

Figure 2 Predictive Modeling

That’s enough theory – let’s get on with the practical applications!

Scope of Predictive Analytics

The applicability of Predictive Analytics across various industries is significant and its potential areas are as follows:

Fraud Detection : Eliminates false identities, detects invalid insurance claims.
Education : Provides insights on strategizing effective learning and teaching techniques.
Advertisement: Gathers customer data across social media platforms and promote ads based on their traits, search data.
Clinical Support : Assists in providing ideal medical option at the point of care and effective diagnosis.
Insurance and Mortgage : Determine the premium plans for the borrower and assessment of borrower’s ability to repay the mortgages.

Industrial Applications:

Human Resources : Helps in strategically analyzing the data to accurately determine behavior patterns, attitudes and capabilities among employees.

Sales : Predicts customer demands and preferences. It helps in optimizing the product pricing based on demand. Ideally used as a decision making tool, that contributes to profitability.

Healthcare :Helps the people to track their medications and sends corresponding data to the doctors. Useful for identifying trends on personal/large scale level, priority in bioinformatics and predictive genomics.

E-Commerce : Allows e-Commerce companies to analyze the user’s past click through behavior, shopping history and product preferences. Further, this will provide relevant results, promotions and recommendations to the users.

Safety: Enables police forces to monitor high threat areas, identify and classify high spot areas based on crime rates

Finance:Widely used for carrying out credit card charges, fraudulent checks and other financial transactions.

Final Thoughts

In an era of Big Data, the demand and usage of predictive analysis is growing rapidly, with the primary objectives of assisting in strategic business decision, enhance their profitability and performance of respective organizations. With promising trends and active research in areas such as the Cloud, in-memory analytics, formulating effective open-source techniques seems Predictive analytics is definitely way forward!