Prescriptive Analytics – Getting Ahead of the Curve to Solve Big Data Problems

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Prescriptive Analytics – Getting Ahead of the Curve to Solve Big Data Problems
In this modern age, data generated by businesses is upsurging.  This unstoppable soaring data needs to be capitalized. That is exactly when data analytics and data science comes into the play. Through business analytics, we can quickly spot trends , predict behavior and support strategic decisions. So, it has been extensively used for enterprise performance management and optimization.  Business analytics is composed of 3 types of analytics: descriptive analytics, predictive analytics, and prescriptive analytics .

The Evolution of Prescriptive Analytics
Business analytics are divided into three stages starting from descriptive analytics through predictive analytics to prescriptive analytics.

1. Descriptive analytics: The simplest Class of Analytics
Purpose: To summarize what happened!
Descriptive analytics is gathering and analyzing raw data to understand what happened in the past.This helps us understand the past behavior and learn how it can affect future outcomes. When you know what happened in the past, you want to know the reason behind it. That is when diagnostic analytics comes into the picture. It asks why it happened which helps you get to the root cause. Descriptive analytics needs to be merged with diagnostic analytics, as both talks about the past. Together, it will tell you what happened and why it happened, so that it becomes even easier for you to predict what might happen; which is the next step (predictive analytics).
Most organizations use descriptive analytics.

2. Predictive analytics: The Advanced Analytics
Purpose: To summarize what can happen!
Predictive analytics is not about ‘what will happen in the future’, as no analytics can do that. It is all about studying historical data, identifying insights, trends and patterns and predicting ‘what might happen next’ using statistical models and forecasts techniques to solve problem up to some extent. Predictive analytics has probabilistic nature. According to a chief scientist in a San Fransisco based company, predictive analytics takes the data that you have got to predict the data that you don’t have.
A few organizations use predictive analytics.

3. Prescriptive analytics: The Final Frontier of Analytics
Purpose: How we can make it happen!
Prescriptive analytics helps businesses to make truly data-driven decisions through simulation and optimization leveraging machine learning and artificial intelligence to give actionable recommendations from the insights provided.
Very few organizations use prescriptive analytics.

PRESCRIPTIVE ANALYTICS: THE MOST ADVANCED AND PROMISING VARIANT OF DATA ANALYTICS

In this competitive world, we have been making business decisions based on what has occurred in the past or what is most likely to occur in the future. Prescriptive analytics helps businesses make the best data-driven decisions with the help of targeted recommendations on the basis of why and how things happen. It makes recommendations taking into the considerations a lot of factors including desired outcome, specific resources, past events and the current situation.
An organization that has the ability to take the right and quick decision in dynamic conditions and ambiguous environment certainly wins.  Prescriptive analytics helps businesses do that. Prescriptive analytics has taken the insight-based actions and analytics maturity model to the next level by suggesting the optimum way to handle the future situation by taking the best decision.

Research Says…
“The prescriptive analytics software market will reach $1.1 billion by 2019” – Gartner
Prescriptive Analytics: A Closer Look
Prescriptive analytics can help businesses get rid of data depot and make optimized decisions. So, make the most of prescriptive analytics techniques and identify the best steps to implement for maximized profits. There are two approaches: simulation and optimization.