The purpose of any analytics program in business is to combine the troves of internally sourced data with data from public and other third-party sources into actionable insight to improve business operations. Of the four analytics disciplines in the analytics portfolio, two — descriptive and diagnostic — are more concrete and give hindsight into what has happened and why. These analytics are typically called business intelligence and are much used in corporate America.
The other two disciplines, predictive and prescriptive, are a step up the analytics ladder. Both give insight, and even foresight, to support decision-making. Predictive and prescriptive analytics incorporate statistical modeling, machine learning, and data mining to give MBA executives and MBA graduate students strategic tools and deep insight into customers and overall operations. Below are examples of real-world applications of these powerful analytics disciplines.
You may be tempted to think of predictive analytics as a fortune-teller who tells you what the future holds. Dr. Michael Wu of Lithium Technologies cautions against that notion. “The purpose of predictive analytics is not to tell you what will happen in the future,” he said. “It cannot do that. In fact, no analytics can do that. Predictive analytics can only forecast what might happen in the future, because all predictive analytics are probabilistic in nature.”
The three keystones of predictive analytics are decision analysis and optimization, transactional profiling, and predictive modeling. Predictive analytics exploits patterns in transactional and historical data to identify risks and opportunities. As Wu said, “You basically take data that you have to predict data you don’t have.”
Here’s another way to look at the differences between business intelligence and predictive analytics: Business intelligence answers the question, “What ZIP code do my most valuable customers come from?” Predictive analytics, however, answers, “How much revenue can I expect from customers in a particular ZIP code?”
On the operational side, businesses are using predictive analytics to maximize efficiency and predict anomalies across the IT infrastructure to prevent interruptions.
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Prescriptive analytics is an emerging discipline and represents a more advanced use of predictive analytics. Prescriptive analytics goes beyond simply predicting options in the predictive model and actually suggests a range of prescribed actions and the potential outcomes of each action. Wu said, “Since a prescriptive model is able to predict the possible consequences based on a different choice of action, it can also recommend the best course of action for any pre-specified outcome.”
Google’s self-driving car is an example of prescriptive analytics in action. The vehicle makes millions of calculations on every trip that help the car decide when and where to turn, whether to slow down or speed up, and when to change lanes — the same decisions a human driver makes behind the wheel.
In the energy sector, utility companies, gas producers, and pipeline companies use prescriptive analytics to identify factors affecting the price of oil and gas to get the best terms and hedge risks.
Predictive and prescriptive analytics are co-dependent disciplines that take business intelligence to unprecedented levels. With both forms of analyses, business executives and leaders gain both insight and foresight.
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