Prescriptive analytics help businesses identify the best course of action so they achieve organizational goals. While figuring out what you should do is a crucial aspect of any business, the value of prescriptive analytics is often missed. There is still an inclination to “go with the gut” when looking at an array of possible scenarios.
A fundamental and important business question is, “what should we do?” It can be answered by prescriptive analytics. Prescriptive analytics solutions enable accurate decision-making for complex problems that involve millions of decision variables, constraints and trade-offs with optimization techniques. These solutions augment a company’s decision support capabilities by providing tools for building and deploying optimization models that are mathematical representations of business problems. Powerful optimization engines then solve these models using sophisticated algorithms and deliver recommendations to business users and decision-makers.
The result? You can get guidance on the actions you should take to meet objectives, such as achieving revenue targets, increasing customer satisfaction, and maximizing profitability and operational efficiency.
Before “what should we do?” there’s often “what could happen?” That’s where predictive analytics comes in. Predictive analytics use advanced algorithms and machine learning to process historical data, “learning” what has happened while uncovering unseen data patterns, interactions and relationships. Then it creates models that show the likelihood of scenarios or outcomes.
You might think that a business could get by with just using predictive analytics and that prescriptive analytics is a “nice to have” add-on. However, that way of thinking misses the mark. An Economist Intelligence Unit report says that 70 percent of business executives rate data science and analytics projects as very important. But only 2 percent say that these same projects have delivered on their promise.
Why? Predictive models delivered by machine learning provide “actionable insights,” but they don’t say what actions you should take based on those insights for the best outcomes. In many cases, a biased human goes with “their gut.” The results are usually not optimal at best and disappointing at worst. To truly benefit from predictive analytics, it’s critical to invest in prescriptive analytics.
Here are some examples that shed some light on the value of adding prescriptive analytics to your predictive capabilities.
