By Greg Silverman, Oct 12, 2020
By now, you likely have a better understanding of the different types of analytics businesses utilize to benefit their strategy. From descriptive and diagnostic, to predictive and, ultimately, prescriptive, each analysis brings different value and insights to an organization.
As each form of analytics becomes more difficult to execute, the more it helps a business obtain foresight to make informed decisions. Therefore, prescriptive analytics — the peak of the analytics ascendancy model — brings businesses the most value, but it is also the hardest to accomplish correctly.
According to Gartner, prescriptive models are a form of advanced analytics that doesn’t just determine what will happen, but tells business decision-makers what the best course of action is to achieve the results they want. As important as this analytics tool is, it cannot be achieved without first having predictive modeling in place.
The difference between predictive and prescriptive analytics is made clear when you understand which business question each strives to answer. While data scientists use both techniques in partnership with historical data and market trends to enhance business processes, the two are used to answer different questions.
After descriptive and diagnostic analysis comes predictive analytics. As the Gartner Ascendency Model shows, predictive analytics forecast what will happen in the business or market. It works best with a wealth of big data on past trends and with help from machine learning and is good at accurately predicting what is likely to happen in the market when certain circumstances are met.
For too long, businesses relied on predictive analytics to guide their decisions and supply chain strategy - but ultimately, these predictions were still malleable and subject to change in response to company and customer decisions. This highlighted the need for further analysis, and with advancements in technology, prescriptive analytics entered the business intelligence arena.
The height of data science uses deep learning to transform predictions into actionable insight. The height of analytics set in a simulation. Through agent-based modeling, behavioral economics, social analysis, and advanced machine learning, prescriptive models are able to handle complex problems to provide decision-makers with a recommendation of the most effective plan to reach their goals. Rather than determining what will happen in the future, prescriptive analytics solutions help optimize strategies to meet predictions with accuracy.
When it comes to predictive vs. prescriptive analytics, it’s not a choice of one or the other. Both analytics solutions bring value to a business, and you cannot have prescriptive without a strong foundation of predictive already in place.
Used in unison, predictive, and prescriptive analytics help organizations optimize their actions to improve planning outcomes. McKinsey has discussed the topic of optimization at length, to ultimately prove it’s more than choosing each action based on the best outcome. Businesses are always making investment decisions based on data and intuition. But in times of uncertainty, choosing the best channel to promote a product or pricing strategy can’t be solved solely by looking backward or rely on gut looking forward.
Optimization is about using advanced software and skilled team members to make decisions that increase sales and drive growth, no matter if the market is thriving or stagnant. Both internal and external data are used to guide business leaders. Prescriptive helps with resource allocation, identifying areas of opportunity, and enables fast cross-functional workflows. McKinsey noted that only a “collaborative planning solution” is capable of effectively aligning resources and opportunities for optimization.
A business cannot simply change one factor, like pricing, and expect positive change to follow. Everything works together - including predictive and prescriptive analytics. Businesses should not choose one over the other, but rather utilize both to their advantage of gaining a forecast of a future outcome and a plan to make it happen.
Business analytics are only as useful as their metrics, the factors that are used to measure their success. However, there are no right and wrong metrics to use. Just as each business is unique, its requirements for identifying progress differ. Ideally, a company has the ultimate goal they want to achieve, like reaching revenue targets or gaining more share in the marketplace. In these instances, overarching metrics would be tracking sales and shares for progress, but it’s also important to monitor other factors as well, such as employee satisfaction and budgets.
It’s important to never focus solely on one or two metrics to measure performance because while sales may be increasing, budgets may become strained, leading to an unsustainable strategy. That is why predictive and prescriptive analytics make the perfect pair and provide businesses with the most value when used together. While predictive analytics determines what will happen next in the market and focuses on internal business elements, prescriptive uses simulation that allows businesses to test different metrics in a controlled twin of the market with outside influences.
Taking all of an organization's data and analytic power to one prescriptive platform harnesses mathematical optimization while providing a unified view of the market that every decision-maker across the company is able to use. Leaders test the prescriptive recommendation against the real market results to gain confidence in the model and continue to test how their actions affect business outcomes to create the best strategy.
Predictive vs. prescriptive analytics is not a debate about which analytic model is best for your business. Each analysis provides value to a business, and you cannot have prescriptive capabilities without predictive analytics.
The Concentric model uses descriptive, predictive, and prescriptive analytics to provide businesses with the process they need to reach their goals. From perfecting product launches to identifying a strategy for capturing sales in current market conditions, the model uses the building blocks of analytical success, so organizations are able to harness the power of prescription.
Contact our team today to learn more about deploying the platform that has already helped organizations across a variety of industries achieve better, faster decision-making.