While you were skimming through a plethora of artificial intelligence and deep learning headlines, prescriptive analytics has quietly been on the rise. In 2017, more companies started looking at prescriptive analytics—Gartner predicts this niche market to grow to $1.1 billion by 2019 – 22 percent compound annual growth rate (CAGR) from 2014.
What is prescriptive analytics? Simply put, prescriptive analytics provides the best options for given situations based on the concepts of optimization. Prescriptive analytics uses the insights revealed by predictive analytics and provides a call to action based on what is found. It analyzes current data sets for patterns and evaluates the outcomes of the multiple scenarios that could be enacted, based on decisions that could be made based on the data, providing decision makers with hypotheticals on the impact of each option.
Prescriptive analytics lies at the far end of the analytics maturity spectrum that starts with descriptive analytics, progresses to diagnostic analytics, predictive analytics, and finally finishes with prescriptive analytics. Today we are seeing increased investment from data discovery and mainstream analytics vendors in basic prescriptive capabilities such as what-if analysis. Approximately 10 percent of organizations currently use some form of prescriptive analytics, according to Gartner, but this will grow to 35 percent by 2020. 1
Right Time, Right Place
Although prescriptive analytics has exceptionally high business impact potential, it can become overwhelming and complex rather quickly. As a result, this area of analytics is often an untapped, truly golden window of opportunity to explore in most organizations. A continued explosion of data combined with vast improvements in prescriptive analytics technology, broader access in mainstream analytics tools, and ease of use should be the perfect formula to develop interest in this domain.
The digital era of big data started the momentum for the predictive and prescriptive analytics market. Organizations are reinventing, utilizing new models, and on a mission to improve business outcomes. Prescriptive analytics, simulation of alternatives, and optimization of options can be extremely useful, providing unbiased tools to efficiently solve planning problems and help improve decision making.
Simulations allow you to evaluate new ideas before you make a complex business decision. This analysis technique lets you test different parameters, such as pricing and costs, to discover opportunities for improvement in your current operations. Simulation helps provide clarity of what might happen if you take certain actions. Applying these results to your business helps you manage risk and make better choices.
Optimization approaches apply linear programming to decision making on problems that can be expressed in terms of a linear objective function and linear constraints on the decision variables. It is a powerful technique for maximizing or minimizing a target variable, such as budget, while satisfying operational constraints.
New Way of Thinking
For many organizations, prescriptive analytics projects introduce a new way of problem solving and thinking. The iterative exercise of developing an accurate prescriptive business model is still a bit of an art that forces you to think through different scenarios and variable combinations.
Large to midsize organizations are moving away from “gut-feel” decision making, and instead are using more sophisticated analytics and fact-based decisions to project future trends and optimize business decisions. 2
There are some common misconceptions that prescriptive and advanced analytics in general require a data warehouse or data scientist. Although both a data warehouse and data scientist are incredible resources, there are simple methods that you can use to get started using a basic spreadsheet. Using spreadsheets, you can start learning how to design prescriptive analytics business models with variables, explore what-if scenarios, and perform simulations of what might happen if certain conditions exist.
To learn more about optimization and simulation techniques, check out Spreadsheet Modeling and Decision Analysis by Cliff Ragsdale and Management Science: The Art of Modeling with Spreadsheets by Powell and Baker. Those books were recommended to me several years ago. I also feel it helps to understand statistics since many algorithms are based on statistical concepts.
1 Forecast Snapshot: Prescriptive Analytics, Worldwide, 2016 https://www.gartner.com/doc/3202617/forecast-snapshot-prescriptive-analytics-worldwide
2 Gartner Hype Cycle for Business Intelligence and Analytics, 2016
Jen Underwood is Founder and Principle of Impact Analytix, LLC. Impact Analytix is a boutique integrated product research, consulting, technical marketing and creative digital media agency led by experienced hands-on practitioners. Jen can be tweeted at @idigdata