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Prescriptive Analytics: An Introduction

Originally published August 20, 2013

“Decision making and the techniques and technologies to support and automate it will be the next competitive battleground for organizations. Those who are using business rules, data mining, analytics and optimization today are the shock troops of this next wave of business innovation.”

-Tom Davenport, Competing on Analytics

What is the prescriptive analytics? Simply put prescriptive analytics provides the best options for given situations based on the concepts of optimization. It 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. A visualization of the analytics continuum by Gartner is shown below.

Figure 1: The Analytics Continuum

Where descriptive analytics is reactive in nature and allows an understanding of what has happened in the past, both predictive and prescriptive analytics support proactively optimizing what is best in the future based on a variety of scenarios. The problems business face today are often quite complex and can be solved by taking multiple courses of action. Prescriptive analytics, modeling and optimization is an area of management science also historically referred to as operations research or decision science. As data-driven organizations continue to recognize that information is a strategic competitive advantage, they will strive towards the prescriptive analytics end of the analytics spectrum.

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. According to a recent Gartner report, only a mere 3% of companies are using prescriptive analytics software today compared to 30% of companies that were surveyed using predictive analytics software. With the continued explosion of data combined with vast improvements in technology, prescriptive analytics adoption is expected to grow substantially in the upcoming years. Already we have seen the academic and commercial education communities creating and offering many more post-secondary and certificate programs in advanced analytics specializations in preparation for the expected future increase in business demand for top analytic talent.

Predictions, Decisions and Effects

Prescriptive analytics can be divided into two primary areas:
  • Optimization: How can we achieve the best outcome?

  • Stochastic Optimization: How can we achieve the best outcome and address uncertainty?
Optimizations are formulated with prescriptive analytics by combining historical data, business rules, mathematical models, variables, constraints and machine learning algorithms. Prescriptive analytics, much like its predictive predecessor, is used in scenarios where there are too many options, variables, constraints and data points for the human mind to efficiently evaluate. It is also used when experimenting in the real world would be prohibitively expensive, risky or take too much time. Sophisticated models, scenarios and Monte Carlo simulations are run with known and randomized variables to recommend next steps, display if/then scenarios and gain a better understanding of the range of possible outcomes.

Some examples of real world prescriptive analytics applications in action that may be familiar to you involve pricing, inventory management, operational resource allocation, production planning, supply chain optimization, transportation and distribution planning, utility management, sales lead assignment, marketing mix optimization and financial planning. For example, airline ticket pricing uses prescriptive analytics to sort through complex mixtures of many factors, demand curves and purchase timing to present seat prices that will optimize profits but also not deter sales. Another highly visible case study example that has been covered in analytics news is UPS’s application of prescriptive analytics in package delivery route optimization. There are many other examples like these in the areas of operations management, finance, human resources, project management, sales and marketing.

Prescriptive analytics software and tools are not talked about much in the mainstream analytics channels right now, but they do exist and have actually been around for quite some time. Business school curriculum operations management courses usually cover one or more prescriptive analytics applications tools and techniques. Several popular prescriptive analytics software tools used in the market today include but certainly are not limited to Excel, Frontline Systems Solver Platform for Excel, SAS Enterprise Guide, IBM SPSS and ILOG, SAP Predictive Analytics and SAP Hana Predictive Analytics Library functions (PAL), KXEN, TIBCO Enterprise Runtime for R, MATLAB, AYATA and RiverLogic. Most prescriptive analytics professionals start exploring and evaluating optimization problems with the free Excel what-if, scenario analysis, and solver options: 1) Scenarios, 2) Data Tables, and 3) Goal Seek and 4) Solver, the Frontline Systems add-in to Excel that allows for more variables. If the optimization problem to solve or needed solution exceeds the base capabilities of Excel, then other prescriptive analytics software tools are sought out.

There are some common misconceptions that prescriptive and advanced analytics in general requires a data warehouse or data scientist. Although both a data warehouse and data scientist are incredible assets, there are other ways that you can begin self-learning prescriptive analytics, optimization, simulation and the process of applying these advanced analytic techniques to address key business challenges. To get started learning more about optimization and simulation techniques, Spreadsheet Modeling and Decision Analysis by Cliff Ragsdale and Management Science: The Art of Modeling with Spreadsheets by Powell and Baker are good books. It also is helpful to have an understanding of statistics.

The prescriptive analytics process is similar to the Cross Industry Standard Process for Data Mining (CRISP-DM). It begins with establishing an appropriate description of the business system to be modeled, including the objective, control factors and constraints. Often the model will evolve from an initial conceptual mental model to a visual model, logical or mathematical model. In order to build a model, you will first need to clearly define a business objective, identify variables and constraints to analyze. You can start to develop an optimization model of the problem using Excel. Often the definition phase of the process alone introduces new questions and advantageous process insights. Much like predictive modeling, coming up with a complete and accurate definition of the business process to model and the prescriptive objective is critical for valid and actionable optimization results. The prescriptive analytics process is highly iterative in nature and does requires close cooperation between analytics experts and the business area experts. Once a model is developed, it could be used for manual human decision reference or possibly embedded into a decision support system application for automated, smart real-time decision making.

Now that the basic concepts of prescriptive analytics have been introduced, future articles in my channel will showcase and dive into specific use cases, the process, describe how and why various models were chosen, the tools, best practices, techniques and APIs to embed prescriptive analytics into applications or business processes, and other aspects of this hot and rapidly growing area of advanced analytics.

SOURCE: Prescriptive Analytics: An Introduction

  • Jen UnderwoodJen Underwood
    Jen Underwood has almost 20 years of hands-on experience in the data warehousing, business intelligence, reporting and predictive analytics industry. Prior to starting Impact Analytix, LLC, she held roles as a Microsoft global business intelligence technical product manager, Microsoft enterprise data platform specialist, Tableau technology evangelist and also as a business intelligence consultant for Big 4 systems integration firms. Throughout most of her career she has been researching, designing and implementing analytic solutions across a variety of open source, niche and enterprise vendor landscapes including Microsoft, Oracle, IBM and SAP.

    As a seasoned industry presenter, author, blogger and trainer, Jen often volunteers her time and gives back to the global technical community in many ways. Recently Jen was honored with a Boulder BI Brain Trust (BBBT) membership, a 2013 Tableau Zen Master (MVP) award and a Dun & Bradstreet MVP. Jen holds a bachelor of business administration degree from the University of Wisconsin, Milwaukee and a post graduate certificate in computer science -- data mining from the University of California, San Diego.

    She may be contacted by email at jen@impactanalytix.com, and her blog can be found here.

    Editor's Note: Find more articles and resources in Jen's BeyeNETWORK Expert Channel. Be sure to visit today!

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