Most humans are curious about the future. This curiosity motivates people to visit oracles, study the stars, consult shamans, get their palms read, stare into crystal balls, and turn over tarot cards. Business leaders are not immune to this curiosity; however, their methods for divining the future generally involves gathering smart people together to perform what-if analysis, identify trends, and produce forecasts. Although each of these activities has value, new analytical tools are available to help business leaders predict what could happen in the future and assist them in deciding how to proceed. Those tools became possible with the creation of large datasets (aka big data) and the maturation of artificial intelligence (AI). Daniel Newman (@danielnewmanUV), a principal analyst at Futurum Research and CEO of Broadsuite Media Group, notes, “Analytics is probably the most important tool a company has today to gain customer insights. This is why the Big Data space is set to reach over $273 Billion by 2023 and companies like Microsoft, Amazon and Google among so many others are so heavily invested in not only collecting data, but enabling data for the enterprise. As AI and machine learning continue to develop, the way we use analytics also continues to grow and change. While in the past, businesses focused on harvesting descriptive data about their customers and products, more and more, they’re about pulling both predictive and prescriptive learnings from the information they gather.”[1]
Types of analytics
There are generally four types of analytics now available to enterprises. What you want from your data determines which kind of analytics you need to apply. George Karapalidis (@gkarapalidis), head of data science at Vertical Leap, explains the purpose of each type of analytics.[2] They are:
1. Descriptive analytics. Descriptive analytics can help discover what happened in the past. As Karapalidis puts it, “Before we learn where to go, we need to know where we came from. That’s the key question descriptive analytics solutions tackle.”
2. Diagnostic analytics. Diagnostic analytics can help explain why something happened. Karapalidis writes, “Diagnostic analytics tools help you uncover the root cause of some problems.”
3. Predictive Analytics. “Predictive analytics,” writes Karapalidis, “‘joins the dots’ between the accumulated and analyzed data points, conveying what and why something happened, into models suggesting what can happen next. It indicates the probability of certain outcomes with high accuracy and takes the guesswork out of your decision-making process.”
4. Prescriptive analytics. Prescriptive analytics informs you what you should do to achieve a particular outcome. It’s a type of analytics made possible by the emergence of cognitive computing technology. Karapalidis notes, “Prescriptive analytics is yet to move from the margins to the mainstream. It’s an emerging area of analysis attempting to answer the complex question of ‘what actions to take if I want to get outcome A?’ Prescriptive tools come up with multiple future outcomes based on your current/past actions; match those futures with your goal and advise you on the action you need to apply.”
Of course, to extract value using analytics you need the right data for the right model. In this article, I want to focus on the fourth type of analytics — prescriptive analytics. Newman insists the future of data analytics is prescriptive analytics.
The future of prescriptive analytics
Mark Pluta, chief technology officer at Blume Global, writes, “The great promise of AI revolves around the initial premise that the technology gets smarter with access to increasing amounts of data. But its full potential encompasses more. The fact is that AI’s power grows even greater when it’s integrated with other technologies, such as analytics.”[3] How you use advanced analytics varies depending on the economic sector in which you are involved.
Prescriptive analytics in the supply chain
In the area of supply chain operations, Pluta asserts the full potential of big data analytics can only be realized when predictive and prescriptive analytics are paired together. Both Abraham Lincoln and the late Peter Drucker have been credited with declaring, “The best way to predict the future is to create it.” Creating the future is exactly what prescriptive analytics is about. Pluta explains, “Predictive and prescriptive analytics … promise to unlock real value … by helping analyze, model, predict and prepare for future changes in the supply chain. Ultimately, these insights may provide ongoing relief and improvement in areas such as reducing waste, streamlining processes, and minimizing costs.” He suggests several supply areas in which the combination of predictive and prescriptive analytics can benefit supply chain operations. They are: Managing supply and demand planning; matching supply chain decisions to financial outcomes; and implementing continual improvements to streamline supply chain operations. He concludes, “Without a crystal ball to predict disasters and unknown variables, organizations need strategies and tools to help avoid disruptions. The advanced capabilities of predictive and prescriptive analytics can serve as a guiding light for the supply chain, analyzing environmental factors and using data to inform decisions, predict, prepare, plan and advise.” Gloria Quintanilla (@chirppoint), a senior marketing specialist at AIMMS, reports a survey by Gartner found, “11% of mid and large-sized enterprises currently have some form of prescriptive analytics. This will grow to 37% by 2022.”[4]
Prescriptive analytics in marketing and sales
Newman writes, “Prescriptive analytics takes three main forms — guided marketing, guided selling and guided pricing. It uses AI and machine learning to guide buyers with less human interaction — prescribing the right buyer, at the right time, with the right content — telling sales people which product to offer using what words — informing you what price to use at what time in which situation. This information allows you to maximize not just sales but price and profit overall.” Like Pluta, Newman believes predictive and prescriptive analytics work best when paired together. He explains, “The benefits of predictive and prescriptive analytics go far beyond sales conversions. They bleed down into time savings, efficiencies, human capital, transaction costs. Predictive analytics, when automated, can allow you to make real-time decisions … for instance, changing prices throughout the day to maximize profit.” Michael Brenner (@BrennerMichael), author of The Content Formula, states, “Prescriptive analytics essentially makes the data you use more valuable by telling you what to use. This transcends predictive insights, which reveal what may happen if a specific decision is made. It’s end-game (but not end goal) data science for marketers because it opens the door to an entirely new playing field of possibilities.”[5]
Concluding thoughts
Mark van Rijmenam (@VanRijmenam), founder of Datafloq, observes, “Prescriptive analytics is about what to do (now) and why to do it, given a complex set of requirements, objectives and constraints. It offers recommendations on how to act upon predictions to take advantage of those predictions and transform an organization accordingly. It leverages predictive analytics and descriptive analytics to derive ideal outcomes or solutions from helping you solve business problems based on foresight achieved from continuously analyzing a wide variety of (un)structured data sources. … In the future, prescriptive analytics will further facilitate analytical development for automated analytics where it replaces the need for human decision-making with automated decision-making.”[6] Peter Bull, chief technology officer at River Logic adds, “Prescriptive analytics is the most advanced form of analytics available today. It has the potential to have the greatest impact on large-scale business objectives like profit, cost, risk, service levels and customer satisfaction, performance efficiency, and more.”[7]
Footnotes
[1] Daniel Newman, “Why The Future Of Data Analytics Is Prescriptive Analytics,” Forbes, 2 January 2020.
[2] George Karapalidis, “Examining the four types of big data analytics,” The Drum, 18 December 2018.
[3] Mark Pluta, “Unlocking value in the supply chain with predictive and prescriptive analytics,” Information Management, 4 November 2019.
[4] Gloria Quintanilla, “Prescriptive Analytics is Going Mainstream. Is your Organization Ready?” AIMMS Blog, 1 October 2019.
[5] Cheryl Hammer, “Prescriptive Analytics: 22 Things the Experts Are Saying About Analytics’ Rising Star,” The Stream by River Logic, 6 March 2019.
[6] Mark van Rijmenam, “What is Prescriptive Analytics and Why Should You Care,” Datafloq, 6 September 2018.
[7] Peter Bull, “4 best practices for tapping the potential of prescriptive analytics,” Information Management, 22 May 2018.
