Somewhere between blind faith and skepticism is the world of prescriptive analytics. Here, machine-generated action items and potential outcomes meet human decision making. Finding the right balance between algorithms and common sense can be tricky, so consider these tips.
Marketing and retail have been two of the most publicized use cases for prescriptive analytics, a type of predictive analytics software which recommends one or more courses of action and shows the likely outcome of each decision.
While prescriptive analytics isn't as mature or widely adopted as descriptive analytics or predictive analytics, Gartner estimates the prescriptive analytics software market will reach $1.1 billion by 2019. Beyond marketing and retail, such tools are starting to be applied in cybersecurity, fraud prevention, supply chain optimization, and resource optimization, among other areas of business.
"Prescriptive analytics can take processes that were once expensive, arduous, and difficult, and complete them in a cost-effective and effortless manner," said Doron Cohen, CEO of Powerlinx, a business-to-business matchmaking service, and chairman of Dun & Bradstreet Israel. "The ROI derived from having more time, energy, and money can then be used to identify new opportunities as a business."
Prescriptive analytics systems learn over time, and there's no guarantee their output will always be reliable. "Application of algorithms is not a substitute for robust investigational methodology or common sense," said Willy McColgan, president and general manager of machine intelligence analytics company Zoomi, in an interview. "We have uncovered non-intuitive patterns and correlations, but data-conversant leaders who can see the confounding factors are still necessary [to] apply a sniff test where we may have made assumptions in our experimental design that do not hold up generally."
John Houston, a principal who leads the Advanced Analytics and Predictive Modeling service area at Deloitte Consulting said in an interview he sees adoption of prescriptive analytics software taking place "from the outside in." While customer-facing solutions are more common now, the number and types of use cases will grow in organizations.
"Future adoption will move into the day-to-day decision-making of knowledge workers, first with tactical, high-frequency decision types and then [to] more strategic, lower frequency decision types," said Houston.
But what, exactly, is the tipping point for companies to embrace prescriptive analytics? According to Forrester Research Principal Michele Goetz, companies start adopting prescriptive analytics when they get focused on why something is happening, and how they can get the information into areas of the business which can immediately act upon it.
"Most organizations that are achieving results from their prescriptive analytics are gaining insight through data science activities," said Goetz, in an interview. "More mature organizations are exploring machine learning and evaluating real-time and near-real-time deployments."
Regardless of where you are in your journey with prescriptive analytics, we've identified eight issues and opportunities to consider as you seek to derive business value from your investments. Once you've reviewed these factors, tell us about your own experiences. Have you worked with prescriptive analytics software? Is it something your company is considering in the year ahead? Can such tools every really replace good old-school common sense? Tell us all about it in the comments section below.