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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.
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