Prescriptive analytics is a form of advanced analytics that uses big data—including historical information and real-time information—not only to anticipate what will happen in the future, but why it will happen, and to recommend actions to take based on those predictions. Using these insights, businesses can make better decisions to optimize situations, mitigate future risk, and gain a competitive advantage.
Prescriptive analytics is typically the third step in a big data analytics strategy, after descriptive analytics (analysis performed to describe an organization's current circumstances, using data like customer feedback, sales numbers, and website traffic to determine where they are at that point in time) and predictive analytics (which uses the same type of data to predict what will happen given the current circumstances).
SEE: Prescriptive analytics: An insider's guide (free PDF) (TechRepublic)
When asked, "Does your organization use prescriptive analytics?" eight technology leaders said yes, while four said no.
"We have used prescriptive analytics decision options to take advantage of future opportunities and mitigate future risks," said Kris Seeburn, an independent IT consultant, evangelist, and researcher. "In real practice, with the analytics, when well-defined and cleaned and had the necessary data identified and earmarked, we could continually and automatically process new data and improve the accuracy of predictions, and provide better decision options once it was clearly defined for all."
However, it can be difficult getting the right data together to achieve this, Seeburn said. Prescriptive analytics is also not fail-proof and can be subject to the same problems found with other types of analytics, such as data limitations and unaccounted for external forces. The effectiveness of prescriptive analytics also depends on how well the decision model captures the impact of the decisions being analyzed, he added.
Organizations already using prescriptive analytics reported doing so for a number of different projects.
"We're piloting the use of prescriptive analytics internally to measure and improve our customer satisfaction score (CSAT)," said Mike Han, CTO of Liferay.
In the security space, "qualitative and quantitative risk analysis utilizes prescriptive analytics models to get an approximate prediction of a future vulnerability being successful," said Terence Jackson, CISO of Thycotic.
"We use prescriptive analytics to predict the next step for our users when using our platform," said Johan den Haan, CTO at Mendix. "Our users are developers building applications. We predict their next step and suggest this next action to them to increase productivity and to teach them how to build better applications. We do this by analyzing all historical data of applications code (in our case visual models) to detect patterns that we can use and then combine it with real-time information from the actions of the user."
For most of the "no" responses, prescriptive analytics was not on their organizations' radar yet, but was expected to be adopted in the coming years.
"Our agency does not currently use prescriptive analytics," said Cory Wilburn, CIO of the Texas General Land Office. "As our current business intelligence and analytics processes continue to mature and evolve, we may look to consider prescriptive analytics in the future."
"It's on the roadmap," said Michael Hanken, vice president of IT at Multiquip Inc.
This month's CIO Jury included:
Michael R. Belote, CTO, Mercer University
Cory Wilburn, CIO, Texas General Land Office
Michael Hanken, vice president of IT, Multiquip Inc.
Kris Seeburn, independent IT consultant, evangelist, and researcher
Mike Han, CTO, Liferay
John C. Gracyalny, vice president of digital member services, Coast Central Credit Union
Johan den Haan, CTO, Mendix
Alan Jacobson, chief data and analytics officer, Alteryx
Amy DeCastro, vice president of HR, Schneider Electric's IT's division
Greg Carter, CTO, GlobalTranz
Shahrokh Shahidzadeh, CEO, Acceptto
Terence Jackson, CISO, Thycotic