5 Predictions for 2016: The Year of Prescriptive Analytics
Blog: Enterprise Decision Management Blog
2016 will be a leap year. NASA’s Juno mission is expected to arrive at Jupiter. The Summer Olympics will take place in Rio de Janeiro. The US will have a presidential election. And in 2016, prescriptive analytics will take center stage as the ultimate destination on the analytics journey.
What do I mean by prescriptive analytics? As one analyst firm defines it:
Prescriptive analytics is a form of advanced analytics which examines data or content to answer the question “What should be done?” or “What can we do to make ____ happen?” and is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristic and machine learning.
Despite incredible advances over the past few years, we’ve barely scratched the surface on the full potential of analytics. With the New Year approaching, I thought it would be fun to make a few predictions about the role of prescriptive analytics and where I believe things are heading in 2016.
- Streaming isn’t just for movies and music, it’s for analytics too
From the Internet of Things to healthcare to cyber terrorism, it’s no longer just about gathering and analyzing data. It’s about gathering, analyzing and acting on data as it happens. With hardware commoditized (or bypassed entirely in favor of the cloud), and open source software (e.g., Apache Ignite, Spark streaming, Storm) coming into its own, it is now economically feasible to squeeze even more value out of data in real time. A cornerstone of prescriptive analytics, Streaming analytics will come of age in 2016. - It’s not quite Spielberg’s Minority Report, but predicting cyber crime is a reality
Antivirus is great. But it’s not bulletproof. Ditto for firewalls and countless other defensive technologies. This is a huge problem that has remained unsolved for years that one cannot build strong enough defenses. Prescriptive analytics is emerging as The Next Big Thing in cyber security. Identifying anomalous behavior and recognizing patterns as they are developing enable analytics to sound the alarm before attackers can harm the organization. Prescriptive analytics will become a must-have security technology. - Lifestyle furniture clothing food analytics becomes part of daily life
In 2016, the Internet of Things will go even more mainstream. From home appliances to cars to automated shopping, “lifestyle analytics” is poised for explosive growth. Groceries delivered without an order having to be placed. Doctors monitoring patients remotely 24/7/52. Biometric security. It’s all coming together thanks to the cloud and all the devices and sensors that surround us. In 2016, lifestyle analytics will be integrating prescriptive analytics into our lives. - The Big Data belly ache – rethinking what’s really important
It seems as though major data breaches are happening daily. From banks to retailers to government agencies, bad guys are accessing personal data at a staggering rate – millions of records at a time. In the upcoming year, I believe businesses that have been collecting Big Data without putting thought into what they really want to collect – what is useful, what is superfluous, what is risky to store – will start to suffer from Big Data indigestion. Businesses need to put greater care into governance, or face serious consequences due to the cost, liability, dangers and headaches involved in storing so much sensitive-yet-unnecessary data spread across the organization. - Beware of wolves in data scientist clothing
A growing number of crowd sourced and open sourced algorithms available today have bugs in them or offer questionable value. Many companies are storing too much data and using iffy algorithms (do you know how it works, why it works, did you test that it works), and hiring practitioners with limited expertise who may apply these resources without knowing where the deficiencies are. This will result in negative impacts to businesses as they rely on the results of the computation. We need to understand, test, and harden our algorithms, and develop more consistent expertise in applying them. In 2016, I see the industry having to deal with challenges created by the flood of open source algorithms and a dearth of qualified analytic scientist practitioners.
What do you think? Do my predictions make sense? Do you disagree? I’d love to hear your thoughts on where analytics is heading in 2016. Regardless of what happens, I know next year will be exciting and full of change. Happy holidays and Happy New Year!
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