The Year Prescriptive Analytics Takes Centre Stage

2016 is going to see a big shift in how businesses take advantage of data analytics.

The Year Prescriptive Analytics Takes Centre Stage

Despite incredible advances over the past few years, we’ve barely scratched the surface on the full potential of analytics. Until now, analytics has been our business guide when making decisions, but this style of analytics technology is just the tip of the iceberg. Prescriptive analytics is taking business decision making to a whole new level, so you better prepare for it to take centre stage this year.

Prescriptive analytics is an advanced form of analytics which uses descriptive and predictive analytics to decipher the best course of action in a business situation. Essentially, instead of just answering the question “what should we do?” prescriptive analytics answers “how can we make ‘X’ happen?”

With the New Year just starting, 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:

  1. 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 analysing data. It’s about gathering, analysing and acting on data as it happens. With hardware commoditised (or bypassed entirely in favour 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.
  2. It’s not quite Spielberg’s Minority Report, but predicting cybercrime is a reality
    Antivirus is great. But it’s not bulletproof. The same goes for firewalls and countless other defensive technologies. This is a huge problem that has remained unsolved for years, that one cannot build strong enough defences for. Prescriptive analytics is emerging as ‘The Next Big Thing’ in cyber security. Identifying anomalous behaviour and recognising patterns as they are developing enable analytics to sound the alarm before attackers can harm the organisation. Prescriptive analytics will become a must-have security technology.
  3. Lifestyle 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.
  4. 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 organisation.
  5. 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 lies ahead for the year of 2016? I would 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. Enjoy 2016!