In many ways I think 2016 was the year that analytics hit the mainstream. At cocktail parties and in coffee shops, 2016 has been the year that we data scientists suddenly felt a bit like – dare I say it? – rock stars. A quick Google search provides a fascinating data point:
- “analytics 2015” returned 28.6 million hits
- “analytics 2016” returned 52.3 million hits
Suffice it to say that in 2017, I believe that analytics, and the many domains it influences, will be a non-stop hit machine in terms of generating news headlines and ideas that fascinate us. Here are my predictions:
- Security/IoT: Is your video cam no longer your friend?
In light of the recent Dyn attack, will 2017 be the year that your IoT device will turn on you? (Think Westworld, Humans, Terminator.) I think the answer is “yes.” In my recent blog, “Real talk: The imminent and very real danger of IoT,” I wrote:
Due to lack of security features, creating an IoT botnet is a great deal easier than phishing users to compromise PCs. Given the ease with which IoT devices can be hacked, we can expect more attacks to follow. Mirai, Japanese for ‘future,’ has given us a view into the future through these attacks, which include data breaches and ransomware attacks through compromised IoT devices.
I predict that in 2017 our personal lives, as well as infrastructure, will be brought down by the devices we design to make things easier. I’m not sure which IoT category or device type it will be, but driverless cars provide – pun alert! – a good conversation vehicle: What is the quality of the security of code inside that fancy new red car? Most consumers are swept away by the shiny red exterior and unaware of downstream security considerations. Many may turn to un-connected cars; for me, that could be a nice air-cooled Porsche circa 1973.
- Enterprises: Lax cyber security? You’re about to be found out
Have a poor security posture? Enterprises that do are about to be exposed. The FICO® Enterprise Security Score brings companies’ security posture to the forefront for partners, enterprises and insurers. These intelligent scores can be used to assess risk in the underwriting process for cyber security insurance, an exploding category that CFO magazine calls a “must have”: “A September  survey by the Risk and Insurance Management Society found that 80% of the companies bought a stand-alone cybersecurity policy in 2016.” I predict that we will see big advances in how ESS is adopted as an important risk assessment tool. Another critical area will be in vendor management, particularly use of the ESS score to continually monitor vendors’ cyber security postures.
- Who’s scoring you now?
FICO revolutionized the operationalization of empirical scores, for ensuring accurate and fair provisioning of credit. I was surprised to learn that FICO® scores are not just sought by credit grantors; they are a fixture in the dating scene.
I believe that businesses and individuals will be getting scored much more in 2017. These will allow us to measure the complex data-filled world around us and boil down the risks we accept in those with whom we interact and do business. For our part, we have recently announced a Safe Driving Score.
As we score more of our lives, we need to keep in mind:
- What does the score measure?
- Is it empirically derived?
- Why should you care about it?
- Who is creating that score and with what data?
- What actions can be taken to improve a score? This point is quite important. There should be transparency on what drives a score, and how you can utilize it as a tool to improve over time.
- Will the AI hype cycle crash?
Well, it is true that the hype cycle is in overdrive, with artificial intelligence powering everything from juice bars to robo investing. How much is too much? I think that we are just at the beginning of the golden age of analytics, in which the value and contributions of AI, machine learning and deep learning will only grow as we accept and incorporate these tools into our businesses.
As part of that, and with a shout-out to my colleague TJ Horan’s prescience that 2016 would be the year of “bad analytics,” we must view AI (including buzz terms du jour like machine learning and deep learning) with a healthy amount of skepticism, and avoid putting blind trust in every claim we hear. Proper model development and governance is a must, and we should watch out for companies purporting to re-invent AI. It’s not the technology solution for every problem, and it can be easy for companies to misuse the algorithms that drive artificial intelligence due to inexperience, or the race to be on the hype cycle.
Cheers to 2017! You can follow Scott Zoldi on Twitter @ScottZoldi to see how many of his predictions come true.