Top 5 Analytics Predictions for 2016
Analytics has never been more needed or interesting. And, 2016 is going to be an exciting year for the technology and its applications. From the Internet of Things to big data to cybersecurity, analytics is poised to make its mark.
Commentary: My view of the world is shaped by where I stand, but from this spot the outlook for 2016 looks pretty exciting! Analytics has never been more needed or interesting.
1. Machine learning established in the enterprise
Machine learning dates back to at least 1950 but until recently has been the domain of elites and subject to “winters” of inattention. I predict that it is here to stay, because large enterprises are embracing it. In addition to researchers and digital natives, these days, established companies are asking how to move machine learning into production. Even in regulated industries, where low interpretability of models has historically choked their usage, practitioners are finding creative ways to use machine learning techniques to select variables for models, which can then be formulated using more commonly accepted techniques. Expect greater interest across academic disciplines, because machine learning benefits from many different approaches. Consider the popular keynote from the INFORMS Annual Meeting last year, where MIT Professor Dimitris Bertsimas talked about “Statistics and Machine Learning via a Modern Optimization Lens.”
2. Internet of Things hype hits reality
The Internet of Things (IoT) is at the peak of the Gartner Hype Cycle, but in 2016 I expect this hype to hit reality. One real barrier is plumbing – there’s a lot of it! One of my colleagues is analyzing the HVAC system on our newest building as an IoT test project. The building is replete with sensors, but getting to the data was not easy. Facilities told him data are the domain of IT, who then sent him to the manufacturer, because while the HVAC system collects the data, it is sent to the manufacturer. “Data ownership” is an emerging issue – you produce the data but may not have access to it. An even larger challenge for IoT will be to prove its value. There are limited implementations of IoT in full production at the enterprise level. The promise of IoT is fantastic, so in 2016 look to early adopters to work out the kinks and deliver results.
3. Big data moves beyond hype to enrich modeling
Big data has moved beyond hype to provide real value. Modelers today can access a wider then ever range of data types (e.g., unstructured data, geospatial data, images, voice), which offer great opportunities to enrich models. Another new gain from big data is due to competitions, which have moved beyond gamification to provide real value via crowdsourcing and data sharing. Consider the Prostate Cancer DREAM Challenge, where teams were challenged to address open clinical research questions using anonymized data provided by four different clinical trials run by multiple providers, much of it publicly available for the first time. An unprecedented number of teams competed, and winners beat existing models developed by the top researchers in the field.
4. Cybersecurity improved via analytics
And as IoT grows, the growing use of sensors must thrill cybercriminals, who use these devices to hack in using a slow but insidious Trojan Horse approach. Many traditional fraud detection techniques do not apply, because detection is no longer seeking one rare event but requires understanding an accumulation of events in context. Similar to IoT, one challenge of cybersecurity involves data, because streaming data is managed and analyzed differently. I expect advanced analytics to shed new light on detection and prevention as our methods catch up with the data. Unfortunately, growing methods for big data collaboration are off limits, because we don’t want the bad guys to know how we’ll find them, and much of the best work is done behind high security clearance.
5. Analytics drives increased industry-academic interaction
The Institute for Advanced Analytics (IAA) at NC State University tracks the growth in analytics masters programs, and new programs seem to pop up daily. Industry demand for recruits fuels this growth, but I see increased interest in research. More companies are setting up academic outreach with an explicit interest in research collaborations. Sometimes this interest goes beyond partnership and into direct hiring of academic superstars, who either take sabbaticals, work on the side, or even go back and forth. For example, top machine learning researcher Yann LeCun worked at Bell Labs, became a professor at NYU, was the founding director of the NYU Center for Data Science, and now leads Artificial Intelligence Research at Facebook. INFORMS supports this academic-industry collaboration by providing academics a resource of teaching materials related to analytics. In 2016, INFORMS will offer industry a searchable database of analytics programs to facilitate connections and the new Associate Certified Analytics Professional credential to help vet recent graduates.
Polly Mitchell-Guthrie serves as chair of the INFORMS Analytics Certification Board and secretary of the INFORMS Analytics Section. She is senior manager of the Advanced Analytics Customer Liaison Group in the Research and Development Division at SAS.
1. Machine learning established in the enterprise
Machine learning dates back to at least 1950 but until recently has been the domain of elites and subject to “winters” of inattention. I predict that it is here to stay, because large enterprises are embracing it. In addition to researchers and digital natives, these days, established companies are asking how to move machine learning into production. Even in regulated industries, where low interpretability of models has historically choked their usage, practitioners are finding creative ways to use machine learning techniques to select variables for models, which can then be formulated using more commonly accepted techniques. Expect greater interest across academic disciplines, because machine learning benefits from many different approaches. Consider the popular keynote from the INFORMS Annual Meeting last year, where MIT Professor Dimitris Bertsimas talked about “Statistics and Machine Learning via a Modern Optimization Lens.”
2. Internet of Things hype hits reality
The Internet of Things (IoT) is at the peak of the Gartner Hype Cycle, but in 2016 I expect this hype to hit reality. One real barrier is plumbing – there’s a lot of it! One of my colleagues is analyzing the HVAC system on our newest building as an IoT test project. The building is replete with sensors, but getting to the data was not easy. Facilities told him data are the domain of IT, who then sent him to the manufacturer, because while the HVAC system collects the data, it is sent to the manufacturer. “Data ownership” is an emerging issue – you produce the data but may not have access to it. An even larger challenge for IoT will be to prove its value. There are limited implementations of IoT in full production at the enterprise level. The promise of IoT is fantastic, so in 2016 look to early adopters to work out the kinks and deliver results.
3. Big data moves beyond hype to enrich modeling
Big data has moved beyond hype to provide real value. Modelers today can access a wider then ever range of data types (e.g., unstructured data, geospatial data, images, voice), which offer great opportunities to enrich models. Another new gain from big data is due to competitions, which have moved beyond gamification to provide real value via crowdsourcing and data sharing. Consider the Prostate Cancer DREAM Challenge, where teams were challenged to address open clinical research questions using anonymized data provided by four different clinical trials run by multiple providers, much of it publicly available for the first time. An unprecedented number of teams competed, and winners beat existing models developed by the top researchers in the field.
4. Cybersecurity improved via analytics
And as IoT grows, the growing use of sensors must thrill cybercriminals, who use these devices to hack in using a slow but insidious Trojan Horse approach. Many traditional fraud detection techniques do not apply, because detection is no longer seeking one rare event but requires understanding an accumulation of events in context. Similar to IoT, one challenge of cybersecurity involves data, because streaming data is managed and analyzed differently. I expect advanced analytics to shed new light on detection and prevention as our methods catch up with the data. Unfortunately, growing methods for big data collaboration are off limits, because we don’t want the bad guys to know how we’ll find them, and much of the best work is done behind high security clearance.
5. Analytics drives increased industry-academic interaction
The Institute for Advanced Analytics (IAA) at NC State University tracks the growth in analytics masters programs, and new programs seem to pop up daily. Industry demand for recruits fuels this growth, but I see increased interest in research. More companies are setting up academic outreach with an explicit interest in research collaborations. Sometimes this interest goes beyond partnership and into direct hiring of academic superstars, who either take sabbaticals, work on the side, or even go back and forth. For example, top machine learning researcher Yann LeCun worked at Bell Labs, became a professor at NYU, was the founding director of the NYU Center for Data Science, and now leads Artificial Intelligence Research at Facebook. INFORMS supports this academic-industry collaboration by providing academics a resource of teaching materials related to analytics. In 2016, INFORMS will offer industry a searchable database of analytics programs to facilitate connections and the new Associate Certified Analytics Professional credential to help vet recent graduates.
Polly Mitchell-Guthrie serves as chair of the INFORMS Analytics Certification Board and secretary of the INFORMS Analytics Section. She is senior manager of the Advanced Analytics Customer Liaison Group in the Research and Development Division at SAS.
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