Virtualization and Cloud executives share their predictions for 2016.  Read them in this 8th Annual VMblog.com series exclusive.

Contributed by Robert Dutcher, Vice President, Marketing at Skytree

Data Finally Finds a Seat in the Boardroom

It was a noisy year in the big data space, with every major technology company coming out with their own artificial intelligence or machine learning offering that promised to fundamentally change business. This traction in the machine learning market will allow AI/ML to attain a new level of maturity and enable these technologies to reach an enterprise-grade status in the coming year. Although enterprises have signed on to the promise of machine learning and big data projects, rarely did we see the findings put into action in key business decisions. In 2016, we will see a shift in how big data is approached by the boardroom, and a deeper understanding of its benefits - and possible limitations - of machine learning and artificial intelligence tools to the bottom line.

Machine learning makes its way into the boardroom

As data scientists overseeing big data projects seek to justify increasing or sustained spend on the resources and tools used to operate these projects, they will have to prove to the boardroom that the expense is ultimately improving business processes. While enterprises are all running their own data projects and understand their importance, data scientists will be taken to task by their board to show marked improvement in a process that will outweigh the cost of using a data analytics tool in production. This will require both data scientists and data vendors to figure out a way to effectively communicate with business executives to show value in the work they are doing.

2016 will be the year big data becomes a boardroom topic, as data scientists bring enhanced visualizations and cost-benefit analysis to the C-suite in a language they can understand. Bringing machine learning into the C-suite and involving executives in the decision-making processes will allow them to make sense of their investments and ultimately make better data-driven decisions.

Open source machine learning reaches its limits in the enterprise

Over the past few years, enterprises have realized that stitching together open source machine learning projects in hopes of having one cohesive solution is a fool's errand. While the projects and code themselves may be free, the expense to build and maintain one solution with multiple open source projects is exorbitant, while the value from results are minimal. This year, enterprises will turn to enterprise-grade solutions to meet their business needs. This will give them greater functionality, flexibility and lower total cost of ownership. Data scientists who have been managing multiple open source solutions to try to run a machine learning project will favor one solution that is able to handle all aspects of the data science process and the ever-growing amount of data in need of analysis.

Machine learning projects in Internet of Things will move from the laboratories to production environments

Internet of Things (IoT) has been the buzz phrase of the past several years, but most IoT initiatives are currently in the form of PowerPoint slides, or tucked away in some dark laboratory. We are well aware that machine sensors are communicating with each other at a mass scale given all of the smart devices introduced into the market, but we have yet to see much actionable insights coming out of IoT initiatives. Few projects have yet to materialize in production and even fewer are producing measurable results.

As the IoT mindset is more commonplace in organizations and there are supportive executives and infrastructure, 2016 will bring these projects out of the dark and into full production. Data scientists will employ machine learning to make use of the massive amount of data being collected from devices and turn it into a useful process improvement. As the data collected and models built for machine learning become more enhanced and accurate, IoT will finally reach its prime time this year and we will see considerable improvement from projects in production.

After nearly a decade of hype around big data and all its promises to the enterprise, we will finally see these projects make a difference that benefits the bottom line next year. The space will become less noisy as enterprise-grade offerings are favored over patchwork open source methods, and the idea of a self-sustaining big data program materializes. Machine learning will continue to make waves by bringing some of the most innovative technologies such as IoT to the forefront and we will finally see it in production. 

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About the Author

Robert Dutcher - Vice President, Marketing at Skytree

Bob has been a marketing leader in technology for over 20 years. Bob joins Skytree from Apigee, where he served as Vice President of Marketing. He has deep domain expertise in data, Business Intelligence and Advanced Analytics and has held leadership roles in the leading BI and analytics companies, including Oracle, Business Objects, SPSS and Moody's Analytics. Bob's extensive domestic and international leadership experience spans the globe, where he has focused on building and managing high impact cross-geographic marketing teams.

Bob holds a B.S. in Physics and M.S. in Astronautical Engineering.