The rapid growth of technology can often outpace human ability to take full advantage of all it has to offer. Without the appropriate processes in place, businesses both in and outside the tech industry might have trouble integrating new tools into their offerings, potentially hurting their service, functionality and safety in the process. That’s where John Thomas (pictured), distinguished engineer and director at IBM Analytics, comes in.

“With the world of exploding data that we are in, with all these devices, it’s very important to have systematic approach to managing your data,” Thomas said. As new languages and frameworks surface daily, Thomas works to assist businesses in their efforts to create environments that support them through data science experience.

Thomas spoke with Dave Vellante (@dvellante) and John Walls (@JohnWalls21), co-hosts of theCUBE, SiliconANGLE’s mobile livestreaming studio, during the IBM Data Science for All event in New York City. They discussed the effect of new technologies on processes, as well as specific use cases for how they are improving functionality across industries. (* Disclosure below.)

‘Machine learning is not an island’

Thomas approaches his work in optimizing businesses through data science experience on three fronts: democratizing machine learning, operationalizing machine learning, and integrating hybrid machine learning.

“Instead of moving data off the platform to do machine learning … bring machine learning to where the data is. … Then have complete flexibility in terms of where you deploy that model,” Thomas said. That flexibility works to create seamless experience across all platforms, a crucial need for businesses looking to improve efficiency and stay competitive.

Thomas’ work with IBM has already proven itself to be a wise investment for businesses. Streamlined data integration has helped in improving customer experience, internal processes, call center assistance, speech to text accuracy, image recognition and fraud detection efforts, he pointed out. “I think perhaps the most important one is that things that used to take weeks or days to train and test now can be done in days or minutes,” Thomas said.

For businesses looking for these kinds of improvements, it’s important to keep in mind that upgrading processes is a process in itself. Companies must not only focus on clean data, but also accessibility, democratization and automation where possible to facilitate the best use of their resources.

“Machine learning is not an island. All these things coming together is what makes these dramatic advancements possible,” Thomas concluded.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the IBM Data Science for All event. (* Disclosure: TheCUBE is a paid media partner for the IBM Data Science for All event. Neither IBM, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE