Episode Summary: The artificial intelligence field is normally seen as burgeoning and new, populated with lots of small, scrappy companies aiming to become the next de-facto solution, with maybe one exception – “Big Blue”. IBM has been involved since the ‘beginning’ and is perhaps best known for Watson, which has from Jeopardy to a range of applications in small and big businesses, as well as the public sector. Swami Chandrasekaran is chief technologist of industry apps and solutions for IBM, and he speaks in this episode about what he sees as some of the low-hanging fruit for applying predictive models to business data. Swami has seen this technology applied in a variety of contexts, from automotive and shipping to telcos and more, providing an informed perspective for industry executives, data scientists, and anyone else interested in the intersection of predictive analytics and business.
Expertise: IT Strategy, Cognitive Computing, Analytics, Applied Machine Learning, NLP/Text Analytics, Cloud Computing, IoT, SOA, BPM, Enterprise Integration and Web/Mobile technologies
Recognition in Brief: Swami Chandrasekaran is the Chief Technologist & Executive Architect for IBM Watson, Industry Applications & Solutions, with a focus on technology strategy and architecture for the industry specific cognitive apps based on the Watson Platform and other IBM analytics components. Swami’s team is also responsible for incubating futuristic industry apps using cognitive, analytics & big data components. Prior to his current role, Swami was the Chief Architect for IBM Software – Business Solutions. He also previously worked for Webify Solutions, which was acquired by IBM in 2006, as well as KPMG, BearingPoint and Ericsson Research.
He holds a Masters in Electrical Engineering from UT Arlington and has filed around a dozen+ patents. Swami is a frequent keynote speaker, a mentor and has authored several books and publications in the areas of natural language processing, data science, BPM, SOA and enterprise integration. We met Swami at the post-event gala of the BootStrap Labs AI conference, and decided to interview him personally for our executive listeners.
Current Affiliations: Chief Technologist & Executive Architect for IBM Watson, Industry Applications & Solutions, Writer at www.nirvacana.com
(1:31) First and foremost, wanted to talk about where yo used low-hanging fruit in applying predictive models to business data…where do you see the ROI there?
(5:25) When it comes to knowing which business problems could be solved with data science…does it make sense for companies to begin consulting with folks who have done this or those who have an experience (in a specific industry and type of company)? Do you find that founders are often bringing someone on for that perspective or are they learning enough to tease out those questions themselves?
(10:18) You and probably many other competitors would aim to bring some of this predictive model building, the fundamentals of applying data science, to the mere mortals of the world; how is that progress going to happen, what are we going to have to do with the technology to move it there?
(16:10) When it comes to predictive (analytics), what’s an example…that might be illustrative of what’s going on (in industry)?
[This interview has been updated as of December 2016.]