Sep 11, 2013 (09:09 AM EDT)
Big Data Profile: Chitra Dorai, IBM
Read the Original Article at InformationWeek
Chitra Dorai, Program Director, Lending Innovation, IBM
Chitra Dorai's first name in Sanskrit means "painting," and her life is a very bright canvas. Dorai is among the most decorated women in technology. She is named on more than 35 patents, has published more than 100 research papers, and holds the title of IBM Distinguished Engineer and Master Inventor.
Not content with research, three years ago she moved into a new global services business unit at IBM. As program director, lending innovation, Dorai is applying her knowledge to customer problems. Her unit recently released a service that melds analytics and mortgage information, which she says aims to reduce the potential of future mortgage-driven market meltdowns.
Name and title: Chitra Dorai, program director, lending innovation.
How long at IBM? I joined IBM at its research headquarters (Thomas J. Watson Research Center), about 17 years ago. For the past three years I have been program director, lending innovation at IBM's global process services unit. For the past few years we have had a focused effort around mortgage origination and servicing. I'm responsible for creating the right analytics strategy and solutions.
Career accomplishment of which I'm most proud: Being able to help clients provide the right help and services to their customers. As we moved from research to the business unit, what we have done is transfer how mortgage processing is done by infusing analytics at all steps of the process. So in digital loan processing, IBM is infusing analytics into the process so that the lenders can provide a differentiated solution and people can get to their housing dreams.
Why do you do what you? What drives me every day and what I get excited about is innovation that matters to the company and to the world. Being closer to clients and customers helps me translate innovation into products and solutions and answers that make a difference to the world. Every PhD student goes through the dilemma of staying in academia or looking outside. I chose an industry research setting because I wanted to solve problems that really matter. I take inspiration from (Richard) Hamming: "If you do not work on an important problem, it's unlikely you'll do important work."
Decision I wish I could do over: I really would have moved to the business side sooner, as I very much enjoy working on problems that are important to clients, and creating and delivering innovation that leads to great value to clients and their businesses.
Most important career influencer: I have many. My teachers from school who nurtured my passion for mathematics and science. My professors at university who taught me how to do scientific research and to communicate it. My colleagues at IBM and outside with whom I have actively collaborated. I would say that I'm shaped by ideas and writings of eminent scientists who have paved the road for people like me. I often read Richard Hamming (the long-time Bell Labs researcher who among other things developed error correcting code for telecommunications).
Top initiatives you are working on: In the context of the mortgage industry and digital loan processing, I'm looking at both sides of loan origination. At building big-data solutions, really understanding and determining the strategic imperative behind why they need big data and envisioning the right architecture and solution in order to address a specific business outcome.
Most disruptive force in my industry: The explosion of data and the improvement of individuals' ability to be able to get their voice heard in so many different channels. The data explosion has been happening for many, many years, but the need to transform that into insights, actionable insights, has become ever more important.
One thing I'm looking to do better: There is an explosion of varieties of big data and with that comes uncertainty about reliability and measurement. When you have this many different disparate sources of data, how do you integrate them into a form that provides the insight that can lead directly to a better operation? How do you rigorously address the uncertainty inherent in data and not be overwhelmed by it and harness that uncertainty to get better insights? At the research level I'm pursuing gaining an in-depth understanding on how to integrate the data that comes with different scales and precision. My work is focused on deriving actionable insights. I want those to stand the test of time as the data changes.
Hardest thing about what you're doing: Moving analytics from merely reporting to embedding them into the operations, so you can change the process behavior or people's behavior. So the hardest part in digital loan processing is the challenge that comes with changing the way the processes work, getting the analytics into the hands of the people for whom it is most useful. It's the last-mile problem of analytics, and it's the most complex part because it involves people, processes and data.
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