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IOD Keynote: Computational Mathematics and Freakonomics

I attended the keynote this morning, on the theme of looking forward: first we heard from Mike Rhodin, an exec in the IBM Software group, then Brenda Dietrich, a mathematician (and VP – finally, a female IBM exec on stage) from the analytics group in IBM Research. IBM Research has nine labs around the world, including a new one just launched in Brazil, and a number of collaborative research facilities, or “collaboratories”, where they work with universities, government agencies and private industries on research that can be leveraged into the market more quickly. I’ve met a few of the BPM researchers from the Zurich lab at the annual academic BPM conference, but the range of the research across the IBM labs is pretty vast: from nanotechnology, to the cloud, to all of the event generation that leads to the “smarter planet” that IBM has been promoting. She’s here from the analytics group because analytics is at the top of this pyramid of research areas, especially in the context of the smarter planet: all of our devices are generating a flood of events and data, and some pretty smart analytics have to be in place to be able to make sense of all this.

The future of analytics is moving from today’s static model of collect-analyze-present results, to more predictive analytics that can create models of the future based on what’s happened in the past, and use that flood of data (such as Twitter) as input to these analytical models.

I have a lot of respect for IBM for trying out their own ideas on systems on themselves as one big guinea pig, and this analytics research is no exception. They’re using data from all sorts of internal systems, from manufacturing plants to software development processes to human resources, to feed into this research, and benefit from the results. When this starts to hit the outside market, it has impacts on a much wider variety of industries, such as telco and oil field development. Not surprisingly, this ties in with master data management, since you need to deal with common data models if you’re going to perform complex analytics and queries across all of this data, and their research on using the data stream to actually generate the queries is pretty cool.

She showed a short video ciip on Watson, an AI “question answering system” that they’ve built, and showed it playing Jeopardy, interpreting the natural language questions – including colloquialisms – and responding to them quickly, beating out some top human Jeopardy players. She closed with a great quote that is inspirational in so many ways, especially to girls in mathematics: “It’s a great time to be a computational mathematician”.