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Brenda Dietrich is a research fellow within the mathematics department of IBM’s research division. Dietrich attended IBM’s launch of Cognos and spoke with eWEEK senior writer Renee Boucher Ferguson about her department’s plans to work with Cognos, and what might be accomplished by applying mathematical techniques to traditional business intelligence.
What is the focus of the mathematics department at IBM?
The IBM research division has had a mathematics department for more than 50 years. Our role is to work with clients on everything from supply chain and product design to demand forecasting to [building] models to better understand and predict customer behavior.
How does that translate in terms of the work you do on a daily basis?
We observe a business situation-with supply chain I spent a lot of time in IBM manufacturing, watching how it’s done. We decide how decisions are made then use mathematics to figure out the best decisions. We instantiate this in software-large pieces of code-that are generally hooked up to ERP [enterprise resource planning] or CRM [customer relationship management] and the algorithm sucks data out of the system and makes recommendations.
How accurate are the recommendations?
Accuracy is a function of data quality, always. Data mining is statistics based and it’s very accurate when it’s in something that is approaching [stability]; you can’t predict something if it hasn’t occurred, if it hasn’t shown up. In general [the predictions] are more accurate than the processes they replace.
How predictable are the scenarios in Cognos’ world of business intelligence and performance management?
We’re hoping to work that out with the Cognos team-the acquisition is only seven days old-and some customers to understand the problems that are at the cutting edge of what Cognos is able to address, [things like] dash-boarding or alerts, to put more predictive modeling into and to project [the] future.
Why is Cognos having such a big impact on your research?
Part of it is this area we worked in [Information On Demand] is growing really rapidly. It started very small but it really does require a full infrastructure to be more effective. Now we’re in a market where customers have historical data. They’ve invested in ERP and as a by-product of investing in ERP you get trace data and this is a gold mine for doing modeling. Ten years ago it didn’t exist.
Are products in the future?
Will your research with Cognos result in actual products?
Probably pieces of an actual product. I don’t think we will be likely to have entire products around our pieces of work.
What type of research have you focused on in the past?
We’ve been involved in the on-demand strategy since it launched two wears ago. We will accelerate that; we will have a lot more friends in the IBM organization that want to work with us. We’ve been working with high performance scientific computing. We do work that supports IBM super computers…to make computers more usable. Information On Demand is aimed at addressing business problems.
Where do you see your work with Information On Demand going?
Computing is become much more valuable as a tool by [many] more people. Let me give you a very simple example called the shortest path to a problem. It’s not hard or complicated-given a map, knowing how long each road is, what is the shortest path between point -A’ and point -B’? Now everyone in the world can solve that with Google. What’s running in the background is an interesting algorithm and it’s running off standard data now accessible to everyone.
My mother-in-law can use Google. If I ever wrote down the algorithm she would have difficulty with me. But she can use [the algorithm]. That’s what I see happening in the world. If we are able to capture the mathematics that are well posed, it opens the world mathematically to everyone, and not just the geeks among us. And people can make better decisions. You won’t follow the route your brother-in-law said is the best route, you will follow the absolute best route. It takes opinion and personal prejudices out of decision making. Everything is based on data.
It’s amazing how the business processes we work with at IBM, how emotion has gone out of the data. Everything is based on data, not on who yells the loudest.
Is there a point where data becomes too pervasive?
One thing we do worry about is do you lose creativity and new ideas when you are doing everything based on encapsulated processes? I think that’s why everything we deliver has a human in the loop. That’s where judgment and creativity still has the capacity to come into it, but based on analysis and information. You can test the model with much [fewer] consequences.