While I'm putting the finishing touches to my PhD thesis (titled The Voting Model for Expert and Blog Search), I thought I'd pick up on a recent related article.
An excerpt from The Numerati has been published on BusinessWeek.com. In the excerpt, Stephen Baker interviews the scientist Samer Takriti while he was working at IBM . Samer, who is a specialist in Operations Research, is working on commoditising workers. Similar to how supply chains and production lines have been modelled and improved, Samer believes that people can be assigned to projects using combinations of their availability, their scost, and their skills/expertise. The idea is to optimise the use of co-workers, leading to a better productivity within an organisation.
What's really interesting here is that this is a real application of expert search technology, being applied not just to satisfy occasional expertise needs ("I'm stuck, who should I ask for help?"), but in daily use to determine work assignments and to increase productivity. A fusion of search technology with constraint optimisation. Tools like these are likely to become invaluable in assigning jobs in global consultancy companies, where managers are unlikely to know everyone at their disposal. Such tools could even be used to identify the best training path for a co-worker to become skilled and productive in a particular area.
Imagine, says Aleksandra Mojsilovic, one of Takriti's close colleagues, that the company has a superior worker named Joe Smith. Management could really benefit from two or three others just like him, or even a dozen. Once the company has built rich mathematical profiles of Smith and his fellow workers, it might be possible to identify at least a few of the experiences or routines that make Joe Smith so good. "If you had the full employment history, you could even compute the steps to become a Joe Smith," she says.
Van drivers have been having their routes assigned automatically for many years. Why shouldn't consultants at IBM be any different? However, Baker points out that some people may be left out by systems (his example, a senior consultant left out because of his high cost, which Takriti counteracts by allowing senior staff members more "time on the bench" than junior staff, because when senior consultants are utilised they get larger cheques). Even still, the concern is this reliance on an expert search system to assign jobs when "expertise relevance" is an even vaguer concept than "document relevance", and expert search systems are not yet (and might never be) as accurate as a travelling salesman solution or a program to optimise a supply chain.
(Via Slashdot)
2 comments:
Craig, congratulations on your imminent doctorhood!
While I can see the virtues of the Operations Research approach to human supply chain management as an optimization program, I'm not convinced that it is the only or best approach. Granted, I'm a bit biased, but I'm more a fan of the solution IBM implemented using Endeca, which emphasizes exploratory search.
Like you, I'm skeptical that people can articulate their needs enough to then trust an procedure to provide them with an optimal solution. Moreover, I think that exploration is key to help people understand trade-offs.
Daniel,
Thanks for the link to the Forrester article.
The workers assignment scenario reported by Baker is indeed a good example of exploratory search.
In fact, this assignment scenario does remind me of your clarification before refinement paradigm and your new washer example illustration.
I am tempted to agree with you. A faceted classification solution might do as well as the solutions based on Operations Research, while having the advantage of keeping the user in the search/decision loop. However, how do we know for sure? We are back to square one and the problem of IR evaluation, especially that of exploratory search.
Iadh
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