A friend (Hi Ken) referred me to this post about search by David Berlind. Now while I think unstructured text in decision automation and Enterprise Decision Management or EDM is going to be more and more important and while I realize I have not blogged much about unstructured text analysis (note to self, write post on this), I actually wanted to drill down in David's phrase "competitive productivity". Here's how he introduced it:
To businesses with a lot of information workers, any technological advancements that can whittle that 25 percent [time spent searching for information] down to 20, 15, 10 or even 5 percent means that respectively, those workers can be spending 5, 10, 15 or event 20 percent more of their time on tasks that contribute more directly (more directly than searching) to competitive advantage. In fact, freeing up time to focus on those activities that contribute to competitive advantage — long-hand for what I'm going to start calling "competitive productivity" (versus plain ole' "productivity")
This is a great concept and highly relevant to anyone thinking about EDM. EDM is about automating and improving decisions, particularly those that require some expert judgment or for which some data exists that could be used to make or improve the decision. Clearly these decisions are often those taken by information workers - underwriters in insurance, loan officers, ad pricing managers, materials master designers, supply chain managers, staffing schedulers, diagnostic engineers, eligibility managers and so on. If I can automate these decisions, at least the most common 85-90-95%, then I likewise free up their time to work on more complex, high-value tasks.
Now some of this is going to overlap - some of the 25% of time spent searching for information is going to be included in the time saved by not having to make the decision manually because it is information being sought to make a decision. I suspect, however, that most of the benefit is additive. The time spent searching for information is probably in support of the most complex decisions and those therefore most likely to be in the small percentage still referred for manual review. The process of automation will free up some of this time though because you will get a lot more context for the referred decision and this will make it easier to search for information. For instance, a referred policy that needs manual underwriting will say why it is being referred and that will focus the need to search for information down to just the information that will resolve that issue.
Each of the case studies above shows how different decisions can be automated and how they free up information worker time for "competitive productivity".