On 2007-02-12 22:17:00, Allan Engelhardt wrote in CYBAEA Journal:
Interest in productivity and how to manage innovation and know-how appears to be growing. Our article on employee productivity gets about four times more hits than the next most popular post. I imagine that more and more people in the West are waking up to the fact that innovation and high productivity is the only thing that keeps jobs here (though the link from USA Today probably didn't hurt).
A recent paper from Harvard considers the optimal implementation of knowledge management. With a slight reformatting for clarity:
We derive three main results [about the optimal management of know-how].
- First, information about successes is typically more useful than information about failures, since successful methods can be replicated while failures can only be avoided. This supports firms' focus on 'best practice'.
- Second, recording mediocre know-how can actually be counter-productive, since such mediocre know-how may inefficiently reduce employees' incentives to experiment. This is a strong-form competency trap.
- Third, the firms that gain most from a formal knowledge system are also the ones that should be most selective when encoding information (i.e., the ones that are most at risk from the competency trap); namely, large firms that repeatedly face problems about which there is little general knowledge and that have high turnover among their employees.
Beyond these main principles, we also show that it may be optimal to disseminate know-how on a plant-level but not on a firm-level, and that storing back-up solutions is most valuable at medium levels of environmental change.
I am not sure I am comfortable with the methodology (I prefer practical results and observed measurements to theoretical mathematics, and in any case they could get to the core of the conclusion with much less differential and integral calculus), but their conclusions broadly rings true. Does anybod have any experimental data in this area?
However, on the third point in particular notice the emphasis on formal knowledge systems. I am thinking that a more comprehensive list along their main dimensions would look something like this (click for larger version):
For what we call unknown problems (problems about which there is little general knowledge
) the main focus is on collaboration: working together to define solutions to the organization's problems. For companies that are mainly faced with known problems the issue is one of sharing this information effectively.
On the other dimension, with low turnover you benefit from an unstructured approach. The task is mainly a socal one of finding the right people with the right expertise or attitude. An emerging, bottom-up approach is entirely appropriste. But in the high-turnover situation you need a way of structuring th ework to ensure that your relatively inexpereinced workforce are addressing the right problems. You need to impose more of a top-down structure for the collaboration or sharing.
In terms of technologies, sharing can be achieved with document management systems while collaboration is more suitable for a wiki. The social element comes from some way of expressing onself (e.g. blogs) and a way of connecting people (RSS aggregators and lists of experts).
On 2009-07-02 20:33:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
I am a sucker for good quality data. I wrote about data.gov, the US Government data site before, and now I find OECD Statistics which has some 300 data sets, many of which seems to be readily accessible (though some may require subscription)
Read more (~53 words).
On 2009-06-16 10:27:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
I like the "multicore" library for a particular task. I can easily write a combination of if(require("multicore",...)) that means that my function will automatically use the parallel mclapply() instead of lapply() where it is available. Which is grand 99% of the time, except when my function is called from mclapply() (or one of the lower level functions) in which case much CPU trashing and grinding of teeth will result.
So, I needed a function to determine if my function was called from any function in the "multicore" library. Here it is.
Read more (~190 words).
On 2009-06-12 10:23:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Somebody on the R-help mailing list asked how to get Rmpi working on his Fedora Linux machine so he could do high-performance computing on a cluster of machines (or a single multicore machine) using the R statistical computing and analysis platform. Since it is unusually painful to get working, I might as well copy the instructions here.
Read more (~414 words, 2 comments).
On 2009-06-09 11:23:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
O’Reilly has published Data Mashups in R as a $4.99 PDF download in their Short Cut series. In 27 pages it takes you through an example of how to combine foreclosure information with maps and geographical information to produce plots like the one here. This is all done with the R statistical computing and analysis platform.
Read more (~108 words).
On 2009-06-01 07:07:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Hugh Miller, the team leader of the winner of the KDD Cup 2009 Slow Challenge (which we wrote about recently) kindly provides more information about how to win this public challenge using the R statistical computing and analysis platform on a laptop (!).
Read more (~456 words).
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