On 2005-11-06 16:14:00, Allan Engelhardt wrote in CYBAEA Journal:
Martin and Dave wonders why knowledge management has failed: the grand (and sometimes successful) projects of the late nineties and early noughties have come to nothing, and today's businesses pay only lip-service to being part of "the knowledge economy". Martin, always perceptive, suggests that the challenge may be cultural.
At CYBAEA we tend to talk about innovation management rather than knowledge management. We prefer to talk about the active utilization of knowledge over the pure gathering of information, which reminds us of dusty libraries run by aging spinsters. But whatever you call it, we agree that businesses are not doing very much about it. With the results we have seen from the brave exceptions, and given that innovation is probably the only thing that keeps your job out of India, this is surprising.
Martin pains a picture of the inflexible organization:
What is frightening is to find so many similarities between our large industrial multi-layered organizations and the former Soviet Union, which proved totally incapable of modernizing itself and eventually collapsed.
There may be some millage in this. Most current managers became successful in a company that was largely hierarchical and where the manager's leadership abilities, this is to say their ability to institute change from the top down, were valued. Dave spells it out:
Business leaders see their leadership role as critical to the organization's success; their frame of understanding is hierarchical -- they tend to believe that knowledge and value increases with experience and that rewards should go disproportionately to identified superstars and up-and-coming leadership candidates.
In this context, innovation management, enterprise social software, and, yes, even knowledge management, whatever you call it, represents a cultural change and therefore a threat. Change is diffucult and why change a formula that works?
Except, of course, that it isn't working very well anymore. All the easy jobs have already gone to India and China. Your job is going next, and you are not going with it. Unless you can innovate and show a clear and sustained benefit of keeping you around.
If you look where innovation has historically happened, you would look to universities and other scientific institutions. That's my background. The management there is traditionally collegiate rather then hierarchical. A system of essentially peers where everybody's contribution is valuable within an established method or way of working, seems to produce the most new insights.
Of course there are problems with a pure collegiate structure. Universities are not usually the best to capitalize on the applications of their innovations, and even just looking within pure research they can lack a certain amount of urgency and accountability.
That, then, is the challenge of the modern Western business. To change its structure to a more collegiate approach and foster innovation without sacrificing the ability to execute. To find the equivalent of the scientific method for successful businesses in the new knowledge economy.
Knowledge management, by whatever name, may help or hinder, but it is clear that it is not about the technology or the systems. It is about changing the way you manage your business. It is about saving your job.
On 2010-07-13 07:47:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
I am not sure apeescape’s ggplot2 area plot with intensity colouring is really the best way of presenting the information, but it had me intrigued enough to replicate it using base R graphics.
The key technique is to draw a gradient line which R does not support natively so we have to roll our own code for that. Unfortunately, lines(..., type="l") does not recycle the colour col= argument, so we end up with rather more loops than I thought would be necessary.
We also get a nice opportunity to use the under-appreciated read.fwf function.
Read more (~535 words).
On 2010-06-22 11:45:00, Allan Engelhardt wrote in CYBAEA Journal:
We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is scary.
We now re-do the analysis four years later and, just because we can, we are using the leading companies of the London stock exchange instead of the largest American companies.
The results still hold. We called it the 3/2 rule: treble the number of workers and you halve their individual productivity. Large companies with ten times the number of employees are ¼ as productive as their smaller competitors.
Employee productivity is a big issue. If all the FTSE-100 companies achieved their average profits per employee, then the index would generate almost £1 trn of additional net profits for the economy.
Read more (~245 words).
On 2010-06-22 11:20:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is mildly scary.
We revisit the analysis for the FTSE-100 constituent companies and find that the relation still holds four years later and across a continent.
Read more (~763 words, 5 comments).
On 2010-06-17 09:05:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Following on from my previous post about improving performance of R by linking with optimized linear algebra libraries, I thought it would be useful to try out the five benchmarks Revolutions Analytics have on their Revolutionary Performance pages.
Read more (~300 words, 2 comments).
On 2010-06-15 10:21:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:
Can we make our analysis using the R statistical computing and analysis platform run faster? Usually the answer is yes, and the best way is to improve your algorithm and variable selection.
But recently David Smith was suggesting that a big benefit of their (commercial) version of R was that it was linked to a to a better linear algebra library. So I decided to investigate.
The quick summary is that it only really makes a difference for fairly artificial benchmark tests. For “normal” work you are unlikely to see a difference most of the time.
Read more (~934 words, 1 comments).
Join the discussion
There are no comments yet. Be the first to comment.