Magazine article CRM Magazine

CRM and Knowledge: All We Know Now Is Just a Dress Rehearsal

Magazine article CRM Magazine

CRM and Knowledge: All We Know Now Is Just a Dress Rehearsal

Article excerpt

KNOWLEDGE and its management have always been subjects of interest, but their importance has increased dramatically in our lifetime. And it's not necessarily the knowledge that exists that we're interested in, but rather our capacity to double it. Knowledge is the raw material of economic progress, not simply information. If information is data in context (and it is), then knowledge is information in action, so doubling it is a measure of economic well-being.

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In the 1980s, Buckminster Fuller, the American futurist and architect, invented the knowledge doubling curve, which has been misunderstood, at least in the context of time. Like many things that double, it's not sufficient that the doubling occur, but that it happens at an ever-increasing rate. Fuller posited that one unit of human knowledge was equal to the knowledge humanity accumulated between year one and 1500 CE. It then doubled by about the year 1750, when the Industrial Revolution began. It doubled again by about 1890, and by 1900, knowledge was doubling every century. Even that wasn't enough. By World War II, knowledge was doubling every 25 years; IBM now predicts that within the next couple of years, it will be doubling every 12 hours.

The idea of managing knowledge is both vital and perplexing. It is perplexing because knowledge has always been something that human minds make out of information. Its synonyms-understanding, comprehension, awareness--all imply a mind acting on information. So given all that, how does one manage knowledge in any systematic way? Logically, knowledge management is the grouping of tools, technologies, and processes that constantly and consistently make the right information available to decision-makers. In that context, information is a substrate on which the minds of decision-makers act, in much the same way an enzyme acts on sugar.

But getting to the level of a substrate takes a great deal of work. Systems have to capture data that analytics run on to produce the raw information. And in the best circumstances, predictive models have to run constantly to ensure that the information in circulation is what's needed at the moment. …

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