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Bridging context and best practice

This article appears in the issue March 2014 (100 Companies) [Vol 23, Issue 3]
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The real promise of KM is better business practice. IT's promise is different, more efficiently meeting technical specifications, delivering better value than a legacy system, better metrics about more data being more available at the right time. The two promises overlap some and are often confused, but they differ. KM is always hostage to what was not anticipated about the realities of practice. As long as IT systems are operating properly, they simply do what they are told, what has been fully anticipated.

IT has come a long way since punched cards, and can certainly help us prepare for real practice—often superbly. But business is something of a battlefield and always holds surprises: unanticipated competitor moves, personal failings, technology developments, all of which drive hindsight and foresight apart. As a result, managers tend to fall into two categories, reflecting two kinds of expectation: (a) that preparation can be so informed and total that practice is fully determined, versus (b) that practice can never be fully anticipated and crucial human input is always needed if practice is to be as good as it can be.

Take "context"—information fully fashioned to the the business' situation and the practices that management desires. Empirical research shows business contexts are constructed from around a dozen types or "dimensions" of information, each specific to the business. A balanced scorecard analysis might suggest some relevant dimensions when a Web-based fashion business has several categories of customer, of UPCs, of line-by-line profit, of in-house and sub-contract designers, of manufacturers, of shipping arrangements, etc.

Call it leadership

To hear those who deliver context management KM tools, the day of total control and fully determined practice has arrived; the IT and KM promises have converged. The reality is that in most cases the context of a specific business's best practice has one or more features not fully covered by the tool chosen. For instance, effective customer service agents must learn to "read" and manage the caller's anxiety, even before they get to use the tool provided to find answers to the caller's questions. As the Xerox service agents used to advise, "Fix the customer, not the machine," because that leads to the long-term customer relationships that build into sustained competitive advantage.

Those who accept that the tool cannot "do the whole job" realize the KM manager's special challenge is to identify the dimensions of best practice that continue to lie beyond the tool's grasp. It is to discover and manage the bridge between the fully known procedures of IT operations and the skills necessary to deal with the ever-surprising uncertainty and ambiguity of real practice. Delivering KM's value is in managing those crucial gap-filling skills, for exploring, training, mentoring, encouraging-what should really be called leadership-all grounded in respect for those people whose un-programmed practice ultimately creates the business' successes.

The history of KM, although different from that of IT, shows major advances too. In the early days of systems engineering, business practices were carefully analyzed and re-engineered as appropriate, and the data requirements for management's chosen processes were spelled out as the IT system's specification. The division between what went on in the IT system and what went on in the business remained sharp: two very different worlds—one within the machine's world and the other in the real world. Bridging between them was problematic and in many cases the resulting IT systems operated perfectly but still failed to deliver business value.

Bridges

KM has made great progress toward better bridging, but must be understood in terms of the human skills eventually necessary to execute best practice. Those are always specific to the situation because no practice is general; there is no "one best way." In contrast, an IT system's coding and algorithms are general, and able to process a defined variety of data at any time.

Systems engineering is always important, but commercially important bridges between the capabilities of IT tools and the business's KM objectives are being built in several new ways. This type of KM future is very different from IT. For instance, big data is attracting attention. The objective is to use ever more powerful analytic techniques to uncover hidden correlations or connections present in the business's work situation. Perhaps blue-themed handsets give rise to fewer service calls. Perhaps categorizing a caller's anxiety first and then choosing scripts accordingly helps improve the agent's performance. In general, big data techniques help business toward more complete preparation. The resulting IT tool handles more of what is required to drive best practice; the need for human skill is lessened.

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