If business was predictable, business theorists could create several models for business operations, make them Open Source, accept donations and consulting, and retire to a place more suitable for thinking. But the unpredictability of business is what makes it interesting. Business is filled with quantitative measurement of itself. Manufacturing companies collect data on raw materials, optimum manufacturing cycles, environmental factors, and just-in-time inventory. Services industries have adapted this analytical perspective of work, examining customer satisfaction rates, average support call time, and other customer based metrics. Service sector companies in the knowledge economy appear to have a much harder time than manufacturers in creating a template for the perfect product. Much of the work is what Robert Reich calls Symbolic/Analytical and requires analysis and decision. Injecting human beings into the business equation sometimes creates a level of unpredictability that cannot be measured. Human unpredictability accounts for two significant problems in the enterprise: first, it makes the modeling of many business processes very difficult due to the inconsistency of human thought and action. Second, it stymies the ability to create a clear model of knowledge management due to the human interaction that needs to occur for knowledge creation and transfer.
KM is not a set of known processes that function consistently. Imagine that your enterprise wants to upgrade its database capabilities. So you license an Oracle database and integrate it into your ERP system. You pay Oracle for some support, add some knowledgeable staff members, conduct training, and pay an integrator to complete the project. Never easy, but once the database is optimized the processes that control the DB and employees is fairly consistent. You can measure all types of metrics from the database and using standard protocols you can guarantee consistent behavior from the database and the people accessing specific kinds of data. This isn’t the case with KM integration. There is no component (like a database) that forces a significant amount of consistency on the processes. In a KM collection effort, some employees feel incentivized to enter information and some don’t’. The sharing of data is inconsistent. Some people share due to relationships and projects. Some prejudicially don’t share due to poor relationships or turf wars. Collaborative exchanges resulting from cross-functional projects might vary and subject matter experts in important areas retire and take a goldmine of untapped information to the flower beds, golf courses, and beach houses of retirement. All of this inconsistency cries for a unified model. But how do we create a model that accounts for human inconsistency and the randomness of some KM exchanges?
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