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What's Next for Knowledge Workers?

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As machines and social networks turn expertise into a commodity, knowledge work migrates from special Roles to nearly everywhere at work. Does this mean that everyone gets smarter, or does it just mean that organizations will be herding even more cats?

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What's Next for Knowledge Workers?

  1. 1. What’s Next for Knowledge Workers ©2013 Malcolm Ryder / archestra As you might expect, a good place to go look for a consensus definition of knowledge worker is Wikipedia, the world's largest common ground for uncommon knowledge. As cited there, Peter Drucker coined the term knowledge worker over forty years ago, referring to one who works primarily with information or one who develops and uses knowledge in the workplace. The easy idea that knowledge is developed from information makes its inventory of information tasks a safe point of reference. However, even the provided evolving chart of knowledge worker roles blurs the distinction between information processing and knowledge processing. Here, "processing" means to make, find, collect, analyze, organize, provide, maintain and track. Meanwhile, we know that information and knowledge are not the same thing. For actual knowledge work, the essential difference between them must be something that is also created, supported, and controlled in use. The essential difference -- namely context -- is arguably what "knowledge work" is most distinctively working on. High-value knowledge work makes the difference between recognizing and applying contexts, or not doing so, in a managed way. Contexts are transformative of information, and contexts are antecedents of knowledge. In other words, knowledge workers perform transformations and create antecedents that result in the availability of needed knowledge for operations. Thereafter, the managed use of available knowledge extends the work. All "processing" treats its subject as a material resource, and through processing, resources can become commodities. Today, the combination of high-powered analytics by machines, with social networking, means that vast amounts of information can be contextualized at extremely high speeds, effectively making knowledge a commodity. An industrial equivalent to this is found in the difference between mass transit and personal automobiles. We might decide that neither mode is more capable of moving people than the other; but the significant difference is not between commodity and non-commodity transportation, anyway. At a given level of consumer cost, each mode commoditizes transportation. Instead, the significant difference is between inconvenience and convenience. Industrialization of transportation commoditizes convenience instead of leaving convenience as a luxury.
  2. 2. Machines and social networks are generating huge quantities of convenient knowledge, which mean that as far as most knowledge consumers are concerned, the bulk of the labor value of knowledge workers is necessarily now moving from knowledge origination to a different aspect of their labor: selectivity. Knowledge Labors Knowledge Information Acquisition Classification Validation Distribution Sourcing Transformations Selections Communication Experts (credentials) Analysts (methods) Managers (authorities) Publishers (reputations) All work is labor. Typically, across the lifecycle of knowledge provision, the division of knowledge labor has aimed for the efficient creation of knowledge that could be validated before distribution. Heretofore, both efficiency and validation have relied on expertise. Expertise was relatively scarce (and therefore a high-value premium), whereas communications technologies had far earlier made distribution itself into a vehicle allowing prepared content to be abundant. Now, things are different. Expertise itself is usually a major intersection of investigation (affecting efficiency) and credibility (affecting validation). Importantly, both of those factors are subject to the constraints or supports of cultural and practical influences -- such as cost allowances, politics, and schedules. These factors could make expertise relatively (and sometimes greatly) inconvenient for some organizations, while more convenient for others. Because automation and social networks have each now radically improved the cost, transparency, and speed of knowledge processing, the development of knowledge is becoming very broadly convenient, and convenient expertise is becoming commoditized. Described in traditional terms of organizational analysis, the quantum leaps in the convenience of expertise translates knowledge work from a "vertical" function managed as a discrete operation, to a "horizontal" function included as a competency within and across most goal-oriented procedures. The customary terms for this – “decentralization” or “democratization” -- reflect new consensus expectations. Those expectations may not be the default for the majority of knowledge work implementations but they are increasingly the default for demand of access to knowledge. An organization-level division of labor is always an exercise in specializations based primarily on three things: labor efficiency, ownership of resource, or accountability of results. Any one of these factors may
  3. 3. be dominant singly, but generally they are all three combined. For example, efficiency in knowledge work has been predicated on expertise; and investment in experts has been predicated on operational outcomes encouraging sponsors. The combination supports “expertise” as a Role, by which sponsors get a high level of accountability for outcomes. But going forward, the organizational value of knowledge work is not going to be anchored so much in the cultivation of superior distinct knowledge workers. Instead, it will be anchored in optimally leveraging today’s tremendously expanded access to knowledge assets. The challenge is to define what "leveraging" means. Most general surveys of the concerns of business operations have been saying for years that the pace of change in the operating environment is the dominant knowledge-related issue confronting competitive organizations. Understandably, few information projects are now as important as the effort to predict change early. Impact analysis of actual work is either equal to or right behind change prediction in importance; but ongoing frequent change makes the focus of impact analysis far less about "quality of execution" and instead about "relevance". The business goal for using knowledge is to achieve sustained relevance in a constantly changing environment of demand-related opportunities and risks. This requires two high-level efforts that provide the necessary awareness of the environment and the logic for action. One is Business Research, and the other is Business Development. The outcome of "successful" Business R&D is timely alignment with environmental conditions, with minimal confusion from the structural complexity of the environment and organization itself. In that light:  Business Research consists primarily of mapping and monitoring the environment.  Business Development consists primarily of prioritizing and integrating production. Knowledge–based Function Competencies Items to Understand RESEARCH of the operating environment Mapping logical entities, boundaries and locations Monitoring events, states, changes DEVELOPMENT of production Prioritizing policy, requirements, scope Integrating inventions (models, designs), transactions (agents, brokers)
  4. 4. This perspective clarifies an agenda for pursuing and processing knowledge, guiding the selection and prioritization of key types of items to understand. Each different business division or operation must identify the specific knowledge items related to its own conduct of competencies; but all the divisions and operations will have the same purposefulness of knowledge effort in common. Through cooperating with each other in the “business R&D” effort, organizations will collectively expose unsuspected patterns of activity; operators will forecast impending opportunities and risks; managers will navigate trade-offs of continuity and course corrections; and teams and partners will collaborate on creating paths to market for new solutions. Those familiar objectives will model the shared perspective used to decide what knowledge to obtain and apply for finding, following and meeting demand.

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