Everything is connected ... really ... Putting meaning to work
We can list a few knowledge work activities that are common in most organizations, including:
- finding and processing information relevant to our objectives and converting that information into purpose-driven communications of various sorts, from e-mail to substantial documents;
- designing information processing applications to make information more useful to people and to other software applications (relational database applications are a good example);
- group knowledge work activities, including discussing the value of the ideas in information; and
- planning and managing the activities of knowledge workers.
There are countless others but, as noted above, we have no formal description, no standards for best practices and no appropriate metrics for such activities. And those high-level descriptions of familiar knowledge work activities mask the core role of meaning in knowledge work.
Putting aside for the moment the cultural and emotional aspects of knowledge work, what are the primary characteristics of such meaning-based work?
- interpreting information;
- converting information into more meaningful and useful forms;
- making judgments on the meaning, relevance and importance of the resulting abstracted information;
- integrating those abstracted objects and connections with prior knowledge; and
- associating those objects with real-world events, products or other realities of importance to the individual and/or the organization.
We already spend much of our time doing precisely those things, but we frame them not as meaning-based activities but as actions performed on information—usually with a variety of established information processing tools. (To illustrate the point, I created the chart on page 24, March KMWorld 2010, which is a simple way of underscoring that the development, transfer and integration of meaning are pervasive activities in businesses.)
Such activities are at the core of what most knowledge-based companies do. They are precisely how we create value. But we are doing those things very poorly because we have focused on the information aspects of the activities, not on what people actually do. To make matters worse, doing those things even marginally well—and in a consistent way that makes sense to us—is still time-consuming, frustrating and exhausting. And we perform those activities largely in isolation and without standards or best practices.
Feedback and metrics
We made a mistake by focusing too much on information, but we also bought into the misleading idea that knowledge work is completely different from physical work.
- We don’t account for feedback as an essential characteristic of work activities. If you strike a nail poorly or move clumsily and scratch a product on the assembly line, you can see or feel the effects. But there are few knowledge processes that give you feedback on the quality and impact of your activities. Most metrics for knowledge work look at bottom-line changes over long intervals. The chain of cause and effect is simply not clear.
- We fail to make explicit, measurable connections between individual knowledge work and group knowledge work activities. Most of the activities of individuals listed above would be more efficient and produce better outputs if they were (1) integrated with related activities performed by other individuals and (2) supported by common resources. Such a transformation is only possible and reasonable if those processes are analyzed carefully and deconstructed into well-defined component tasks. That’s exactly what happened in manufacturing.
That’s so wrong! In spite of the superabundance of information, it’s still all about how we work, what we do and how we work together.
Technologies and practices of meaning
With or without a formal Taylor-like process for analyzing knowledge work—even without acceptance among managers and theorists that meaning is the infrastructure of all work—practices and technologies that address and leverage meaning are insinuating themselves into knowledge-based activities. That’s a good thing. The adoption of meaning-based (or “semantic”) approaches and technologies is inevitable, but it is also painfully slow, and we are losing the opportunity to make dramatic short-term improvements in productivity and competitiveness because we are still so obsessed with information.
Many forms of information technology are, indeed, devoted to conversion of information into meaning or deal directly with meaning. Consider the semantic aspects of the following applications:
- Relational databases are meaning-based. A relational schema deconstructs real-world relationships into tables and relationships among tables.
- Project management techniques and tools associate the availability of specific skills with specific actions and specify the order in which such actions must take place.
- Personal information management software helps individuals organize their personal information resources and connect those resources to their activities.
- Social network analysis (SNA), insofar as it includes connections between ideas and people, traces the flow of meaning through organizations and unstructured groups.
But those applications are not framed as tools for capturing, managing and leveraging meaning. More recent practices and technologies do make that connection, but they do so as stove-piped applications, not as part of an overall semantic infrastructure for knowledge work.
Best-known brand of semantic technology
Today, the most visible meaning-based technology is the Semantic Web. There is no shortage of explanations of the Semantic Web. I can recommend Jeffrey Pollock’s Semantic Web for Dummies, but you are well served by first reading James Hendler’s post, “What is the Semantic Web really all about?” (http://network.nature.com/people/jhendler/blog/2009/06/16/what-is-the-Semantic-web-really-all-about).
Hendler, an expert and innovator in computer ontologies, was co-author of the Scientific American article that was primarily responsible for bringing Tim Berners-Lee’s new vision to a much broader audience, so he brings much more authority to his observations than, well, just about everyone else except Tim B-L himself. Hendler defines the foundations of the Semantic Web: “The Semantic Web is based on the relatively straightforward idea that to be able to integrate (link) data on the Web, we must have some mechanism for knowing what relationships hold among the data, and how that relates to some real-world context.”