I’ve been exploring the role of deliverables in understanding and improving knowledge work for a while. In January, I took another shot at articulating the link in a column in the Enterprise Systems Journal putting deliverables at the center of the challenge of improving knowledge work.

Knowledge work does not produce standardized, well-defined outputs. Instead, the value of its outputs depends on how well they match the unique needs of their users. No one is interested in a spreadsheet full of someone else’s data; no teacher is likely to value a copy of a paper you’ve submitted to another class. Understanding what aspects and features of a knowledge work product are most valuable to its intended user is key to focusing efforts on producing the desired deliverable. [The Fundamental Secret to Improving Knowledge Work – ESJ].

Our experience in industrial settings encourages us to look at the output as something that is already well-defined and well-understood. We focus on process changes that will produce the output more quickly or more cost-effectively. When we are doing knowledge work, we do better to focus on the deliverable longer and more mindfully. At a minimum we need to understand the user’s definition of quality, the balance between uniqueness and uniformity that will meet this level of quality, and the conditions that must be met to declare the work done.

Back in December I wrote a column for the Enterprise Systems Journal on the notion of triangulation as a key data collection and analysis strategy that is increasingly relevant in an economy characterized by information abundance. My central point was that:

In organizational (and other) settings where you are attempting to make sense of—or draw useful inferences from—a multitude of noisy and conflicting sources, the principles of triangulation offer a workable strategy for developing useful insights in a finite and manageable amount of time.

In navigation, the more widely and evenly dispersed your sightings, the more precisely you can fix your position. Focus your data collection on identifying and targeting multiple sources of input that represent divergent, and possibly conflicting, perspectives. Within an organization, for example, work with supporters and opponents, both active and passive, of a proposed reorganization or systems deployment to develop an implementation strategy. When evaluating and selecting a new application, seek out a wider assortment of potential references, vendors, and analysts. [Trust, Verify, and Triangulate]

Since that column, I’ve watched several of the recurring discussions (e..g Doc Searls, Eric Norlin, and Kent Newsome) about the changing relations between MSM (Main Stream Media) and new media forms such as blogs. Thinking about the contrasts between information collection and analysis strategies sheds some light on this debate. We used to live in a world with a handful of authoritative sources we learned to trust. With a bit more sophistication we added verify’to trust. Both those strategies work in a world of small numbers of sources, but breakdown in the world of multiple, conflicting, and contradictory sources. Triangulation then emerges as a viable alternative.

Tags: triangulation, gatekeepers