Being a “smart” organization—to learn quickly and apply that learning to making changes—is essential for survival in this age of hyper-competition, global power shifts, and technological change. In “dumb” organizations, the flows of knowledge and idea into and through the organization are limited and slow. Those flows are restricted by the organization’s command-and-control culture, the maze-like organizational and business structure, and limitations imposed by closed communication technology. There are three matching and inter-linked solutions to improve flows: reducing unnecessary complexity, moving to a collaborative culture, and using an Enterprise Social Networking (ESN) technology. The focus of this chapter is a step-by-step approach to justify, design, measure, and roll out an ESN suite.
TopIntroduction: The Rise Of The Need To Be Smart
Organizations did not always need to be smart.
During most of the 20th Century, America and most western industrialized countries enjoyed unprecedented economic growth. U.S. real per capita GDP increased 3.4 times during the 60 year period between 1946 and 2006 (MeasuringWorth, 2013).
With the economy growing (relatively) steadily, and with (largely) the same competitors, organizations could just keep doing what they had done the year before - plus or minus a bit. The key to success was the optimization of existing processes and finding economies of scale. Instead of being “smart,” organizations needed to be consistent and reliable. Taylorism and “scientific management” was the ascendant management approach (or was it an ideology?).
All that has now changed.
The world is no longer stable, there is hyper-global competition, new technologies are disrupting every industry, there is a seismic shift in geopolitical realities (with power shifting from the West to the East), demographic changes are massive, youth unemployment is a serious risk factor, government fiscal cliffs are increasingly common, financial institutions are in crisis, and climate change is beginning to impact everything. Whether organizations are selling into the marketplace or providing public services, being reliable just is not good enough anymore. Now, organizations must be “smart.”
Knowledge management as a discipline has largely relied on the the Data-Information-Knowledge pyramid - the DIKW pyramid when “Wisdom” is added - as a guide. Some of the earliest KM thinkers - Ackoff (1989), Adler (1986), and Zeleny (1987) - contributed to developing this model. Widely used by practitioners, vendors, consultants, and scholars, the DIKW pyramid is commonly depicted as that shown in Figure 1.
The model suggests that each of the four elements is separate and distinct from the others - and that they exist in a hierarchical relationship. Somehow one of the elements can be transmuted “upward” to the next state (e.g. from “data” to “information”) by some unknown process.
In practice the DIKW pyramid tends to cause a number of problems for KM practitioners. One problem is that there is no agreement on the boundaries between the different elements; for example, Chaim Zins (2007) collected 130 definitions of data, information, and knowledge from 45 scholars! Nor is there any agreement on how one element becomes the next, higher, element - it appears to be a process more akin to alchemy than to science. More fundamentally from a KM practitioner’s point of view, the model offers no guidance as to where to focus the KM work within an organization so as to deliver business results - and no hint as to how to go about that effort.
Hence KM programs disappear with regularity down a number of rabbit holes:
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Just-in-case data and information collection and organization (in the hope that the collected data and information will be useful to someone someday).
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“Build it and they will come” syndrome - the stores of content (data? information?) are expected to find an audience by themselves.
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Treating people as passive users.
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Ignoring (largely) the context of the people and their work.
The siren call of the DIKW pyramid’s simplicity has dashed many a KM effort on the rocks of reality. When one tries to use the DIKW pyramid to develop a solution to the problem of “dumb” (not dynamically adaptive) organizations, the model suggests the problem must be addressed by finding the data and turning it (refined or alchemically altered) into information that can then be yet again processed (by whom, in what way?) into knowledge leading to business results. But what data or information or knowledge is processed? How does that processing occur? The D, I or K is delivered to whom? For what use?
The DIKW model does not help in the search for a way to make organizations smarter. What is needed is a new model for knowledge management that meets the following criteria: