Organizational Knowledge Design

I see my friend Dave Snowden is extending/rethinking/refining his Cynefin framework, which emboldens me to follow through on an idea that won’t leave me.  More completely, an idea that challenges some basic axioms and cliches that have haunted my PowerPoint/Keynote presentations for years.

Headed for my personal trash bin:  ‘People, Process, Technology.’  There are 298,000 hits on the Google machine for that phrase (in quotes) – so some will no doubt disagree.

From a theoretical point of view, the Cynefin framework was a giant first clue that this triplet was endangered, and yet I found myself using it years after knowing better.  From a practical perspective, the more we learn about successful open government examples and social business experiments – the evidence became clearer.

The enemy:  Process.  In retrospect, this is obvious, and it is possible I am the last one to realize this.  For complex systems, for innovation efforts, for creativity – process engineering is not only the wrong approach, it is a mistake.  These epiphany is still in its embryonic phase, but it may be that any work that is amenable to a ‘process‘ should be automated as much as possible. For the rest, technology should enable serendipity rather than predictive process.

It may be that simple.  It may be People, Organization, Technology; although we should not be in a rush to replace a failed triplet with another.  In discussions about ‘social business,’ we describe some fairly radical organizational structures.  In fact, my definition for Social Business is as follows:

Social Business refers to an organization whose structures and processes defer to the natural systems of human interaction. This transformation from the 19th century industrial age organizational model is enabled by conversation-centered technologies that allow for low-latency, low-effort flow of information across the workforce – laterally as well as vertically.  It is characterized by flattened decision cycles, real-time situational awareness, creativity, and a capacity for agility realized through adaptive responses to changes in its environment.  

This definition needs to be shorter and may be missing some elements.  Nevertheless, this is where I am right now.  How did I get here?  From a casual conversation that went like this:

“Why can’t our internal collaboration platforms be more like Facebook or LinkedIn?”

Immediately, it struck me.  This isn’t the right question.  The right question is:  “Why do we work in organizations where natural interactions and instincts are discouraged?”  The reason that consumer social media technologies experience a high adoption rate – without the ‘benefit’ of corporate training – is because they align with human aspiration.  We want to share with friends.  We want to strengthen our tribal affiliations.  We want to help where help is needed.  We solve business problems over lunch.  We sketch out innovative ideas on bar napkins.  This is how we live – but not how most of us work.

Others have written about new organizational structures, such as heterarchy, wirearchy, et al.  We cannot fall into the trap of the last decade, where “flat organizations” were supposed to destroy hierarchy.  Sociology is not extinct.  But radical new organizations are possible and are in fact happening.  A dear friend now works for a consulting firm where people come together into ad hoc teams to tackle projects.  The firm itself is just the backplane, providing health care, office space, etc – in exchange for a percentage of revenue.  The consultants/engineers/developers/project managers self-organize around opportunities.  The morale is high, the reputation is strong, and the life balance is exquisite.  This model does not suit junior employees, and would not work for many areas outside professional services – but it represents a triumph of natural systems over machine processes.  It maximizes crew methodologies for client value.

Let’s consider that the unit of analysis is not the process, but the organization.  People, Organization, Technology.  Let’s run this up the flagpole and count the salutes.

This is not the original title for this blog, the change is a bow towards the good folks on the ACT-KM listserv, some of whom are checking for a pulse on the teenaged wrist of Knowledge Management.

The social graph used to be analog, fleeting and personal – which extended to our metaphors:  “Whom do you call?”  “When you spin your office chair around, who are you looking to?” “Whom do you trust to not steer you wrong?”    Unless phone calls were recorded and transcribed, the conversations were fleeting.  Water cooler chatter died away as people returned to their desks. Each participant taking with them their own knowledge of the interaction; based on their past experience, their cognitive biases evidenced through learning filters, and the random noise that affects the metaphorical learning that helps us navigate our day.  While the sociology of trust relationships has not changed, and human cognition is still very much an analog function; the digitization of our interactions has increased dramatically over the past decade.  The implications are profound for organizations and should be reflected and exploited in any competent KM strategy.

When these interactions are instead in a discussion forum, a wiki, or as a result of blogged comments, or using an instant messenger tool, or email, etc., the exchange of information is – at least theoretically – available for trend analysis; for addition to the corpus of organizational understanding; or for use by people who were not party to the original conversation, perhaps separated by time and distance.

With this new digitization of organizational conversations, the opportunity for an organization to understand, leverage, and enhance the social graphs represented by its members interactions is very real.  The organization considered mature at KM, therefore, is one that learns from the conversations among its members – scaling the water cooler experience and aware of trends across the knowledge exchanges that fill our day.

Amazon.com has a business model that focuses on the ‘interest graph,’ the potential connections among people who share interests but who are not connected otherwise.  By analyzing buying habits and interests over time, Amazon developed a “recommendation engine” the suggest purchases it has observed trending among “people like you.”

This concept of an interest graph can be brought into the enterprise as well – extending to expertise across organizational members who are unaware of people working on projects and problems similar to themselves.  The idea that an organizational knowledge system can provide a level of work recommendation follows naturally – connecting otherwise disconnected workers based on common interests and need.

We often speak of KM in terms of generations – although few agree on the precise phases that have been experienced since the mid-1990s.  One construct looks back and considers the phases as 1) document-centric, 2) person-centric, and 3) network-centric.

We used to base interventions (1st generation) under the KM umbrella believing that ‘organizational knowledge’ was contained primarily in our work artifacts; documents, spreadsheets, project plans, etc.  Efforts to improve an organization’s KM were thus focused on repositories, with entreaties to “share your knowledge.”  The tools were crude at first, evolved to become web-based and now are extended to ubiquitous mobile platforms.  Early on, however, it became clear that the “provide and pray” approach failed to address the sociological challenges – there is a long human journey for most organizations to advance to becoming a truly collaborative enterprise.  Core aspects of the organization, from process to management incentives and beyond, must adjust to realize this transformative vision.  Technology alone never addresses the individual behaviors that have to adapt such that the organizational change can occur.

This led to a 2nd generation of KM, which recognized that not only did technology not change behaviors, but that knowledge is actually biologically determined.  The knowledge structures in the mind of the receiver will never match those held in the mind of the sender.  Expertise is personal, exchanged only as a result of a volunteer effort by the person, and transferred over trusted social networks.  This led to a move away from (but not an abandonment of) document-centric architectures.  We remain habituated to linearly constructed formal documents, standard procedures and checklists that guide our work and transfer best principles from previous workers and generations.  This is the intellectual capital for most organizations, and resonates with the learning styles that begin with primary and secondary education.  (This too, is subject to flux, as revolutions in education will reverberate in the workplace – challenging this presumption for some countries – beginning around the year 2025.)  Nevertheless, the focus for 2nd generation KM was to connect these experts to knowledge seekers, and facilitate the exchange of trusted information across space and time within an (often distributed) enterprise.

The 3rd generation of KM, building on the first two, began to recognize that these networks represent the core organizational value.  The valuable intellectual capital – that which truly distinguishes a firm in the market – exists in conversations between members of the organization.  The 2nd generation is true enough for individual knowledge – we learn from others who volunteer their experiences, knowledge is not ‘transferred’ or ‘shared,’ it is learned.  However, for organizational knowledge, the focus is on these conversations.  This leads to the need to balance document repositories (databases and electronic document systems); ecosystems where expertise can be located quickly and questions are answered with accuracy and timeliness (email); and (3rd generation) advancing the health of the social graphs within organizational subcultures (social network analysis, emerging social business tools).

This generational path – representing almost two decades of the KM discipline – has been influenced by research in cognitive science and neuroscience, advances in information technologies, and the broad consumerization of social tools – often referred to as social media.  Globally, most people are not active on these consumer tools; however in certain countries and for certain cohorts, the expectation that one will ‘live out loud’ is beginning to transform the workplace.

“Working out loud” refers to the fact that workers are embracing the behaviors that drive them in their leisure time to Facebook, LinkedIn, Twitter, Foursquare, etc., within the workplace itself.  This transformation of the workforce, combined with enterprise-class social tools, represents the operationalization for this 3rd generation of KM.  While references to this 3rd generation date back a decade, it is only in the past few years that people have altered their social behaviors to embraced the digitization of their social networks. The realization of this 3rd generation means that the informal networks and the interactions there that have always been critical to decision-making can now be leveraged, analyzed, extended, and advanced for dramatic increases in organizational value.

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* I am in revolt against expensive royalty-free photos, and will just use pictures from my own collection until I calm down.