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Decentralized knowledge management

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These past few months of lockdown amid the pandemic have had a vast and lasting impact on our lives and work. Although many have suffered and will continue to do so, the fact is that the information management industry has boomed. Almost without exception, every technology vendor in the space has seen revenues and activity increase significantly. During this period, work has decentralized dramatically, driving an uptick in the need for information management tools and systems. However, decentralization, though a boon to technology vendors, poses a unique set of challenges and risks for information and knowledge managers to grapple with. For all the talk of people working remotely—and when or if they will return to their traditional office environments—there has been little discussion about how to effectively manage information and knowledge in such a decentralized world.

Like it or not, we are living in an era of rapidly expanding micro silos—be that the use of Box, Dropbox, Google Drive, Microsoft, or even Slack. Everyone is using whatever they like, and that is not going to change. The challenges ahead are obviously multifold and unprecedented—and it’s at seemingly chaotic times that conceptual frameworks and methodologies can come into their own.

Take KM, for example; historically the methodology at a high level at least (defined by Nonaka) was to socialize—externalize—combine—internalize (SECI). This four-step SECI method has become the ground zero methodology for anyone entering the KM space. But does that approach still serve us well, or is it time to update or at the very least question it?

The SECI methodology, in all fairness has been around a long time, but the technologies available to manage digital knowledge sources have advanced considerably since those early days. Today, there is a fair argument that those ideas are outdated, and that instead today there is instead a need to capture—analyze—govern.

All we know for sure is that there is a need to manage knowledge effectively, and that the landscape has changed dramatically. Whether the original methodology still stands is debatable, but it's a good and timely debate to have.

As for frameworks, at Deep Analysis, we developed one to help us to at least think through today’s broad, needs. It’s just a simple model framework, but realistically, KM is as much an abstract art form as management science, so conceptual and simple work just fine.

To explain the Deep Analysis framework, one can imagine that particularly in today’s decentralized world, there is a need to automate as much as possible. For the sheer volume of information and data scattered across locations, mean manual curation is essentially impossible.

The counterbalance to automation is the need to remain adaptable. You can’t automate everything, nor should you, and you still need guidelines and tools to ensure you adapt to changes in the right way.


Governance, be that simply the need to manage knowledge assets or to stay in compliance with regulatory concerns, is increasingly important.

In the bottom left of the image, you can see the word “veracity.” In a world of “alternative” facts, the subject of “veracity” and the “truth” of knowledge sources are becoming paramount.

In the middle of the framework is the holy grail of “insight,” for, as we value unless we can gain actionable insight from it. Note that insight can be human-derived or artificially driven. Ultimately then, this is just a framework to try and bring some order to what can appear at times to be unmanageable chaos. Feel free to use our framework, or indeed, improve or adapt it in your own workplace.

The time to innovate

The bottom line is that I would argue that now is the time to innovate and to rethink the practicalities of KM. The past 6 months have been the fastest-changing and most disruptive time in generations. The foundations and approaches of KM need to be adapted to our rapidly changing times. There will be no returning to the old ways; the new “norm” will not look similar to the old. Decentralized data silos and ad hoc working practices are here to stay.

As an industry analyst, I can tell you with absolute certainty that no technology vendor has an out-of-the-box solution that can magically fix today’s or tomorrow’s information and knowledge management problems. Even so, technology is itself at the root of many of today’s challenges (easy access to cloud applications, for example). Likewise, technology will be a central part of solutions to the challenges that technology has created. But for technology to be of any value, we will need dedicated and engaged knowledge managers to design, implement, manage, and operate it. You may like our framework or hate it. You may remain devoted to Nonaka’s method, or you may want to think about new methodologies. Either way, any methodology or framework is better than nothing, particularly in such fast-changing times.

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