Blog Topics: Brainspace Enterprise, Knowledge Management

The Key to Knowledge Management and Innovation is Knowledge Flow, Part 2

This is the second post in a two-part series titled “The Key to Knowledge Management and Innovation is Knowledge Flow”. In it, I look at the history of knowledge management, where we are at today, and what the next generation of knowledge management systems must do in order to deliver on its orignal promise. You can read the first post here

“The only long-term sustainable competitive advantage is to learn faster than your competitors.”

Arie De Geus, Head of Strategic Planning, Royal Dutch Shell

So, what is “knowledge flow” and why does it matter?

Major organizational objectives are influenced by knowledge flow (or the lack thereof).

To move along in a stream • to circulate • to stream or well forth • to issue or proceed from a source • to come or go as in a stream • to proceed continuously and smoothly

Optimizing knowledge flow is the difference between simply “managing” knowledge and leveraging a company’s collective intelligence. In short, the importance of knowledge flow comes down to better alignment of work product and insight in context, better distribution of employees’ work, and better discovery of organizational intelligence.


There are two types of knowledge that exist within a company: knowledge in the form of Work Product and insight. Work Product is the physical manifestation of one’s insight into a formal document or asset. Insight is the thought and opinion that exist around it. Knowledge management systems to date have largely concentrated on organizing Work Product, and insight ellusively resided in emails, notes, conversations and, at worst, inside someone’s head . In recent years, enterprise social networks have promised to free this insight, but the results have been underwhelming. While such systems have lowered the friction around sharing such insight, their nature displays that insight in a fleeting, non-contextual way via chat, status updates, and siloed conversations.

Knowledge management systems of the next iteration must continue to lower friction around publishing but also allow that insight to be surfaced at the correct time and in the correct context.


The parts of a written or spoken statement that precede or follow a specific word or passage, usually influencing its meaning or effect.

Aligning insight and work product produces an influenced understanding. Consequently, that understanding (by both people and machines) can be used to surface the right knowledge at the right time.

Distribution (Working Out Loud)

When knowledge is recorded, it deserves more than to be sent to the corner of a file system only to be accessed through a properly crafted search query. Organizations need to do a better job of inserting such knowledge into the general consciousness of the organization. It needs to find it’s way to the daily activities that employees engage it. It needs to be given the ability to find it’s way where it’s most relevant.  Dynamic and alive.

Bryce Williams understood this fundamental shift and in 2011 coined the phrase “Working Out Loud” as a way to help distribute the insight around an employee’s work.  He defines working out loud as follows:

Working Out Loud   =   Observable Work   +   Narrating Your Work 

Conceptually, this ideas has merit, but it has a fatal flaw in that it asks yet again that the employee bear the burden of sharing. Friction. The benefit is dependent upon participation. To overcome this, technology needs to take over this role, leverage an employee’s natural behaviors, and syndicate what is important to other interested parties.

Which brings me to the next important feature a knowledge flow platform must offer.


Imagine an employee who is searching and collecting information around their company’s competitors. Rather than narrate that they are doing such a thing, a next generation KM system will recognize this pattern and connect this activity with others interested in competitive intelligence. “John Employee seems to be working on gaining a better understanding of Company XYZ, perhaps you should share your presentation on XYZ or schedule some time to chat with John”. Or, “It looks like you are researching company XYZ, here are some presentations your co-workers have created in the last 6 months.”

 Augmented intelligence combines the power of machine learning with human curation and intuition producing an outcome greater than the sum of its parts. Knowledge Management systems need to push knowledge as much as allow employees to pull it.   A system that learns from employee behavior and understands the concepts around what people are interacting with can begin to surface work product in context. 

IDC has a well quoted stat which explains that 16% of a knowledge worker’s time is spent looking for information, and 44% of the time they can’t find it.  A knowledge management system that understands more about its participants and what they are currently engaged in can significantly reduce the time that employees spend looking for things. Such a knowledge management system, as a direct benefit of this type of understanding, would reduce the amount of rework, eliminate redundancy of efforts, and increase desired outcomes by providing the correct information by which to make decisions. The former has always been the promise of knowledge management, but with the advancements in machine-learning and associated technologies, technology is finally in a place to begin to deliver on it. 

Knowledge Management as an initiative has struggled to find a meaningful ROI, but a study done in 2012 by Global Consulting, Inc got fairly close. In a very scientific and specific approach, Global Consulting evaluated the implementation of a Knowledge Management system that gets closer to what we’ve been describing. While “time savings” was the primary measure of the study, they identified 6 other areas that were affected by a robust KM system.

Figure 13.5 Time Savings as a Portion of KM Benefits

The study concluded that in the time savings category alone there was a 1014% ROI when the KM system was implemented. You can read the study in detail here.

A next generation KM system that furthers the intelligent, in-context discovery of knowledge will only increase the ROI on KM initiatives. While KM systems will continue to tie specific ROI numbers to all of the seven areas of potential benefit, the results are real. Moreover, these types of systems (and investments therein) are unavoidable for companies who wish to remain competitive in an increasingly disruptive business environment.

Summing it up

Get flowing

Knowledge Management has always been part of progress. Its place as an organizational initiative will remain a priority, but the mechanism by which companies attempt to amplify organizational intelligence need to change. Finding ways to promote the understanding and transfer of knowledge is where companies should be spending their resources.  They should think twice before investing in initiative that furthers the categorization and organization of data and begin to explore solutions that enable the flow of knowledge. That is the ability to lower friction for contribution, better distribute, and surface knowledge in context. Those companies that will create surely have an advantage in the years to come.

Thoughts? Agree disagree? Where do you think the future of knowledge management is headed? Let us know in the comments or on Twitter.

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