In a December 2014 press release, International Data Corporation senior vice president and chief analyst Frank Gens predicted that the worldwide information and telecommunications industry would continue to grow due to the new wave of information, cloud, big data and mobility solutions, but that the underlying story driving this growth would not be cost reduction, but innovation.

While most executives view their investments into the next generation of knowledge and collaboration management platforms as a way to drive down capital expenditures and other costs, the true value of these platforms will be driven by the innovation — and subsequent competitive advantages — they create. 

Organizations must recognize this coming wave of competitive innovation and take the right steps to ensure they remain at the forefront of this movement.

How We Approach Knowledge Management Today 

Knowledge management has changed dramatically over the last decade, and even now — with the rise of cloud-based content management and collaboration platforms merged with machine learning and social networking capabilities — the rate of change is outpacing organizational ability to keep up. Features are developed and released within days, rather than multi-year cycles, and organizations are trying to adjust.

The basic needs of knowledge management have not changed: the ability to capture and store knowledge assets and intellectual property, and provide simple yet powerful search capabilities so employees can easily discover and retrieve them. However, the sheer volume of content is making meeting both of these needs a difficult proposition. And the more difficult it is to locate and retrieve your content and business-critical data, the less likely end users are to embrace the platform. 

Adoption and engagement are key measurements of a knowledge management initiative's success, and yet most organizations struggle with this because they do not truly understand the failures of their platforms.

The knowledge management problem is a “Big Data” problem: most organizations do not have a strategy, and so they store everything, amassing content and rich media, and are unable to tap into the collective knowledge they hold.

Graphing our Knowledge

Most of us have experienced changing teams, changing roles or changing companies — and we have all witnessed one of the most flagrant gaps in our corporate knowledge management schemes: losing knowledge as a result of these changes. Similarly, most of us remember that in years past, when you joined a team or started a new role, part of your employee on-boarding was to receive "The Binder: The Source of All Collective Knowledge and Business Process Wisdom."

Both are clear examples of how hard it is to transfer our knowledge, expertise and information assets back and forth. Upon leaving a role or an organization, it can be an enormous, if not impossible task to articulate and capture everything about your job within one or more documents. And it is an enormous, if not impossible task to join a company or team and assume you will learn all you need to know about the new role from the on-boarding binder placed in front of you.

Whether leaving a team or joining one, the problem is the same: how are we collecting, protecting and disseminating our greatest assets — the skills, knowledge and experiences of our people?

The next generation of knowledge management platforms need to take into account the various methods through which we share our skills, knowledge and experience. We need the ability to capture or create knowledge in every form, from documents and process flows, to interviews, Q&A, video and audio recordings, and our various social activities and other indicators.

But that's the easy part: collecting information, in all of its forms. The hard part will be to organize it, to correlate disparate artifacts into meaningful streams of information. Through the automated classification and tagging of content — print, video, audio — and subsequent translation using data mining and machine learning techniques to identify patterns, our knowledge management systems will slowly begin to match the human brain's ability to discover deep insights into the knowledge we hold.

The good news is that we live in an exciting time for knowledge management innovation. Many of the core technologies that will make this next generation of knowledge management possible are becoming available through leading collaboration and KM platforms.

Companies such as Microsoft, Google, Facebook and others are working on social graph efforts, where the relationships between document artifacts and business processes are joined by and expanded upon by the relationships of readers, authors, team members, administrators, and anyone else who may interact with any single node, amplifying the relationships between nodes exponentially.

Learning Opportunities

Preparing for Future Systems

While no single system or methodology for capturing knowledge assets can be applied to every organization, experienced consultants and knowledge management practitioners have used best practices that you can apply to any existing or new environment. The greatest risk to your organizational information assets in the face of the rapid growth of data is to do nothing. However, by taking some tactical steps, you can begin shaping a unique, personalized strategy:

Identify all knowledge assets

Begin by identifying and cataloguing all of your knowledge assets. This includes documents, diagrams, visual and audio media, business processes and all other intellectual property. This in itself can be a massive undertaking, with many company assets reaching into the terabytes and petabytes. However, you cannot develop a strategy if you do not understand where and what your assets are.

Add context to content 

As you catalogue your knowledge assets, provide as much detail around each artifact. Many organizations begin by classifying their content types and fundamental taxonomy, applying relevant metadata to help identify and map their current state, versions and histories, and the relationship of each artifact to each other, to projects, business teams and divisions, personnel, and so forth.

Recognize and classify patterns

Rarely will you be able to identify all of the relevant context surrounding all of your artifacts. It is usually an iterative process, requiring tools and methodologies to help automate the process (such as automated contextual tagging) and human-driven classification, or folksonomy. Much of this can be provided through social interactions, and by encouraging a culture of accountability to ensure that content is properly classified when interactions occur. For example, a search result may uncover content that has been improperly or insufficiently tagged. The end user should be able to add additional metadata, or flag the artifact as requiring further research.

Discover and articulate insights

Beyond the surface of taxonomy, content types and classification, insights may not be identifiable through machine learning and automation, but are found through human interaction. The tools and methodologies you employ must allow for the identification and association of business insights, providing a deeper, richer layer to your knowledge assets.

How you implement these steps in your own organization depends on the maturity of your knowledge management practices and systems, and on how sincere your pursuit is of discovering the insights within your collected wisdom. The most difficult first step of any company is recognizing knowledge management's value lies not in the collection of artifacts itself, but in the spark of innovation captured within. The steps outlined here may seem like empty platitudes to some, but they are critical steps forward to leveraging the collective wisdom of your organization.

A box full of documents sitting in a warehouse, hidden away under lock and key might be secure, but provides no value to your business. Making the box and its content readily available to your end users opens opportunities. Describing each item and connecting them and their potential value to each team member opens further opportunities. 

Innovation enters when you point out specific links within those documents and how they relate to someone's current project, and then bringing in team members to discuss, share ideas and add to the documents.

The more interactive your end users are with your knowledge assets, the stronger the value achieved. That's what the future of knowledge management will certainly deliver — better access and measurable value.

Title image by Joshua Earle

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