Knowledge Mobilization (KMb): Multiple Contributions & Multi-Production Of New Knowledge

Tag Archives: data-noise

The Knowledge Exchange Cycle


Knowledge mobilization (KMb) can be challenging. Constant meetings, conferences, workshops, articles, blogs, emails, text messages, questions, problem solving, stakeholder involvement – or lack thereof – and the ongoing cycle of sifting through information and data/information noise to gain knowledge can begin to feel like you are sinking in an infinitely vast ocean of opinions, beliefs, ideas and ideals, statistics, and research “evidence”. Once you gain knowledge of something and exchange further knowledge with others, new knowledge seems to appear to refute previous knowledge. One moment a research study suggests certain findings. The next, a new study seems to contradict those findings, requiring you to constantly re-examine your knowledge and the knowledge of others. A brief definition of knowledge mobilization is making knowledge (particularly research knowledge) useful to society. Let’s face it – sometimes it seems such never-ending knowledge contradictions are preventing us from making any knowledge useful to society.

Yet I’m optimistic! One of the most powerful and enduring lessons I have learned in my almost decade of promoting and supporting knowledge mobilization efforts is that the multitude of contexts, sources, findings and views aren’t necessarily keeping us from knowledge – this is knowledge: fluid knowledge. I’ve talked and written about this at length in person and in previous KMbeing blog posts, as well as in the papers and book chapter I co-authored.

The notion of looking at these “contradictions” of knowledge in a valuable way is one I feel bound to reiterate. Why? Because by adopting this approach to the fluidity of knowledge we can dramatically increase our opportunities for influencing policy-makers, clarify positions for various stakeholders, develop understanding and build trust within different environments, and forge meaningful relationships in various contexts of knowledge transfer and exchange as our knowledge continues to evolve.

In short, we can recognize that knowledge is never stagnant – or we can be stuck in knowledge silos. All we have to do is remember that each interaction – each knowledge exchange – is filled with unlimited and profound possibilities for impact. But remember, impact is also never stagnant. Impact occurs and is also transformed by new knowledge – the fluidity of knowledge.

Knowledge Exchange Cycle

So, how do we make each knowledge exchange count and not become inundated by the infinitely and often overwhelming bombardment of varying knowledge? By approaching each knowledge exchange practically and purposefully.

There are three components to each effective knowledge exchange. Combined, they form what I call a Knowledge Exchange Cycle. When you consider all three elements with one another, they can produce a powerfully productive approach to developing our own knowledge and advancing our collective knowledge. Simply remember these three elements in each interaction:

Speak & Listen Carefully

Put Knowledge in Context

and Transform Knowledge Collaboratively.

This funny video clip shows the importance of speaking and listening carefully, being open and paying attention to context.



Speak & Listen Carefully:  Speaking and listening carefully is the key to effective communication. But few people get it right. That’s because it takes meaningful practice and focus to connect with others, detect different meanings, recognize multiple perspectives, and determine what kind of knowledge is being exchanged. When you master being truly present in your communication, you can become an amazing speaker and – more importantly – an amazing listener. This means that when you’re not speaking you’re fully engaged, mindful of the moment and paying attention to the other people sharing their knowledge with determined focus. Remember, to give other people the space to be heard. Don’t become a constant speaker without also being a compassionate listener! The give and take of speaking and listening carefully also means asking for the knowledge “evidence” of others, and taking the time to understand the general benefit of the knowledge being exchanged. When you feel confident that you understand someone else’s knowledge, take a moment to briefly summarize to ensure you and others understand the knowledge being exchanged.

Put Knowledge In Context: Once you understand the essence of the knowledge being exchanged, you’re ready to put that knowledge in context to better understand how this knowledge is being used and understood in a particular (and often different) context. When you put knowledge in context people will be able to place the knowledge in circumstances that may not always fit within our own frameworks or social benefit. This requires some diplomacy. You need to be both responsive and adaptable. Determine the context by adjusting your approach and understanding of your own knowledge accordingly. The key is to be open to knowledge that may be different from your own to wholly grasp the applicability to your own context. It’s important to connect to their purpose and passion for the knowledge they exchange from the context in which they are situated to also connect it to the knowledge you provide. You may also need to show them how their knowledge is uniquely situated within their own environment in whatever drives them for benefit within their own society – while also anchoring their knowledge in an understanding of whatever drives you in your own knowledge that may be different. Whatever the situation, frame the knowledge exchange openly and speak from your heart. Let people know why their knowledge matters in connecting to your own knowledge to transform it by the next step.

Transform Knowledge Collaboratively: In this part of the knowledge exchange cycle you must show a desire to turn your knowledge (and sometimes differing knowledge) into action collaboratively. Knowledge exchange should ultimately be about making a difference in the world. Transform exchanged knowledge collaboratively! You spoke and listened carefully. You put knowledge in context. You need to continue to speak and listen carefully. Now you need to transform the knowledge exchanged collaboratively. And you need to continue to speak and listen carefully. Maybe you need to help them make a decision. Maybe you need to shift your thinking and look at your own knowledge differently. This is your chance to think about how you can advance knowledge – yours and others – into something useful – beyond individual contexts – yet also considering how to be adaptive within individual contexts.

As you engage in the Knowledge Exchange Cycle remind yourself of the risk in not speaking and listening carefully, not thinking about context, and not acting collaboratively. In order to not feel like you’re drowning in the vast ocean of knowledge exchange, all any of us can do is mindfully consider the knowledge shared by and with us in the moment. This Knowledge Exchange Cycle provides a framework for you to build knowledge relationships carefully, be open to and understand different contexts, and make and support ways to transform knowledge collaboratively – in every moment of knowledge exchange. In this sense, knowledge mobilization can be challenging. As someone who has used mindfulness meditation in my daily life for over 25 years, mindfulness is not always easy. And just like mindfulness meditation, with mindful knowledge exchange, the more you do it, the better and more efficient you will become.  I encourage you to keep the Knowledge Exchange Cycle in mind in your next knowledge encounter – you may find you are one step closer to transforming knowledge to make the world a better place.



Relevant-Signal To Data-Noise Ratio

signal noise

In science and engineering we often hear about the signal to noise ratio – a concept that compares the level of a desired signal to the level of background noise.  Although this is a technical term commonly used for electrical signals or biochemical signaling between cells, it can also be applied in the world of social media. In my own social media use I call this relevant-signal to data-noise ratio.

How often do we sift through Twitter feeds or Google search results to find what is relevant to our online research while also being inundated with data-noise?  I always keep this in mind when I’m doing digital research.  I can often find my Twitter feed filled with tweets that are relevant to digital research – and plenty more that are simply data-noise. Understanding the social media concept of relevant-signal to data-noise ratio can help us use social media in a more effective and productive manner and keep us focused on the more relevant information and knowledge sharing that makes using social media – especially for knowledge mobilization (KMb) – a better and more valuable experience.

As a community-based digital researcher, I was involved in a research project and book chapter publication with the Knowledge Mobilization Unit at York University, working with York University’s Executive Director of Research & Innovation Services,  Dr. David Phipps and York’s KMb knowledge broker,  Krista Jensen.  Our research project looked at Applying Social Sciences Research for Public Benefit Using Knowledge Mobilization and Social Media.  One of my contributions to this project was analyzing online profile keywords used on Twitter to advance our understanding of how individuals might use a social media platform like Twitter to connect and form collaborative relationships and like interests. Like interests are the foundation of communities of practice.

This important concept of relevant-signal to data-noise ratio  can be conceptualized by the following equation:

R-S:D-N = A (amount) of relevant-signal

                 = A (amount) of data-noise = 50

Basically, what this formula means is that the relevant-signal to data-noise ratio is equal to the average amount of what is a relevant-signal divided by what is the average amount of data-noise. To use this equation, for example, on a Twitter feed of someone I’m following on Twitter, I will often seek the keywords that are relevant to my digital research on a page of profile tweets. This can easily be done using the Ctrl-F Find function on any computer. I type in the keywords I’m looking for and – for convenience sake – I hold the amount of data-noise is going to be at least half or fifty-percent – as in a 50-50 chance.  This is why I have the amount of data-noise equal to 50.

When I find my keywords at least twenty-five-percent (25%) of the time or more (at least half of my 50-50 chance of finding data-noise), I will continue to follow this Twitter feed. If the amount is less than 25% – it’s filled with too much data-noise for what is relevant to my research interests, and I often make the decision to un-follow. I find this equation very helpful in making decisions about who to follow by weeding-out more of the data-noise.

All real measurement is disturbed by noise – and social media is no exception. As a research tool, social media is now being recognized as a valid part of gathering, exchanging and creating new knowledge, and as part of doing valid research.  However, many are still not effectively using social media in the best possible way to do this, and are still being swamped by a deluge of information and data-noise not relevant to knowledge sharing interests.  Or worse, people feel they need to connect broadly so as not to “miss anything”.  Remember, social media is NOT a popularity contest.  Attempts to measure or analyze your online success with what can be called as vanity metrics is irrelevant. It’s quality NOT quantity that counts in social media – so you may have to un-follow and eliminate some of that data-noise to find the relevant signal. I hope this relevant-signal to data-noise ratio equation is helpful for you in this process.