Connecting existing information to facilitate new insights is the hallmark of knowledge management. But most knowledge management focuses on the means of the infrastructure rather than the actual method of learning. Until we address the fact that knowledge workers still spend 10-weeks a year navigating information repositories and disjointed toolkits searching for and struggling to wrangle the information they need, we will continue to fail to translate the power of collective intelligence into better and faster decisions.
I don’t say this to discredit knowledge management. On the contrary, the infrastructure connecting people to data is fundamental. We’re just not using it correctly. Think of it as a subway system: today, there is a vast network of platforms and connectors linking people to data. But it has grown into an unmanageable labyrinth which users must navigate. And we keep trying to solve it with another tool – another platform, another search, another silo. Think of all the money we’ve spent trying to solve complexity with more complexity and only aggravated the problem.
We don’t just need more platforms; we need a different paradigm – one where knowledge navigates to us. And we can use the same infrastructure. But instead of people going between disparate platforms and servers to find data, people stay in the platforms where they work and data navigates to them. The notion of the right knowledge finding the right user at the right moment is becoming an achievable reality through the marriage of machine learning, smart UI, and APIs. And this synthesis of technology will untap collective intelligence and catalyze new insights.
Communicate complex ideas
Adopting storytelling best practices to business learning is essential. Despite the fact business content is often serious, dense, and/or confidential, to achieve effective and efficient knowledge transfer it must also be engaging and enjoyable. Be they colleagues, clients, or customers, business audiences are still people. And as stated above, they are people for whom dynamic and personalized content has become an expectation. How we compose and consume business knowledge today is evolving.
A decade ago, Qwiki won the highly regarded “TechCrunch’s Disrupt SF” prize for melding text, audio, video, and images into a seamless interface, generating a dynamic movie of whatever you search for. At the time, it really did seem like something from the future. But today, how we consume and distribute business knowledge is adapting to be more personal and dynamic. From a distribution perspective, knowledge feeds from the likes of LinkedIn, Reddit and Instagram raise content tailored to our interests and network. Platforms like Medium and Quora enlist the crowd to elevate voices. And content itself is no longer dry and static. It has adapted to quicker consumption. New mediums like dynamic boards or digital libraries make it possible to physically explore topics and mental maps. And gamification can improve the last 5% - 10% of the road to promote sharing and accelerate learning.
This is not just corporate communications waking up to best practices. It is a response to the complexities of today’s ideas and the competitive necessity of knowledge transfer. Advancing the notion of collective intelligence and unlocking the potential it contains requires us to compile stories from more sources and voices. The better we can consume knowledge, synthesize and adopt it into our own work, and then connect our contributions and insights to others, the more quickly the knowledge cycle can revolve. This means our business run faster, smoother, and in the right direction.
Eventually, AI will adapt any content to our personal proficiency and consumption preferences. Text will be rewritten to our vocabularies and preferred syntax while also expanding our understanding of language and concepts. Multi-media will auto-generate and engage us through different mediums triggering deeper learning. But even today, flexible content and teaching platforms allow individuals to choose the content most relevant and effective for them. As knowledge workers – both consumers and producers of information and ideas – we must learn and apply the theory and practice of pedagogy. and human-centered communication to benefit from and contribute to the knowledge cycle.
Engaging new audiences
The last step in the Knowledge Cycle is connecting our insights and contributions with others. Too often, sharing happens within a closed loop community. The myriad reasons for this include social, geographic, and confidentiality reasons. Even intra-company sharing is often limited to team silos, and it gets exponentially more complex when you cross institutional boundaries. But eventually, any closed knowledge loop will exhaust its intrinsic ideas and stall.
New information is the fuel of the knowledge cycle. As knowledge workers, we are constantly trying to ingest new ideas and the latest information. Without effective communication and knowledge transfer, this is tremendously difficult. Which isn’t to say it doesn’t happen. But it’s why we spend over two months a year just searching for information. And on the flip side, as knowledge producers, this means that there are audiences thirsty for what we know, but often we struggle to engage them. If people willing to share have information which others need, why is it so difficult to connect?
Well, the same factors that inhibit our consumption of information affect our contributions. After spending weeks sourcing information from across silos to produce a new knowledge asset, where do we then store the asset? That’s right – in the same places from which we struggled to search and source information. If individual users already have difficulties navigating through the knowledge-overload for themselves, how can we assume that the same user can navigate knowledge successfully and reliably to other users?
Again, this challenge cannot be addressed by adding complexity and introducing further places to search and collaborate. The solution lies in the combination of machine and human intelligence. We need to provide vehicles for human expressions of value, capture that data, and marry it with machine learning to understand users and content and manage the dissemination of knowledge to need. When this happens – when insights from one knowledge worker stream into the workflows of colleague, client, and prospect – that is when innovation and progress occur and organizations capitalize on opportunities. That is when we have unleashed enterprise intelligence.
Today we are at the advent of a new paradigm where knowledge navigates to us. This paradigm will continue to grow deeper and broader through more sophisticated interfaces and machine learning, but even in its present manifestation, it is changing how leading enterprises form insights and communicate them to gain competitive advantage. So how can you establish and maintain this advantage and prepare for future enablement?
So how effectively do you and your organization form insights and communicate complex ideas to new audiences? Join the conversation on LinkedIn.