Dave Snowden champions knowledge sharing during his keynote at KMWorld 2022

Building on early knowledge mapping work, Dave Snowden, chief scientific officer, The Cynefin Co, opened up the first day of sessions at KMWorld 2022 discussing how small things create resilience and sustainable change while large initiatives are more all or nothing.

Typically based on an ideal future state definition, large initiatives produce a more linguistic conformance approach than real change. It’s more difficult to allocate blame with smaller initiatives, and the right people usually get the credit. If something small fails, we are likely to learn from it. Getting to the right metaphor is important. The estuarine metaphor is a physical image that people understand. It’s not all about linear flows; tides matter.

He emphasized that any approach to strategy needs to be dynamic and non-linear, allowing for fractal or, maybe better, holographic representation (break it and the picture is still there in the shards) for a fluid integration of strategy with operations and tactics.

“We live, as the Chinese say, ‘in interesting times,’” Snowden said. “What we need is a renaissance, not an enlightenment. We don’t want to go back to that society, but we want to keep the knowledge from it.”

The knowledge management function is important, but it never lives up to its aspiration. You need to start journeys with a sense of direction instead of goals, Snowden explained.

The COVID pandemic separated us all from each other and brought communication through technology rapidly to the surface. However, connecting to people virtually isn’t the same as doing so in-person.

“We need to be in positions where we’re inherently uncomfortable,” Snowden said. “The connections you make create radical changes.”

Knowledge can only ever be volunteered, it cannot be conscripted, he explained. We only know what we know when we need to know it. And we can always know more than we can say and can say more than we can write down.

Narrative handles the communication between deeply tacit knowledge and the explicit, he said. We learn more through failure than success. We leave open the possibility of what we can be in the process.

There are three core frameworks for naturalizing sense-making:

• Cynefin: What type of decisions you can make is dependent on the type of system you are in
• Flexuous curves: To everything there is a season, and a time to every purpose
• Moryd (estuarine): Mapping different constraints in the flow of knowledge

The importance of cross-team collaboration

David Clarke, founder of Synaptica, focused on cross-team collaboration for knowledge discovery during his keynote after Snowden.

For over 25 years Synaptica has been helping clients to organize, categorize, and discover enterprise knowledge. Individually and collectively these three tasks require people from different teams and departments to collaborate, to understand each other’s roles, and to share knowledge. The common goal is to make search more relevant and knowledge more discoverable.

Achieving this goal requires the coordinated effort of content specialists, information scientists, data scientists, and computer scientists. Clarke discussed how to promote cross-team collaboration that pulls together stakeholders responsible for content, metadata, taxonomy, databases, information architecture, and search.

Information science deals with classification and retrieval of information. Taxonomy and knowledge management systems are used in this area, he explained. It is human curated and defines knowledge.

Data science is about the extraction of information often from noisy data sources, it is math and statistics based. It is computational and it infers knowledge.

Synaptica helps people discover, categorize, and organize this information. The company offers horizontal solutions for various vertical knowledge domains, Clarke explained.

Dealing with the Great Resignation

Jeff Evernham, VP of product strategy, Sinequa discussed how companies can thrive despite the great resignation by retaining and delivering knowledge across the enterprise.

The Great Resignation affects organizations in many ways and contributes to the loss of critical knowledge. As employees leave, much of their expertise goes with them, resulting in knowledge gaps. Meanwhile, current employees don't have the resources or past knowledge to do their best work and perform at the top of their profession.

If unchecked, employees give up on trying to find and leverage institutional knowledge, and organizations lose their competitive edge and begin to atrophy.

Intelligent search is the key to unlocking organizational information and surfacing insights that are crucial to success in today's market, Evernham explained.

“With everyone going remote [due to COVID], we had a sudden reliance on systems and had to find a better way to do things,” Evernham said.

According to a survey fielded by Sinequa and CMSWire, one-third of business users never find the information they need frequently or most of the time.

The solution, Evernham recommended, is intelligent search that unifies content across the organization. Sinequa offers such a solution that can quickly get the information needed within the enterprise. Neural search is a giant leap forward in this area, Evernham said.

Developing and successfully using powerful recommender systems

Andreas Blumauer, founder and CEO, Semantic Web Company Inc., closed the morning keynotes with a comprehensive overview of possible uses of recommender systems and why they should be central building blocks in the digital workplace, especially in enterprise information systems such as knowledge and content hubs, customer experience platforms, or support systems.

In knowledge-intensive workflows, where decisions have to be made continuously, users benefit from accurate suggestions. In recommender systems, the business objects (documents, experts, products, suppliers, etc.) that best fit the respective context are automatically brought into the spotlight. The possible applications are many.

“Semantic AI, the fusion of ML and graph, takes an enterprise AI strategy to the next level,” Blumauer said.

There are two types of recommender systems: collaborative filtering and knowledge-based recommender. Augmented decision-making systems are at the core of your digital workplace, he said.