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Building a governance model for enterprise knowledge at KMWorld 2023

As the proliferation of data continues to grow at massive quantities, enterprise knowledge grows in tandem; with the advent of various generative AI (GenAI) tools, this knowledge growth will only continue to complicate the world of traditional KM.

Visibility will be a key differentiator in the evolving world of KM, where leaders and decision makers who are able to answer various questions about their KM strategy—such as, “??How many of your business decisions are automated?” “How many business rules does your organization have?” “How secure are they?” and more—will come out above the rest.

Implementing a top-level KM governance model—which makes the aforementioned questions answerable—brings reason to a world of overwhelming knowledge quantities. By reducing risk and uncertainty for a proprietary KM journey, organizations can derive immense value from their existing (and future) knowledge assets.

Art Murray, CEO, Applied Knowledge Sciences, Inc. and director, enterprise of the future program, International Institute for Knowledge and Innovation, led KMWorld 2023’s workshop, “Building a Governance Model for Enterprise Knowledge,” to explore the seven major facets of organizational knowledge governance that, in the face of the proliferation of KM assets, place KM joy at the forefront.

Knowledge governance is fundamentally associated with risk. According to Murray, being unaware of the business rules that your enterprise employs introduce various risks, both internal and external. He further explained that understanding and managing risk is the key to achieving greater resilience.

To address risk, a mix of human efforts and technology is necessary for successful knowledge governance. Humans meet a cognitive demand while machines are designed to meet a computational demand; together, they achieve what neither one can accomplish alone for governance.

“Humans have strengths and weaknesses, and machines have strengths and weaknesses,” said Murray. “You need to achieve a balance between both.”

Though tempting, Murray warned against hyper-automation in establishing knowledge governance.

“Hyper-automation is not digitizing everything,” he explained, adding that hyper-automation is achieving a business outcome through a redesigned automated process with no or minimal human intervention.

“How to manage [automation], to keep it from getting away from you, is knowledge governance,” said Murray.

Succeeding at change management is another identifier of having a formidable knowledge governance in place. The reason why many change initiatives fail is because they do not adhere to a change cycle, which leads organizations through a change as it occurs. Murray pointed to examples of these change cycles, including “The Story Thinking Cycle” and an “Option Outline.”

“Another reason you need knowledge governance is because many organizational decisions are made on the fly,” said Murray. “It’s bringing adult supervision into the equation.”

Knowledge governance fills in the missing pieces that these sorts of decisions prompt, providing:

  • Relevance and accuracy of the data
  • Visibility into your data analytics models and sources of “intelligence”
  • More effective risk mitigation
  • Human “sense-making” of the inputs and outputs

With the proliferation of AI and its generative iteration, AI and automation needs sound governance, according to Murray. Governance can reduce confusion in understanding AI’s decision-making, establish thresholds and parameters for keeping humans “in-the-loop,” incorporate semantics and situational context, and help achieve alignment between organizational AI, KM, and master strategies.

A successful knowledge governance implementation should work to achieve these seven attributes:

  1. Rule of Law, or fair legal frameworks enforced by an impartial regulatory body
  2. Transparency
  3. Responsiveness
  4. Consensus-oriented
  5. Equity and inclusiveness
  6. Effectiveness and efficiency
  7. Accountability
  8. Participation

Notably, Murray explained that knowledge governance is not “some lofty tribunal that doles out punishment.” Instead, it should exist as a participatory asset that involves everyone in an organization.

He further divided knowledge governance into seven facets, which include:

  1. Roles and responsibilities are clearly defined
  2. Competencies required for each role are known, developed, and nurtured
  3. Processes for knowledge vetting and assurance are in-place, understood, and practiced
  4. Links to organizational performance are known and measured
  5. Training plan is formulated and content is developed
  6. Evangelization/socialization processes are in place
  7. Reward and recognition systems are in place

Ultimately, Murray boiled down his message into a central theme, explaining that “it’s not all about technology evolution; we humans need to keep evolving as well.” Adapting to the changing world of KM will be an ongoing, changing practice, though when guided by key principles, can continue to elicit successful knowledge management.

KMWorld returned to the J.W. Marriott in Washington D.C. on November 6-9, with pre-conference workshops held on November 6.

KMWorld 2023 is a part of a unique program of five co-located conferences, which also includes Enterprise Search & Discovery, Enterprise AI World, Taxonomy Boot Camp, and Text Analytics Forum.

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