Ready for KM? A Knowledge Management Assessment Tool
A Knowledge Management Assessment Tool
A knowledge management assessment tool. Is my organization ready for knowledge management? That may not seem like a common question, but it should be. Knowledge Management remains a critical investment for every organization. Understanding the value of intellectual property, nurturing innovations, capturing organizationally-specific expertise and leveraging data about customers for sales, marketing and product development all require an appreciation for knowledge. An appreciation for knowledge, however, isn’t enough. Good stewards of knowledge invest in practices and infrastructure to support the retention, enhancement, sharing, measurement and integration of knowledge across the organization.
An organization need not “check all the boxes” in order to declare its readiness to embark on knowledge management. Most organizations include elements of knowledge management without reflectively evaluating their readiness. The readiness exercise for all but the most recent start-ups, more of an assessment in search of gaps than one aimed at the development of a new strategy or plan. Given that many organizations do not consider knowledge strategically, the assessment may result in a new element in the strategic plan that emphasizes, and it may produce tactical actions for functions and departments.
The impact of ML on KM
The advent of machine learning systems that leverage large data sets requires augmentation of some of the traditional knowledge management roles, but it is not that different from feedback loops associated with human learning. Those new to a role bring a preconceived idea of what that role entails, and through experience and feedback, they refine their model and their ability to execute to the quality and time parameters set in organizational goals. Experts in a domain define the features, models, goals and their values, as well as practices and incorporate them into formal and informal learning opportunities. In machine learning, the list includes algorithms and thresholds, and the output includes visualizations, but essentially those are augmentations to the human learning environment.
The scope of knowledge represents the largest difference in machine learning processes, as most machine learning implementations, regardless of the extent of the data sets, focus on a very narrow sub-domain problem in which they apply prediction, recommendations and clusters. The human learning cycle, in contrast, is inclusive and whole-person, encompassing not only skills but social norms, orthogonal processes, and other contextual knowledge that the machine learning scenario will never call upon, nor be expected to know.
This last point sets up a key knowledge expectation and one that goes to the root of the human-automation question. Because the machine learning scenario can produce a highly effective, perhaps more effective than pure human results at a relatively low cost, especially when overhead and longevity of employment are considered, machine learning solutions appear to offer more economic benefits than humans doing the same work. And in those narrow cases of highly repetitive actions that require precision machine learning outperforms humans in perseverance, and can at a minimum, augment human capabilities in day-to-day execution.
Humans, however, have the ability to do many other kinds of work, and to contribute in many other ways than a highly efficient, but narrowly cast machine learning algorithm. Many of the list items in this knowledge management assessment reflect purely human capabilities. While it may at some point be possible for a machine to synthesize disparate information and produce meaningful business innovations that go beyond the random combination of ideas accidentally resulting in a viable idea—human sense-making brings with it a rich and adaptive ability to contribute to the overall knowledge of the organization, and therefore its ongoing intellectual capability. Machine learning to date, however, is only capable of contributing narrow expertise applied to a domain where the organization collects enough data to effectively make a repetitive set of predictions or classifications.
Knowledge management permeates and diffuses throughout organizations. The most emergent areas, including areas that purport to mimic aspects of human intelligence, require the most attention because they potentially disrupt, augment or eliminate existing knowledge flows. The decisions about implementation, about how the organization continues to maintain knowledge and expertise, and how new knowledge and expertise integrates, all require human decision making, action and perhaps intervention.
How this list is organized
The knowledge management assessment provides a high-level checklist of essential technologies, policies and practices and the use of space, aligned with the methodology found in Management by Design. The list offers some qualifications and definitions, but it does not detail next level assessments for the suitability, implementation, or practices for individual choices. The assessment asserts that if a technology, policy or practice, or use of space be in place a higher likelihood of organizational readiness exists.
We have organized the items in logical groupings that will make the category clear. Organizations using the checklist may choose alternative ways of organizing the list.
Knowledge Management Assessment: Policy and Practice
General operational expectations
Management encourages Design Thinking.
Knowledge management takes place within information grass-roots efforts and through sponsored policies and practices.
Organizational and personal knowledge are viewed as strategic assets.
A well-funded internal education program exists.
Motivation and incentive programs reward knowledge-sharing, knowledge reuse, and knowledge enhancement.
Employees understand what the organization is trying to accomplish, and executives can clearly articulate the organizational strategy.
Knowledge management is viewed as an on-going cross-disciplinary investment, and not as a project.
The organization follows formal intellectual property processes and accounting practices.
People join and participate in communities.
Human Resources and organization
Job descriptions include knowledge components and knowledge creation, sharing and assessment expectations.
Formal mentoring requires participation and accountability from both the mentor and mentee.
The organization provides time explicitly designated for reflection, discovery, and synthesis.
Continuous learning is encouraged, and time is made available to pursue learning.
The company has an appreciation for intellectual assets.
Communities of practice organize expertise and act as an expert governance structure.
Membership in communities of practice, interest, and passion is voluntary.
The organization explicitly invests in social capital.
A current/futures skills and competency database is maintained and used as input to strategic workforce planning processes.
Goals and objective descriptions include the knowledge required to fulfill them (knowledge components).
Any downsizing is reviewed for its knowledge impacts.
The organization has identified its core knowledge components.
Knowledge gaps are identified and plans are in place to fill them.
Knowledge balance sheets track knowledge value and performance.
Business intelligence systems are used to identify strategic patterns in structured data.
Communities of practice are consulted for strategic input.
Customers are graded on their ability to help the organization learn.
The organization creates and maintains knowledge maps.
The organization uses knowledge as a substitute for inventory.
The enterprise actively attempts to leverage intellectual assets to add shareholder value.
New ideas are examined for their patent, license of copyright potential.
A formal system exists for the management of tangible intellectual assets.
Intellectual asset valuation processes are documented.
Unused intellectual assets are offered for sale.
The organization has adopted knowledge-based and account-based marketing approaches.
Customer knowledge is maintained and leveraged in a Customer Relationship Management system.
Knowledge is recognized as integrated with business functions, not as a separate function.
Competitive intelligence is gathered, shared and used to drive decisions.
The knowledge that represents competitive advantages is identified and documented.
Post project reviews include documenting lessons learned.
Adequate budget allocated for training and internal development.
During economic downturns, training is protected.
Communities of practice manage education budget.
Mentoring programs formalize knowledge transfer of skills and context.
Career paths and career shift processes exist.
e-learning is deployed and employees can use it in an “on-demand” mode.
Review discussions include educational goals and objectives.
Taking time to learn on the job (read, talk, etc.) is encouraged and supported.
Executives can clearly articulate knowledge management goals.
Employees know how knowledge management affects them, their role, their department.
Storytelling is a common form of communication.
When a message is communicated, it is crafted for all available channels to obtain the most rapid dissemination.
Key messages on the intranet are updated regularly.
Common policy and practices that support knowledge sharing.
Rewards for sharing knowledge.
Funding for communities of practice.
Regularly schedule knowledge sharing events.
Reviews include knowledge sharing as a criterion for advancement or salary increases.
All projects start with a “what do we know” session.
The organization’s core competitive differentiators are understood by the majority of employees.
Employees and contractors are given time to share.
The organization encourages cross-organizational grassroots knowledge sharing.
A community exists for community leaders to discuss how to improve communities.
Lessons learned and practice reflections are viewed only as a starting point for adapting and evolving the organization.
Processes are documented.
Documented processes are stored in a process library.
Processes are regularly reviewed for efficacy, appropriate technology, and automation potential.
Lessons learned systems capture process improvement suggestions as they occur.
Employee orientation includes job-specific process training.
Processes document the knowledge required to perform/support tasks.
Rapid response systems exist where required (competitive threat, natural disaster, etc.).
Knowledge Management Assessment: Technology
Line-of-business users employ the knowledge features of transactional systems.
The knowledge features of transactional systems integrate with the collaboration platform.
Domain experts define machine learning features, models, goals and values, algorithms, and thresholds.
The content repository includes document life-cycle management (Enterprise Content Management).
Documents include metadata about classification, use, state, etc.
People outside of a business unit or practice area can join a community.
Advanced visualization and mapping tools are used to communicate relationships between content and concepts.
A common global collaboration platform has been adopted and deployed.
The collaboration environment includes integrated synchronous and asynchronous features.
Communities of Practice and other discussions take place within the context of the collaboration platform.
Remote and mobile users can fully participate in the collaboration environment.
Knowledge Management Assessment: Space
Knowledge about space is considered during the design of work experiences.
Spaces are made available for quiet and personal reflection.
Conference rooms and other meeting spaces are stocked with knowledge capture and brainstorming supplies.
Conference rooms and other meeting spaces easily incorporate the collaboration platform.
To use the Knowledge Management Assessment as an on-going evaluation tool, specific goals must be set for each item as part of the KM planning process. Monitoring and reporting methods need to be incorporated.
More on Knowledge Management from Serious Insights:
Daniel W. Rasmus, Founder and Principal Analyst of Serious Insights, is an internationally recognized speaker on the future of work and education. He is the author of several books, including Listening to the Future and Management by Design.
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