Analysis, design of KM system

A knowledge management system blueprint is essentially a roadmap for building and steadily improving a KM setup—down to the architecture, components, and deployment path—so organizations can turn the right knowledge into usable advantage without losing opportunities. The central theme is that KM only delivers leverage when knowledge is made explicit and correctly positioned for decision-making; simply collecting information or storing content isn’t enough.

The blueprint frames KM as a practical sequence: design the system architecture, ensure interoperability, plan for performance and scalability as user counts grow, and manage repositories through their full lifecycle. Repositories must be treated as living assets that can become outdated, redundant, or even harmful if obsolete knowledge remains accessible. The blueprint also forces trade-offs early: whether to build capabilities in-house or outsource them, and how to balance future scalability with security against “penetration” by hackers and malware.

At the heart of the argument is the distinction between explicit and tacit knowledge. IT systems are portrayed as strong enablers for explicit knowledge—sharing, applying, validating, and distributing it through structured repositories. But IT does not naturally solve tacit knowledge, which is person-to-person and often requires community practices, collaboration, and a culture of knowledge sharing to convert experience into codified form. If tacit knowledge never becomes explicit, the organization can’t exploit it through the KM system. Even when knowledge is explicit, leverage can still fail if it is wrongly interpreted; the system must therefore ensure correct transformation and “context” around knowledge.

The blueprint also emphasizes a workflow for knowledge flow across organizational units. Ideas originate in multiple departments, get refined through evaluation and filtering, and then emerge as usable knowledge only after being articulated in explicit form. The system’s design should make that flow frictionless: encourage sharing, support codification, and ensure that refined outputs remain interpretable and actionable.

On the architecture side, four major components are highlighted: repositories, collaborative platforms, networks, and culture. Repositories act as knowledge hubs that store and retrieve content, but they must add context and support validation, maintenance, annotation, and distribution through strong interfaces. A key distinction is made between information repositories (data without context) and knowledge repositories (information enriched with decision-relevant meaning). Knowledge is further categorized into declarative (definitions and concepts), procedural (processes and standard actions), and causal (cause-effect relationships that justify decisions). Context is the final layer that determines whether knowledge actually helps someone choose the right action.

The transcript warns that integrative or composite repositories can create liabilities such as information overload and maintenance difficulty, especially as technology and knowledge evolve quickly. It cites Arthur Andersen Consulting Group’s Lotus Notes-based knowledge architecture as an example where repositories grew too large and redundant in a short time, making storage and upkeep difficult.

Finally, the design includes open and distributed access using standard protocols (e.g., intranet, HTML, TCP/IP), plus aggregation and data mining approaches that go beyond keyword search. The system should support skill databases and expert locators (Yellow Pages-style directories), automated categorization using metadata, personalized content filtering and push delivery, and collaborative channels for sharing explicit knowledge in multiple media formats. The goal is a flexible, secure KM system that remains useful over time and delivers the right knowledge to the right people.