The Big Book of Knowledge Management: Workforce Learning, Knowledge Capture, and Decision Making Review
The Big Book of Knowledge Management by Jay Liebowitz is a broad survey of KM foundations and applications with a focus on workforce learning and decision support. It connects knowledge capture to training, analytics, and managerial judgment.
Overview
Coverage includes KM strategy, knowledge capture and retention, learning programs, decision support systems, expert systems, and performance measurement. Industry vignettes show where KM lifts safety, quality, and speed.
Summary
Liebowitz links capture methods—interviews, knowledge harvesting, storytelling, structured templates—to learning pathways and decision aids. He stresses leadership sponsorship, integration with operations, and metrics that reflect outcomes: fewer errors, faster onboarding, better choices under uncertainty.
Authors
Jay Liebowitz is a long-time KM scholar-practitioner. His approach is pragmatic, offering checklists and implementation steps more than theory debates.
Key Themes
Retention of critical know-how; learning as a KM output; analytics and decision aids as application layers; governance that keeps content current and useful.
Strengths and Weaknesses
Strengths: breadth, concrete capture techniques, and decision-oriented framing. Weaknesses: limited depth on modern data platforms and AI augmentation; some examples are traditional enterprises. Treat it as a starter kit to customize.
Target Audience
Operations leaders, KM and L&D teams, safety/quality managers, and consultants tasked with reducing knowledge risk and improving decisions.
Favorite Ideas
Knowledge risk mapping; structured harvests from departing experts; coupling lessons learned to training modules and dashboards.
Takeaways
Capture what matters, tie it to learning and decision points, and measure results in fewer errors and faster competence. KM earns its keep when it sharpens choices.
