BBA212-Session 2-KM

Knowledge Management Overview

  • Course Information: BBA212 Knowledge Management eu Business, SESSION 2, Carlos Canivell Cretchley, www.euruni.edu

Key Topics

  • Progression from Data to Knowledge

  • Definitions: Data, Information, and Knowledge

  • Types of Knowledge and Challenges

  • Measuring Knowledge Management

  • Importance of Data Analysis and Data Scientists

Classification of the Human Mind (According to Russell Ackoff)

  • Data: Symbols

  • Information: Processed data that provides answers to questions (who, what, where, when)

  • Knowledge: Application of data and information, answers how questions

  • Understanding: Appreciation of why

  • Wisdom: Evaluated understanding

Evolving Definitions of Knowledge

  • Knowledge increasingly viewed as a complex and personal concept that encompasses more than just information.

  • Longman Online Dictionary: Defines knowledge as "the information, skills, and understanding gained through learning or experience."

Knowledge Pyramid

  • Knowledge: Know-how, understanding, experience, insight, intuition, and contextualized information.

  • Information: Contextualized, categorized, calculated, and condensed data.

  • Data: Facts and figures that relay specific information without organization.

Detailed Definitions of Key Terms

Data

  • Defined as unstructured facts and figures, lacking organization and contextual relevance (Thierauf, 1999).

Information

  • Data becomes information when contextualized, categorized, calculated, and condensed (Davenport & Prusak, 2000).

  • Provides relevance and purpose, answering questions on trends and patterns (Ackoff, 1999).

  • Information technology aids in transforming data into information, providing a larger context.

Knowledge

  • Knowledge is tied to application and understanding, influenced by personal experiences (Davenport & Prusak, 2000).

  • Gamble and Blackwell (2001): Defines knowledge as a mix of experience, values, contextual information, expert insight, and grounded intuition.

  • Exists in minds and is embedded within organizational routines and practices.

Importance in Knowledge Management (KM)

  • A deep understanding of knowledge is critical for successful KM implementation.

  • The distinction between data, information, and knowledge allows exploration of how knowledge can be accessed, shared, and utilized.

Types of Knowledge

  • Explicit Knowledge: Codified, found in documents.

  • Tacit Knowledge: Non-codified, based on personal experiences.

Challenges of Knowledge Management

Security

  • Right level of security is crucial. Some information should be protected while accessible to authorized users.

Motivational Challenges

  • Building a culture that encourages learning and knowledge sharing requires more than just technology.

Technological Adaptation

  • Keeping up with technology and ensuring efficient transfer and utilization of knowledge is challenging.

Measurement of Knowledge

  • Knowledge quantification is complex due to its basis in human relationships and experiences.

  • Focus should be on distributed purpose over mere results.

Leadership and Data Accuracy

  • Knowledge leaders should encourage collaboration and accurate data maintenance for effective KM.

Measuring Progress in KM

  • Organizations must assess KM objectives, evaluate maturity, identify weaknesses, and track accomplishments to gauge progress effectively.

  • Metrics should be simple, focus on knowledge use rather than participation, and highlight success stories.

Role of Data Analysts and Data Scientists

Data Analysts

  • Analyze data to uncover trends, create visual representations (charts and graphs), and solve tangible business needs.

  • Responsibilities may include analyzing sales patterns, marketing campaign successes, and internal attrition impacts.

Data Scientists

  • Interpreting data while also possessing coding and modeling expertise is key.

  • They can conduct advanced data analysis, build statistical models, and create automation tools and frameworks.

Knowledge Management Challenges Perpetually Encountered

  • Challenges include organizing knowledge, rigid classifications, a lack of incentives, and issues with documentation.

  • Knowledge management processes often lag behind organizational realities, leading to diminished quality when not integrated with core work.

Future of Knowledge Management

  • Continues to evolve with changing practices and technology. (Additional resources include a video on the future of KM).