Understanding Knowledge Management and Decision Making

The Knowledge Management Landscape

- Knowledge management and collaboration systems are experiencing significant growth in corporate and government software investment.
- Research in knowledge and knowledge management has seen explosive growth in the fields of economics, management, and information systems.
- Knowledge management and collaboration are closely related, as knowledge needs to be communicated and shared to be useful.
- The production and distribution of information and knowledge are crucial in the information economy, with a significant portion of the labor force and GDP attributed to knowledge and information sectors.
- Many large business firms recognize the importance of knowledge management for their value creation and realize that knowledge is vital to their intangible assets.
- Well-executed knowledge-based projects have been known to yield high returns on investment, although measuring the impact of such investments can be challenging.

Key Dimensions of Knowledge:

1. Distinction between data, information, knowledge, and wisdom:
- Data is a flow of events or transactions captured by an organization's systems, useful for transacting.
- Information is derived from organizing data into meaningful categories.
- Knowledge involves discovering patterns, rules, and contexts where the information is applicable.
- Wisdom is the collective and individual experience of applying knowledge to problem-solving.

2. Individual and collective attributes of knowledge:
- Knowledge takes place inside people's heads but can also be stored in libraries, records, and business processes.
- Tacit knowledge refers to undocumented knowledge residing in employees' minds, while explicit knowledge is documented.
- Knowledge can be found in various forms, such as emails, voice mails, graphics, unstructured documents, and structured documents.

3. Knowledge has a location:
- Knowledge is a cognitive event that involves mental models and maps within individuals. - It has both a social and an individual basis.

4. Knowledge is situational:
- Knowledge is 'sticky' and not easily transferable or universally applicable. - It is enmeshed in a firm's culture and works only in specific situations.

- Knowledge is conditional and contextual, requiring an understanding of when and how to apply it.

Organizational Learning and Knowledge Management:

- Organizations create and gather knowledge through various learning mechanisms, such as data collection, measurement, experimentation, and feedback.
- Organizational learning involves adjusting behavior, creating new business processes, and changing management decision-making patterns based on acquired knowledge.
- Rapidly sensing and responding to the environment through effective learning mechanisms increases the survival chances of organizations.

2. The Knowledge Management Value Chain

Knowledge Acquisition:
1. Acquire knowledge through repositories, online networks, and data analysis.
2. Systematic data and external sources are crucial for a coherent knowledge system.

Knowledge Storage:
1. Store knowledge in databases, document management systems, and expert systems. 2. Management support and proper document updating are essential for effective storage.

Knowledge Dissemination:
1. Utilize collaboration tools and platforms for sharing knowledge.
2. Training programs, networks, and a supportive culture aid in knowledge dissemination.

Knowledge Application:
1. Apply knowledge to practical problems and decision-making.
2. Integrate knowledge into business processes and key application systems.

Building Organizational and Management Capital:
1. Establish roles like chief knowledge officer and knowledge managers.
2. Foster communities of practice for knowledge sharing and collaboration.
3. Utilize software environments for collaboration and communication in COPs.

3. Types of Knowledge Management Systems

Enterprise-wide knowledge management systems:
1. Collect, store, distribute, and apply digital content and knowledge across the entire organization.
2. Include features like information search, structured and unstructured data storage, employee expertise location, portals, search engines, collaboration tools, and learning management systems.

Knowledge work systems:
1. Specialized systems for engineers, scientists, and knowledge workers to discover and create new knowledge.
2. Examples include computer-aided design (CAD), visualization, simulation, and virtual reality systems.

Intelligent techniques in knowledge management:
1. Utilize diverse technologies like data mining, expert systems, neural networks, fuzzy logic, genetic algorithms, and intelligent agents.
2. Serve different purposes, such as discovering knowledge, distilling knowledge into rules, and finding optimal solutions for problems.

4. Enterprise Content Management Systems

- Businesses manage both structured and semistructured knowledge assets.
- Enterprise content management systems help capture, store, retrieve, distribute, and preserve knowledge.
- These systems handle both formal documents and semistructured information like emails.
- They provide access to external sources, facilitate communication, and incorporate social networking tools.
- Leading vendors in enterprise content management software include Open Text, EMC, IBM, and Oracle Corporation.
- Barrick Gold uses Open Text tools to centralize information, access critical data, and promote knowledge sharing.
- Classification schemes or taxonomies are important for organizing knowledge.
- Enterprise content management systems offer capabilities for tagging, interfacing with databases, and creating knowledge portals.
- Digital asset management systems help firms in publishing, advertising, broadcasting, and entertainment store and distribute unstructured digital data.
- These systems ensure consistency and prevent redundant work.

Collaboration and Social Tools and Learning Management Systems

1. Importance of Social Bookmarking:
- Social bookmarking facilitates information sharing and searching.
- Users save bookmarks to web pages and tag them with keywords for organization. - Folksonomies, user-created taxonomies, are used to categorize bookmarks.
- Popular social bookmarking sites include Delicious, Slashdot, and Pinterest.

2. Learning Management Systems (LMS):
- LMS tools manage, deliver, track, and assess employee learning and training.
- Contemporary LMS support various modes of learning, such as CD-ROM, web-based classes, and live instruction.
- LMS consolidates mixed-media training, automates course administration, and measures learning effectiveness.

3. Example of LMS Implementation:
- CVM Solutions, LLC uses Digitec's Knowledge Direct LMS for supplier management training. - Knowledge Direct provides an online portal for course access and administration features.
- It offers tools for student registration, assessments, reminders, and reporting.
- Company-branded logins and company administrators are supported.

Knowledge Work Systems Knowledge Workers and Knowledge Work

1. Knowledge workers include researchers, designers, architects, scientists, and engineers who create knowledge and information for the organization.
2. They possess high levels of education and often belong to professional organizations.
3. Knowledge workers exercise independent judgment in their work and are involved in creating new products and improving existing ones.

4. They play three critical roles within the organization:
- Keeping the organization updated with external knowledge in technology, science, social thought, and the arts.
- Serving as internal consultants in their areas of expertise, providing insights on changes and opportunities.
- Acting as change agents, evaluating, initiating, and promoting change projects.

5. These roles are vital to both the organization and the managers working within it.

Requirements of Knowledge Work System

1. Knowledge workers rely on office systems and specialized tools for increased productivity. 2. Specialized knowledge work systems require powerful graphics, analytical tools, and document management capabilities.
3. Sufficient computing power is needed to handle complex calculations and sophisticated graphics.

4. Quick and easy access to external databases is crucial for knowledge workers.
5. User-friendly interfaces enable efficient task performance without extensive training.
6. Knowledge workstations are optimized for specific tasks, such as 3D CAD systems for design engineers and access to large financial databases for analysts.

Examples of Knowledge Work Systems

Major knowledge work applications include:
1. CAD systems: Computer-aided design automates the creation and revision of designs, saving time and costs associated with physical prototypes.

- CAD software allows easy testing and modification of designs on a computer. - Provides design specifications for tooling and manufacturing processes.
- Supports data supply for 3-D printing (additive manufacturing).

2. Virtual reality systems: Offer advanced visualization, rendering, and simulation capabilities. - Creates computer-generated simulations close to reality.
- Users can immerse themselves in virtual environments with specialized equipment.
- Applications include medical education, vehicle design, and manufacturing assessment.

- Augmented reality (AR) enhances visualization by overlaying virtual imagery onto the real-world environment.

3. Financial workstations: Specialized systems used by the financial industry for investment management and analysis.

- Integration of data from internal and external sources.
- Streamlines access to contact management data, market data, and research reports. - Improves efficiency and accuracy in the investment process.

4. Virtual reality applications on the web: Utilize Virtual Reality Modeling Language (VRML) for interactive 3-D modeling.

- Organizes various media types to create simulated real-world environments.

- Platform-independent and operates over desktop computers with low bandwidth requirements.

- Used for applications such as plant walkthroughs and construction planning.

5. HyperPlant by DuPont: A VRML application for accessing 3-D data over the internet.
- Enables engineers to explore plant structures and reduce construction mistakes virtually.

These applications demonstrate the diverse uses of knowledge work systems in industries ranging from design and manufacturing to healthcare and finance.

Decision-Making and Information Systems ● Types of Decisions

Unstructured decisions:
1. Requires judgment, evaluation, and insight for novel and nonroutine problems. 2. No agreed-on procedure for making these decisions.
3. Examples: Senior executives making long-term goals or entering new markets.

Structured decisions:
1. Repetitive and routine decisions with a definite procedure.
2. Do not require treating each decision as new.
3. Semistructured decisions have a clear-cut answer for part of the problem. 4. More prevalent at lower organizational levels.

Decision scenarios at different levels:
1. Senior executives: Face unstructured decision situations.
2. Middle management: Deals with structured decisions and some unstructured components. 3. Operational management and employees: Make highly structured decisions.

These points summarize the characteristics of unstructured and structured decisions, as well as how decision scenarios differ across organizational levels.

● The Decision-Making Process

Intelligence:
- Involves discovering, identifying, and understanding organizational problems.
- Seeks to determine the reasons, locations, and impacts of the problems.
Example: Apple conducted intelligence to understand the declining market share of its iPhone in certain regions and the reasons behind it. Through market research, customer feedback, and analysis of competitors, Apple identified the problem and its causes.

Design:
- Involves identifying and exploring various solutions to the identified problem.
- Focuses on generating potential approaches and strategies.
Example: After identifying the declining market share issue, Apple's design phase involved exploring solutions such as enhancing product features, introducing competitive pricing strategies, and expanding into new markets.

Choice:
- Involves selecting the most suitable solution from the alternatives identified in the design phase.
- Considers factors such as feasibility, effectiveness, and alignment with organizational goals. Example: Apple's choice phase consisted of evaluating different solutions and selecting a strategy to address the declining market share. They decided to launch a new model with advanced features and a competitive price to regain market share.

Implementation:
- Involves executing and putting the chosen solution into action.
- Includes allocating resources, coordinating activities, and monitoring progress.
Example: Apple implemented their chosen solution by manufacturing and launching the new iPhone model, conducting marketing campaigns to create awareness, and collaborating with distribution channels to ensure availability.

Monitoring:
- Involves ongoing evaluation of the implemented solution to assess its effectiveness.
- Allows for adjustments or modifications if necessary.
Example: Apple closely monitored the market response to the new iPhone model, tracking sales figures, customer feedback, and market share trends. They analyzed the impact of their solution and made further adjustments based on the data collected.

Real-life case study: Apple's decision to introduce the iPad
Intelligence: Apple identified a potential market opportunity for a tablet device that could bridge the gap between smartphones and laptops.
Design: They explored various design options, considered hardware specifications, software capabilities, and pricing strategies.

Choice: Apple chose a solution that emphasized a sleek design, user-friendly interface, and integration with their existing ecosystem.
Implementation: They manufactured and launched the iPad, collaborated with app developers, and marketed the device to create demand.

Monitoring: Apple monitored sales, customer feedback, and competition to assess the success of the iPad and made subsequent iterations and improvements based on user preferences and market trends.

● Managers and Decision Making in the Real World

Managerial Roles:
- Managers play key roles in organizations, ranging from decision-making to administrative tasks.
- Classical model describes functions of managers as planning, organizing, coordinating, deciding, and controlling.
- Behavioral models suggest that managerial behavior is less systematic and more informal than the classical model implies.

Managerial Roles and Information Systems:
- Henry Mintzberg identified 10 managerial roles categorized into interpersonal, informational, and decisional roles.
- Information systems can support most areas of managerial life, but not all roles.
- Examples of supporting systems include management information systems, executive support systems, telepresence, social networks, email, webinars, and decision-support systems.

Challenges in Decision Making:
- Information Quality: High-quality decisions require high-quality information.
- Management Filters: Managers interpret information through filters and biases, affecting decision-making.
- Organizational Inertia and Politics: Organizations resist major changes and decision-making is influenced by various interest groups.

Impact of Information Systems on Decision Making:
- Information systems are not universally helpful for all managerial roles.
- Investments in information technology do not always result in positive outcomes due to information quality, management filters, and organizational culture.

Real-World Example: Financial Crisis of 2008:

Wall Street firms didn't realize how risky the investments in complex mortgage securities were because they had biased information and simplified the measurement of risks. The focus was on making quick profits rather than accurately assessing the risks involved, which resulted in bad decisions. Additionally, the organizations were resistant to change and blamed external factors instead of acknowledging their own mistakes when their performance suffered.

● High-Velocity Automated Decision Making, Business Intelligence in the Enterprise

- A growing number of decisions in organizations are now automated and made by computer algorithms.
- Examples include search engines like Google, which decide which URLs to display in a fraction of a second, and stock trading platforms that execute orders in milliseconds.

- Computer algorithms, large databases, high-speed processors, and optimized software enable fast and precise automated decision making.
- Humans, including managers, are often eliminated from the decision-making process due to their slower speed compared to automated systems.

- However, this also means that organizations are making decisions faster than managers can monitor or control.
- Lack of control over automated decisions can lead to issues, as seen in the 'Flash Crash' in the U.S. stock markets in 2010 and other breakdowns in computerized trading systems.

- The Simon framework of intelligence, design, choice, and implementation is captured by the algorithms in high-velocity decision environments.
- Humans who develop the software have already identified the problem, designed a solution method, defined acceptable solutions, and implemented them.

- Care must be taken to ensure the proper functioning of these systems and additional safeguards may be necessary to observe their behavior, regulate performance, and potentially turn them off if needed.

● What is Business Intelligence?, The Business Intelligence Environment

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