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  1. Data Warehouse Defined
    • A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format
    • “The data warehouse is a collection of integrated, subject-oriented databases design to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time”
  2. Characteristics of DW
    • Subject oriented
    • Integrated
    • Time-variant (time series)
    • Nonvolatile
    • Summarized
    • Not normalized
    • Metadata
    • Web based, relational/multi-dimensional
    • Client/server
    • Real-time and/or right-time (active)
  3. Data Mart
    -A departmental data warehouse that stores only relevant data
  4. Dependent data mart
    A subset that is created directly from a data warehouse
  5. Independent data mart
    A small data warehouse designed for a strategic business unit or a department
  6. Operational data stores (ODS)
    A type of database often used as an interim area for a data warehouse
  7. Oper marts
    An operational data mart.
  8. Enterprise data warehouse (EDW)
    A data warehouse for the enterprise.
  9. Metadata
    Data about data. In a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use
  10. Three-tier architecture
    • Data acquisition software (back-end)
    • The data warehouse that contains the data & software
    • Client (front-end) software that allows users to access and analyze data from the warehouse
  11. Two-tier architecture
    • First 2 tiers in three-tier architecture is combined into one
    • … sometime there is only one tier?
  12. DW Architecture Considerations: Issues to consider when deciding which architecture to use:
    • Which database management system (DBMS) should be used?
    • Will parallel processing and/or partitioning be used?
    • Will data migration tools be used to load the data warehouse?
    • What tools will be used to support data retrieval and analysis?
  13. Ten factors that potentially affect the architecture selection decision:
    • Information interdependence between organizational units
    • Upper management’s information needs
    • Urgency of need for a data warehouse
    • Nature of end-user tasks
    • Constraints on resources
    • Strategic view of the data warehouse prior to implementation
    • Compatibility with existing systems
    • Perceived ability of the in-house IT staff
    • Technical issues
    • Social/political factors
  14. Data integration
    Integration that comprises three major processes: data access, data federation, and change capture.
  15. Enterprise application integration (EAI)
    A technology that provides a vehicle for pushing data from source systems into a data warehouse
  16. Enterprise information integration (EII)
    An evolving tool space that promises real-time data integration from a variety of sources
  17. Service-oriented architecture (SOA)
    A new way of integrating information systems
  18. Extraction, transformation, and load (ETL)
    -a data warehousing process that consists of extraction (i.e. reading data from a database), transformation (i.e. converting the extracted data from its previous form into the form in which it needs to be so that it can be placed into a data warehouse or simply another database), and load (i.e putting the data into the data warehouse).
  19. ETL Process
    Data Integration and the Extraction, Transformation, and Load (ETL) Process
  20. Issues affecting the purchase of and ETL tool
    • Data transformation tools are expensive
    • Data transformation tools may have a long learning curve
  21. Important criteria in selecting an ETL tool
    • Ability to read from and write to an unlimited number of data sources/architectures
    • Automatic capturing and delivery of metadata
    • A history of conforming to open standards
    • An easy-to-use interface for the developer and the functional user
  22. Direct benefits of a data warehouse
    • Allows end users to perform extensive analysis
    • Allows a consolidated view of corporate data
    • Better and more timely information
    • Enhanced system performance
    • Simplification of data access
  23. Indirect benefits of data warehouse
    • Enhance business knowledge
    • Present competitive advantage
    • Enhance customer service and satisfaction
    • Facilitate decision making
    • Help in reforming business processes
  24. Data cube
    • A two-dimensional, three-dimensional, or higher-dimensional object in which each dimension of the data represents a measure of interest
    • Grain
    • Drill-down
    • Slicing
  25. Best Practices for Implementing DW
    • The project must fit with corporate strategy
    • There must be complete buy-in to the project
    • It is important to manage user expectations
    • The data warehouse must be built incrementally
    • Adaptability must be built in from the start
    • The project must be managed by both IT and business professionals (a business–supplier relationship must be developed)
    • Only load data that have been cleansed/high quality
    • Do not overlook training requirements
    • Be politically aware.
  26. Risks in Implementing DW
    • No mission or objective
    • Quality of source data unknown
    • Skills not in place
    • Inadequate budget
    • Lack of supporting software
    • Source data not understood
    • Weak sponsor
    • Users not computer literate
    • Political problems or turf wars
    • Unrealistic user expectations
    • Architectural and design risks
    • Scope creep and changing requirements
    • Vendors out of control
    • Multiple platforms
    • Key people leaving the project
    • Loss of the sponsor
    • Too much new technology
    • Having to fix an operational system
    • Geographically distributed environment
    • Team geography and language culture
  27. Things to Avoid for Successful Implementation of DW
    • Starting with the wrong sponsorship chain
    • Setting expectations that you cannot meet
    • Engaging in politically naive behavior
    • Loading the warehouse with information just because it is available
    • Believing that data warehousing database design is the same as transactional DB design
    • Choosing a data warehouse manager who is technology oriented rather than user oriented
    • (…see more on page 356)
  28. Real-time DW (a.k.a. Active Data Warehousing)
    • Enabling real-time data updates for real-time analysis and real-time decision making is growing rapidly
    • Push vs. Pull (of data)
  29. Concerns about real-time BI
    • Not all data should be updated continuously
    • Mismatch of reports generated minutes apart
    • May be cost prohibitive
    • May also be infeasible
  30. Data Warehouse Administration
    • Due to its huge size and its intrinsic nature, a DW requires especially strong monitoring in order to sustain its efficiency, productivity and security.
    • The successful administration and management of a data warehouse entails skills and proficiency that go past what is required of a traditional database administrator.
    • Requires expertise in high-performance software, hardware, and networking technologies
  31. DW Scalability: The main issues pertaining to scalability:
    • The amount of data in the warehouse
    • How quickly the warehouse is expected to grow
    • The number of concurrent users
    • The complexity of user queries
    • Good scalability means that queries and other data-access functions will grow linearly with the size of the warehouse
  32. DW Security
    Emphasis on security and privacy
  33. Extraction, transformation, and load (ETL)
    -a data warehousing process that consists of extraction (i.e. reading data from a database), transformation (i.e. converting the extracted data from its previous form into the form in which it needs to be so that it can be placed into a data warehouse or simply another database), and load (i.e putting the data into the data warehouse).
  34. The Inmon Model: the EDW approach
    -Inmon's approach emphasis top-down development, employing established database development methodologies and tools, such as ERD, and an adjustment of the spiral development approach. The EDW approach does not prelude the creation of data marts. The EDW is the ideal in this approach because it provides a consistent and comprehensive view of the enterprise.
  35. The Kimball Model: the data mart approach
    -Kimball's data mart strategy is a "plan big, build small" approach. A data mart is a subject-oriented or department-oriented data warehouse. It is a scaled-down version of a data warehouse that focuses on the requests of a specific department, such as marketing or sales. This model applies dimensional data modeling, which starts with tables. Kimball advocated a development methodology that entails a bottom-up approach, which in the case of data warehouses means building one data mart at a time.
  36. BPM is…
    • Business Performance Management
    • A real-time system that alert managers to potential opportunities, impending problems, and threats, and then empowers them to react through models and collaboration
    • Also called, corporate performance management (CPM by Gartner Group), enterprise performance management (EPM by Oracle), strategic enterprise management (SEM by SAP)
    • BPM refers to the business processes, methodologies, metrics, and technologies used by enterprises to measure, monitor, and manage business performance
  37. BPM encompasses three key components
    • -A set of integrated, closed-loop management and analytic processes, supported by technology …
    • -Tools for businesses to define strategic goals and then measure/manage performance against them
    • -Methods and tools for monitoring key performance indicators (KPIs), linked to organizational strategy
  38. BPM versus BI
    • BPM is an outgrowth of BI and incorporates many of its technologies, applications, and techniques
    • Same companies market and sell them
    • BI has evolved so that many of the original differences between the two no longer exist (e.g., BI used to be focused on departmental rather than enterprise-wide projects)
    • BI is a crucial element of BPM
    • BPM = BI + Planning (a unified solution)
  39. BPM cycle
    • 1. Strategize
    • 2. Plan
    • 3. Monitor/analyze
    • 4. Act/adjust
  40. Common tasks for the strategic planning process:
    • Conduct a current situation analysis
    • Determine the planning horizon
    • Conduct an environment scan
    • Identify critical success factors
    • Complete a gap analysis
    • Create a strategic vision
    • Develop a business strategy
    • Identify strategic objectives and goals
  41. Strategic objective
    A broad statement or general course of action prescribing targeted directions for an organization
  42. Strategic goal
    A quantified objective with a designated time period
  43. Strategic vision
    A picture or mental image of what the organization should look like in the future
  44. CSF
    • Critical success factors
    • Key factors that delineate the things that an organization must excel at to be successful
  45. Strategy gap
  46. Four sources for the gap between strategy and execution:
    • Communication (enterprise-wide)
    • Alignment of rewards and incentives
    • Focus (concentrating on the core elements)
    • Resources
  47. Operational planning
    Operational plan: plan that translates an organization’s strategic objectives and goals into a set of well-defined tactics and initiatives, resources requirements, and expected results for some future time period (usually a year)
  48. Operational planning can be
    • Tactic-centric (operationally focused)
    • Budget-centric plan (financially focused)
  49. Financial planning and budgeting
    • An organization’s strategic objectives and key metrics should serve as top-down drivers for the allocation of an organization’s tangible and intangible assets
    • Resource allocations should be carefully aligned with the organization’s strategic objectives and tactics in order to achieve strategic success
  50. A comprehensive framework for monitoring performance should address two key issues:
    • What to monitor
    • -Critical success factors
    • -Strategic goals and targets
    • How to monitor
  51. Diagnostic control system
    A cybernetic system that has inputs, a process for transforming the inputs into outputs, a standard or benchmark against which to compare the outputs, and a feedback channel to allow information on variances between the outputs and the standard to be communicated and acted upon.
  52. Pitfalls of variance analysis
    • The vast majority of the exception analysis focuses on negative variances when functional groups or departments fail to meet their targets
    • Rarely are positive variances reviewed for potential opportunities, and rarely does the analysis focus on assumptions underlying the variance patterns
  53. Harrah's Closed -Loop Marketing Model
    • 1. Define Campaign Objectives & Test Outcomes
    • 2. Execute Marketing Campaign
    • 3. Track Linked Transactions
    • 4. Evaluate Campaign Effectiveness
    • 5. Learn & Refine Campaigns & Approaches
  54. The Hackett Group’s benchmarking results indicate that world class companies:
    • Are significantly more efficient than their peers at managing costs
    • Focus on operational excellence and experience significantly reduced rates of employee turnover
    • Provide management with the tools and training to leverage corporate information and to guide strategic planning, budgeting, and forecasting
    • Closely align strategic and tactical plans, enabling functional areas to contribute more effectively…
  55. Performance measurement system
    • A system that assists managers in tracking the implementations of business strategy by comparing actual results against strategic goals and objectives
    • Comprises systematic comparative methods that indicate progress (or lack thereof) against goals
  56. KBI
    • Key performance indicator
    • A KPI represents a strategic objective and metrics that measures performance against a goal
  57. Distinguishing features of KPIs
    • Strategy
    • Targets
    • Ranges
    • Encodings
    • Time frames
    • Benchmarks
  58. Outcome KPIs vs. Driver KPIs
    • Outcome KPIs: (lagging indicators e.g., revenues)
    • Driver KPIs: (leading indicators e.g., sales leads)
  59. Operational areas covered by driver KPIs
    • Customer performance
    • Service performance
    • Sales operations
    • Sales plan/forecast
  60. The drawbacks of using financial data as the core of a performance measurement:
    • Financial measures are usually reported by organizational structures and not by the processes that produced them
    • Financial measures are lagging indicators, telling us what happened, not why it happened or what is likely to happen in the future
    • Financial measures are often the product of allocations that are not related to the underlying processes that generated them
    • Financial measures are focused on the short-term returns…
  61. Good performance measures should:
    • Be focused on key factors
    • Be a mix of past, present, and future
    • Balance the needs of all stakeholders (shareholders, employees, partners, suppliers, …)
    • Start at the top and trickle down to the bottom
    • Have targets that are based on research and reality rather than be arbitrary
  62. An effective performance measurement system should help:
    • Align top-level strategic objectives and bottom-level initiatives
    • Identify opportunities and problems in a timely fashion
    • Determine priorities and allocate resources accordingly
    • Change measurements when the underlying processes and strategies change
    • Delineate responsibilities, understand actual performance relative to responsibilities, and reward and recognize accomplishments
    • Take action to improve processes and procedures when the data warrant it
    • Plan and forecast in a more reliable and timely fashion
  63. BSC
    • Balanced scorecard
    • A performance measurement and management methodology that helps translate an organization’s financial, customer, internal process, and learning and growth objectives and targets into a set of actionable initiatives
    • "The Balanced Scorecard: Measures That Drive Performance” (HBR, 1992)
  64. The meaning of “balance”
    BSC is designed to overcome the limitations of systems that are financially focused
  65. Nonfinancial objectives fall into one of three perspectives:
    • Customer
    • Internal business process
    • Learning and growth
  66. In BSC, the term “balance” arises because the combined set of measures are supposed to encompass indicators that are:
    • Financial and nonfinancial
    • Leading and lagging
    • Internal and external
    • Quantitative and qualitative
    • Short term and long term
  67. BSC aligning action and strategies: A six-step process
    • Developing and formulating a strategy
    • Planning the strategy
    • Aligning the organization
    • Planning the operations
    • Monitoring and learning
    • Testing and adapting the strategy
  68. Strategy map
    A visual display that delineates the relationships among the key organizational objectives for all four BSC perspectives
  69. BPM architecture
    The logical and physical design of a system
  70. BPM system consists of three logical parts:
    • BPM Applications
    • Information Hub
    • Source Systems
  71. BPM system consists of three physical parts:
    • Database tier
    • Application tier
    • Client or user interface
  72. BPM applications
    • Strategy management
    • Budgeting, planning, and forecasting
    • Financial consolidation
    • Profitability modeling and optimization
    • Financial, statutory, and management reporting
  73. Performance dashboards
    • Visual display used to monitor operational performance (free form…)
    • Performance dashboard is a multilayered application built on a business intelligence and data integration infrastructure that enables organizations to measure, monitor, and manage business performance more effectively - Eckerson
  74. Performance scorecards
    Visual display used to chart progress against strategic and tactical goals and targets (predetermined measures…)
  75. Dashboards and scorecards both provide
    visual displays of important information that is consolidated and arranged on a single screen so that information can be digested at a single glance and easily explored
  76. Three types of performance dashboards:
    • Operational dashboards
    • Tactical dashboards
    • Strategic dashboards
  77. Dashboard design
    “The fundamental challenge of dashboard design is to display all the required information on a single screen, clearly and without distraction, in a manner that can be assimilated quickly" -(Few, 2005)
  78. What to look for in a dashboard
    • Use of visual components (e.g., charts, performance bars, spark lines, gauges, meters, stoplights) to highlight, at a glance, the data and exceptions that require action
    • Transparent to the user, meaning that they require minimal training and are extremely easy to use
    • Combine data from a variety of systems into a single, summarized, unified view of the business
    • Enable drill-down or drill-through to underlying data sources or reports
    • Present a dynamic, real-world view with timely data updates
    • Require little, if any, customized coding to implement, deploy, and maintain
  79. Collaboration
    • “… making joint effort toward achieving an agreed upon goal.”
    • Meeting is a common form of collaboration
  80. Why Collaborate?
    • Make decisions
    • Review
    • Build trust
    • Synergy
    • Share the vision
    • Share information
    • Share work
    • Solve problems
    • Build consensus
    • Socialize
  81. Ineffective collaboration
    • Waiting to speak
    • Domination
    • Fear of speaking
    • Misunderstanding
    • Inattention
    • Lack of focus
    • Inadequate criteria
    • Premature decisions
    • Missing information
    • Distractions
    • Wrong people
    • Groupthink
    • Poor grasp of problem
    • Ignored alternatives
    • Lack of consensus
    • Poor planning
    • Hidden agendas
    • Conflict
    • Inadequate resources
    • Poorly defined goals
  82. Collaboration is Essential
    • No one has all the …
    • Experience
    • Knowledge
    • Resources
    • Insight, and
    • Inspiration
    • …to do the job alone
    • Bottom line:
    • Collaboration is difficult, expensive, and yet essential for today’s organizations
  83. Meetings
    • (a form of collaboration)
    • Joint activity
    • Equal or near equal status
    • Outcome depends on participant’s knowledge, etc.
    • Outcome depends on group composition
    • Outcome depends on decision-making process
    • Disagreement settled by rank or negotiation
  84. The Ideal Meeting
    • Dozens of people attends
    • Everyone …
    • talks at once
    • hears everything
    • understands
    • remembers
  85. Traditional Meetings
    Only ONE person can speak at a time
  86. GSS Meetings
    By using the computer everyone can SPEAK and be understood simultaneously
  87. Communication Support
    • Vital
    • Needed for collaboration
    • Modern information technologies provide inexpensive, fast, capable, reliable means of supporting communication
    • Internet / Web
  88. Supporting Communication: Evolution of Communication
    • Word of mouth
    • Delivery persons
    • Horseback
    • Snailmail
    • Telegraph
    • Telephone
    • Radio
    • Television
    • Videoconferencing
    • Internet / Web…
  89. Group Support Systems
    • Goal: to support groupwork
    • Increase benefits / decrease losses of collaboration
    • Based on traditional methods
  90. Nominal Group Technique
    “Individuals work alone to generate ideas which are pooled under guidance of a trained facilitator”
  91. Delphi Method
    “A structured process for collecting and distilling knowledge from a group of experts by means of questionnaires”
  92. Electronic Meeting System (EMS)
  93. GSS – Important Features
    • Process Gains:
    • Parallelism ( simultaneous contributions )
    • Larger groups can participate
    • Anonymity ( promotes equal participation )
    • Focus on content not personalities
    • Triggering ( stimulates thinking )
    • Synergy ( integrates ideas )
    • Structure ( facilitates problem solving )
    • Record keeping ( promotes organizational memory )
    • Process Loses:
    • Free-riding
    • Flaming
  94. Benefits of Anonymity
    • Ideas considered on merit not source
    • Overcome fear of speaking up
    • More ideas leads to more quality ideas
    • Defuses tough political discussions
  95. GSS Enabling Technologies
    • Decision room
    • Multiple use facility
    • Web-based
  96. The Decision (Electronic Meeting) Room
    • 12 to 30 networked personal computers
    • Usually recessed into the desktop
    • Server PC
    • Large-screen projection system
    • Breakout rooms
    • Need a Trained Facilitator for Success
  97. Few Organizations Use Decision Rooms
    • High Cost
    • Need for a Trained Facilitator
    • Requires Specific Software Support for Different Cooperative Tasks
    • Infrequent Use
    • Different Place / Different Time Needs
    • May Need More Than One
  98. Other Technologies
    • Multiple Use Facility
    • -Cheaper
    • -Still need a facilitator
    • Web-based
    • -Cheaper: no extra hardware needed
    • -Still need facilitator
  99. Collaborative Planning, Forecasting, and Replenishment (CPFR)
    An industry-wide project in which suppliers and retailers collaborate in planning and demand forecasting in order to ensure that members of the supply chain will have the right amount of raw materials and finished goods when they need them
  100. Collective Intelligence
    • A shared intelligence that emerges from the intentional cooperation, collaboration, and/or coordination of many individuals.
    • Examples: Wikipedia, video games, online advertising, learner-generated context, …
    • In order for CI to happen:
    • -Openness
    • -Peering
    • -Sharing
    • -Acting globally
  101. Creativity
    Definition: Creativity is a characteristic of a person that leads to production of acts, items and/or instances of novelty
  102. Creativity is fostered by
    • Freedom
    • Permission-to-fail
    • Allow and Enable rather than Structure and Control
  103. Software that shows creativity
    • Intelligent Agents (Softbots)
    • Creativity is an intelligent behavior
  104. Software that facilitates human creativity
    • ThoughtPath: promotes outside-the-box thinking
    • Creative WhackPack (Creative Think): whack you out of your habitual thought process
    • IdeaFisher: provides language specific universality - thesaurus
  105. Knowledge management
    The active management of the expertise in an organization. It involves collecting, categorizing, and disseminating knowledge
  106. Intellectual capital
    The invaluable knowledge of an organization’s employees
  107. Knowledge is
    • information that is contextual, relevant, and actionable
    • understanding, awareness, or familiarity acquired through education or experience
    • anything that has been learned, perceived, discovered, inferred, or understood.
    • In a knowledge management system, “knowledge is information in action”
  108. Characteristics of knowledge
    • Extraordinary leverage and increasing returns
    • Fragmentation, leakage and the need to refresh
    • Uncertain value
    • Uncertain value of sharing
  109. Knowledge-based economy
    The economic shift from natural resources to intellectual assets
  110. Explicit (leaky) knowledge
    • Knowledge that deals with objective, rational, and technical material (data, policies, procedures, software, documents, etc.)
    • Easily documented, transferred, taught and learned
    • Examples…
  111. Tacit (embedded) knowledge
    • Knowledge that is usually in the domain of subjective, cognitive, and experiential learning
    • It is highly personal and hard to formalize
    • Hard to document, transfer, teach and learn
    • Involves a lot of human interpretation
    • Examples…
  112. Knowledge management systems (KMS)
    A system that facilitates knowledge management by ensuring knowledge flow from the person(s) who know to the person(s) who need to know throughout the organization; knowledge evolves and grows during the process
  113. Learning organization
    An organization capable of learning from its past experience, implying the existence of an organizational memory and a means to save, represent, and share it through its personnel
  114. Organizational memory
    Repository of what the organization “knows”
  115. Organizational learning
    • Development of new knowledge and insights that have the potential to influence organization’s behavior
    • The process of capturing knowledge and making it available enterprise-wide
    • Need to establish corporate memory
    • Modern IT helps…
    • People issues are the most important!
  116. Organizational culture
    • The aggregate attitudes in an organization concerning a certain issue (e.g., technology, computers, DSS)
    • How do people learn the “culture”?
    • Is it explicit or implicit?
    • Can culture be changed? How?
    • Give some examples of corporate culture: Microsoft, Google, Apple, HP, GM, …
  117. Why people don’t like to share knowledge:
    • Lack of time to share knowledge and time to identify colleagues in need of specific knowledge
    • Fear that sharing may jeopardize one’s job security
    • Low awareness and realization of the value and benefit of the knowledge others possess
    • Dominance in sharing explicit over tacit knowledge
    • Use of a strong hierarchy, position-based status, and formal power
    • Insufficient capture, evaluation, feedback, communication, and tolerance of past mistakes
  118. Why people don’t like to share knowledge:
    • Differences in experience and education levels
    • Lack of contact time and interaction between knowledge sources and recipients
    • Poor verbal/written communication and interpersonal skills
    • Age, gender, cultural and ethical defenses
    • Lack of a social network
    • Ownership of intellectual property
    • Lack of trust in people because they may misuse knowledge or take unjust credit for it
    • Perceived lack of accuracy/credibility of knowledge
  119. Most knowledge management initiatives have one of three aims:
    • To make knowledge visible
    • To develop a knowledge-intensive culture
    • To build a knowledge infrastructure
  120. Knowledge creation is
    the generation of new insights, ideas, or routines
  121. Four modes of knowledge creation:
    • Socialization
    • Externalization
    • Internalization
    • Combination
    • Analytics-based knowledge creation?
  122. Knowledge sharing
    • Knowledge sharing is the willful explication of one person’s ideas, insights, experiences to another individual either via an intermediary or directly
    • In many organizations, information and knowledge are not considered organizational resources to be shared but individual competitive weapons to be kept private
  123. Knowledge seeking
    • Knowledge seeking (knowledge sourcing) is the search for and use of internal organizational knowledge
    • Lack of time or lack of reward may hinder the sharing of knowledge or knowledge seeking
  124. Process approach to knowledge management attempts to codify organizational knowledge through formalized controls, processes and technologies
    Focuses on explicit knowledge and IT
  125. Practice approach focuses on building the social environments or communities of practice necessary to facilitate the sharing of tacit understanding
    Focuses on tacit knowledge and socialization
  126. Hybrid approaches to knowledge management
    • The practice approach is used so that a repository stores only explicit knowledge that is relatively easy to document
    • Tacit knowledge initially stored in the repository is contact information about experts and their areas of expertise
    • Increasing the amount of tacit knowledge over time eventually leads to the attainment of a true process approach
    • Hybrid at 80/20 to 50/50
  127. Supply-driven vs. demand-driven KM
  128. Knowledge repository
    is the actual storage location of knowledge in a knowledge management system. Similar in nature to a database, but generally text-oriented
  129. KMS usually follow a six-step cycle:
    • Create knowledge
    • Capture knowledge
    • Refine knowledge
    • Store knowledge
    • Manage knowledge
    • Disseminate knowledge
  130. KMS are developed using three sets of core technologies:
    • Communication
    • Collaboration
    • Storage and retrieval
  131. Technologies that support KM
    • Artificial intelligence
    • Intelligent agents
    • Knowledge discovery in databases
    • Extensible Markup Language (XML)
  132. AI methods used in KMS:
    • Assist in and enhance searching knowledge
    • Help for knowledge representation (e.g., ES)
    • Help establish knowledge profiles of individuals and groups
    • Help determine the relative importance of knowledge when it is contributed to and accessed from the knowledge repository
    • Scan e-mail, documents, and databases to perform knowledge discovery, determine meaningful relationships and rules
    • Identify patterns in data (usually through neural networks and other data mining techniques)
    • Forecast future results by using data/knowledge
    • Provide advice directly from knowledge by using neural networks or expert systems
    • Provide a natural language or voice command–driven user interface for a KMS
  133. Intelligent agents
    • Intelligent agents are software systems that learn how users work and provide assistance in their daily tasks
    • They are used to elicit and identify knowledge
    • Combined with enterprise knowledge portal to proactively disseminate knowledge
  134. Knowledge discovery in databases (KDD)
    • A machine learning process that performs rule induction, or a related procedure to establish (or create) knowledge from large databases
    • a.k.a. Data Mining (and/or Text Mining)
  135. Model marts
    Small, generally departmental repositories of knowledge created by employing knowledge-discovery techniques on past decision instances. Similar to data marts
  136. Model warehouses
    Large, generally enterprise-wide repositories of knowledge created by employing knowledge-discovery techniques. Similar to data warehouses
  137. Extensible Markup Language (XML)
    XML enables standardized representations of data structures so that data can be processed appropriately by heterogeneous information systems without case-by-case programming or human intervention
  138. Web 2.0
    The evolution of the Web from statically disseminating information to collaboratively creating and sharing information
  139. Knowware
    Technology tools (software/hardware products) that support knowledge management
  140. Enterprise knowledge portals (EKP)
    An electronic doorway into a knowledge management system…
  141. Electronic document management (EDM)
    A method for processing documents electronically, including capture, storage, retrieval, manipulation, and presentation
  142. Content management systems (CMS)
    An electronic document management system that produces dynamic versions of documents, and automatically maintains the current set for use at the enterprise level
  143. Software development tools
    • Knowledge harvesting tools
    • Search engines
    • Knowledge management suites
    • Knowledge management consulting firms
    • Knowledge management ASPs
  144. Integration of KMS with other business information systems
    • With DSS/BI Systems
    • With AI
    • With databases and information systems
    • With CRM systems
    • With SCM systems
    • With corporate intranets and extranets
  145. Chief knowledge officer (CKO)
    • The person in charge of a knowledge management effort in an organization
    • Sets KM strategic priorities
    • Establishes a repository of best practices
    • Gains a commitment from senior executives
    • Teaches information seekers how to better elicit it
    • Creates a process for managing intellectual assets
    • Obtain customer satisfaction information
    • Globalizes knowledge management
  146. Skills required of a CKO include:
    • Interpersonal communication skills
    • Leadership skills
    • Business acumen
    • Strategic thinking
    • Collaboration skills
    • The ability to institute effective educational programs
    • An understanding of IT and its role in advancing knowledge management
  147. The CEO, other chief officers, and managers
    • The CEO is responsible for championing a knowledge management effort
    • The officers make available the resources needed to get the job done
    • -CFO ensures that the financial resources are available
    • -COO ensures that people begin to embed knowledge management practices into their daily work processes
    • -CIO ensures IT resources are available
    • Managers also support the KM efforts by providing access to sources of knowledge
  148. Community of practice (CoP)
    A group of people in an organization with a common professional interest, often self-organized for managing knowledge in a knowledge management system
  149. KMS developers
    • The team members who actually develop the system
    • Internal + External
  150. KMS staff
    Enterprise-wide KMS require a full-time staff to catalog and manage the knowledge
  151. Implementing a good KM strategy can: Reduce…
    • loss of intellectual capital
    • costs by decreasing the number of times the company must repeatedly solve the same problem
    • redundancy of knowledge-based activities
  152. Implementing a good KM strategy can: Increase…
    • productivity
    • employee satisfaction
  153. Criteria (performance dimensions):
    • 1. Creating a knowledge-driven corporate culture
    • 2. Developing knowledge workers through leadership
    • 3. Fostering innovation
    • 4. Maximizing enterprise intellectual capital
    • 5. Creating an environment for collaborative knowledge sharing
    • 6. Facilitating organizational learning
    • 7. Delivering value based on stakeholder knowledge
    • 8. Transforming enterprise knowledge into stakeholders’ value
  154. Expert location systems (know-who)
    Interactive computerized systems that help employees find and connect with colleagues who have expertise required for specific problems—whether they are across the county or across the room—in order to solve specific, critical business problems in seconds
  155. Financial metrics for knowledge management valuation
    • Focus knowledge management projects on specific business problems that can be easily quantified
    • When the problems are solved, the value and benefits of the system become apparent
  156. Nonfinancial metrics for knowledge management valuation—new ways to view capital when evaluating intangibles:
    • Customer goodwill
    • External relationship capital
    • Structural capital
    • Human capital
    • Social capital
    • Environmental capital
  157. Causes of knowledge management failure
    • The effort mainly relies on technology and does not address whether the proposed system will meet the needs and objectives of the organization and its individuals
    • Lack of emphasis on human aspects
    • Lack of commitment
    • Failure to provide reasonable incentive for people to use the system…
  158. Factors that lead to knowledge management success
    • A link to a firm’s economic value, to demonstrate financial viability and maintain executive sponsorship
    • A technical and organizational infrastructure on which to build
    • A standard, flexible knowledge structure to match the way the organization performs work and uses knowledge
    • A knowledge-friendly culture that leads directly to user support
    • A clear purpose and language, to encourage users to buy into the system
    • A change in motivational practices, to create a culture of sharing
    • Multiple channels for knowledge transfer
    • A significant process orientation and valuation to make a knowledge management effort worthwhile
    • Nontrivial motivational methods to encourage users to contribute and use knowledge
    • Senior management support
  159. Artificial intelligence (AI)
    • A subfield of computer science, concerned with symbolic reasoning and problem solving
    • AI has many definitions…
    • Behavior by a machine that, if performed by a human being, would be considered intelligent
    • “…study of how to make computers do things at which, at the moment, people are better
    • Theory of how the human mind works
  160. AI Objectives
    • Make machines smarter (primary goal)
    • Understand what intelligence is
    • Make machines more intelligent and useful
  161. Signs of intelligence…
    • Learn or understand from experience
    • Make sense out of ambiguous situations
    • Respond quickly to new situations
    • Use reasoning to solve problems
    • Apply knowledge to manipulate the environment
  162. Test for Intelligence
    • Turing Test for Intelligence
    • A computer can be considered to be smart only when a human interviewer, “conversing” with both an unseen human being and an unseen computer, can not determine which is which.
  163. Symbolic Processing: AI …
    • represents knowledge as a set of symbols, and
    • uses these symbols to represent problems, and
    • apply various strategies and rules to manipulate symbols to solve problems
  164. A symbol
    • is a string of characters that stands for some real-world concept (e.g., Product, consumer,…)
    • Examples:
    • -(DEFECTIVE product)
    • -(LEASED-BY product customer) - LISP
    • -Tastes_Good (chocolate)
  165. Reasoning
    Inferencing from facts and rules using heuristics or other search approaches
  166. Pattern Matching
    Attempt to describe and match objects, events, or processes in terms of their qualitative features and logical and computational relationships
  167. Knowledge Base
  168. Advantages of AI
    • More permanent
    • Ease of duplication and dissemination
    • Less expensive
    • Consistent and thorough
    • Can be documented
    • Can execute certain tasks much faster
    • Can perform certain tasks better than many people
  169. Advantages of Biological Natural Intelligence
    • Is truly creative
    • Can use sensory input directly and creatively
    • Can apply experience in different situations
  170. The AI Field
    • AI is many different sciences and technologies
    • It is a collection of concepts and ideas
    • -Linguistics; Psychology; Philosophy; Computer Science; Electrical Engineering; Mechanics; Hydraulics; Physics; Optics; Management and Organization Theory; Chemistry;
    • -Physics; Statistics; Mathematics; Management Science; Management Information Systems; Computer hardware and software; Commercial, Government and Military Organizations; …
    • AI provides the scientific foundation for many commercial technologies
  171. AI Areas
    • Major…
    • Expert Systems
    • Natural Language Processing
    • Speech Understanding
    • Robotics and Sensory Systems
    • Computer Vision and Scene Recognition
    • Intelligent Computer-Aided Instruction
    • Automated Programming
    • Neural Computing Game Playing
    • Additional…
    • Game Playing, Language Translation
    • Fuzzy Logic, Genetic Algorithms
    • Intelligent Software Agents
  172. AI is often transparent in many commercial products
    • Anti-lock Braking Systems (ABS)
    • Automatic Transmissions
    • Video Camcorders
    • Appliances
    • -Washers, Toasters, Stoves
    • Help Desk Software
    • Subway Control…
  173. Expert Systems (ES)
    • Is a computer program that attempts to imitate expert’s reasoning processes and knowledge in solving specific problems
    • Most Popular Applied AI Technology
    • -Enhance Productivity
    • -Augment Work Forces
    • Works best with narrow problem areas/tasks
    • Expert systems do not replace experts, but
    • -Make their knowledge and experience more widely available, and thus
    • -Permit non-experts to work better
  174. Expert
    A human being who has developed a high level of proficiency in making judgments in a specific domain
  175. Expertise
    • The set of capabilities that underlines the performance of human experts, including
    • -extensive domain knowledge,
    • -heuristic rules that simplify and improve approaches to problem solving,
    • -meta-knowledge and meta-cognition, and
    • -compiled forms of behavior that afford great economy in a skilled performance

    • Nonexperts outnumber experts often by
    • 100 to 1
  176. DENDRAL
    • Applied knowledge (i.e., rule-based reasoning)
    • Deduced likely molecular structure of compounds
  177. MYCIN
    • A rule-based expert system
    • Used for diagnosing and treating bacterial infections
  178. XCON
    • A rule-based expert system
    • Used to determine the optimal information systems configuration
  179. Structures of Expert Systems
    • 1. Development Environment
    • 2. Consultation (Runtime) Environment
  180. Three major components in ES are:
    • Knowledge base
    • Inference engine
    • User interface
  181. ES may also contain:
    • Knowledge acquisition subsystem
    • Blackboard (workplace)
    • Explanation subsystem (justifier)
    • Knowledge refining system
  182. Knowledge acquisition (KA)
    The extraction and formulation of knowledge derived from various sources, especially from experts (elicitation)
  183. Knowledge base
    A collection of facts, rules, and procedures organized into schemas. The assembly of all the information and knowledge about a specific field of interest
  184. Blackboard (working memory)
    An area of working memory set aside for the description of a current problem and for recording intermediate results in an expert system
  185. Explanation subsystem (justifier)
    The component of an expert system that can explain the system’s reasoning and justify its conclusions
  186. Knowledge Engineering (KE)
    A set of intensive activities encompassing the acquisition of knowledge from human experts (and other information sources) and converting this knowledge into a repository (commonly called a knowledge base)
  187. The primary goal of KE is
    • to help experts articulate how they do what they do, and
    • to document this knowledge in a reusable form
  188. Declarative Knowledge
    • Descriptive representation of knowledge that relates to a specific object.
    • Shallow - Expressed in a factual statements
    • Important in the initial stage of knowledge acquisition
  189. Procedural Knowledge
    • Considers the manner in which things work under different sets of circumstances
    • Includes step-by-step sequences and how-to types of instructions
  190. Metaknowledge
    Knowledge about knowledge
  191. Different ways of representing human knowledge include:
    • Production rules (*)
    • Semantic networks
    • Logic statements
  192. Forms of Rules
    • IF premise, THEN conclusion
    • -IF your income is high, THEN your chance of being audited by the IRS is high
    • Conclusion, IF premise
    • -Your chance of being audited is high, IF your income is high
    • Inclusion of ELSE
    • -IF your income is high, OR your deductions are unusual, THEN your chance of being audited by the IRS is high, ELSE your chance of being audited is low
    • More Complex Rules
    • -IF credit rating is high AND salary is more than $30,000, OR assets are more than $75,000, AND pay history is not "poor," THEN approve a loan up to $10,000, and list the loan in category "B.”
  193. Two types of rules are common in AI:
    Knowledge rules and Inference rules
  194. Knowledge rules (declarative rules),
    • state all the facts and relationships about a problem
    • Knowledge rules are stored in the knowledge base
  195. Inference rules (procedural rules),
    • advise on how to solve a problem, given that certain facts are known
    • Inference rules contain rules about rules (metarules)
    • Inference rules become part of the inference engine
  196. Inference
    is the process of chaining multiple rules together based on available data
  197. Forward chaining
    • A data-driven search in a rule-based system
    • If the premise clauses match the situation, then the process attempts to assert the conclusion
    • Data-driven: Start from available information as it becomes available, then try to draw conclusions
  198. Which Forward/Backward chaining to Use?
    • If all facts available up front - forward chaining
    • Diagnostic problems - backward chaining
  199. Backward chaining
    • A goal-driven search in a rule-based system
    • It begins with the action clause of a rule and works backward through a chain of rules in an attempt to find a verifiable set of condition clauses
    • Goal-driven: Start from a potential conclusion (hypothesis), then seek evidence that supports (or contradicts with) it
    • Often involves formulating and testing intermediate hypotheses (or sub-hypotheses)
  200. A typical process for developing ES includes:
    • Knowledge acquisition
    • Knowledge representation
    • Selection of development tools
    • System prototyping
    • Evaluation
    • Improvement /Maintenance
  201. Knowledge engineer
    An AI specialist responsible for the technical side of developing an expert system. The knowledge engineer works closely with the domain expert to capture the expert’s knowledge
  202. Knowledge engineering (KE)
    The engineering discipline in which knowledge is integrated into computer systems to solve complex problems normally requiring a high level of human expertise
  203. Expert system shell
    • A computer program that facilitates relatively easy implementation of a specific expert system
    • (e.g., ExSys or Corvid)…
  204. Choosing an ES development tool
    • Consider the cost benefits
    • Consider the functionality and flexibility of the tool
    • Consider the tool's compatibility with the existing information infrastructure
    • Consider the reliability of and support from the vendor
  205. Problem Areas Addressed by ES
    • Interpretation systems
    • Prediction systems
    • Diagnostic systems
    • Repair systems
    • Design systems
    • Planning systems
    • Monitoring systems
    • Debugging systems
    • Instruction systems
    • Control systems, …
  206. ES Benefits
    • Capture Scarce Expertise
    • Increased Productivity and Quality
    • Decreased Decision Making Time
    • Reduced Downtime via Diagnosis
    • Easier Equipment Operation
    • Elimination of Expensive Equipment
    • Ability to Solve Complex Problems
    • Knowledge Transfer to Remote Locations
    • Integration of Several Experts' Opinions
    • Can Work with Uncertain Information
    • … more …
  207. Problems and Limitations of ES
    • Knowledge is not always readily available
    • Expertise can be hard to extract from humans
    • -Fear of sharing expertise
    • -Conflicts arise in dealing with multiple experts
    • ES work well only in a narrow domain of knowledge
    • Experts’ vocabulary often highly technical
    • Knowledge engineers are rare and expensive
    • Lack of trust by end-users
    • ES sometimes produce incorrect recommendations
    • … more …
  208. ES Success Factors
    • -Most Critical Factors
    • Having a Champion in Management
    • User Involvement and Training
    • Justification of the Importance of the Problem
    • Good Project Management
    • -Plus
    • The level of knowledge must be sufficiently high
    • There must be (at least) one cooperative expert
    • The problem must be mostly qualitative
    • The problem must be sufficiently narrow in scope
    • The ES shell must be high quality, with friendly user interface, and naturally store and manipulate the knowledge
  209. Longevity of Commercial ES
    • Only about 1/3 survived more than five years
    • Generally ES failed due to managerial issues
    • -Lack of system acceptance by users
    • -Inability to retain developers
    • -Problems in transitioning from development to maintenance (lack of refinement)
    • -Shifts in organizational priorities
    • Proper management of ES development and deployment could resolve most of them
  210. Coding (implementing) the system
    The major concern at this stage is whether the coding (or implementation) process is properly managed to avoid errors…
  211. Assessment of an expert system
    • Evaluation
    • Verification
    • Validation
  212. Assessment of an expert system: Evaluation
    • Assess an expert system's overall value
    • Analyze whether the system would be usable, efficient and cost-effective
  213. Assessment of an expert system: Validation
    • Deals with the performance of the system (compared to the expert's)
    • Was the “right” system built (acceptable level of accuracy?)
  214. Assessment of an expert system: Verification
    • Was the system built "right"?
    • Was the system correctly implemented to specifications?
  215. Defining the nature and scope of the problem
    Rule-based ES are appropriate when the nature of the problem is qualitative, knowledge is explicit, and experts are available to solve the problem effectively and provide their knowledge
  216. Identifying proper experts: A proper expert should have a thorough understanding of:
    • Problem-solving knowledge
    • The role of ES and decision support technology
    • Good communication skills
  217. Two Basic Explanations
    • Why Explanations - Why is a fact requested?
    • How Explanations - To determine how a certain conclusion or recommendation was reached
    • -Some simple systems - only at the final conclusion
    • -Most complex systems provide the chain of rules used to reach the conclusion
    • Explanation is essential in ES
    • Used for training and evaluation
  218. Explanation
    • Human experts justify and explain their actions
    • … so should ES
    • Explanation: an attempt by an ES to clarify reasoning, recommendations, other actions (asking a question)
    • Explanation facility = Justifier
  219. Explanation Purposes…
    • Make the system more intelligible
    • Uncover shortcomings of the knowledge bases (debugging)
    • Explain unanticipated situations
    • Satisfy users’ psychological and/or social needs
    • Clarify the assumptions underlying the system's operations
    • Conduct sensitivity analyses
  220. Firing a rule
    • When all of the rule's hypotheses (the “if parts”) are satisfied, a rule said to be FIRED
    • Inference engine checks every rule in the knowledge base in a forward or backward direction to find rules that can be FIRED
    • Continues until no more rules can fire, or until a goal is achieved
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2013-12-17 04:54:02

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