Chapter 15: KNOWLEDGE-BASED INFORMATION SYSTEMS

What is Knowledge? Data: Raw facts, e.g., Annual Expenses = $2 million Information: Data given context, e.g., current annual expenses of $2 million are $1 million above last year’s annual expenses of $1 million Knowledge: Information given meaning & application, e.g., Additional expenses were used for R&D which resulted in a new product line which will increase our revenues by 50%. Can use this to set next years budget and provide accurate revenue forecasts.

Knowledge ctd. Organizational Learning: using organizational experience to create new SOPs, processes, & products/services Knowledge Management: The set of processes developed in an organization to create, gather, store, maintain, and disseminate the firm’s knowledge Chief Knowledge Officer (CKO): Senior executive in charge of knowledge management program

Process of Organizational Learning (Hine et al. 1998)

Types of Knowledge Explicit Knowledge –Structured internal knowledge. Has been documented and in computer files & databases, policy & procedure manuals, etc. Tacit Knowledge –Expertise and experience of organizational members that has not been formally documented Organizational Memory –The stored learning from an organization’s history, used for decision-making and other purposes

Knowledge Work and Productivity SHARE KNOWLEDGE DISTRIBUTE KNOWLEDGE CREATE KNOWLEDGE CAPTURE, CODIFY KNOWLEDGE GROUP COLLABORATION SYSTEMS OFFICE AUTOMATION SYSTEMS ARTIFICIAL INTELLIGENCE SYSTEMS KNOWLEDGE WORK SYSTEMS

Artificial Intelligence (AI) Computer-based systems that try to mimic human intelligence –Natural Language –Robotics –Artificial Vision –Expert Systems –Others (neural nets, genetic algorithms)

Business Interests in AI Store information in organizational memory so expertise not lost when personnel turns over Create a mechanism not subject to feelings, fatigue, worry, crisis Eliminate routine / unsatisfying jobs Solutions to problems that are too complex for humans to do in short periods of time

Natural Language Processing List names and addresses of clients from the midwest whose year to date order total is less than $50,000 Show me the names and addresses of our midwestern clients whose year to date balance is less than $50,000 List names and addresses of clients with a year to date balance of less than $50,000 who are headquartered in the midwest SELECT Name, Address FROM Client WHERE Region = “Midwest” AND YTD_Ord < 50000

Expert Systems A knowledge-intensive program that captures the expertise of humans in limited domains of knowledge Rule - based expert system –AI system based on IF - THEN statements Knowledge base –Model of human knowledge Inference engine –Search through rule base can be either Forward chaining: Take input, search rules for answer Backward chaining: begin with goal; seek information until goal is achieved or not. Expert System Shell –Programming environment of expert system

Examples of Expert Systems CLUES –Loan underwriting Blue Cross Blue Shield –Medical underwriting system United Nations –Calculates employee salaries XCON –Mainframe configuration

Case-Based Reasoning (CBR) uses database of cases and solutions User describes problem System searches database for similar cases System asks more questions Finds closest fit and retrieves solution Modify solution as required Stores problem and new solution in case base

Other Intelligent Techniques Neural Networks –Software attempts to emulate brain processes Fuzzy Logic –Tolerates ambiguity using nonspecific terms within if-then rules (for example: if truck is close to loading dock then sound warning) Genetic Algorithms –Uses models of organisms to promote evolution of solution Intelligent Agents/bots –Carries out specific, repetitive tasks