Week 7 Amare Michael Desta

1. Decision Support & Executive Information Systems: Week 7Amare Michael Desta

2. Data, Information and Knowledge is needed to … • To manage internal operations • React to the external environment, to potential threats and opportunities • Manage/Minimise risk • Generate knowledge, ideas and, through this, • New way(s) of doing things and • New Products & Services may be achieved

3. The Naïve View Data is what we find in databases • But how does the database ‘know’ what data to hold? Information is what we find in IS and it allow us to ask questions of the data. - But how does the information system ‘know’ what questions we will want to ask?

4. Data & Data Values • Data – that which is given • In problem solving (decision making) • What is known or assumed to be true • Typically a member or members of one or more collection or sets of ‘objects’ • E.g. in Mathematics – Given a line and a point not on that line … • Data = any one individual member of the set of lines and any one individual member of the set of points that satisfies the condition.

5. Data & Data Values • In engineering we move from the individual to the particular • From the mathematical concept of a line to the practical realisation of this particular line from here to there.

6. Relational Data Tables

7. Data, Measurement and Observation • A chicken and egg situation • There can be no observation without knowledge • We have to decide what something is – to categorise it before we can measure it and record the data values.

8. Reason • Reason derives from the same root meaning as ratio and also connected with relation • Connected to the idea of the balance • To weigh the evidence • To put things in proportion

9. Decision Theory 1 Decisions consist of: • A set of possible courses of action • A set of outcomes form each action • A state of the world

10. Decision Theory 2 Decision making contexts can be classified in terms of the Information and knowledge available • Certainty • Risk • Uncertainty

11. Rationality When modelling peoples behaviour economists and management scientists usually assume that people are rational which means that: • They always choose their best option the one that maximises their payoff • Which means they have the knowledge and ability to determine what their best option is!

12. Bounded Rationality Problems with assuming rational actors • It is very easy to provide counter examples from experience • Most people are not in possession of enough information (data) to determine what their best option is • Most people do not have the necessary knowledge to determine their best option even given the necessary information

13. Bounded Rationality Herbert Simon introduced the term bounded rationality as a more realistic view of decision making: • BR is NOT irrationality • Actors make the best decision (act rationally) given • limited information • Limited knowledge • Limited resources

14. Learning & Knowing process Requires an understanding of: • Know who • Know where • Know when • Know what • Know about • Know how

15. Learning & Knowing 2 Categorisation & Knowledge • Similarity – common properties • Difference – distinct properties • Contiguity – at the same place and or at the same time

16. Knowledge Management in Organisations Knowledge Management, (KM), is: Systemically and actively managing and leveraging stores of knowledge in organisation

17. Knowledge Management Systems, (KMS) • KMS – are sophisticated decision support systems • KMS – Support Decision Support Systems • KMS – Are systems for managing the knowledge processes of an organisation

18. Information and Knowledge Work Systems DATA WORKERS: People who process & Disseminate organization’s paperwork INFORMATION WORK: Work consists primarily of creating, processing information KNOWLEDGE WORKERS: People who design products or services or create new knowledge for organization

19. Knowledge Processes 1 • A Mechanistic View People as Computers • Creation • Acquisition • Transmission • Storage • Retrieval

20. SHARE KNOWLEDGE DISTRIBUTE KNOWLEDGE NETWORKS PROCESSORS GROUP COLLABORATION SYSTEMS OFFICE AUTOMATION SYSTEMS DATABASES ARTIFICIAL INTELLIGENCE SYSTEMS KNOWLEDGE WORK SYSTEMS SOFTWARE CAPTURE, CODIFY KNOWLEDGE CREATE KNOWLEDGE Knowledge Management and IT

21. Organisational Knowledge

22. Bohn’s Stages of Knowledge

23. Data, Information & Knowledge in Use

24. Data Relationship of Data, Information & Knowledge Prior Expectation The Agent’s Knowledge Base The World Data Filters Information Knowledge: a disposition to act

25. Action Decision Knowledge Information Data Processing Hierarchy • Centrality of data • (Wilson 1996) • Does data always lead to information? • Does information always lead to knowledge? • And where does good judgement come from?

26. Intelligence in Data Processing Systems Processing Data Entry Report Manipulation USERS Data Collection Knowledge is a pervasive characteristic of information systems Data Systems & Knowledge

27. Data Information Data & Information: System Perspective Decoding Encoding Receiver Sender Computer System

28. Information Systems • Information systems process data and turn it into information that can be used for management decision-making • Knowledge is used to design, encode and operate IS • Knowledge is needed to decode the information that comes out of IS • IS professionals need to understand the human (perceptual) processes involved in the encoding and decoding process

29. Usual Approach Data Information Knowledge Actions Results Benefits-Driven Approach Data, Information & Knowledge: Linear Models

30. Data, Information & Knowledge Cyclic Model Accumulate Knowledge (Experience) Knowledge Format, Filter Summarise Interpret, Decide, Act Information Data Results

31. Information & Knowledge: Sharing & Transmission • Information • Capture, creation and dissemination • Releasing the Value • by use and re-use Knowledge – transmission(s) • Explicit to Explicit • Tacit to explicit • Explicit to tacit. • Tacit to Tacit. Nonanka (1991)

32. Information & Quality – the main issues • Accurate • Appropriate detail and accuracy for the user • Meaningful • to user • Up to date - information is very time sensitive. • Out of date information is misleading if not useless. • Available • at point of need/use. related to decision-maker's context • Complete and comprehensive • Providers the receiver with all they need to know

33. Information & Quality • Format • in a form that aids assimilation. • Cost effective • costs of production and delivery lower than the benefits derived. e.g. a decision taken on the basis of the information provided could result in reduced costs, increased sales/revenue, better utilisation of resources • Must be secure BUT .... the potential value of information depends on its quality.

34. Shift in Importance Knowledge Information Data Represented in Technology Historical Trends • Massive structural shifts in Western economies

35. The Changing Economic Eras

36. The Shift to Information & K Work • Shift away from agriculture and manufacturing to services • Outsourcing of manufacturing to the Developing World. • Trend towards knowledge-based manufacturing • Increased growth in knowledge-based products and knowledge-enhanced goods – mobile phones, CD’s, digital photos, electronic journals • Growth in information and Knowledge-based services, particularly in advanced economies • Massive growth in information based occupations & knowledge work. In the US, over 85% population works in services, with 65% in high skilled areas. • Fastest growth areas: education, communications and information, computing, electronics and aerospace industries

37. Key Drivers of Change • Political Changes • Collapse of Communism, formation of major economic and political alliances • Business Changes • Growth of free trade, deregulation, emergence of new producers, globalisation • Technological Changes • Biotechnology, telecommunications and computing • Social Changes • Stakeholder Society, spread of egalitarian ideal

38. The New Economy: Key Points • Key driver is INNOVATION rather than production efficiency (quality rather than quantity) • Knowledge is the main source of innovation • Economic wealth depends on knowledge creation, production and distribution • Organisations are increasingly information and knowledge-based • Workforce is more skilled and knowledgeable • State and employers invest heavily in research and development in science and technology • Greater investment in education and training

39. The Emergence of IM & KM • IM & KM are new fields of study • Multi-disciplinary • Focus on information and managing expertise not on technology – IT underpins information and knowledge management • New type of professional may be required – one who understands information, Knowledge , IT and business

40. Information Use: Management Issues • What information do we need? • What information do we have? • Where is the information held and in what form? • How much does it cost to acquire and process information? • How can we tell if it adds any value?

41. Knowledge Use: Management Issues • What information is needed to create knowledge? • What explicit knowledge do we have? Where can we find it? • What implicit knowledge do our employees have? How can we capture and use it? • How much value does knowledge add? • How can we cultivate knowledge within the organisation?

42. Why Knowledge Management? • Organisations have lots of useful knowledge lying around that could be used to their advantage • But identifying it, finding it and leveraging it can be problematical • A knowledge intensive culture promotes knowledge creation and knowledge sharing

43. Taxonomy of Knowledge • Tacit – rooted in actions, experience & context • Explicit – articulated, generalized • Social - know who – collective • Declarative – know about • Procedural – know how • Causal – know why • Conditional – know when • Relational – know with • Pragmatic – best practice

44. Basic Knowledge Processes • Knowledge creation • Knowledge storage & retrieval • Knowledge transfer • Knowledge application

45. Knowledge Creation • Development of new tacit/explicit knowledge – individual & social • Modes: • Socialization, externalisation, internalisation, combination • IS • Data mining & data warehousing • CSCW, intranets • Brainstorming at a distance

46. Knowledge Storage & Retrieval • Organisational memory • Documents (hard & soft), databases, expert systems, plus tacit knowledge • Supports status quo • May not always be easy to interpret

47. Knowledge Transfer – can be achieved • Between individuals, groups, explicit sources, organisations Depends on: - perceived value of source unit’s knowledge, - willingness to share, - willingness to listen, - richness of transmission channel (implications for IS) - absorptive capacity of recipient.

48. Issues (i.e. Problems) in Practice • Using KM for strategic advantage • Obtaining top management support • Motivating staff to contribute • Identifying relevant knowledge • Evaluation • Verification • Design & development • Sustaining progress • Security

49. Tacit Knowledge • “We know more than we can tell” • Hard to formalise & communicate • Driving a car • Explicit knowledge may imply tacit knowledge • Polimorphic knowledge, relating to social behaviour, can only be learned through experience and socialisation

50. The Role of Experts • Usually provides a certain status • Unlikely to give away years of experience for nothing • Experts often linked in a community of practice • Experts often disagree • Experts can be wrong but may be more likely to spot things going wrong and have sufficient judgement to change course