organizational analyses 2500 Problem Solving Case Study and Proposal Report
doode
OrganisationsasDataInformationandKnowledge1.pptxOrganisational Analysis
Organisations as Data, Information and Knowledge
‹#›
Aims
Understand the differences between Data, Information and Knowledge
Understand Data Analysis
‹#›
Data, Information and Knowledge
Data, Information and Knowledge are not synonyms
Data
Information
Knowledge
‹#›
Defining Data
Data as an elemental building block
Facts and figures which relay something specific
Not organized in any way
Data represents unorganized and unprocessed facts (raw)
Provides no further information regarding patterns, or context
Unstructured facts and figures that have the least impact on the typical manager (Thierauf, 1999)
Consider this argument:
A person cannot be 34 years and 62 years in age
However, there could be two files on that same person, one listing 1981 and one listing 1948 the same time
Data is always “correct”
Information can be wrong
‹#›
Information
Information is:
contextualised data
Information is categorised data
For data to become information, it must be contextualised
Information paints a bigger picture; it is data with relevance and purpose (Bali et al 2009). It may convey a trend in the environment, or perhaps indicate a pattern of sales for a given period of time.
The human brain is needed to assist in contextualisation, interpretation
Data
Information
‹#›
Difference Between Data and Information
Information has data that is calculated or condensed (Davenport & Prusak 2000)
Information is found "in answers to questions that begin with such words as who, what, where, when, and how many" (Ackoff 1999).
Data
Information
Within organisations, much effort is spent in turning data into information, particularly in larger firms that generate large amounts of data across multiple departments and functions
Information technology is subservient, it is usually invaluable in the capacity
Value Add
‹#›
Knowledge
Knowledge is much deeper
Knowledge is understanding the assumptions we make
Knowledge is understanding how we learn
Knowledge is our personal map or picture of the world
Knowledge is what we know
Knowledge is awareness
Data
Information
Knowledge
‹#›
Definition of Knowledge
‹#›
Philosophy of Knowledge
The philosophy of knowledge: meta-theory as ontology and epistemology
Knowledge is much deeper
‹#›
Individual Knowledge
Knowledge is closely linked to doing and implies know-how and understanding. The knowledge possessed by each individual is a product of his experience, and encompasses the norms by which he evaluates new inputs from his surroundings (Davenport & Prusak 2000).
‹#›
Organisational or Collective Knowledge
"Knowledge is a fluid mix of framed experience, values, contextual information, expert insight, and grounded intuition that provides an environment and framework for evaluating and incorporating new experiences and information. It originates and is applied in the mind of the knowers. In organizations it often becomes embedded not only in documents or repositories, but also in organizational routines, practices and norms”
Gamble and Blackwell (2001)
‹#›
RMIT University
12
The Process of Analysing Knowledge
Knowledge generation consists of a number of interrelated steps with a certain direction of determinism:
Three levels:
Metatheoretical (the foundation of all theories):
A: Ontology: our assumptions about reality. What is ‘real’?
B: Epistemology: How do we gain knowledge of the world? What counts as knowledge? How do we know this is the ‘real’ nature of reality?
Methodological: Devices used to uncover data or ‘reality’.
Theoretical: set of ideas intended to explain facts, events, and the nature of reality.
‹#›
RMIT University
13
The Process of Generating Knowledge
The meta-theoretical, theoretical and methodological levels are organically interrelated.
Different meta-theoretical assumptions ‘determine’ the criteria of scientific explanation (theory), choice of methodology, procedure for theory construction and what one considers to be data.
Meta-theoretical Assumptions
Methodology
Theory/Knowedge
Determines
Produces
‹#›
Knowledge is much deeper
But we will explore this later in the lecture on “Learning Organisations”
‹#›
Data – As Abstraction
Data modelling naturally progresses from functionality
Like systems of production, process flows convert data as the “raw material”
Sales Order
Manufacturing Instruction
Data out/Input
Data Input
Data out
‹#›
Data Modelling
Semantics - understanding what data means
How data is “consumed”, or changed
‹#›
Data Modelling
Why is it necessary to model data?
‹#›
Imagine, if you were managing a health service
Importance of Data Modelling
‹#›
Entity Relationship Modelling
Manager
Subordinates
1
1...N
Data Modelling
‹#›
Modelling Data Structures
Nomenclature, rules, semantics
Manager
Subordinates
1
1...N
Department
1
1
‹#›
Taxonomy
Manager
Subordinates
Employees
Parent/Super-type
Child/Sub-Type
‹#›
Contextualisation - Data Dictionaries
Descriptions of entities and attributes used in an organisation
Contains aliases
Data type, e.g. integer, string, float
Rules, e.g. age
‹#›
Data Modelling Tools
Data modelling with IDEF1x
Used to produce information models that represent the structure and semantics of data within an enterprise
Bruce, T. (1992). Designing Quality Dababases with IDEF1X Information Models. Dorset House Publishing
‹#›
The Role of Standardisation - Information Interoperability Frameworks
The next part of the lecture discusses “interoperability frameworks”
Frameworks to manage data, information and knowledge
‹#›
Systems Integration - Interoperability
The ability to transfer and use information in a uniform and efficient manner across multiple organisations and information technology systems
Delivering integrated services
Managing areas of joint activity
Seeking efficiencies by re-using our information holdings
Responding to community expectations to collaborate across businesses (including government)
‹#›
Australian Government Interoperability Frameworks
The Role of Standardisation and Interoperability Frameworks
Standards aim to ensure that systems can be harmonised within and among organisations
Different parties can independently develop technologies that work together
Consumers and users can be instantly familiar and comfortable with new systems
Products and technologies and that new players can more easily enter the market.
‹#›
Australian Government Information Interoperability Frameworks
Principles for Australian Government information management
Authoritative data sources
Protocols for shared/re-use of information across public and private sector
Legal policy and administrative requirements Information lifecycle management
Technical Technical Interoperability Framework Harmonisation of standards for transport, messaging, description, discovery and security.
‹#›
Parting Thought
Is information interoperability synonymous with knowledge interoperability?
‹#›
Lecture Summary
Data
Information
Knowledge
‹#›
References
Ashworth, C. M. 1988, Structured systems analysis and design method (SSADM). Information and Software Technology, 30(3), 153-163.
Bruce, T. (1992). Designing Quality Dababases with IDEF1X Information Models. Dorset House Publishing
Uschold, M. e. (1998). Knowledge level modelling: Concepts and terminology. Knowledge Engineering Review, 13(1)
Guarino, N. 1995. Formal Ontology, Conceptual Analysis and Knowledge Representation. International Journal of Human and Computer Studies, 43(5/6): 625-640.
Thomas R. Gruber, 1995, Toward principles for the design of ontologies used for knowledge sharing?, In International Journal of Human-Computer Studies, Volume 43, Issues 5–6, 1995, Pages 907-928)
Kakabadse, N. K., Kakabadse, A., & Kouzmin, A. (2003). Reviewing the knowledge management literature: towards a taxonomy. Journal of knowledge management, 7(4), 75-91.
Nonaka I., Von-Krogh G., & Voelpel S., 2006, Organizational knowledge creation theory: Evolutionary paths and future advances, Organization Studies, 27: 1179–1208.
Nonaka, I. (1994). A dynamic theory of organizational knowledge creation.Organization science, 5(1), 14-37.
Nonaka, I., & Toyama, R. (2003). The knowledge-creating theory revisited: knowledge creation as a synthesizing process. Knowledge management research & practice, 1(1), 2-10.
Nonaka, I., Toyama, R., & Konno, N. (2000). SECI, Ba and Leadership: a Unified Model of Dynamic Knowledge Creation. Long range planning, 33(1), 5-34.
Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS quarterly, 107-136.
‹#›
