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Week 9 Database Technologies Knowledge Management 1

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Week 9 Database Technologies Knowledge Management

Last week Decision support Systems (DSS)

Definition (refined)Emphases associated with MIS and DSS Purpose, objectives (what support do DSS provide?)

ClassificationsStructure (components) Users (managers and staff specialists)

Development (including end-user development)Benefits (and limitations)2

This week

Database Technologies Data Data On

warehousesmarts

Line Analytic Processing (OLAP)

Data

mining

Knowledge Management

3

Data warehouses (1)

A data warehouse is a subject-oriented integrated time variant non-volatile collection of data in support of managements decision making processInmon from Chaffey (2003)4

Data warehouses (2)

Subject-oriented

customer, product, ...

Integrated

collected from diverse sources, internal and external

Time variant

accurate at some frozen point in time, not time of access, not right now

Non-volatile

static, not updated in DW, transferred from volatile TPS periodically

in support of managements decision-making process

for Management Support Systems

5

Data warehouses (3)

Accessed by BI applications, which retrieve data from DW for analysis using OLAPTypically contain large volumes of data measured in gigabytes or terabytes

1 gigabyte = 1 billion bytes or 1000 megabytes

1 terabyte = 1 trillion bytes, or 1000 gigabtyes6

Data warehouses (4)

Contain multi-dimensional data,e.g. sales data by

customer (and customer groupings)

product (and product categories)time period e.g. month, quarter, year

geographic regione.g. area of town, district, country, world7

Active Data Warehouse

8http://www.teradata.com/resources/white-papers/Enabling-the-Agile-Enterprise-with-Active-Data-Warehousing-eb4931/

Data marts (1)Similar to the concept of a data warehouse, except for departmental rather than organisational use

specifically designed for the information needs of a particular group rather than just based on data that happens to exist

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Data marts (2)Similar to the concept of a data warehouse, except

may be derived from a data warehouse

to support particular information needs

designed for ease of access usability

Definition depends on which author(s) you read10

On Line Analytical Processing (OLAP) (1)

Functionality for real-time analysis of multi-dimensional data Term is used to cover end-user software or or both the software and the data

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On Line Analytical Processing (OLAP) (2)

OLAP allows users to navigate through multi-dimensional data (a hypercube)

which dimensions to view?

time, area, sales, products, customers, income, profit...

how to aggregate the data?

profit per customer, sales per employee, trends over time...

slice and dicedata mining...

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On Line Analytical Processing (OLAP) (3)

OLAP allows users to navigate through multi-dimensional data (a hypercube)

13 http://www.wseas.us/e-library/conferences/2010/Faro/VIS/VIS-12.pdf?CFID=149242481&CFTOKEN=71970357

accessed 28/11/2012

On Line Analytical Processing (OLAP) (4)

Looking at different dimensions and aggregates in visual form

14 http://www.wseas.us/e-library/conferences/2010/Faro/VIS/VIS-12.pdf?CFID=149242481&CFTOKEN=71970357

accessed 28/11/2012

Data mining

Used to identify

in the data within a data warehouse Has applications in Customer Relationship Management (CRM) analysis of loyalty card data analysis of web-site activity15

Data mining

Identifying patterns, trends or correlations in the data...

Association

one event is connected to another event one event leads to a later event new patterns that may lead to new ways of organising the data gathering & documenting groups of facts not previously known discovering patterns in data leading to reasonable predictions16

Sequence or path analysis

Classification

Clustering

Forecasting

MIS, EIS/OLAP and Data MiningMIS Who are in the top 20% of our customers? EIS / OLAP Who are the top 20% customers for a particular product range and/or in a particular geographic region and/or in a particular time period? Data mining What are the characteristics of our top 20% of customers?17

Geographic Information Systems

Details of thefts of motor vehicles are shown hotspots can been seen18

trends and patterns can be examined over time

Geographic Information Systems

Not much activity here: is it a safer area? better lit? an area where there is very little parking? a factory, supermarket, football pitch...

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Geographic Information Systems

A lot of activity here: is it a riskier area? less well lit? activity displaced from another area made more secure? an area where there is more parking? near a factory, supermarket, football pitch, in a residential area?

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Data, information or knowledge?

An analogy to clarify the nature of data, information and knowledge...

a geographical map

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Data

The names of certain areas and their map references would be considered data knowing simple

the location of a town on a map is the town in this area?yes / no answer

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Information

Details of distances and direction between different areas would be information enables

travel between different sites how much further to go? A quantifiable

answer (miles, km, light years)

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Knowledge

Details of routes constitutes knowledge

fast motorway route railway link slow but picturesque roads linking the areas

What is the purpose of the journey?

Route chosen will depend on nature of visit:

business leisure pleasure of the journey24

Information as a resource

Information is 1 of 3 classes of resource:Financial Human

Information25

Knowledge management (KM) (1)

What is knowledge? Data literally, that which is given

collection of facts, measurements, statistics

Information processed data timely accurate complete relevant appropriately presented within cost limits26

Knowledge management (KM) (2)

What is knowledge?

information that is contextual, relevant and actionable... has strong experiential and reflective elementsTurban (2001)

Applying managerial experience to problem-solvingChaffey (2003)

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Knowledge management (KM) (3)

What is knowledge? Knowledge assets: organisational knowledge regarding how to efficiently and effectively perform business processes and create new products and services that enables the business to create valueLaudon & Laudon (2004)

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Why do we need KM? (1)Every day, knowledge essential to your business walks out of your door, and much of it never comes back.

Employees leave, customers come and go

and their knowledge leaves with them....miserable song...

This information drain costs you time, money and customers

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Saunders (2000) from Chaffey (2003)

Why do we need KM? (2)

Islands of information

each report is constructed for a single purpose continents are bigger and more difficult to createWu (2002)

Types of knowledge (Polanyi, 1958)

Explicit Tacit

One goal of KM is to turn tacit knowledge into explicit knowledge30

Intellectual capital = competence x commitment (Ulrich, 1998)

Turban (2001)

Types of knowledge (1)

Holsapple and Whinston (1996): Descriptive - knowing what Procedural - knowing how Reasoning - knowing whyKnowledge an organisation has

Presentation - delivering knowledge Linguistic - communicating knowledge Assimilative - maintaining knowledge

Communicating, understanding and learning of knowledge in order to use it

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(from Turban, 2001)

Types of knowledge (2)

Clarke (1998)

Advantaged

can provide competitive advantage

Base

integral to the organisation, provides short-term advantage best practices

Trivial

no major impact on organisation32

(from Turban, 2001)

What is Knowledge Management?

Knowledge management (KM) Processes, tools and techniques used to collect, manage and disseminate knowledge within an organisation Enhance organisational learning Create an organisational memory KM initiatives run by Chief Knowledge Officer (CKO)The key to knowledge management is capturing intellectual assets for the tangible benefit of the organisationTurban (2001)33

Knowledge ManagementKnowledge Management(performing knowledge actions on knowledge objects)

=Knowledge Actions(organising, storing, gathering, sharing, disseminating, using)

Knowledge Objects(data, information, experience, evaluations, insights, wisdom)34

*

Organizational Value of Metrics for Communities of Practice

35

http://wiki.nasa.gov/cm/wiki/?id=2702 accessed 28 November 2012

36

Delen D and Al-Hawamdeh S A, 2009 DOI: 10.1145/1516046.1516082

Bidirectional Knowledge Management Process ModelTechnology approach

Data

Supply-driven: DIKARActionKnowledge

Results

Information

Business-value approach

Demand-driven: RAKID37

Turban, Sharda & Delen (2011), after Murray, P (2002) Knowledge Management as a Sustained Competitive Advantage

How is KM applied? (1)

Davenport et al (1998) from Turban (2001):

Create knowledge repositories Improve knowledge access Enhance the knowledge environment Manage knowledge as an asset38

How is KM applied? (2)

Turban et al (2011):

Create created as people develop new ways of doing things

Capture identified and represented in a meaningful way

Refine placed in context tacit knowledge with explicit facts

Store stored in appropriate format to allow access

Manage update, review, verify, ensure relevance and accuracy

Disseminate made available in useful format, where and when required39

How is KM applied? (3)

Organisational knowledge repository may include

structured internal knowledge (explicit) external knowledge of competitors, products and markets (competitive intelligence) informal internal knowledge (tacit)40

KM activities

Knowledge management system processes are designed to manage knowledge: creation through learning capture and explication sharing and communication through collaboration access use and re-use archivingTurban (2001)

Similar to the data life cycle for MIS...41

Data Life Cycle

42

from: https://securosis.com/blog/data-security-lifecycle-2.0 accessed 28/11/2012

Data Life Cycle

KPMG data life cyclehttp://mscerts.programming4.us dated 2010; accessed 28/11/201243

Knowledge Life Cycle

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http://wiki.nasa.gov/cm/wiki/?id=2702 accessed 28 November 2012

Share knowledge

Distribute knowledge

Group collaboration systems groupware intranets Artificial Intelligence expert systems neural nets fuzzy logic genetic algorithmsCapture & codify knowledge

Office systems WP and DTP electronic diary/calendar Knowledge Work Systems CAD Virtual reality

Create knowledge

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KM applications: summary

Share knowledge Group collaboration systems groupware, intranets

Distribute knowledge Office systems WP, DTP, imaging & web publishing, e-calendars, desktop DB

Create knowledge Knowledge work systems CAD, virtual reality, investment workstations

Capture and codify knowledge AI systems ES, ANN, fuzzy logic, genetic algorithms, intelligent agents46

Laudon & Laudon (2004)

KM applications: integration

KMS with DSS/BI DSS/BI run models - KMS applies knowledge integrate with models & data

KMS with AI KM not AI method - KMS could include ES which has relevant rules

KMS with databases and IS KMS gathers knowledge from documents and databases (KDD)

KMS with CRM predict customer needs, increase sales, improve service to clients

KMS with SCM combine tacit and explicit knowledge to optimise supply chain performance

KMS with Corporate intranets and Extranets KMS developed on intranets & extranets enhance collaboration47

Turban et al (2011)

Further Reading

Chaffey, D. (ed.), 2003, Business Information Systems, 2nd ed., FT Prentice Hall EIS, DW, data marts and data mining: chapter 6, pages 257 - 263 Knowledge management: chapter 1, pages 28-30 Laudon, K. & Laudon, J., 2004, Management Information Systems, 8th ed., Pearson Prentice Hall Database Trends: chapter 7, pages 234-238 EIS: chapter 11, pages 363-364 Knowledge management: chapter 10, pages 313-327 Turban E. & Aronson J.E., 2001, Decision Support Systems and Intelligent Systems (6th edition), Prentice Hall Business Publishing Enterprise DSS: pages 306-321 DW and data mining: pages 130-132 + 141-151 Knowledge management: pages 346-366 + 370-375 Turban E. Sharda R & Delen D, 2011, Decision Support Systems and Business Intelligence Systems (9th edition), Prentice Hall Business Publishing Islands of information: http://www.dmreview.com/article_sub.cfm?articleId=4505 (accessed 21/11/2011) Delen D and Al-Hawamdeh S A, 2009, Holistic Framework for Knowledge Discovery and Management, Communications of the ACM, Vol 52, No 6, p 141-145; DOI: 10.1145/1516046.1516082 http://www.teradata.com/resources/white-papers/Enabling-the-Agile-Enterprise-withActive-Data-Warehousing-eb4931/ (accessed 28/11/2012)

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Multiples of bytes as defined by IEC 60027-2Name kilobyte megabyte gigabyte terabyte petabyte SI prefix Symbol Multiple kB 103 MB 106 (or 220) GB 109 (or 230) TB 1012 (or 240) PB 1015 (or 250)49