Week 9

Database Technologies Knowledge Management

1

Last week

Decision support Systems (DSS)

Definition (refined)

Emphases associated with MIS and DSS

Purpose, objectives (what support do DSS provide?)

Classifications

Structure (components)

Users (managers and staff specialists)

Development (including end-user development)

Benefits (and limitations) 2

Database Technologies

Data warehouses

Data marts

On Line Analytic Processing (OLAP)

Data mining

Knowledge Management

This week

3

A data warehouse is a

◦ subject-oriented

◦ integrated

◦ time variant

◦ non-volatile

collection of data in support of management’s

decision making process

Inmon from Chaffey (2003)

Data warehouses (1)

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 management’s decision-making process’

for Management Support Systems 5

Accessed by BI applications, which retrieve data from

DW for analysis using OLAP

Typically 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 gigabtyes

Data warehouses (3)

6

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 region

e.g. area of town, district, country, world 7

Active Data Warehouse

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

8

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

9

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 read…

10

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

11

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 dice’

data mining...

12

On Line Analytical Processing (OLAP) (3)

OLAP allows users to navigate through multi-dimensional data

(a ‘hypercube’)

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

accessed 28/11/2012

13

On Line Analytical Processing (OLAP) (4)

Looking at different dimensions and aggregates in visual form

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

accessed 28/11/2012

14

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 activity 15

Data mining

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

Association

one event is connected to another event

Sequence or path analysis

one event leads to a later event

Classification

new patterns that may lead to new ways of organising the data

Clustering

gathering & documenting groups of facts not previously known

Forecasting

discovering patterns in data leading to reasonable predictions

16

MIS

“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?”

MIS, EIS/OLAP and Data Mining

17

Geographic Information Systems

Details of thefts of motor vehicles are shown

• “hotspots” can been seen

• trends and patterns can be examined over time

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Geographic Information Systems

19

Not much activity here:

● is it a safer area?

● better lit?

● an area where there is very little parking? a factory, supermarket, football pitch...

Geographic Information Systems

20

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?

Data, information or knowledge?

An analogy to clarify the nature of data,

information and knowledge...

a geographical map

21

Data

The names of certain areas and their map

references would be considered data

knowing the location of a town on a map

is the town in this area?

simple yes / no answer

22

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…)

23

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 journey

24

Information as a resource

Information is 1 of 3 classes of resource:

Human

Information

Financial

25

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 limits

26

Knowledge management (KM) (2)

What is knowledge?

“information that is contextual, relevant and actionable...”

“… has strong experiential and reflective elements”

Turban (2001)

“Applying managerial experience to problem-solving”

Chaffey (2003)

27

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 value”

Laudon & Laudon (2004)

28

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.

This information drain costs you time, money and customers”

Saunders (2000) from Chaffey (2003)

29

...miserable song...

Why do we need KM? (2)

‘Islands of information’

each report is constructed for a single purpose

continents are bigger and more difficult to create

Wu (2002)

Types of knowledge (Polanyi, 1958)

Explicit

Tacit

Intellectual capital = competence x commitment (Ulrich, 1998)

Turban (2001)

One goal of KM is to turn tacit

knowledge into explicit knowledge

30

Types of knowledge (1)

Holsapple and Whinston (1996):

◦ Descriptive - knowing what

◦ Procedural - knowing how

◦ Reasoning - knowing why

◦ Presentation - delivering knowledge

◦ Linguistic - communicating knowledge

◦ Assimilative - maintaining knowledge

Knowledge an

organisation has

Communicating,

understanding

and learning of

knowledge in

order to use it

(from Turban, 2001)

31

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 organisation

(from Turban, 2001)

32

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 organisation” Turban (2001)

33

Knowledge Management

Knowledge Management (performing knowledge actions on knowledge objects)

= Knowledge Actions

(organising, storing, gathering, sharing, disseminating, using…)

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

34

*

http://wiki.nasa.gov/cm/wiki/?id=2702

accessed 28 November 2012

Organizational Value of Metrics

for Communities of Practice

35

Delen D and Al-Hawamdeh S A, 2009

DOI: 10.1145/1516046.1516082

36

Bidirectional Knowledge Management Process Model

Data

Information

Knowledge

Action

Results Supply-driven:

DIKAR

Turban, Sharda & Delen (2011),

after Murray, P (2002) “Knowledge Management as a Sustained Competitive Advantage”

Demand-driven:

RAKID

Technology approach

Business-value

approach

37

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 asset

38

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 required 39

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

◦ archiving” Turban (2001)

Similar to the data life cycle for MIS...

41

Data Life Cycle

from: https://securosis.com/blog/data-security-lifecycle-2.0

accessed 28/11/2012

42

Data Life Cycle

43

KPMG data life cycle http://mscerts.programming4.us

dated 2010; accessed 28/11/2012

Knowledge Life Cycle

http://wiki.nasa.gov/cm/wiki/?id=2702

accessed 28 November 2012

44

Share

knowledge

Distribute

knowledge

Capture

& codify

knowledge

Create

knowledge

Artificial Intelligence

•expert systems

•neural nets

•fuzzy logic

•genetic algorithms

Knowledge Work Systems

•CAD

•Virtual reality

Office systems

•WP and DTP

•electronic diary/calendar

Group collaboration systems

•groupware

•intranets

45

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 agents Laudon & Laudon (2004)

46

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 collaboration

Turban et al (2011)

47

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-with-

Active-Data-Warehousing-eb4931/ (accessed 28/11/2012)

Further Reading

48

Multiples of bytes as defined by IEC 60027-2 SI prefix

Name Symbol Multiple

kilobyte kB 103

megabyte MB 106 (or 220)

gigabyte GB 109 (or 230)

terabyte TB 1012 (or 240)

petabyte PB 1015 (or 250)

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