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CS5500 Data Management and

Business Intelligence

Session 1:

Module Overview . . .

Data, Information and Knowledge in Business

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Who am I?

Dr Allan Tucker, Brunel University:

Research Lecturer

Research Interests:

Data Mining

Machine Learning

Artificial Intelligence

Bayesian Networks

Time Series

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Session Learning Outcomes

The learning outcomes for this session are

that you:

Know the module learning outcomes, structure

and forms of assessment

Recognise the value, role and conceptual

difficulties associated with data, information and

knowledge in business environments

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Part 1

Module Overview . . .

A Reminder to Read the Study Guide!

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Module Aim

The aim of the module is to:

Develop knowledge and skills to support the

development of business intelligence solutions in

modern organisational environments

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Module Learning Outcomes

The learning outcomes for CS5500 are:

1.Describe and discuss the key concepts of data

management and business intelligence their value and

implications for information systems development anduse.

2.Apply the appropriate practical skills/abilities required

to implement business intelligence solutions

3.Reflect critically on the theory and appropriate use ofdata management and business intelligence in

organisational environments.

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Module Structure

The structure of the module is thus:

Session Lecturer Workshop

1 Data, Information and Knowledge Dr. Allan Tucker SAP BW Orientation

2 Data Warehousing (I) Relational Design Dr. Allan Tucker Reporting and QueryDefinition

3 Data Warehousing (II) Data Integration Dr. Allan Tucker Exception Reporting

4 Data Warehousing (III) Architectures Dr. Allan Tucker Creating SAP BW

InfoObjects

5 Data Warehousing (IV) Information Modelling Dr. Allan Tucker Creating SAP BW

InfoCubes

6 Datamining (I) The Concepts Dr. Allan Tucker Master Data Staging

7 Datamining (II) Advanced Analysis Dr. Allan Tucker Loading Transaction Data

8 Break out / Datamining (III) and Business

Intelligence

Dr Allan Tucker

Prof. Xiaohui Liu

Visualising and Analysing

Data (I)

9 Business Intelligence: Strategy and Practice Guest Visualising and Analysing

Data (II)

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Assessment

CS5500 is assessed wholly by coursework:

Coursework details Structured essay (1500 words) addresses Learning Outcomes 1

and 3 Technical appendix containing outputs of SAP BW workshops + 500-

1000 words of integrating text - addresses Learning Outcome 2

Brunel submission For both full and part-time students . . .

Submission 05/04/09 at 23.00 GMT via u-Link

NITH submission Full-time student submission 25/01/09 at 23.00 GMT

Part-time student submission 15/02/09 at 23.00 GMT

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Reading Material

The reading for the module is:

Core Virtual study pack (see Web base on u-Link)

Supplementary Connolly, T. and Begg, C. (2005), Database Systems: A Practical

Approach to Design, Implementation and Management, 4th Edn.,Addison Wesley, Essex.

Hand, Mannila, Smyth (2001), Principles of Data Mining, MIT Press

MacDonald, K., Wilmsmeier, A., Dixon, D. C. and Inmon, W. H.(2005), Mastering the SAP Business Information Warehouse:

Leveraging the Business Intelligence Capabilities of SAPNetWeaver, Wiley, New York.

Howson, C. (2008) Successful Business Intelligence: Secrets toMaking BI a Killer App, McGraw-Hill

Galit Shmueli, Nitin R. Patel, Peter C. Bruce (2007) Data Mining forBusiness Intelligence, Wiley.

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Brunel Contact Details

Contact details are:

Dr. Allan Tucker ([email protected])

Room SJ128h in St. Johns

Post questions to u-Link discussion board

Surgery hour 13.00 14.00 on a Wednesday

Meetings by appointment . . .

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Part 2

Data, Information and Knowledge in Business

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Hypercompetition in Business

Competitive advantage is difficult to sustain: Traditional business model

Legally restrain competition

Segment market to avoid head-to-head competition Build barriers to entry . . .

Many challenges now exist

Customer taste changes

Rapid technological change

Globalisation of markets Global alliances in many industries

Advantage is thus continually created, eroded,destroyed and recreated

Slide 12CS5550 Session 01

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Some Theories on Response

Management/IS theories emerging as a

response to rapidly changing environments:

Enterprise agility

Sensing and responding

Dynamic capabilities

Ability to integrate, build and reconfigure internal

and external competencies

Absorptive capacity

Organisational routines and processes by which

firms deal with knowledge

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Sensing and Responding

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Responding Intelligently

Some definitions of intelligence:

The ability to acquire and apply knowledge and

skills (OED)

The ability to learn, understand and make

judgments orhave opinions that are based on

reason (Cambridge)

The ability to apply knowledge to manipulate

one's environment (Merriam-Webster)


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The Data Explosion

If you feel like you are drowning in

information, its because you are.

Advance of IT and the Internet

Massive increase in ability to:

Record: Electronic records and forms

Store: Data Warehouses (more of this later) Analyse: Data Mining and Visualisation (more later)

Risk of Information Overload

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Problems with Core Concepts

A note of warning: Typical hierarchical view

Data as the raw material

Information as structured data Knowledge as personalised information

Inverse view

Knowledge must exist before information can beformulated and before data can be used to forminformation

Potential implications

Knowledge does not exist outside of an agent

Shaped by needs and initial stock


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Toward the Utopian Organisation

The emerging response to hypercompetitionequates to business intelligence:

Corporate performance management

Measure, decide and react . . . (backward BI)

Proact to adapt strategy, goals and process . . . (forward BI)

Flexible technology infrastructure and architecture

Processes not application specific

Service-orientation

Managing time-to-action

Data latency, analysis latency and decision latency

Business Intelligence = Data Integration + Data Mining &OLAP


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An example

A primer for thought on business

intelligence:

Fighting crime with zeros and ones . . .(http://www-03.ibm.com/innovation/us/adv/special/overview/nypd/ high_wmv.html)

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Components of BI Architecture


Some Jargon: Source Systems (Enterprise Resource Planning systems)

Extract Transform Load Data Warehouses & Data Marts

Querying and Reporting

On Line Analytic Processing

Data Mining

Dashboards

See Glossary in Study Guide!

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SAP BWWarehousing Solution


Source: SAP AG

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Session Summary

This session has examined the:

Module learning outcomes, structure and forms of

assessment

Value, role and conceptual difficulties associatedwith data, information and knowledge in business

environments

Understanding of business intelligence


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Key Reading

Key reading:

Study pack to support concepts discussed (e.g.,):

Anderson (1999)

Overby et al. (2006)

Tuomi (1999)

Zahra and George (2002)

Next Session : Relational Models

Chapters 3, 4 and 5 in Connolly and Begg (2005).

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Workshops

Thursdays: 11am - 1pm

Fridays: 2pm - 4pm

Allocations of Students to workshop sessions on U-Link

Students work in pairs though both are given accounts

Workshop 1: SAP BW Orientation (BSI need not attend)

From Workshop 2 it will be necessary to document yourwork see Assessment on U-Link

cs5500 session 01 at2

Documents