FIT3003 Business intelligence and data warehousing - Semester 2, 2013

Automation and the use of technological tools have resulted in the accumulation of vast volumes of data by modern business organisations. Data warehouses have been set up as repositories to store this data and improved techniques now result in the speedy collection and integration of such data. OLAP technology has resulted in the faster generation of reports and more flexible analysis based on the data repositories. Business intelligence (BI) can be considered as the art of exploring and analysing this data, extracting relevant information and identifying patterns, and turning such information and patterns into knowledge upon which actions can be taken. This unit will explore the concepts of BI, including the emergence of BI and factors influencing BI, technology requirements for BI and will provide hands on experience on designing and building business intelligence systems.

Mode of Delivery

Clayton (Day)

Contact Hours

2 hrs lectures/wk, 2 hrs laboratories/wk

Workload requirements

Students will be expected to spend a total of 12 hours per week during semester on this unit as follows:

• 2 hour lecture and
• 2 hour tutorial (or laboratory) (requiring advance preparation)
• a minimum of 2 hours of personal study per 1 hour of contact time in order to satisfy the reading and assignment expectations.

Unit Relationships

Prerequisites

One of FIT1004, FIT2010, FIT1013, BUS1010, BUS3112, CSE2316, CSE3316

Chief Examiner

Campus Lecturer

Tutors

Academic Overview

Learning Outcomes

At the completion of this unit students will have -A knowledge and understanding of:

• the role of Data Warehousing (DW) as opposed to operational databases;
• the definition and the need of Business intelligence (BI);
• DW development methodology;
• dimensional models compared to ER models;
• DW architectures, ETL and data quality issues;
• how DW can support BI;
• BI tools, techniques and OLAP;
• Data Mining (DM) techniques;
• Data Mining Tools.

Developed attitudes that enable them to:

• recognise the value of DW and BI for a business organisation;
• adapt a critical approach to DW and BI technology in a business context;
• appreciate the value of DW for effective management support and decision making;
• understand the importance and value of BI tool and techniques compared to traditional data analysis techniques;
• appreciate the value BI tools and DM for providing knowledge for decision making, in ways unavailable with traditional techniques.

Gained practical skills to:

• create dimensional models;
• create DW architectures suitable for different organisations and requirements;
• interpret results from OLAP and dimensional models;
• create data analysis models using BI tools;
• interpret results from BI and DM tools.

Demonstrated the communication skills necessary to:

• document and communicate DW architectures and BI techniques;
• work in a team during DW architecture design and BI model development;
• communicate and coordinate during the team activities.

Unit Schedule

Week	Activities	Assessment
0		No formal assessment or activities are undertaken in week 0
1	Introduction to Business Intelligence and Data Warehousing
2	The Dimensional Data Warehouse
3	Data Cubes and Online Analytical Processing (OLAP)	Assignment 1 available to students Week 3
4	Guest Lecture
5	Applying the Dimensional Model with Microsoft BI Tools
6	MDX for Complex Analysis
7	Introduction to Business Data Mining and the Customer Life Cycle	Assignment 1 due Week 7
8	Data Mining Techniques 1
9	Data Mining Techniques 2	Assignment 2 available to students Week 9
10	Data Exploration and Mining with Microsoft Tools
11	Delivering BI and Performance Management
12	Revision	Assignment 2 due Week 12
	SWOT VAC	No formal assessment is undertaken in SWOT VAC
	Examination period	LINK to Assessment Policy: http://policy.monash.edu.au/policy-bank/ academic/education/assessment/ assessment-in-coursework-policy.html

*Unit Schedule details will be maintained and communicated to you via your learning system.

Assessment Summary

Examination (2 hours): 60%; In-semester assessment: 40%

Assessment Task	Value	Due Date
Assignment 1 - SQL Server and Data Warehousing	20%	Week 7
Assignment 2 - Data Mining	20%	Week 12
Examination 1	60%	To be advised

Teaching Approach

Lecture and tutorials or problem classes

Assessment Requirements

Assessment Policy

Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)

Academic Integrity - Please see the Demystifying Citing and Referencing tutorial at http://lib.monash.edu/tutorials/citing/

Assessment Tasks

Participation

• Assessment task 1

  Title:

  Assignment 1 - SQL Server and Data Warehousing

  Description:

  Students will be required to use a given case study to complete the following tasks:

  1. Design and build data cubes based on a given data mart.
  2. Develop OLAP queries.
  3. Carry out OLAP analysis and propose recommendations to address issues identified in the case study.

  This is an individual assignment.

  Weighting:

  20%

  Criteria for assessment:

  1. Correctness and understanding - there may be more than one "right" answer in many cases. We will look for answers that reflect understanding of the underlying principles and theories.
  2. Completeness - that you have answered all parts of each question. 
  3. Presentation - that you have presented your answers in a suitably formatted report style.
  4. Use of evidence and argument - you are able to explain your position by using logical argument drawing on the theory presented in the unit.

  Due date:

  Week 7

• Assessment task 2

  Title:

  Assignment 2 - Data Mining

  Description:

  Students will be required to use several given data sets (related to customers and their buying behaviour) to complete the following tasks:

  1. Carry out a data mining based exploration of the data sets.
  2. Analyse the findings, and describe and profile the customers and their behaviours.
  3. Recommend strategies for improving business performance, upsell and cross-sell, and also target marketing, based on the findings.

  This is an individual assignment.

  Weighting:

  20%

  Criteria for assessment:

  1. Correctness and understanding - there may be more than one "right" answer in many cases. We will look for answers that reflect understanding of the underlying principles and theories.
  2. Completeness - that you have answered all parts of each question. 
  3. Presentation - that you have presented your answers in a suitably formatted report style.
  4. Use of evidence and argument - you are able to explain your position by using logical argument drawing on the theory presented in the unit.

  Due date:

  Week 12

Examinations

• Examination 1

  Weighting:

  60%

  Length:

  2 hours

  Type (open/closed book):

  Closed book

  Electronic devices allowed in the exam:

  None

Learning resources

Feedback to you

• Informal feedback on progress in labs/tutes
• Graded assignments with comments
• Solutions to tutes, labs and assignments

Extensions and penalties

Returning assignments

Assignment submission

Online submission

If Electronic Submission has been approved for your unit, please submit your work via the learning system for this unit, which you can access via links in the my.monash portal.

Prescribed text(s)

Brian Larson. (2008). Delivering Business Intelligence with Microsoft SQL Server 2008. () McGraw Hill.

Michael J. A. Berry and Gordon Linoff. (2011). Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. (2nd Edition) John Wiley & Sons.

Other Information

Policies

Graduate Attributes Policy

Student services

Monash University Library

Disability Liaison Unit

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