The role of Data Warehousing (DW) as oposed to operational databases;

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The role of Data Warehousing (DW) as oposed to operational databases;

FIT3003 - Business intelligence and data warehousing

Synopsis
This unit provides students with an understanding of Business Intelligence (BI) systems and the infrastructure needed to support 

them. Over the past decade OLAP 20 tools, data mining and other data analysis techniques have been used to obtain value from data 

in ways not possible with earlier tools. Topics covered include the nature and purpose of BI, the relationship between BI and data 

warehousing, design issues related to BI tools and data warehouses, and common data analysis techniques such as OLAP, data mining 

and other computational techniques. The differences between these kinds of systems and other, more traditional information systems 

will be highlighted.

Objectives

At the completion of this unit students will have - A knowledge and understanding of:
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• the role of Data Warehousing (DW) as oposed to operational databases;



• the definition and the need of Business intelligence (BI);

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• DW development methodology;



• dimensional models compared to ER models;



• BUS architecture;

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• DW architectures, ETL and data quality issues;



• how DW can support BI;

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• BI tools, techniques and OLAP;

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• Data Mining (DM) techniques;



• Data Mining Tools.

• Developed attitudes that enable them to:

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• recognise the value of DW and BI for a business organisation;



• adapt a critical approach to DW and BI technology in a business context;

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• 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.