FIT5195 Business Intelligence and Data Warehousing

FIT5195 Business Intelligence and Data Warehousing
By Dr Rob Meredith
About this podcast
Business Intelligence is a significant part of the IT industry but it is poorly understood by many. BI systems are fundamentally different to other IT systems in terms of users, their use of the system and the development processes required. This podcast is a recording of lectures and other material from Monash University's postgraduate unit on the topic covering both BI application design as well as data warehouse design.
In this podcast

Business Intelligence

Data Warehousing

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Latest episodes
May 25, 2017
A brief discussion of the exam and revision of topics from throughout the semester.
May 25, 2017
Discussion of governance issues for business intelligence and data warehousing, looking at the Blue Cross Blue Shield case study.
May 23, 2017
A short summary of the key ideas covered in the unit, what to expect for the exam and how to prepare for it.
May 23, 2017
Scott Yaworski is a consultant with Avanade and is a leading expert on big data, internet of things and analytics.  Formerly with Cognos and IBM, Scott has over 25 years of experience in providing business intelligence to clients in the mining, oil and gas, transport and healthcare sectors, as well as a strong interest in clean energy. In this lecture, Scott talks about the challenges of big data and business intelligence, discussing some of the latest technologies for data collection and visualisation.
May 18, 2017
Recording of class discussion of eight cases of data warehousing failure.  The discussion looks at how failure can mean different things for different people, as well as common themes across the cases.
May 16, 2017
IT governance is the allocation of decision making rights and responsibilities in regard to IT resources in an organisation.  Our argument throughout the semester has been that BI/DW systems are fundamentally different to other kinds of IT systems, requiring different approaches to design, implementation and management.  In this week's lecture, we look at a case of a BI/DW project that failed despite using an orthodox IT governance approach.  The successful coda to the project differed in that it didn't adhere to the orthodox approach.  We conclude, therefore, that traditional IT governance may not be appropriate for these systems. In the second half of the lecture, we look at the idea of these systems being chaotic and subversive, and draw inspiration from the management literature on managing creative processes to derive guidelines for BI/DW governance.
May 9, 2017
We begin this week by finishing off last week's topic, with a discussion of Agile methods for BI and DW projects.  We then move on to this week's topic of looking at case studies of different BI and DW projects, demonstrating how pragmatism and working out what decision support really means for each individual setting is critical to delivering a successful outcome.
May 2, 2017
This week we look at development methodologies for both BI applications and data warehouses.  We look at various lifecycle models.  Central to managing development on these projects is handling the evolutionary nature of the systems and the user requirments.  We discuss different kinds of evolution and how various lifecycle models manage them.
April 11, 2017
In the first half of the lecture, we look at the most technical topic we'll cover this semester, and one of the largest technical challenges in any BI/DW project: designing and developing the ETL process.  In the second half, we look at the related issue of data quality and why it is such a challenge for BI/DW projects.
April 4, 2017
Our final week of looking at data modelling for BI/DW.  The key message is pragmatism and not being ideologically wedded to one particular modelling approach.  Of primary concern must be the improvement of decision making in the organisation - everything else, including engineering, data management, etc., is secondary to this aim. We also take a look at possible technical data warehouse architectures, including various data mart approaches.