Learning Outcomes:

At the end of the module/unit the learner will be able to:

• Use financial forecasts including projected cash flow, profit and balance sheet positions;
• Design probabilistic modelling of the real analysed business processes;
• Use statistical estimation theory for aggregating the results of random entries with a given distribution law and the execution of deterministic computations with the random generated entries;
• Use simulations to calculate ROI and IRR on the basis of selected inputs regarding cost and revenue of operations and different scenarios;
• Integrate disparate data sources into a single coherent framework for real-time reporting and detailed analysis within the extended enterprise;
• Assemble a BI Team;
• Design the scope of the BI Team and set up their objectives;
• Design the data warehouse, data marts or other sources that enable BI reporting and analytics;
• Use their knowledge of BI and related processes to correctly-balance resources to accurately capture data. The learner will gain important insight on where to be more lenient when it comes to data collection and cleansing;
• Practice common analytic operations on data such as: Query by multiple criteria, “Slice and dice”, Drill Down, and Roll up;
• Use Hammer and Champy’s suggested seven reengineering principles to streamline the work process;
• Use Business Intelligence to increase the competitive advantage of a company through intelligent use of available data;
• Plan BPR or CIP in line with the expectations of the business, mainly combining jobs and processes to be executed in natural order by the fewest employees possible, empowering the employees, and increasing flexibility;
• Design dashboard or BI end-user interface to convey BI system information at a glance.

The learner will be able to:

• Define a Data Warehouse (DW)
• Understand the role of a DW for doing BI
• Describe typical DW architecture and processes
• Describe the ETL process to populate a DW
• Describe different techniques used in data warehousing
• Describe and distinguish between Kimball and Inmon approaches to data warehousing
• List different data pre-processing techniques to clean data
• Use the MS BI Suite to build ETL processes
• Describe the role of data marts in a DW
• List examples of use of data marts for different business needs
• Describe multidimensional data modelling
• Describe the difference between a normalised and denormalised database schema
• Define and distinguish between star schemas and snowflake schemas
• Define the Cube data structure
• Describe methods to use the data in a cube
• Define OLAP
• Design different data models
• Describe conceptual, logical and physical models
• Define Business Intelligence
• List different BI applications
• List different methods of data delivery in doing BI
• Use MS Excel to do basic data analysis
• Use MS Excel to access a cube structure
• Describe the role of static reports and dashboards in BI
• List different components that can be used in a dashboard
• Describe the role of charts in BI
• Describe the use of in-memory tools to analyse data
• Define the role of Analytics in BI
• Describe different data mining techniques

The learner will be able to:

• Apply BI data for data analysis
• Capitalise on the strengths of Microsoft Excel as BI tools
• Use different BI tools for data analysis
• Create dashboards through excel
• Present effectively key findings to users
• Deal with errors and appropriately document their work

After a number of years in software development mainly using JAVA and web applications, moved to the field of Business Intelligence. Gained experience in both Data Warehouse and BI development, and also lead BI technical and operational teams.

Nowadays Colin manages the function of Business Intelligence for a large organisation within the telecommunications industry.

Vincent has over 11 years of experience in the Remote Gaming Industry as a Business Intelligence Analyst and Consultant. He occupied several key important roles in various organisations such as: Student Representative of the Faculty Board of Science (University of Malta), President of the Science Student Society, President of the Malta Statistics and Operations Research Organisation (MSTOR), Financial Officer of the National Youth Council, Deputy CEO and Chief Regulatory Officer within the Malta Lotteries and Gaming Authority.

Dr. Vincent Marmarà lectures statistics at the Department of Management (FEMA) at the University of Malta. Vincent is a fellow member (FRSS) of the Royal Statistical Society (UK). Dr. Marmarà provided research consultancy services to various organisations and supervised / is currently supervising BSc, BCom, MBA and MSc research students at the University of Malta. Vincent published several academic articles on international journals and other scientific surveys in Malta. At present he is one of the partners and co-owner of the statistics research company, Sagalytics.

Simon keeps himself up-to-date on the latest advances in Excel and helps customers use these new tools & features to increase their productivity & efficiency. His hands-on experience applying it at industrial scale, puts him in a unique position to advise, train, & assist others in their adoption of these new tools.