inf604 Business Intelligence I (Complete module description)

No participant requirement

Objective of the module/skills:
Current module provides basics of business intelligence with focus on enterprises and strong emphasis on data warehousing technologies. Students of the course are provided with knowledge, which reflects current research and development in a data analytic domain.
Professional competence
The students:

• name and recognize the role of business intelligence as past of daily business process
• being able to analyse advantages and disadvantages of different approaches and methods of the data analytics and being able to apply them in simple case studies
• obtain theoretical knowledge about data collection and modelling processes, including most applicable approaches and best practices

Methodological competence
The students:

• being able to execute typical tasks of business intelligence, and also being able to deepen knowledge on different approaches and methods
• gain a hans on experience and being able to understand advantages and disadvantages of different methods and being able to use obtained knowledge in most efficient ways

Social competence
The students:

• build solutions based on case studies given to the group, for example solving the issue of a factless fact table
• discuss solutions on a technical level
• present obtained case studies solutions as part of the exercises

Self-competence
The students:

• critically review provided data and information

Data warehouse technology together with business intelligence are increasingly being used by business in order to get better decision support and enrich ongoing rocesses with data-rich decisions. Data warehouse technology enables an integration of data from heterogeneous sources, whether business intelligence builds data rocessing on top of it. For instance, business intelligence allows to build reporting on very large volumes of data (including historical) coming primary from data warehouse.
As past of the current module following contents are taught: 

• Definition and scope of business intelligence.
• Procedures and objectives of data warehousing.
• Process of extracting, transforming and loading (ETL) of data.
• Phases of data modelling, data capturing and reporting in conjunction with a plausible case studies/scenarios.
• Prospects for further and evolving topics for business intelligence (e.g. Adaptive Business Intelligence, In-MemoryComputing. etc.)
• Introduction to Data Mining.
• Case studies based practical exercises and assessments in order to impart practical knowledge.

• Gómez, J. M., Rautenstrauch, C., & Cissek, P. (2008). Einführung in Business Intelligence mit SAP NetWeaver 7.0. Springer Science & Business Media.
• Ariyachandra, T., & Watson, H. J. (2006). Which data warehouse architecture is most successful?. Business intelligence journal, 11(1), 4.
• Jensen, C., Pedersen, T. B., & Thomsen, C. (2010). Multidimensional databases and data warehousing. Morgan & Claypool Publishers.
• Haneke, U., Trahasch, S., Hagen, T., & Lauer, T. (2010). Open Source Business Intelligence: Möglichkeiten, Chancen und Risiken quelloffener BI-Lösungen. Hanser.
• Müller, R. M., & Lenz, H. J. (2013). Business intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg.
• Sabherwal, R., & Becerra-Fernandez, I. (2013). Business intelligence: Practices, technologies, and management. John Wiley & Sons.
• Awe, O. W., Liu, R., & Zhao, Y. (2016). Analysis of energy consumption and saving in wastewater treatment plant: case study from Ireland. Journal of Water Sustainability, 6(2), 63-76.
• Adamson, C. (2010). The complete reference star schema. McGraw-Hill.
• Linstedt, D., & Olschimke, M. (2015). Building a scalable data warehouse with data vault 2.0. Morgan Kaufmann.
• Schnider, D., Jordan, C., Welker, P., & Wehner, J. (2016). Data warehouse blueprints: business intelligence in der Praxis. Carl Hanser Verlag GmbH Co KG.

Module Capacity:
unrestricted

Links:

http://www.wi-ol.de