BM9503 - Business Intelligence and Information Systems

BM9503 is delivered using a combination of lectures, workshops and webinars.

Lectures will be used to provide you with an overview of different forms of data, and also to introduce you to the functionality of various business intelligence systems. In the 2-hour weekly workshop, you will gain first hand experience of using such systems in a IT lab environment, working with data sets to produce dashboards that will result in practical assets that can be used when seeking placement or graduate employment. The webinars will be used to provide you with walkthroughs of more complex systems and also to discuss how such analysis informs decision making.

To complement timetabled sessions, the module’s Blackboard site will be populated with supporting information such as information on how to access and work with BI systems, and practical exercises to help you assess your progress (solutions to these tasks will also be provided). The module reading list will direct you to a combination of academic and non-academic sources which will enable you to appreciate the role of business intelligence in various contexts.

Support will be provided to you by the module tutor and the academic teaching team. Workshops are delivered in IT labs in small groups of around 20 students, allowing ample opportunity for students to obtain 1-2-1 feedback on their progress. Equally, the weekly webinars will be used to further consolidate understanding and provide another channel for students to seek support when required.



Your module is supported by a dedicated Blackboard site, which hosts lecture materials, IT workshops exercises and data files, alongside assessment details and various support facilities such as recordings of certain lectures and IT applications. This will also house the module reading list and host your webinar sessions.

You will have a wide-ranging electronic reading list that comprises of various textbooks whose contexts will reinforce the lecture and IT workshop inputs, alongside academic reports, conference papers and journal articles that showcase the application of various quantitative techniques presented in the module.

All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)

Knowledge & Understanding:
• Develop knowledge of how data analysis can inform data-driven business decision making using Business Intelligence tools [MLO1]
• Develop an understanding of how business intelligence can inform and contribute to the success of a business by surfacing knowledge from underlying data [MLO2]
Intellectual / Professional skills & abilities:
• Apply and demonstrate the role of data and data structures as an aid to decision making; justify the selection of models and techniques to assist in the development of BI solutions. [MLO3]
• Apply introductory analytical techniques to the analysis and interpretation of business data to justify the chosen business intelligence approach [MLO4]

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
• Gain awareness on the ethical considerations on data and business intelligence applications [MLO5]

Formative assessment:
Formative assessment will be provided on an ongoing basis throughout the module, where feedback will be provided during the practical workshops. The weekly workshops will embed the taught materials from the lectures, re-enforcing the theoretical concepts alongside practice-based activities.

Weekly webinar sessions are a further channel for formative feedback on both the theoretical and practical aspects of the module and on the tasks that underpin the summative assessment.

Summative Assessment:
Consists of two separate but inter-related activities and outputs.

Output 1: Will be a poster presentation in week 6 to justify the chosen data that will be used in the BI dashboard. The poster should establish justification for the chosen structured/unstructured data, how it is connected, along with descriptive elements to define how you have prepared and linked the data. The poster presentation will make up 30% of the marks for the module. [MLO1] and [MLO3]

Output 2: Will be in the form of a written report that discusses key methodological outputs, a discussion of your chose dataset, the final dashboard (as an appendix) and how it helps to inform the business of the key insights emerging from the data. The written report will make up 70% of the overall marks for the module. [MLO1 – MLO5]

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The module will equip you with skills to help you to develop skills for working with intelligence (BI) in future study, employment, work placement or graduate roles, commonly used in business and its impact. The module will provide you with a theoretical understanding of various business intelligence methods and data-handling applications supported through practical tutor-led workshops.

You will be assessed on your understanding and proficiency in data management, analysis and presentation using appropriate tools. The assessment will aim, wherever possible, to simulate “real-world” tasks and scenarios. You will therefore be required to work on a series of tasks over the course of the semester in preparation for the final assessment.

The data-handling and interpretative skills learned on this module are directly transferable to your further academic study and working life and are vital in a wide range of graduate roles. The skills and tools identified for use in the module are based upon research and collaboration with a variety of businesses. In line with this, and to deepen your understanding of the practical content of the module, you will also be expected to engage in additional reading to aid your understanding of the importance of the use of data modelling, analysis and interpretation in both business and academic settings, thereby engaging in research-led learning.