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Big Data and Business Analytics

  • Module code: BB7591
  • Year: 2018/9
  • Level: 7
  • Credits: 15
  • Pre-requisites: None
  • Co-requisites: None

Summary

This module is designed to introduce business students to the exciting world of business intelligence and big data science with a particular focus on its use to inform strategic decision-making policies. Today's managers need to understand where and how to use business intelligence and big data analytics, to allow them to utilise new sources of customer, product, and operational data, which coupled with data science will allow them to optimise key business processes and KPIs. In addition, they need to understand the process by which data analytics and big data can add value to the business by providing new insights into sources of competitive advantage. The module does not attempt to convert business managers into data scientists; its aim is to help business managers think like data scientists, so they can collaborate with IT specialist and data scientists in order to provide critical input into the operationalisation of business strategies.

Aims

  • To develop knowledge of the nature of data and best practices for data visualisation.
  • To appreciate the challenges arising as a result of the interaction between IT capability, business demands as well as limitations placed on adoption of data driven decision making within organisations.
  • Be aware of the range of software tools and their application to extract meaningful patterns within large data sets and significant relationships between KPIs and other business variables and communicate these effectively.
  • To develop reasoning, communication and presentation skills appropriate to the role of a business manager adopting a data driven decision making approach.

Learning outcomes

On successful completion of the module, students will be able to:

  • Critically evaluate the potential for data driven strategy implementation in business.
  • Demonstrate an understanding of challenges arising as a result of the interaction between IT capability and business demands and adopt appropriate strategies to address them.
  • Apply competently business analytics software tools and their application to business intelligence and big data analytics.
  • Communicate effectively a strategy for data driven decision making integration in a business context.

Curriculum content

  • Business potential of data driven decision making
  • Data visualisation approaches
  • Data warehousing and governance
  • Data science for business stakeholders: Review of business intelligence tools and their business implementations 
  • Challenges arising as a result of the interaction between IT capability and business demands and strategies to address them.
  • Ethical and Legal challenges of IT and big data
  • Organisational challenges for data driven decision strategy implementation

 

 

Teaching and learning strategy

Formal class time will be used to introduce topics and generate debate plus provide the opportunity for participative learning by means of case studies, practical workshops (e.g. Tableau), and student presentations. The sessions will be interactive and include a mix of teacher-led learning, problem solving, and student-led learning via real life work examples to illustrate applications of relevant business intelligence big data analytic techniques. Class time will also be used to discuss and review the preparation of the assignment. This will be underpinned by self-directed learning through the use of the relevant MBA text, together with relevant software tools, simulation to expose students to the difference between traditional ERP and real time data analytics reporting (e.g. ERPSim - S/4HANA), on-line journals and case studies. Interactive learning outside the classroom will be supported by means of tutor-supported Canvas activities and by learning sets.

Breakdown of Teaching and Learning Hours

Definitive UNISTATS Category Indicative Description Hours
Scheduled learning and teaching Full time 40 Executive & Russia 32
Guided independent study Full time 110 Executive & Russia 118
Study abroad / placement
Total (number of credits x 10) Full time 150 Executive & Russia 150
Total (number of credits x 10) 150

Assessment strategy

The assessment consists of two elements:

  • The first element will be in the form of a set of written professional PowerPoint presentation slides based on a topic of student's choice focusing on the critical evaluation of the challenges implementation of business analytics poses for a business. The presentation slides will count for 15% of the module mark.
  • The second element will be a professional business report and will count for 85% of the module mark. The report will be based on the same topic as the PowerPoint presentation slides and will allow students to apply knowledge gained in the module to a real-life situation and further develop their critical understanding of the challenges posed by application of data analytics in a business context.

Formative assessment will be an on-going feature of the module due to the problem-oriented nature of the teaching and learning approach. Opportunities for formative assessment and feed forward are afforded through the PowerPoint slides, interactive sessions in the form of class discussion, and through regular individual and group exercises.

Mapping of Learning Outcomes to Assessment Strategy (Indicative)

Learning Outcome Assessment Strategy
1) Critically evaluate the potential for data driven strategy implementation in business. Coursework
2) Demonstrate an understanding of the challenges arising as a result of the interaction between IT capability and business demands and adopt appropriate strategies to address them. Coursework
3) Apply competently business analytics software tools and their application to business intelligence and big data analytics. Group Presentation Coursework
4) Communicate effectively a strategy for data driven decision making integration in a business context. Group Presentation Coursework

Elements of Assessment

Description of Assessment Definitive UNISTATS Categories Percentage
Group Presentation Practical Examination 15%
Individual Assignment Coursework 85%
100%
Total (to equal 100%) 100%

Achieving a pass

It IS NOT a requirement that the element of assessment is passed in order to achieve an overall pass for the module.

Bibliography core texts

Lin, N. 2015 Applied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics, Pearson Education.

Bibliography recommended reading

Schmarzo, B 2015, Big Data MBA: Driving Business Strategies with Data Science, John Wiley & Sons, Incorporated, Hoboken.

Maheshwari, A 2014, Business Intelligence and Data Mining, Business Expert Press, New York.

Verhoef, P. 2016 Creating Value with Big Data Analytics, Routledge.

Siegel, E, & Siegel, E 2013, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, John Wiley & Sons, Incorporated, Somerset.

Loshin, D 2013, Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph, Elsevier Science, San Francisco.

Liebowitz, J (ed.) 2013, Big Data and Business Analytics, CRC Press, Boca Raton.

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