Improving your decision-making with data and technology
- Module code
- Module leader
- Karen Rial-Lovera
- Module level
- Module credits
- Min study time
- 150 hours
- Contact Hrs within study time
- 15 hours plus 15 hours module orientation (distance learning) 80 hours directed and independent distance learning via a range of student activities 40 hours assessment preparation
- Teaching period
- Semester 1 or 2
Organisations that fail to take account of all of the relevant data fail to maintain currency within their marketplace. However there is a wealth of data available and a multitude of mechanisms, both traditional and emerging, that can be employed to interrogate it. In turn, valuable data analytics relies on sound methods of capture, collation, curation, analysis and collaboration. Identifying the strategically important data sets and understanding the scope of technology to support these stages and derive valuable business intelligence is critical when making decisions around technological investment.
This module starts by exploring the foundation principles of (firstly) the fundamental role of data mining and analytics in business intelligence, (secondly) the continually evolving ethical challenges this presents and (thirdly) the use of analytical output in policy development. Students are supported to develop reflective, proactive leadership in order to maintain strategic advantage and to define appropriate frameworks for this.
The student will hone their critical analysis skills in relation to innovative technological solutions, such as Blockchains, Google Analytics, Precision farming, and how they align with answering strategically important decisions. They will also evaluate how these solutions interact and impact on people, on production systems and on organisational culture.
The aspects studied within this module link with S1, S2 and S4 in that it exercises the skills of reflective practice, innovation and leadership as applied to the use of technology and data in business and policy development. The concepts and exercises undertaken will provide a foundation which can be drawn upon further in the course, for example for modules P1, B3 and B4, and will be further developed when undertaking M1 or M2 modules.
To achieve credit for this module, students must be able to:
- Analyse, appraise and apply appropriate frameworks for the use of data in business intelligence
- Provide justified arguments for the selection and interrogation techniques used in deriving business intelligence from a range of available sources
- Critically analyse how innovative technological applications in data management may impact on an organisation using named examples
- Evaluate the role of leadership in defining strategic approaches to data use in informed business decision making
|First Sit||Coursework: Critical evaluation of the role of a chosen technology towards the promotion of strategic and evidence-based business decision making (3000 words)||100%|
|Referral (capped at 40%)||Coursework: Critical evaluation and, for resubmissions, a reflection upon first sit coursework (3250 words)||100%|
- Mastering Business Research Methods Series. 2015-2017. Sage Publishing.
- Additional resources:
- Albright S.C. and Winston, W.L. (2019). Business Analytics: Data Analysis & Decision Making. 7th edn. CENGAGE Learning Custom Publishing.
- Foster, L., Diamond, I. and Jefferies, J. (2015) Beginning statistics: an introduction for social scientists. 2nd edn. SAGE Publications Ltd.
- Genstats, Nvivo and Tableau User Guides
- Norman, A.T. (2017). Blockchain Technology Explained: The Ultimate Beginner’s Guide About Blockchain Wallet, Mining, Bitcoin, Ethereum, Litecoin, Zcash, Monero, Ripple, Dash, IOTA And Smart Contracts. Create Space Independent Publishing Platform.
- Provost, F., Fawcett, T. (2013) Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly Media.
- Scherbaum, C.A. and Shockley, K.M. (2015) Analysing quantitative data: for business and management students. SAGE Publications Ltd.
- Whigham, D. (2007) Business data analysis using excel. Oxford University Press.