Strategic and HR Analytics

Module description

In this module you will deepen your understanding of how to identify and analyse data and information essential to making strategic and HR decisions.  In addition to introducing various concepts, theories and frameworks from strategic management and human resources management that can be used to help identify and evaluate important sources of data and information relevant to key strategic and HR decisions and leverage points, the module will develop understanding about how unstructured text can be converted to structured data and then analysed using various dimension reduction techniques.  Key concepts taught include pattern recognition, classification, categorisation, and concept acquisition.  Additionally, the module will also cover recent developments in the field such as machine learning, deep learning and neural networks and their role in the analytics toolkit.  

Full module specification

Module aims

It is increasingly necessary to be able to provide data driven facts and use them effectively in order to proactively address critical performance issues and strategically support business and HR activities.  We will cover how data driven strategic and HR analytics and metrics provide information that helps drive strategic business decisions and deliver competitive advantage over competitors.  We will cover how key strategic and HR deliverables are identified, how data analytics can be used to solve strategic and HR problems, how strategic performance frameworks and measurement systems are designed and implemented, and how different metrics, benchmarks, indicators, research and gathering techniques, analysis and reporting can be used to support decision-making.  Key concepts, theory and best practices related to workforce psychology and analytics will also be discussed, including text analysis, machine learning and network analytics.

ILO: Module-specific skills

• 1. P2: Demonstrate knowledge and understanding of key business processes and structures, and the role of business analytics in decision support.
• 2. P3: Critically analyse and discuss current issues and influences relevant to the ongoing development of business analytics, and its application.
• 3. P4: Draw on knowledge of current research and practice to identify and apply appropriate analytics methods and tools to a range of business situations.
• 4. P5: Create, manage, interrogate, interpret and visualise data from a wide range of different sources, types and including structured and unstructured forms.

ILO: Discipline-specific skills

• 5. P6: Critically analyse the use of data within a business context, identifying strengths and limitations.
• 6. P8: Work with stakeholders from a range of backgrounds to identify the need for, design, develop, and deploy, analytics solutions within a business environment
• 7. P10: Contribute effectively to managerial decision processes within a business context.

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

Re-assessment notes

Re-assessment will be in nature to the original assessment, but the topic, data, and materials must be new.

Syllabus plan

The following content will be covered in an integrated fashion throughout the course

• Defining business strategy and building the case for HR as a strategic function
• Identifying the strategic and HR architecture and key deliverables
• Recognising how HR analytics supports strategic business decisions and goals
• Designing and implementing strategic performance frameworks and measurement systems that are aligned with business strategy
• Understanding workforce psychology and how data analytics can be used to solve HR problems
• Using a systematic approach to collecting, analysing and interpreting data right for the organisation and industry
• Building and improving measurement systems in order to gain significant business insights, for example, using descriptive and diagnostic analytics, benchmarking, and balanced scorecards
• Leveraging information technology to record, retrieve and report on HR information and convert data, metrics, benchmarks and indicators into strategic decision–making information
• Ethics, regulation and governance in HR analytics
• Introduction to machine learning in HR analytics
• Using text based data to generate insights

Indicative learning resources - Basic reading

Guided reading will be provided via the module ELE pages, the type of information that you might be expected to review includes:

• Volberda, H., Morgan, R., and Reinmoeller, P. (2011). Strategic Management Competitiveness and Globalization: Concepts and Cases. Cenage.

• Cascio, W. (2018). Managing human resources, 11th edition. New York: McGraw Hill.

• Bauer, T., Erdogan, B., Caughlin, D., & Truxillo, D. (2019). Human Resource Management: People, Data, and Analytics, Sage.
• Isson, J. P., & Harriott, J. S. (2016). People analytics in the era of big data: Changing the way you attract, acquire, develop, and retain talent. Hoboken: Wiley.
• Fink, A. A., & Sturman, M. C. (2017). HR metrics and talent analytics. In D. G. Collings, K. Mellahi, & W. F. Cascio (eds.), Oxford handbook of talent management (pp. 375-395). Oxford, UK: Oxford University Press.

Module has an active ELE page?

Yes

Origin date

09/01/2020

Last revision date

06/10/2021