Business analytics as organisational problem solving

Topic Description

Business analytics (BA) are rapidly becoming a key function of organisations, seen as an enabler for better decisions and improved business performance (Delen & Zolbanin 2018). At its essence, BA is about transforming data into actionable insight.

Despite the rapid growth of BA in recent years, questions remains as to how organisations derive business value from it, and how they can best align BA efforts with their business goals and strategy (Hindle & Vidgen 2018). Readily available data sets and advances in techniques and technologies provide opportunities for organisations, but both methodological approaches (Hindle & Vidgen 2018) and BA skills in the workforce (Seddon et al. 2017) are lacking to allow them to take full advantage.

As argued by Holsapple et al. (2014), business analytics should be properly regarded as contributing to organisational problem solving, supported by evidence, including hard facts and reliable measurements, but also justified estimates and credible explanations: the real-world is too complex for hard facts alone to be sufficient, even when available, so that domain knowledge and expert, unbiased insight is an essential complement. Human judgement and sense-making is key to understanding which data patterns and insights emerging from business analytics are really meaningful and actionable, and research also shows that collectives often outperform individuals in many complex tasks.

Hence our research questions: To which extent does complex problem solving provide a sound methodological basis for effective BA? Can differently shaped and heterogeneous groups within a business organisation outperform domain experts in business data science? How are these groups to be composed and interact to maximise the development of meaningful and actionable data interpretations from BA?

Building on research on Complex Problem Solving, Data Science, Business Process Management and Collective Intelligence this PhD project aims to:

1. provide problem modelling capabilities based on observable phenomena which can be related directly to evidence, coupled with structured problem solving processes in which stakeholders participate in knowledge discovery, validation and risk mitigation activities
2. develop a framework for a deep integration of business and analytics, so that meaningful interpretation of evidence can take place within the business context in support of decision making, and close, fruitful collaborative practices are established between business and data stakeholders
3. develop a collaborative platform to support the human process of making sense of BA. A specific attention will be put in developing new interfaces for organisational engagement in collective business data interpretation, with new interactive visualisations to improve the organisation's capability to make sense of business analytics and convert them into actionable insights.

This project will also be supervised by Anna De Liddo ( https://kmi.open.ac.uk/people/member/anna-de-liddo ) and Jon Hall ( https://www.open.ac.uk/people/jgh23 ).

Skills Required:

You will have a Computing/Data Science/Business analytics background. Previous experience of manipulating data set in a computational fashion is essential. Familiarity with traditional and emerging techniques for data science and business analytics is desirable.

As for all PhD programmes, you will also have a track record of academic research at Masters level or beyond.

Background Reading:

Acito, F. & Khatri, V. (2014), ‘Business analytics: Why now and what next?’, Business Horizons 57(5), 565–570.

Delen, D. & Zolbanin, H. M. (2018), ‘The analytics paradigm in business re- search’, Journal of Business Research 90, 186–195.

Hall, J. G. & Rapanotti, L. (2017), ‘A design theory for software engineering’, Information and Software Technology 87, 46–61.

Hall, J. G. & Rapanotti, L. (2019), 'Business analytics as organisational problem solving: an application in higher education', to appear. (Please request a draft copy from lucia.rapanotti@open.ac.uk)

Hindle, G. A. & Vidgen, R. (2018), ‘Developing a business analytics methodology: A case study in the foodbank sector’, European Journal of Operational Research 268(3), 836–851.

Holsapple, C., Lee-Post, A. & Pakath, R. (2014), ‘A unified foundation for business analytics’, Decision Support Systems 64, 130–141.