Public Policy Analytics: Cases & Issues


Units: 6


The purpose of this course is to focus on the significant challenge of replicating the private sector’s successful use of big data analysis and predictive analytics in the public sector.


The reason the challenge is so great is that governments’ relationships with their citizens are far more complex than the relations companies have with their customers. And there are other complexities here – constraints on public sector resources, skills shortages and competing political agendas. Perhaps the most important variable is the growing demand from citizens to participate in the government decisions that affect them, both as individuals and as members of communities.


Yet the challenge needs to be met as the rewards for successful use of predictive analytics in the public sector are potentially so great. Already, we see signs of analytics being used by governments to add public value is areas such as suicide reduction; early identification of public health problems; traffic management; optimizing public infrastructure investment; and improving educational outcomes.


This course looks closely at some practical issues that have emerged so far with a view to equipping students with the background and skills needed to successfully deploy predictive analytics when their turn comes. To do that, the course looks closely at practical cases with important lessons. Most of the cases reveal application of solutions which are technically sound but which meet hurdles of accountability, transparency, legitimacy and citizen empowerment.


For instance, how can an algorithm used to identify children at risk of mistreatment be hailed as a success in Pennsylvania, called “useless” in New Zealand and be the subject of successful legal action by citizens in Scotland? Are data techniques applied to the criminal justice systems prone to racial bias? What rights should citizens have over the dissemination and use of data collected on them by governments? What redress should citizens have when data anonymized by governments is re-identified?


These and other cases raise policy, ethical and management issues. In considering these issues, the course traverses the various levers available to governments such as legislation, administrative reform and participative democracy as well as the pitfalls.


All of these matters will be considered in a classroom that places a premium on student participation. This is a very new area and the course will work best if we take the journey together. The ultimate aim is to develop sound understandings of the principles which public policy professionals using big data analytic techniques can apply to achieve successful outcomes for both governments and their citizens.  


Learning Outcomes:

Students completing this course will be able to:

  • Understand the context and critical success factors for using predictive analytics successfully to address public policy problems and service delivery by governments
  • Diagnose the role of value judgements in this context and evaluate the remedies suggested for their impact
  • Design governance regimes which represent best practice for safeguarding ethics
  • Understand the current debate over machine bias

Identify practical opportunities for successful use of data analytics in government service delivery