WHat is Theme C?

Data quality and use and their connections to project performance - Lead by Professor Terry Williams, University of Hull, Paolo Quattrone, University of Edinburgh

It is important to understand the role of data in the governance and management of projects. Does good data help to ensure successful delivery? If we can demonstrate the link this should lead to improvements in the data we collect which will in turn enable the design and deployment of more sophisticated analytical methods capable of unlocking the latent knowledge embedded within existing data-sets.

The IPA is committed to improving the quality of the data that it collects across the GMPP and more broadly, and is keen to link GMPP with other data sets from within and outside of government. It also seeks to understand how GMPP knowledge, both explicit and tacit, can be made accessible to project and programme communities in such a way that it fosters continuous improvement and improved critical reflection on performance.

We need to understand:
a) How projects behave and the data that represents that
b) Embedded within an analytical process within the organisation
c) How the data can be used predictively
d) But this all needs an understanding of how individuals, groups and organisations behave
e) And how we deal with this needs to be associated with the eventual performance of the project (which is the point of the exercise!).
We therefore have five different levels of study, which need to inform each other.

Our Projects

1. Visualising the data in the GMPP (Manchester)
2. How are review recommendations formulated, why are they not always followed; how does data form the recommendations for action or inaction; what impact does this have on project outcomes (Hull, Manchester, Oxford)
3. Identify and estimate benefits/value initially and following this through the lifecycle across the organisation to benefits realisation (Phase 1 only) (Hull, Edinburgh, Cranfield, Manchester)
4. Reference Class Forecasting and Major Project Data (Said Business School)
5. Application of Machine Learning to Major Project Data for the purpose of predictive analysis (Said Business School)
6. International comparison of good practises focusing on central authority bodies' (like IPA) project intelligence practices in order to establish and improve the quality of practical analysis and academic research (Cranfield)