Bringing data science to P3M maturity assessments

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Many project delivery professionals will have been involved with project, programme, portfolio maturity assessments. There are a number of frameworks available, providing guidance in the development of an organisation’s ability to deliver projects, programmes and portfolios effectively and efficiently, both internally or externally.

The internal perspective is about developing the organisation’s ability so that more projects and programmes deliver their objectives (effectiveness) and less investment is wasted (efficiency).

The external perspective concerns the reassurance of stakeholders. For example, where a customer is about to invest in a project or programme that is to be delivered by a third party, they are likely to be more confident of the performance of a third party that has demonstrably achieved a high level of capability maturity.

So that's the context established, so how can data science make a difference? Could we automate all or part of the maturity assessment process?

Risk management – a user case

Let’s unpack a specific user case. As a director I wish to understand how good my organisation is at risk management and whether it is applied consistently in projects and programmes?

Most organisations would deploy a team of assessors to investigate; but in an age of austerity such an approach seems somewhat anachronistic, and expensive.

The application of data science provides an alternative approach, for example by accelerating and improving data capture and processing and providing high quality real-time feedback. Here’s some possibilities

  1. We can use algorithms to assess the materiality of any differences between a risk register and its subsequent updates. Has the risk register been thoroughly reviewed, or has the project or programme manager simply saved the latest version with a minor change, to give the impression that it is up to date and all is well with the world?
  2. We can track the life cycle of risks. How effective have risk management responses been? For example, were interventions undertake belatedly thereby reducing their effectiveness?
  3. We can assess optimism bias by comparing baseline with actual results of different risk types, including impact and probability.
  4. We can assess which risks emerged late. Was the eventuation of the risk foreseeable, or were the project team ‘asleep on the job?’
  5. We can use machine learning to rank the quality of risk definition. Are the risks generic and obvious, or are they forensic and specific?
  6. We can assess the type of risks that are applicable for a specific type of project and assess the degree to which the project manager has captured them.

All the above help to address the specific use case, whilst at the same time they are changing the conversation, and relationship, between the project manager and senior management.

For example, senior management will gain new insights into how well risk is being managed in their organisation, including the ability to make comparisons across an entire portfolio of projects and programmes.

This is no longer restricted to the realms of fantasy football league. With advanced data analytics real time information becomes available, about the performance of individual project and programme managers. In the new world there will be nowhere to hide!

Risk management is just a start

And risk is just one of 20+ functions, processes or perspectives that might be assessed for its level of maturity. Similar use cases can be developed for every aspect of project, programme and portfolio management - from business case and benefits management to resource management and scheduling.

So, data-science lead to the demise of P3 maturity assessors as organisations become increasingly reliant on their own performance information? Most likely organisations will still want the comfort blanket of an independent assessment, and the score rating that goes with it. Assessors too will need to be at the top of their game, as their reported findings and recommendations can be checked against available data!

Join us if you can on Tuesday 12 March in Bristol for the SWWE branch benefits management seminar, where we will explore this increasingly important topic from a benefits management perspective.

Martin Paver

Posted by Martin Paver on 5th Mar 2019

About the Author

Martin Paver BEng(Hons) MBA CEng MIMechE MAPM RPP is the founder of the London and Bristol Project Data Analytics Meetups, a community of over 2,200 people with an interest in leveraging the exhaust plume of data that is emitted from projects.

He is a Fellow of the APM, Registered Project Professional and Chartered Engineer. He is the CEO and founder of Projecting Success, a consultancy that is pushing new boundaries in advanced project data analytics.

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