Delivering Projects and Programmes with Data Science and Machine Learning

Delivering complex projects and programmes can be a hard challenge. We must scope out, design and build robust projects. We must then make predictions on their evolution in highly uncertain environments. This event was held on 12 September 2024.

The good news is that the burgeoning world of project data science can provide solutions to these challenges. We can use data science techniques to quantify our world, build prediction models, and then make predictions using those models to steer our projects to calmer waters and a safer journey.

In this interactive event, James presented the power of data science in guiding our projects, using a range of worked examples. He illustrated a selection of data science techniques to deliver projects and programmes, and reviewed how machine learning and predictive analytics can be used to help us “learn from the past to predict – and shape - the future”.

There were some short workshop exercises to help illustrate the points made, and to provide learning opportunities – so that we could learn from our mutual experiences on how data science is transforming project and programme delivery.

James has very kindly shared a link to Project Science Insights where there are various case studies included for your information.

Speaker-James Lea, Director, Project Science Ltd

James Lea is a Fellow of APM and a Fellow of the British Computer Society. He is the founder of Project Science, a consulting business specialising in the ‘physics of projects’ that provides answers to the question - how can we accelerate our project and programmes? Project Science also publishes apps that encode James’ expertise, in particular, predictive analytics for agile projects that use Jira.

As a physicist James applies a sceptical empirical approach to plan, control and predict project and programme outcomes. By applying the ‘laws of project physics’, using data and mathematics we can carve a path to success. In addition, James is a keen practitioner of ultra-low defect approaches that reduce risk and volatility, improve efficiency and cost-effectiveness, and ensure quality.

James contributes to the APM through talks and webinars, and is a published author in the APM Project Journal.