We all know projects are extremely complex system that are influenced by extremely large number of factors. Often project failure can be due to hidden causes that can sneak up on project managers. Data holds the key to preventing this, as it allows us to peer into the past and better understand our mistakes.
In many projects, one of the main major issues holding us back is that we lack the quantity of useful data needed to perform in depth project data analytics and the data we do have is
In our most recent Meetup, we had a talk from Highways England on how they are transforming their approach to project delivery. With deployment of advanced data analytics across various stages of their projects including the A14 and Lower Thames Crossing, data analytics is becoming a standard in how they deliver projects. Another step into their data journey, includes upskilling their existing project workforce to align with this new approach, of which they have utilised the Project Data Academy
Effective project data analytics requires data; lots of data. There is a split across industry into how this challenge is tackled, divided into various camps. I’ve summarised them into 5 areas but I recognise that it is difficult to capture all the nuances within such a short blog.
1. Closed systems. This is where a few companies are heading, creating their own vaults of data to shape their own models and analysis. We are seeing these starting to proliferate, from waste to project perfor
In 2017 we met with people from the NHS and Network Rail to discuss the challenge of learning from experience and how advanced data analytics holds the key. I then developed a paper with Dr Stephen Duffield that summarised our research into 20,000 lessons lesson; we concluded that the process just doesn’t work. We take the complexity of a project and boil the experience down into a few trite paragraphs that are often statements of the obvious. Even today, a recent paper from Grant Mills et al hi