How do we judge how good a project professional is at their job? Is it by the way they follow process? Or as Jeff Bezos reported “I want people who are right most of the time?” How do we measure how right someone has been?

We’ve been giving a lot of thought to this recently and believe that there is a lot that we can bring to the debate through advanced data analytics.

• Estimators. How good were you at estimating something compared to your peers? What is the difference between forecast and out-turn? How do we normalise this based around project complexity or emergence?
• Risk managers. How good were you at pre-empting risk? How effectively was the risk budget invested? How many issues sprang up out of the blue?
• Stakeholder managers. How much delay was created by challenges with stakeholder management and how does this compare with other benchmarks.
• Change managers. How much of the change could have been anticipated and tackled earlier? On a scale, how proactively is change managed? What is the impact of tackling change late? Are the change managers plugged into the lead indicators and picking up change at the earliest point possible?

This is the tip of the iceberg and there is a lot we can do. But is this just another set of KPIs that no-one is interested in?

By surfacing it we can help to move away from turning the handle on process and pivot towards a more evidence driven approach.

Formula 1 works on very fine margins. Every member of the pitlane and engineering team are measured against a set of KPIs. The driver’s performance is measured against every metre of the road and compared against their peers. But in project delivery we don’t even measure what these margins are or how we can optimise them.

This situation is no longer acceptable and change is coming. Only then will we really drive transformational change.

We work with >60 different organisations and have a deep grasp of where they are struggling and where others are outperforming them. We’ve boiled it down to the following key factors:

• Vision, strategy and ambition. They have a clear understanding where they want to get to and how quickly.
• Senior commitment. They have board level support who show an interest in driving through change. Having a nod from the board is very different to having clear support.
• Governance. Senior members of the organisation are involved with driving progress, unblocking obstacles and holding people to account. They tackle the cross organisational issues that often drag transformational projects down.
• Understanding of constraints. They have a good understanding of their constraints. But they don’t get suffocated by them. They work around them, use the evidence of impact to influence others and build momentum.
• Transformation. They understand that this isn’t the job of ‘someone in the data team’. It requires a data culture that runs deep into the heart of the project delivery organisation. As such, the organisation treats it as a transformation project rather than a bolt on. The organisation has a good understanding of the various strands of activity that need to be developed in parallel.
• Iterative approach. We’ve seen some organisations who try and tackle the challenge of data in a massive programme of change. But they often get bogged down in organisational design, consultation and much more beyond. Those organisations who are pulling away are running pilots and driving change in rapid spirals rather than a big bang.
• Developing talent. They recognise that some members of their project teams will become increasingly obsolete as new methods gain traction. Rather than trying to find data talent within an overheated market they invest in developing their own people. Step 1 is to highlight the need for change and the opportunities that it presents. Step 2 is to inspire them, create a fear of missing out, show them a path. Then sign them up to the Project Data Academy.
• Open source. The organisations who are making rapid progress tend to be those who are working on open source solutions. Clients are beginning to understand the opportunities that this provides, leaving them with solutions that they can iterate and build on for collective benefit.
• Data. The more advanced organisations are also beginning to understand that the data they collect and the problems that they aspire to solve aren’t always aligned. They are beginning to get to grips with how they close these gaps.

Most interesting, there are clearly organisations out there who are working within a walled garden, developing products and services to sell or differentiate themselves from others. But our sense is that those who work collaboratively will outperform their peers. Particularly those who are agile and responsive. They’ll move more quickly together than alon