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.
