When we deliver projects there is always a rush to move on to the job. Some organisations undertake a critique of the project to enable reflective learning, but do they have the evidence that they need to conduct a thorough forensic review?
For instance, many organisations manage risks on the basis of their current status and trend, but wouldn’t the real insights come from how the risk developed over its lifecycle?
- What event created the inflexion in the risk profile?
- What was the risk management action effective? Could it have been delivered more efficiently?
- Did the schedule give appropriate attention to risks (e.g. via schedule risk analysis)?
How would you adjust the risk impact and probability score, management actions and contingency/risk drawdown if you were to manage a similar project in the future? By including this feedback loop we can develop the data to underpin machine learning and understand the accuracy in our ability to anticipate and manage risks. Its a future which is eminently possible, but it depends on high quality data.
Connected data By reviewing the risk data, we also need to consider which of the risks developed into issues, which risks ‘fizzled out’ and which risks needed concerted action to ensure that they didn’t impact delivery. How much risk drawdown budget was required? To what extent did the risks impact the schedule, budget, performance or realisation of benefits? What is the connection between risks and issues? Were lessons identified to help improve how the risks and issues can be managed in the future? Was the issue resolved at a portfolio level or is there an element of residual risk that needs to be considered in future projects?
By joining these datasets together we can develop a high level model for the project and how it evolved. As this data is aggregated and combined it provides us with insights that wouldn’t otherwise have been possible. It provides the foundation for forecasting, for prioritising management effort, for identifying issues before they emerge. It enables the project manager to take early action to avoid what is avoidable.
Exploiting data The data acquired throughout the course of a project is a significantly under-utilised asset. The capabilities already exist to enable us to leverage this connected data, but the organisational imperative hasn’t yet emerged to make this a reality. As a community, we need to be bold and to drive a vision for change, piloting emerging methods within a safe environment. Its a future that we all know is coming; our challenge is how we prepare and engage in it.
Young professionals I would encourage all the young, aspiring projects managers out there to begin to explore the intersection between project management and data analytics. Its a skillset that will rapidly increase in value as data delivers competitive advantage and unlocks a wide range of productivity improvements that will be increasingly within our reach. Its an exciting future for all of us.