How might we create a repeatable way to assign data to risk factors and map their relationships to build a shared, trusted approach to asset risk management?
Challenge
We’ve made great strides in understanding asset health - but when it comes to asset risk, we’re still wrestling with complexity. Risk management requires deeper insight into the factors: likelihood, consequence, cause of failure and mitigation - pulled from diverse data sources that live in different formats and systems.
The challenge is to design and test a repeatable method of allocating data to the correct risk factors for any given system of assets while also mapping the relationship between these factors. This will lead to the creation of a “common currency” - a standardised way of measuring and communicating asset risk that everyone across the organisation can understand, trust, and apply consistently - making asset risk management actionable, comparable, and transparent.
What will we do?
This sprint will run as a years’ worth of work in a week, aiming to compress months of effort into a single intense week. The team will bring together data owners, asset experts, analysts, and digital specialists to test and iterate a consistent methodology for asset risk assessment. Building on the work already underway with tools like Bowtie, the sprint will focus on surfacing, structuring, and aligning the data and insight needed to feed a robust risk model.
The ultimate goal? A working prototype that allows for consistent and scalable risk scoring across our asset base - one that can grow with us, while giving the business a reliable way to make decisions based on real exposure.
How we will do it?
The sprint will blend elements of system mapping, data exploration, and risk modelling. We’ll bring in real-world use cases and test our approach in short, intense development loops. Participants will work in collaborative pods, experimenting with different risk logic, data inputs, and visualisation.
The team will map out key blockers - such as siloed data, subjective scoring, and inconsistent terminology - and rapidly prototype solutions to overcome them. We’ll also explore opportunities to automate risk capture or updates where appropriate and begin building the business case for embedding this approach post-sprint.
Target Outcomes
By the end of the sprint, we aim to deliver:
- A functioning proof-of-concept model (or interactive dashboard) to demonstrate the approach.
- Clear prioritisation of asset types or systems where this model can add the most value.
- A roadmap for testing, scaling, and integrating the model into existing processes and tools.
- Defined roles and responsibilities for keeping the approach live and evolving.
Who will benefit from this sprint?
Risk managers, asset owners, and planning teams will gain a clearer and more actionable view of their risk landscape. Decision-makers will benefit from more consistent data to guide investment and intervention. The wider organisation will benefit from having a shared, reliable way to talk about risk across asset types and departments. Regulators will see more transparent, auditable evidence of how risk is managed and mitigated across critical infrastructure and customers and communities ultimately benefit from more resilient, reliable services and fewer unexpected failures.
