Utilities traditionally have analyzed risk by looking at the likelihood and impact of a particular end result (for example, an outage, wildfire or tree strike). Then, they made investments to mitigate the riskiest parts of the system.
The fundamental flaw with this approach is that looking at the end result of risk does not factor in the most important considerations: What decisions can management make that actually will minimize the risk and how can management put to use limited budgets to get the most return in terms of risk buy-down?
This session will explore a new data-driven approach to risk management, specifically targeted on how utilities are beginning to leverage data science to maximize the risk-spend efficiency of various mitigation strategies.
- How data science can be used to reduce operations and maintenance spend and optimize capital spend for risk mitigation
- Examples of utilities using this approach across the enterprise, from storm resiliency and wildfire risk to vegetation management and more
- How to get started with data science for grid resiliency and risk mitigation
Tom Martin, Managing Director of Product, Data Science