How an organization makes critical decisions is the result of multiple factors, including its history, culture, technology, data, and people. If we imagine an organization’s decision-making function as a marketplace where different stakeholders work together to achieve the best outcome for the business — then risk is the common currency used to quantify and express the pros and cons for any decision. Risk models, either data-driven machine learning models or physics-based analytical models, produce the quantitative metrics that inform decision-making. When these risk models are scaled to inform enterprise-level decision-making, they provide the requirements that can foster an organization’s digital transformation. This presentation will uncover, soup-to-nuts, Exponent’s collaborative approach with Pacific Gas and Electric (PG&E) to deploy risk models that enabled a mutually beneficial overhaul of their digital systems. By stitching together otherwise separate tools, data sets, and processes into an engineering-driven risk framework, all utilities can achieve their own comprehensive, science-based process for enhanced decision-making.
- Understand, through a detailed example, how to scale from a proof-of-concept risk model to a production-ready digital solution using forward-leaning requirements derived from multidisciplinary subject matter experts collaborating from the start.
- Learn how developing and deploying a comprehensive risk model provides software and data system requirements that enable your organization to achieve its digital transformation objectives.
- Recognize that risk is a common currency used by different stakeholders to ensure that your organization’s decision-making consistently seeks the optimal solution.
- Learn how a solution is agnostic to the technology it is deployed across, thereby enabling its use in any environment.