Data projects are inherently interdisciplinary operations, and insights gained from wrangling these different aspects into a cohesive, actionable strategy offer a template for other organizations seeking to excel at business intelligence. Data-driven business transformations can be daunting efforts riddled with multiple interdependencies and hidden technical debt. This presentation will use an ongoing, collaborative effort involving PG&E and Exponent to develop a data-driven, risk-based framework for asset management and operability assessment as a lens for understanding how effective business transformations can occur at the business unit and enterprise levels.

Session Takeaways:

• Define business transformation and what a data-driven approach means.
• Describe what is hidden technical debt and how it can be mitigated through the interplay of people, process, data, and technology demands.
• Assess the need for a business transformation through the lessons learned from an existing data-driven, risk-based project that has been deployed for asset management and operability assessment decision making.
• Identify how to employ a balance of institutional knowledge, data management, and insight to develop business intelligence tools for effective decision making.