BEGIN:VCALENDAR VERSION:2.0 PRODID:-//swoogo.com//NONSGML kigkonsult.se iCalcreator 2.27.21// CALSCALE:GREGORIAN BEGIN:VEVENT UID:078e2bf5c148cab6d43591334de5886a339cfe48@swoogo.com DTSTAMP:20240328T112315Z DESCRIPTION: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 ex cel at business intelligence. Data-driven business transformations can be daunting efforts riddled with multiple interdependencies and hidden techni cal debt. This presentation will use an ongoing\, collaborative effort inv olving PG&E and Exponent to develop a data-driven\, risk-based framework f or asset management and operability assessment as a lens for understanding how effective business transformations can occur at the business unit and enterprise levels.\n\nSESSION TAKEAWAYS:\n\n \n\n * Define business transf ormation and what a data-driven approach means.\n * Describe what is hidde n technical debt and how it can be mitigated through the interplay of peop le\, process\, data\, and technology demands.\n * Assess the need for a bu siness transformation through the lessons learned from an existing data-dr iven\, risk-based project that has been deployed for asset management and operability assessment decision making.\n * Identify how to employ a balan ce of institutional knowledge\, data management\, and insight to develop b usiness intelligence tools for effective decision making.\n\n DTSTART:20210505T180000Z DTEND:20210505T184500Z LAST-MODIFIED:20240328T112315Z LOCATION: SEQUENCE:0 STATUS:CONFIRMED SUMMARY:602: Data-driven Business Transformations to Enable Enterprise-lev el Insights TRANSP:OPAQUE X-ALT-DESC;FMTTYPE=text/html:
Data projects are inherently interdisciplin ary operations\, and insights gained from wrangling these different aspect s into a cohesive\, actionable strategy offer a template for other organiz ations seeking to excel at business intelligence. Data-driven business tra nsformations can be daunting efforts riddled with multiple interdependenci es and hidden technical debt. This presentation will use an ongoing\, coll aborative 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.
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