Episode 81: [Value Boost] How to Frame Data Problems Like a Decision Scientist

Data science training programs often jump straight into technical methods without teaching one of the most critical skills for project success - problem framing. Without proper framing, data science projects are doomed to fail, right from the start, as data scientists find themselves solving the wrong problems or building models that don't address real business decisions. In this Value Boost episode, Professor Jeff Camm joins Dr. Genevieve Hayes to reveal the specific problem framing framework that decision scientists use to ensure they're solving the right problems from the start, dramatically improving their success rates compared to traditional data science approaches. You'll discover: The medical doctor approach to diagnosing business problems by distinguishing symptoms from root causes [02:09] The critical question that reveals what decisions actually need to be made [04:53] How to turn model "failures" into valuable strategic insights for management [06:24] Why thinking beyond the data prevents you from building technically perfect but business-useless solutions [10:04] Guest Bio Prof Jeff Camm is a decision scientist and the Inmar Presidential Chair in Analytics at the Wake Forest University School of Business. His research has been featured in top-ranking academic journals and he is the co-author of ten books on business statistics, management science, data visualisation and business analytics. Links Connect with Jeff on LinkedIn Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE