Most data scientists follow the same predictable process: gather requirements, collect data, build models, and only at the very end create visualisations to communicate results. This traditional approach seems logical, but what if it's actually working against us?

In this episode, David Cohen joins Dr. Genevieve Hayes to reveal how flipping the script on data visualisation - moving it to the beginning of projects rather than the end - can dramatically improve stakeholder buy-in and project success rates.

This episode reveals:

1. Why the traditional bottom-up data communication approach often misses the mark [02:36]
2. How moving visual storytelling to the start of a project can transform stakeholder engagement [06:40]
3. The gamified workshop framework that turns requirement gathering into collaborative problem-solving [08:50]
4. The counterintuitive first step that immediately improves data project outcomes [20:28]

Guest Bio

David Cohen is a data and AI strategy consultant, with a background in supporting the F500 clients of both Big 4 and boutique consulting firms. He is the founder of Superposition, a consulting firm that builds collaborative workshops focused on data & AI-related use cases.

Links

• Connect with David on LinkedIn
• Superposition website
• Superposition YouTube channel
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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:

1. The medical doctor approach to diagnosing business problems by distinguishing symptoms from root causes [02:09]
2. The critical question that reveals what decisions actually need to be made [04:53]
3. How to turn model "failures" into valuable strategic insights for management [06:24]
4. 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

Most data scientists have never heard of decision science, yet this discipline - which dates back to WWII - may hold the key to solving one of data science's biggest problems: the 87% project failure rate. While data scientists excel at building models that predict outcomes, decision scientists focus on modelling the actual business decisions that need to be made - a subtle but crucial difference that dramatically improves success rates.

In this episode, Prof Jeff Camm joins Dr. Genevieve Hayes to explore how decision science approaches problems differently from data science, why decision science approaches lead to higher success rates, and how data scientists can integrate these techniques into their own work.

This episode reveals:

1. The fundamental difference between modelling data and modelling decisions [04:12]
2. Why decision science projects have historically had higher success rates than current data science efforts [10:42]
3. How to avoid the "ill-defined problem" trap that kills most data science projects [21:12]
4. The medical doctor approach to understanding what business problems really need solving [22:28]

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