S7 | Ep 7 | From Cost Centre to Revenue Engine: How to Make Data Pay for Itself (and Then Some) with Edward Chenard, Fractional CDAO — Driven by Data: The Podcast

In Episode 7, of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Edward Chenard, Fractional Chief Data and Analytics Officer, where they discuss how data leaders can break free from the cost centre trap and drive measurable, quantifiable business value, having personally delivered over $2.5 billion in revenue across Fortune 500s and start-ups alike, which includes;

How a career in product management at GE laid the foundation for an outcome-first approach to data leadership.
Why not having a seat at the table is not an excuse and why the biggest commercial wins came from several levels below the C-suite.
How to read the type of organisation you are in and choose your influence strategy accordingly.
Why the shift from AI-as-tool to AI-as-strategy matters for how data leaders position themselves now.
Why data teams are correctly perceived as overheads.
Building a personalisation platform at Best Buy that generated over $1 billion in revenue.
Why identifying the metrics the C-suite actually obsess over is the only way to get and keep their attention.
Why delivering what the job description says is the riskiest career move a data leader can make.
How a predictive shipping tool built in four months turned a century-old logistics company into a recognised innovator.
Why agreeing attribution with business stakeholders before the work starts is the only way to get the credit you deserve.
Why the individual contributor mindset of doing what you are told actively works against data leaders when they step into leadership roles.
Why data leaders need to think like intrapreneurs, owning a P&L and speaking the language of finance, VCs, and private equity rather than just tech.
Why a background in international business and theology turned out to be better preparation for data leadership than a technical degree.
Why philosophy and physics majors often outperform computer science graduates in data roles because thinking through problems without solid facts is the real differentiator.
Why IT cultures that lead with process are structurally incapable of delivering transformation.
Why training your team on AI regardless of what the C-suite thinks is a leadership obligation, not insubordination.
How Edwards frameworks for moving data teams from dashboard builders to decision-makers are publicly available.
Why it is human as the loop, not human in the loop, and why AI will quickly expose those who have been faking it.

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