CTOs & Heads of Data - Biggest Contributors to Infrastructure Costs

I’m working on a new research paper around a question I think deserves more attention "What are the biggest cost contributors in modern data infrastructure?" Cloud data platforms have made it easier than ever to scale. But they’ve also made costs much harder to understand. Compute, storage, pipelines, architecture decisions, idle resources, inefficient workloads, governance... the bill can grow in places teams don’t always expect. Rather than writing another paper based purely on reports and benchmarks, I want to hear from the people actually dealing with this every day. So I’m interviewing CTOs, Heads of Data, Data Engineering leaders, architects, and other industry peers to understand what’s really driving infrastructure costs inside their organisations. What surprised you? Where do you see the most waste? And what actually helped you bring costs under control? If you work closely with data infrastructure, I’d love to include your perspective. Would you be open to a 30-minute conversation with me? Comment “Interested” below or send me a message, and I’ll reach out. Hopefully, we can turn the collective experience of the industry into something genuinely useful for data teams.