How Data Teams Scale Project Management Without Slowing Down

Cam Crow, Director of Data and Analytics at Vacatia, joins The Tech Trek to unpack what happens when a startup outgrows informal ways of working. This episode looks at how data teams can introduce project management frameworks without killing speed, how to manage stakeholder demand as complexity rises, and why the right operating model matters even more as AI begins to reshape analytics work.

Cam shares a practical view from the middle of real growth, from startup scrappiness to acquisitions, migrations, and a much wider stakeholder base. He explains when process becomes necessary, how to build trust during that shift, and where AI is starting to change both delivery workflows and the future of business insights.

In this episode

• Why early stage teams should add process cautiously, not by default

• The moment speed and quality start breaking under too many competing requests

• How public communication and domain based stakeholder channels reduce friction

• Why planning routines matter as much for stakeholders as they do for the data team

• Where AI fits today, from faster delivery to semantic layers that support better answers

Highlights

00:00 Cam Crowe joins the show to discuss project management frameworks through the lens of data, startup growth, and stakeholder alignment

01:58 Why Cam resisted formal sprint planning in the startup phase and why that made sense at the time

05:58 The tipping point where too many priorities start hurting both velocity and quality

11:49 How moving conversations out of direct messages and into domain channels changed team operations

15:03 Inside the two week development cycle and the planning week that keeps stakeholders engaged

21:08 How Cam is thinking about AI, semantic layers, and the future of on demand analytics

A standout idea from this conversation, process should be added conservatively, only when the business truly needs it.

Practical takeaways

• Do not formalize too early, but do not wait until the system is already breaking

• Make prioritization visible once demand exceeds capacity

• Use shared channels instead of one to one communication to reduce bottlenecks

• Build stakeholder rituals into the operating model, not just team rituals

• Treat AI readiness as an infrastructure challenge, not just a tooling decision

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