The Data ROI Blueprint: Turn Data Costs into Defensible Assets

Most data functions don’t struggle for lack of tools, people or data. They struggle because nobody can draw a clear line from what the team does to what the business earns. More software, more hires and a bigger data lake rarely close that gap, and often widen it. Over four weeks, we fix the structure underneath: what data you invest in, when you hire, how technical results translate into business value, and how the team is organised.

I draw on more than a decade building and leading data functions: founding the data science practice at Go1, rebuilding experimentation and product analytics at Xero, and starting the Search Quality function at SEEK. Each week works on your own organisation. You’ll finish with an operating plan for your data function that shows what it costs, what it returns, and how it will be built from here, written so your CEO and CFO can follow it.

Who this is for

• Heads of data or analytics and CDOs whose function is questioned at budget time, or who can’t yet state its return in a sentence with a number in it.

• CEOs, CPOs and founders of growing companies deciding when to hire data people, whom to hire first, and how to organise them.

• Data leaders whose team is busy but not clearly valued: buried in ad hoc requests, or producing dashboards that few people use.

Who this isn’t for

• Leaders whose main challenge is getting specific AI systems into production and governed. The AI Industrialist is built for that.

• Senior individual contributors aiming for their first leadership role. Start with The Plateau Breaker.

• Data functions whose value is already clear and well funded. You may not need this yet.

Many organisations pour money into event data (clicks, transactions, logs) and neglect the reference data that gives those events meaning: who the customer is, what the product is, where it happened. Without that, event data is expensive to store and hard to use. This week we audit what data you hold and where your spend goes, separating the “things” (customers, products, locations) from the “activities” (what those things do). You’ll leave with a map of your core data assets, showing where investment is wasted on unlinked events and where better reference data would pay off fastest.

A common and costly mistake is hiring data scientists before the product can support them, like hiring a top chef before you’ve built the kitchen. This week we assess how mature your product and data really are, and decide what you need now: user research, solid reporting, or advanced modelling. Then we sequence hiring and infrastructure so investment follows real demand instead of running ahead of it. You’ll leave with a phased hiring and infrastructure plan tied to milestones in your product.

Model accuracy and forecast error mean little to a CFO. This week we build the bridge: a value tree that links what your data team measures to the revenue, cost and retention numbers the business runs on, across your whole portfolio of data work. We’ll also cover the experiment design mistakes that make results look better or worse than they are. You’ll leave with a value tree for your data function and a short list of the initiatives that move the business most, which is the basis of your budget case.

Centralised data teams drift away from the business. Fully embedded data people lose standards and career paths. This week we design a hub-and-spoke model: a central team that owns standards, tooling and hiring, with people embedded in the product and business teams they serve. We’ll also cover what keeps strong, research-trained people: interesting problems, clear decision rights and room to grow, not office perks. You’ll leave with an operating model, clear decision boundaries between the hub and the spokes, and a retention plan for your key people.