Many organisations believe they are data-driven because they have dashboards, warehouses and data scientists. The infrastructure exists, the capability exists and yet data teams often struggle to connect to the business’ larger objective and as a result, fail to create impact.

The issue is rarely skill. It’s alignment.

When data operates adjacent to product and commercial teams, it can slide into a reporting function. When it shares purpose and accountability with them, it becomes a decision making partner.

Over time I’ve seen this play out in different ways. The structure varies, but the pattern is consistent: alignment determines whether a data team reports on the business or helps steer it.

The three data team archetypes

1. The reporting factory

Hidden away in a back corner, often reporting into a CFO or COO, this team runs on requests. Product, marketing or operations raise tickets and the data team responds with dashboards or analysis. Success is measured by turnaround time and output volume.

They are busy and often technically competent. But they are reactive. They describe what happened rather than shaping what happens next.

At scale, this becomes a service desk or kanban model. Insight is produced, consumed and forgotten.

2. The research lab

At the other end is the research-heavy team. Strong modelling skills. Long experimentation cycles. Deep analytical capability.

The challenge here is cadence and accountability. Product thinks in quarters. Engineering thinks in sprints. Research often operates on exploratory timelines that don’t neatly align.

Without clear commercial ownership, experiments drift. Research cycles extend. The work may be intellectually impressive but is disconnected from measurable outcomes.

3. The aligned value partner

The most effective model I’ve seen isn’t defined by org chart. It’s defined by shared intent and coordinated execution.

Data teams may operate in centralised, embedded or hybrid models. The structure matters less than whether they share the same product and commercial objectives and are accountable for the same outcomes.

But alignment doesn’t stop at goals. It has to extend into delivery.

If a data initiative influences a feature release, a pricing change or a growth experiment, it must be integrated into the same delivery rhythm as the teams implementing it. That often means sprint alignment, shared checkpoints and participation in roadmap trade-offs.

Exploratory work still requires flexibility. Not every model fits neatly into a two-week sprint. But exploration cannot be open-ended. It needs decision points tied to real initiatives and visible milestones.

In high-performing organisations, data work is neither isolated nor blindly forced into engineering rituals. It is deliberately integrated where impact depends on timing and structured where depth is required.

Shared goals define direction. Coordinated delivery ensures contribution.

Injecting Product Thinking into Data

The real shift isn’t technical. It’s introducing product thinking into how we work with data.

A reporting mindset asks:
“What data do you want in your report?”

A product mindset asks:
“What decision are we trying to improve?”

In one organisation I worked with, the data team operated largely on instinct. They were capable and proactive, but without product ownership or defined commercial objectives. They built reports for the business and features for customers they believed might add value.

There was no shared roadmap. No clear success metric. Insight was produced, but it wasn’t anchored to a product decision or commercial goal. Adoption was low because the work wasn’t tied to a defined outcome.

The turning point came when we introduced product discipline into the function. Work was aligned to specific initiatives such as improving conversion, launching premium features and increasing engagement across key segments. Every piece of analysis had a decision attached to it. Every initiative had a measurable target.

The conversation changed. Instead of asking what we could build, the team started asking what we were trying to move. Their work stopped being analytical output and began shaping the direction of the business.

Measuring the Right Things

If you measure a data team by dashboards delivered, you will get dashboards.

If you measure them only by model accuracy, you will get increasingly sophisticated models that may lag behind business priorities.

We’ve seen this pattern before in engineering. Volume is not value.

If you want your data teams to drive impact, measure impact instead:
• Experiment cycle time
• Customer adoption of data-driven features
• Uplift attributable to intelligence initiatives
• Revenue influenced by data-enabled products
• Reduction in manual decision-making

These metrics tie data capability directly to business outcomes.

Technical excellence still matters. Governance still matters. But they are enablers, not endpoints.

AI Raises the Stakes

As AI becomes more accessible, these structural choices matter even more.

Access to models is no longer the differentiator. What differentiates organisations is how quickly proprietary data can be translated into product behaviour.

Without product thinking, AI becomes another isolated experiment or reporting layer. With alignment, it becomes smarter ranking, improved intent prediction or measurable conversion uplift.

AI doesn’t replace the need for discipline. It amplifies it.

If data teams are not connected to business objectives, AI initiatives drift. If they are aligned, intelligence compounds into advantage.

It’s Not About Org Charts

It’s tempting to treat this evolution as a structural problem. Centralise. Decentralise. Add a product owner.

Those decisions help, but they aren’t the core shift.

The real transformation happens when data stops operating as a function that produces answers and starts operating as a function that shapes decisions.

Data maturity isn’t about tooling or scale. It’s about whether your data capability has clear purpose and is connected to outcomes.

When that alignment exists, structure becomes secondary and cadence becomes manageable. Data stops operating adjacent to the business and starts influencing its direction.

Without that alignment, dashboards multiply and impact doesn’t.