Build Strategy First. Choose Technology That Deserves It

Meet Dylan Anderson

Dylan leads data strategy at Perfusion, a London consultancy that helps organisations turn ambition into measurable results. He began his career in management consulting before moving into data and AI, fascinated by how deeply they shape modern business.

Today, he advises global enterprises on data modernisation, governance, and long-term value creation — helping them connect technical work to commercial goals.

“Your best tech leaders probably haven’t coded for years,” Dylan says. “They’re solving organisational problems, not writing scripts.”

1. Start with strategy, not software

Most struggling transformation programmes share a common starting point: they begin with a software purchase instead of a plan.

Dylan recommends reversing the order. Define the business outcome first, then identify the data and technology needed to reach it. When goals lead, tools follow naturally. When tools lead, teams fall into endless migrations and duplicated effort.

A healthy data stack should be the expression of strategy — not the substitute for one.

2. Listen before you lead

Great data leadership starts with curiosity. Dylan urges leaders to spend time with business teams before prescribing new tools or processes.

“Most problems aren’t technical,” he says. “They’re about alignment and language.”

Stakeholders don’t talk in APIs or pipelines; they talk in customers, revenue, and efficiency. Bridging that gap turns data from a service into a partnership. When teams feel heard, adoption follows — because people trust what they’ve helped to shape.

3. Treat data as a business function

One of the biggest obstacles to a healthy data culture is structure. When data sits entirely under IT, it loses influence.

Dylan argues that data should function as a business enabler, directly responsible for improving customer experience, reducing costs, and driving growth.

The most successful operating models link technical metrics to business outcomes. Success isn’t measured by the number of dashboards delivered, but by how clearly the organisation can decide.

4. Ask for honest technology

When Aaron asks what vendors should do differently, Dylan doesn’t hesitate: “Be realistic about what your product can and can’t do.”

He believes the best partnerships come from transparency. Vendors who admit their limits and help clients design around them build long-term trust.

“Every stack is unique,” he says. “Legacy systems, team structures, odd edges — it’s never one-size-fits-all. The best vendors guide you through that instead of selling more licences.”

Honest technology wins because it earns confidence, not just contracts.

5. Simplify before you automate

AI promises a lot, but Dylan warns that automation without clarity only multiplies confusion.

“The most exciting use of AI right now isn’t chatbots,” he says. “It’s improving data quality and governance — the quiet, essential work that makes everything else possible.”

AI works best on clean, well-structured data. Simplify your models, retire unused assets, document transformations, and then automate. The result is a system that scales with confidence instead of chaos.

Putting it all together

A technology stack that deserves its place is one you can explain in business terms.

Start by understanding the decisions you’re trying to improve. Listen to the people making them. Design for clarity, then choose technology that serves that design.

Teams that work this way might feel smaller, but they perform bigger. They build momentum through trust and make data a genuine advantage instead of an expensive problem.

Your next move

Take an honest look at your current data estate. Which tools genuinely serve the strategy, and which just create noise?
Revisit how your data function fits within the business. Does it sit close enough to decision-makers?

Then rebuild with intent — not by adding more, but by aligning what you already have. Every dataset, dashboard, and pipeline should earn its place by improving how people decide, not by increasing how much data they can collect.

Dig Deeper

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