The Data Strategy Trap: Why "Rubbish In, Rubbish Out" is Rubbish Advice

The Data Strategy Trap: Why "Rubbish In, Rubbish Out" is Rubbish Advice

How to escape the platform paradox and actually deliver AI value that matters

The Comfortable Lie We Keep Telling Ourselves

You all know the scene. An executive meeting, AI on the agenda, and someone drops the line: "Of course, it all depends on having good data. Rubbish in, rubbish out."

Heads nod. Everyone feels wise. Meeting moves on.

And there it is—the most expensive, least effective playbook in enterprise transformation just got triggered.

Here's what's actually happening: "Good data first" is analysis paralysis wearing a business suit.

The Playbook That Always Fails (But Feels So Right)

Watch the next 24 months unfold like clockwork:

• Hire the data czar (Chief Data Officer, Chief AI Officer, or the ambitious "both")
• Pick your platform poison (Databricks, Snowflake, Palantir—doesn't matter which)
• Build the team (data engineers, governance folks, change managers)
• Launch the big infrastructure play (18 months, $10M+, detailed project plan)
• Roll out the compliance theatre (mandatory training sessions everyone forgets)

Two years later? You've got infrastructure. You've got process documents. You might even have a few demo projects that look impressive in steering committee slides.

What you don't have: AI that's actually changing customer outcomes.

Been there?

Why We Keep Getting This Wrong

The trap is seductive because it plays to IT's strengths. Infrastructure projects? We're brilliant at those. Vendor selection? We've got frameworks. Governance rollouts? There's a methodology for that.

Most data isn't valuable enough to deserve gold-plated treatment.

No point polishing every piece of brass when only some of it's actually brass.

The real question isn't "How do we control all our data?" It's "What data actually moves the needle for customers?"

That changes everything.

From Control to Value: Centre and Edge

Forget the old "top-down versus bottom-up" thinking. It's not about executive mandates fighting grassroots rebellion. It's about centre and edge working together.

Centre: Clarity Over Control

Use what you already have: InfoSec assessments, Business Continuity Planning: to answer one critical question: What data, if it disappeared tomorrow, would actually hurt?

Not governance for the sake of ticking boxes. Not because "data is the new oil." Because some information is genuinely critical to operations, safety, or customer delivery.

The result: A clear view of what actually matters, from a whole-of-business perspective.

Edge: Value Over Volume

Get your hands dirty. Map your value streams exploration to shipping, order to delivery, whatever drives your business.

Pick a domain where better data translates to better decisions.

Break it down to Standard Operating Procedures. Every step gets one question: "Do we capture data here? Is it reliable, usable, and does someone actually make better decisions because of it?"

Where data is missing or messy, that's not a governance problem, that's a value opportunity.

The Cultural Shift That Actually Matters

Digital teams can't be tourists in the business.

They can't build solutions FOR operators from the comfort of head office. They need to be WITH users, embedded in the daily grind of work, decisions, and customer calls.

This isn't just about methodology. It's about identity. Centre provides the guardrails. Edge creates the value. Neither works alone.

Beyond the Strategy Document Factory

No one writes an electricity strategy. No one has a breathing strategy document. Yet we're compelled to create AI strategies and data strategies like they're some special category of business activity. AI is the How, you should already know Where to Play and How to Win, now consider the systems that change.

What if the real work isn't documents but making the invisible visible?

• Centre: Clear sight lines on risk and governance
• Edge: Tangible opportunities for customer value
• Throughout: Let value pull you forward, not process push you around

When every workflow runs on data that's trustworthy, useful, and actionable, something shifts. "Rubbish in, rubbish out" becomes irrelevant background noise.

Where Theory Meets Tuesday Morning

The companies that crack this code don't start with platforms, strategy etc, they start with problems worth solving.

They embed digital capability where value gets created, not where the org chart suggests it should live.

They build AI that works in the real world of equipment breakdowns, shift handovers, and urgent customer situations.

Because that's where the gap between AI theory and daily reality actually gets bridged.

Next time someone drops "it all depends on good data" in a meeting, ask them: "Good for what, exactly?" Their answer will tell you whether you're about to fund another expensive platform project—or actually deliver something that matters.

The real work happens where theory meets work. Where customer value gets created. Where AI transforms from PowerPoint slides into systems that people actually use to do better work.

That's the only place transformation actually delivers.