Data Modernisation Journey #029

Why I Stopped Selling Data Migrations and Started Building AI-Ready Foundations

Read time: 4 minutes.

Note: I have recently rebranded the newsletter from "The Data Modernisation Playbook" to "Data Modernisation Journey" because I believe Data Modernisation isn't a one-off effort; it's an ongoing journey that can't be contained within a playbook.

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A few months ago, a mentor asked me a question that changed everything:

"Khurram, your clients migrate to modern platforms, but what are they trying to achieve?"

I rattled off the usual answers: faster queries, lower costs, better scalability, cloud flexibility.

He smiled and said: "That's not what they want. They want to use AI to transform their business. Everything else is just infrastructure."

He was right.

After 15 years of focusing on moving data faster, I realised I am solving the wrong problem in 2025. Modern businesses don't want better data platforms; they want AI capabilities. But they can't get there without the foundation.

Today I'm sharing why I've restructured my entire approach around three interconnected pillars: Data, Governance, and AI.

Here's what we're covering:

• Why data modernisation alone isn't enough anymore

• The missing link between your migration and AI success

• My new framework for building AI-ready foundations

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The Pattern Every Client Wants

Here's what I hear in almost every first conversation now:

• "We want to use AI for customer insights..."

• "Can we automate our reporting with machine learning?"

• "How do we build predictive models for our business?"

But when I ask about their data foundation, I get:

• Legacy systems with inconsistent definitions

• No data governance strategy

• Teams that can't agree on basic metrics

The disconnect is obvious: You can't build AI on chaos.

I used to think my job was getting data from Point A (legacy) to Point B (cloud). Now I realise it's getting organisations from Point A (reactive) to Point C (AI-driven); and Point B is just infrastructure.

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Why Data + Governance + AI Must Work Together

Gartner recently revealed that 60% of AI projects running without AI-ready data will be abandoned by next year. Primarily, the reason for this failure is that data governance and AI are not separate issues; they form a single interconnected challenge.

You can't do AI without governance.

Machine learning models trained on inconsistent data produce technically accurate but business-meaningless results. Your "customer churn" prediction is worthless if marketing and finance define churn differently.

You can't govern what you can't access.

Try implementing data governance on siloed legacy systems. You'll spend more time hunting for data than governing it. Modern platforms make governance scalable.

You can't access what you haven't modernised.

Legacy systems weren't built for the volume, variety, and velocity that AI requires. You need modern infrastructure as the foundation.

The insight: Data Modernisation, Governance and AI aren't sequential projects. They're one integrated transformation.

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My New Framework: Foundation → Governance → Intelligence

Instead of selling "migrations," I now design AI-ready foundations.

Here's how the approach has changed:

Phase 1: Modern Data Foundation

We are not just transferring data; we are creating the necessary infrastructure for AI. This includes:

• Real-time data access for machine learning training

• Scalable computing for processing models

• Flexible storage options for both structured and unstructured data

Phase 2: Governance at Scale

This is a challenge that many organisations face. We implement:

• Semantic layers to ensure consistent business logic

• Data catalogues to enhance discovery and lineage

• Quality frameworks that AI can reliably trust.

Phase 3: AI Enablement

Now the magic happens:

• Feature stores for ML-ready data

• Automated model training pipelines

• Real-time inference capabilities

The key difference is that each phase enables the next. You're not just migrating, you're building capabilities.

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What This Means for Your Modernisation Project

If you're planning a data modernisation project, ask yourself:

Are you building infrastructure or capabilities?

Infrastructure gets you faster queries and lower costs. Capabilities get you predictive insights and automated decisions.

Do your teams agree on what data means?

If your finance and marketing teams calculate metrics differently today, they'll have the same problem on your new platform, except now they can create conflicting dashboards faster.

What's your AI goal in 12 months?

If you can't answer this, you're over-engineering your data platform and under-thinking your governance strategy.

My experience: Organisations that start with AI outcomes in mind make completely different architecture decisions than those focused purely on migration.

The Questions I Ask Every Client Now

Instead of "What platform do you want?" I ask:

1. What business decisions do you want AI to help with?

2. Who needs to trust the results?

3. How consistent are your current metric definitions?

4. What happens if your AI model is wrong?

These questions help determine if they require infrastructure, governance, or a combination of both to succeed with AI.

The pattern I've learned is that organisations that can't answer these questions aren't ready for AI, no matter how modern their data platform becomes.

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Here's what I've shared today:

• Modern businesses want AI capabilities, not just better infrastructure. Data modernisation is the foundation, not the destination.

• Data, governance, and AI must work together. You can't skip governance and expect AI to work, just like you can't govern data you can't access.

• Start with AI outcomes in mind. This changes every decision about platforms, architecture, and governance strategy.

My challenge to you: Before your next modernisation project, define what AI success looks like for your business. Then work backwards to the foundation you need.

What I'm curious about: Are you seeing the same pattern? Clients requesting AI capabilities but struggling with fundamental data foundations?

Hit reply and let me know what you're observing. I'm always learning from others' experiences.

P.S. - Next week I'll dive deeper into semantic layers as the governance foundation for AI. Machines need consistent definitions even more than humans do.

P.P.S. - I'm working on a framework for "AI-Readiness Assessment" based on this integrated approach. If you'd like early access when it's ready, reply with "AI READY" and I'll add you to the list.

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That’s it for this week. If you found this helpful, leave a comment to let me know ✊

About the Author

Khurram, founder of BigDataDig and a former Teradata Global Data Consultant, brings over 15 years of deep expertise in data integration and robust data processing. Leveraging this extensive background, he now specialises in organisational financial services, telecommunications, retail, and government sectors, implementing cutting-edge, AI-ready data solutions. His methodology prioritises value-driven implementations that effectively manage risk while ensuring that data is prepared, optimised, and advanced analytics.