Perfect Data Is a Myth. Operational Data Is Reality.

Perfect Data Is a Myth. Operational Data Is Reality.

In manufacturing, changing master data isn't a cleanup task—it's an operational event.

Why Manufacturing AI Starts With Master Data — Not Perfection

There's a lot of talk about perfect data when it comes to AI.

Clean data. Real-time data. Every event logged. Every edge case covered.

All of that matters.

But in practice, something matters more.

Defining your master data and actually running the business on what you already have.

Yes, good data is important. Yes, logging is important. Yes, real-time data is sometimes critical.

But most organizations don't fail at AI because they lack perfect data. They fail because they never decide what data is authoritative, and they wait too long to start.

Once your business is operating on real data—orders, customers, inventory, pricing, operations—you can't just "fix it later" overnight. Changing data definitions impacts systems, people, processes, and trust.

That's reality.

The real progress comes from:

• Clearly defining master data (even if it's imperfect)
• Accepting that your data will evolve while the business runs
• Using what you have today instead of waiting for an ideal future state
• Improving quality incrementally, without stopping the world

AI doesn't need perfection to create value. It needs clarity, consistency, and momentum.

Start where you are. Be explicit about what's "true enough." Keep moving forward.

Perfection can come later—if it ever needs to.

Think this argument fits your event? Tell me about the room — the calendar is selective.