Data Harmonization & Normalization: The Real Prerequisite for Scalable Manufacturing Intelligence

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Sergio Hernández is AG Solution's Consultancy Director with years of experience managing digital transformation and MES global sites projects. In this article, he's sharing his thoughts on why data fragmentation is the main obstacle to achieve scalable value from digital manufacturing transformation.

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Everyone's talking about Model Context Protocols and the 5th Industrial Revolution. AI agents managing production lines. Autonomous plants makereal-time decisions. The promise is compelling.

But here's what I keep seeing: manufacturers chasing these advanced capabilities while their data foundation crumbles beneath them.

You can't deploy AI agents across multiple plants when Plant A calls a cleaning cycle "Pre-clean" and Plant B logs it as "System Wash." You can't benchmark performance across geographies when temperature readings come in Fahrenheit from Texas and Celsius from Munich. And you certainly can't build autonomous operations on top of semantic chaos.

The uncomfortable truth? Data fragmentation remains the biggest obstacle to extracting real, scalable value from digital manufacturing transformation.

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Why This Affects Everyone (Not Just the Digitally Immature)

Whether you're launching your first MES project or managing a global rollout across multiple sites, the challenge persists. Without harmonized and normalized manufacturing data, you can't scale, compare, automate, or reuse anything meaningfully.

This isn't just about better dashboards or cleaner KPIs. It's about deploying digital applications at scale:

• AI agents that work across multiple assets and plants
• Performance benchmarking that actually compares like with like
• Reusable digital workflows for golden batch optimization, OEE tracking, energy management
• The resilient, interoperable data layer that autonomous operations require

Most manufacturers discover this reality the hard way.

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The Semantic Problem Hiding in Plain Sight

Here's where terminology matters more than most realize.

Normalization ensures consistency in format and structure. It's the technical preprocessing that improves data integrity and reduces redundancy. Converting all temperature readings to °C. Ensuring time stamps follow ISO format. Essential, but straightforward.

Harmonization aligns meaning across data sources. It's what enables different plants, systems, and suppliers to speak the same language. One site's "Pre-clean" becomes another's "Cleaning" – but harmonization maps both to a shared definition that enables unified logic and analysis.

The real challenge lies in creating a data catalog that bridges local semantics to global standardized meaning.

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The Maturity Paradox

If you've deployed MES globally, you've probably discovered this: enforcing a fully harmonized data model at the source level is nearly impossible.

Historical naming conventions vary by plant. Changes to source systems are risky, costly, and disruptive, with unclear ROI. Each site evolves semi-independently, even when using the same MES vendor.

Ironically, less digitally mature companies have an easier time building it right from the beginning. However, many are still tempted by quick wins and low-hanging MES deployments that lack long-term data model design.

The mature manufacturers face a different challenge: untangling years of semantic drift while keeping production running.

‍Beyond Technology Selection

This isn't about which UNS architecture you've implemented or which cloud provider you chose.

It's about the semantic chaos underneath your operations and your ability to bring meaning to it.

The manufacturers succeeding with advanced applications aren't necessarily the ones with the most sophisticated technology stack. They're the ones who've solved the foundational data modeling challenge first.

What Works: Lessons from the Implementation Front

When helping manufacturers untangle data modeling complexity, these approaches consistently deliver results:

Study your data before your technology. Operations and phases often appear identical but get categorized differently across sites. Audit with that lens first. Map the variants before you try to fix them.

Leverage AI tools for preprocessing acceleration. Don't rely solely on keyword or linguistic matching – it misses too much context. Use models that detect relationships, patterns, and correlations between seemingly different terms.

Let AI reconstruct your workflows. Generative AI can now rebuild production workflows from raw data, clustering operations to reveal alignment opportunities across plants. This makes comparison possible, though a comprehensive harmonization strategy remains the ultimate solution.

Build your semantic model and commit to it. Once you've gathered insights, establish a consistent semantic model aligned with standards like ISA-95. Define your physical model, procedural structure, and terminologies as non-negotiables.

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The Foundation That Changes Everything

Harmonizing and normalizing manufacturing data is complex work. No question.

But once you align terminology and map structures across your operations, everything else becomes a technical execution challenge. The right people with the right skills can solve technical problems.

That alignment is what unlocks AI agents, autonomous plants, and AI-driven operations. Not the latest protocol or platform – the semantic foundation that makes them possible.

The manufacturers preparing for MCP and autonomous operations aren't starting with the technology. They're starting with their data story. And getting that story straight changes everything that comes after.

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