A Master Data-Led Approach to CMC Data Strategy 

Written By Preeti Desai, Sr. Manager, Client Success and Colin Wood, Strategy & Solutions Leader, Life Sciences

In the previous blog, we established that we can no longer afford to treat CMC data as something created just for submissions. This data holds immense operational and strategic value for analytics, process optimization, and automated regulatory submissions. But to unlock that value, data quality and structure are paramount. 

We also looked at how implementing a CMC data model — a foundational framework that organizes and links entities such as materials, manufacturing processes, test methods, and experiments transforms fragmented information into an integrated system of scientific truth 

There’s growing enthusiasm in pharma to apply ontologies to CMC (Chemistry, Manufacturing, and Controls) and regulatory data, and for good reason. Ontologies can bring semantic meaning, relationships, and machine-readability to data models. But attempting to use ontologies to cleanse and standardize legacy data is often misguided and inefficient. 

Anyone who’s dealt with legacy data knows how messy it can be: 

• If the material code starts with ‘TMP’, it was temporary and might not be valid 

• Between 2020–2022, we used different naming conventions 

• Batch numbers used to include site codes, but now they don’t 

These kinds of inconsistent business rules are often undocumented, approximated, and full of edge cases. 

Now imagine trying to model all that historical inconsistency into an ontology. You’d have to: 

• Capture every exception, outdated meaning, and local rule 

• Maintain conflicting definitions and overlapping hierarchies 

• Build logic that explains how things used to be, not just how they should be 

This quickly becomes unmanageable and defeats the purpose of ontologies, which are meant to add clarity and meaning, not capture legacy confusion. 

In an era where life sciences organizations are increasingly turning to knowledge graphs, ontologies, and semantic data layers to drive digital transformation, a foundational truth is often overlooked: You cannot infer meaning from data that lacks structural and referential integrity. 

When it comes to structuring CMC data, the two most essential pillars are: 

1. A Blueprint: A data model that defines entities, relationships, and constraints 
2. A Backbone: A governing Master Data Management (MDM) system that ensures the reliability and consistency of that data across sources and lifecycles 

Data Model Without MDM Is an Incomplete Scaffold 

A data model is the abstraction of your CMC domain. It defines: 

• What entities exist (e.g., Batch, Manufacturing Process Step, Test, Stability Study) 

• What attributes they hold (e.g., batch expiry date, test name, manufacturing site code, stability study start date) 

• How they relate (e.g., a drug product is a formulation composed of ingredients, which are substances, manufactured at specific sites) 

But in practice, even the most elegant data model fails without high-quality data that populates it, which is where MDM comes in. 

Without MDM: 

• Entity uniqueness is compromised — e.g., the same material could be listed under 5 different names 

• Hierarchy and versioning are ambiguous — e.g., which version of a manufacturing process step applies to which submission 

• Data provenance is unclear — e.g., is the acceptance criteria for pH range sourced from R&D specs or commercial process validation 

A model without governed data is like a periodic table filled with scribbles. The structure is there, but the contents are unreliable, so no inference can be made. 

Why MDM Without a Data Model Leads to Confusion and Data Debt 

On the flip side, deploying MDM without a foundational data model turns your MDM into a glorified data registry — a collection of fields with no semantic or structural consistency. 

Let’s take an example:
Suppose you’re managing “Packaged Medicinal Product” as a master data domain. Without a model, this could be: 

• A free-text field in ELN 

• A picklist in SAP 

• A coded term in a regulatory XML schema 

• A synonym list held in a reference data system 

Without a model defining the context and relationships — i.e., how Packaged Medicinal Product relates to Manufactured Item, Pharmaceutical Product, strength, route of administration, container, your MDM becomes disconnected fragments rather than a unified source of truth. 

MDM provides governance. The data model provides structure for the data and allows business meaning to be assigned to each entity and attribute.  

They are co-dependent — not optional. 

Only when these two are in place does the third layer — ontologies and knowledge graphs — can begin to generate value. This delivers semantic meaning for the data, allowing richer insights to be inferred from the data.  

The next blog in the series will highlight the growing importance of IDMP in the CMC data model with the recent ICH M4Q updates. We will also cover how organizations can confidently begin layering semantic technologies such as ontologies and knowledge graphs to unlock new capabilities in automation, compliance, and analytics. Stay tuned!