In life sciences organizations a data migration is often treated like a set of technical checklist: Extract. Map. Load. Validate. Go Live! But anyone who has worked on any data migration knows the truth:

Migration isn’t really about moving data. It’s about moving meaning.

Meaning is where things get complicated, especially when organizations underestimate the differences between structured and unstructured data. This is a common challenge we’ve seen with our clients, especially when modernizing .

The Illusion of Data Readiness

Most migration programs begin with confidence. After all, the data exists, it’s stored somewhere, it’s been used for years, so how hard can it be? Then the first mapping workshops begin… the cracks start to show:

• • Submission types or study metadata don’t match controlled vocabularies across regulatory and clinical systems
  • Status fields are full of free text: study milestones, complaint records, or CAPA statuses captured inconsistently instead of following lifecycle states
  • Product hierarchies only make sense to one team due to differing interpretations
  • Critical decisions about safety case assessments, investigation outcomes, or deviations can be, and often are, buried in comments, emails or spreadsheet notes

The issue isn’t a lack of data. It’s that years of business logic are hidden inside unstructured information. Systems can’t interpret or migrate assumptions. Suddenly this becomes a much bigger problem.

What began as a simple mapping exercise quickly becomes an effort to untangle undocumented processes, institutional knowledge, and years of historical workarounds.

Don’t worry, you are not alone. We’ve seen firsthand how quickly complexity multiplies once that hidden logic is uncovered.

Structured Data is Predictable. Unstructured Data is Personal.

Understanding the difference between structured data and then unstructured information surrounding it is critical. Structured data is easy. It lives in defined fields, it supports reporting, it drives workflows, and it can be migrated using clear, defensible mapping rules.

Unstructured data is different. It contains insight, often extremely valuable insight, but it is hidden within narrative, interpretation, and context. It hides in documents, trackers, comments, file names, or “temporary” excel sheets that quietly became the system of record.

In many life sciences organizations, the most important regulatory knowledge isn’t stored in the system. It’s stored around the system. And that’s where migrations become truly challenging!

A Simple Example

Here’s a simple, yet very common, example. Consider a site selection tracker used in a clinical program. On the surface, it looks structured. Why? The data appears well-structured:

• • Clearly defined columns (STUDY_NUMBER, SI_COUNTRY, SI_SITE, SI_SITE_STATUS)
  • A reason field (DC_REASON_NON_SELECTION)
  • Consistent row-based records

From a migration perspective, it looks like a straightforward mapping exercise.

Why It Isn’t Really Ready

Once you examine the content, key information lives in unstructured comments.

Structured fields contain conflicting information. In Record 2, SI_SITE_STATUS is selected as “Ongoing” yet the DC_REASON_NON_SELECTION field lists “Low recruitment projections.” If the site is indeed Ongoing, it raises a logical question: why does a Reason for Non-Selection exist at all? This suggests the fields are being used inconsistently, without validation rules in place, or that there is not a clearly enforced process for system use (if it is even defined). As a result, this record contains conflicted structured data, making it challenging to determine the true state of the site without manual investigation. Remember when we thought this would be a straightforward mapping exercise?

Important decisions are hidden in narrative text. In Record 3, the DC_REASON_NON_SELECTION comment is ““Site has no psychologist and stated it would be difficult to organize one. Site declined participation.” The structured field says “Does not have required staff” but the comment contains additional operational logic: it specifies a missing role (psychologist), identifies a feasibility constraint, and captures the site’s final decision. A target system cannot easily interpret or structure that information.

Multiple concepts are embedded in a single field. The comment in Record 3 actually contains several data elements all collapsed into a single free-text field:

Structured fields are left blank. While the explanation appears in comments! Record 4’s DC_REASON_NON_SELECTION field is blank, but the comments indicate variations like, “Site lacks sufficient experience”, “Investigator has limited trial experience”, and “Site has not conducted similar studies”.

To a human reader, these records make sense (mostly, I am looking at you, Record 2). To a system, they are entirely inconsistent. When migrated into a structured platform, Record 3 will be reportable and visible in analytics. Record 4 will not.

This is how unstructured behavior quietly undermines structured intent. This is exactly where migrations become more than technical exercises. They become exercises in data governance.

Target Systems Expose Legacy Exceptions

Today’s modern enterprise platforms (QMS, CTMS, RIM), safety databases, or regulated content management systems are built to enforce structure. They require:

• • Controlled vocabularies
  • Mandatory fields
  • Valid relationships
  • Consistent master data
  • Full traceability

That’s a good thing: it improves compliance, visibility, and efficiency. But it also reveals an uncomfortable truth: your legacy systems don’t just store data; they also store a legacy of exceptions. Those exceptions were often managed manually by experienced people, not by processes. When you migrate into a structured system, those manual workarounds suddenly have nowhere to hide.

The Real Risk: Migrating Years of Clutter

In regulated environments there’s an instinct to migrate everything “as-is.” Teams don’t want to lose audit trails, history, or critical business context. That instinct is understandable and logical. But migrating unstructured data blindly into a structured system creates a new problem: a modern platform filled with legacy clutter.

From the moment the system goes live, clutter and data exceptions affect user trust and efficiency. Technically, everything works, but users hesitate. They double-check. They export to Excel “just to be safe”. Slowly, spreadsheets start creeping back in. The migration succeeded on paper, but failed in adoption, and is no longer the trusted source of truth.

Poor data structure also prevents organizations from taking advantage of the AI and analytics capabilities built into most modern and emerging platforms. When critical information is buried in comments or captured inconsistently, systems cannot identify trends, recurring issues, or relationships between records, such as patterns across complaints, deviations, safety events, or site performance. Without structured data, the platform cannot surface insights or enable automation, reducing it to little more than a repository rather than an intelligent operational tool.

The Mindset Shift That Ensures Data Readiness

The most successful migration programs don’t ask: “How do we move everything?” They ask: “What do we need to trust on Day One?” That subtle shift changes everything, leading to smarter, more strategic decisions.

Not everything needs to become a structured field in your target system, but the meaning behind the data must remain clear and remain interpretable. One practical way we approach this with our clients, regardless of their volume of data, is by separating data into three categories early on.

1. 1. Workflow-critical and compliance-driving (must be structured)
   2. Contextual but still valuable (evaluate and transform selectively)
   3. Historical reference (archive with traceability)

Of course, there are other valid approaches, and each one has strengths within specific situations. The right strategy always depends on the organization, its risk profile, and its future operating model. The key is to make conscious decisions rather than defaulting to “migrate it all.”  Successful migrations treat the effort as a business transformation, not just a technical transfer.

A Simple Decision Framework for Data Migration

Here’s a simple principle that we’ve found is a great starting point:

1. 1. If data drives a workflow, reporting, or compliance decision, it must be structured.
   2. If data provides useful operational context, evaluate it and structure it selectively.
   3. If data provides historical reference only, it belongs in documents or a well-managed archive.

Final Thought: Don’t Just Migrate. Modernize.

Migrations rarely fail because of technology. They fail when unstructured legacy knowledge is assumed to be structured truth. Don’t fall into that trap by starting with tools and mapping spreadsheets. Start with the harder questions: What is our source of truth?  Which data do we trust enough to run the business on?

Life sciences organizations are moving toward greater automation, AI-driven insights, and real-time regulatory visibility. None of that works without reliable, structured data. The migration process should force organizations to face those questions, especially if they have been avoided for years. Once they are answered honestly and completely, migration stops being just a technology project. It becomes a transformative step toward true digital maturity.

By assessing your data landscape before you start your migration, you’ll be able to define clear ownership, and build a pragmatic strategy for what to structure, transform, or archive. Instead of a system replacement, you’ll have the opportunity to strengthen compliance, improve transparency, and lay the foundation for automation and AI.

Get Started the Right Way

We know it is tempting to jump straight into platform selection and implementation, but modernizing any complex, regulated environment will not be a simple ‘lift and shift’ to a new technology. If you try to approach it that way, you will delay your progress, multiply your workload, and fail to realize the full potential of your target system. After over two decades helping clients navigate technology transfers, fme’s experts can guarantee that “one-click export” you are promised is a beautiful story told by people who have never had to do or pay for the actual work.

Start your modernization initiative correctly with a clear and detailed analysis of your existing data and document repositories. You’ll quickly discover proprietary data formats and custom-built applications that are incompatible with modern databases and tools, siloed systems with limited integration capability, data consolidated from multiple sources with inconsistent data structures and standards, and legacy systems with redundant or incomplete data and metadata.

fme’s Migration Readiness Evaluation

fme’s Migration Readiness Evaluation service is specifically designed and proven to provide proactive guidance on your data and document landscape BEFORE embarking on extensive solution deployments. We take a hard look at the source system’s technical stack: how it was built, how accessible it is, what’s custom, what’s obsolete, and how long it’s realistically going to take to get clean, validated data out of it.

Instead of being caught unaware during your deployment, we identify potential risks and common pitfalls in a Migration Readiness Report that summarizes the current state and quality of your data and documents. We also offer Risk Mitigation Recommendations with proactive solutions to minimize potential risks and ensure you stay on time and on budget.

Download this datasheet to learn the details and benefits that fme’s Migration Readiness Evaluation can provide, and then contact us to discuss your unique challenges. We’d love to help you on your transformation journey.

About the Author

Wendy Gilhooley
Wendy Gilhooley has over 25 years of global experience delivering leading edge IT-related services and solutions in highly regulated industries. For the last 15 years Wendy has been focused on the complex challenges of life sciences firms struggling to modernize their Regulatory Information Management (RIM) systems. Her depth of knowledge allows her to bridge the gap between business and IT teams, and provide a wealth of technical and industry best practices to increase client business value and deliver measurable results.