Supercharging Chromatography Workflows with FAIR Data Use Cases

Table of Contents

Introduction

In partnership with Astrix, ZONTAL recently hosted a webinar exploring how organizations can modernize chromatography workflows through FAIR data principles.

Led by Kelsey Boyle, Director of Product Management at ZONTAL, the session focused on a growing challenge across life sciences: how to manage increasingly complex chromatography data while ensuring it remains usable, comparable, and accessible across systems, teams, and time.

What emerged was a clear theme—chromatography is foundational to R&D, but the way its data is managed has not kept pace with the demands placed on it.

The Growing Complexity of Chromatography Data

Chromatography sits at the center of scientific discovery. It supports everything from early-stage research to regulatory submissions, method validation, and product characterization.

But as techniques evolve, so does the complexity of the data. Larger datasets, more advanced instrumentation, and a wide range of proprietary formats have created an environment where data is no longer just growing—it is fragmenting.

Across organizations, chromatography data is often distributed across ELNs, instrument software, file systems, and legacy platforms. Add in global operations, regulatory requirements, and mergers and acquisitions, and the challenge becomes even more pronounced.

The issue is not a lack of data—it is the inability to unify and use it effectively.

Why FAIR Data Matters

This is where FAIR data principles come into focus.

FAIR—Findable, Accessible, Interoperable, and Reusable—provides a framework for ensuring that data is not only stored, but structured in a way that enables it to be used across systems and over time.

In chromatography workflows, this becomes particularly important. Scientists need to compare results across instruments, replicate methods across sites, and validate outcomes in a consistent way. Without standardization, even simple comparisons become time-consuming and error-prone.

FAIR data shifts the conversation from “where is the data?” to “how can we use it immediately?”

From Siloed Workflows to Connected Systems

A recurring theme in the session was the fragmentation of chromatography workflows.

Methods may live in one system, instrument data in another, and results in yet another. When teams attempt to replicate a method or validate results across different environments, they often encounter inconsistencies driven by differences in software, hardware, or configuration.

This creates friction in areas where precision is critical.

By centralizing data into a unified platform, organizations can begin to connect these workflows. Methods, results, and metadata become part of a single, searchable ecosystem rather than isolated artifacts.

The impact is immediate—faster access, better comparability, and reduced dependency on manual processes.

Enabling Digital Method Transfer

One of the most practical challenges highlighted in the webinar is method reproducibility.

When methods are transferred between sites, partners, or contract organizations, even small differences in instrument configuration can lead to variation in results. Traditionally, this requires significant manual effort to troubleshoot and align.

A digital approach changes this dynamic.

By standardizing methods and normalizing parameters across instruments, organizations can enable consistent execution regardless of location or vendor. Methods become portable, comparable, and easier to validate.

What was once a manual, iterative process becomes streamlined and repeatable.

Unlocking the Value of Historical Data

Chromatography data is not just relevant in the moment—it plays a critical role in intellectual property, regulatory submissions, and long-term research.

Yet much of this data remains locked in legacy systems or scattered across environments.

When brought into a unified, FAIR-aligned platform, this data becomes searchable and usable. Scientists can identify prior experiments, legal teams can access supporting evidence, and organizations can reduce the risk of missing critical information during audits or litigation.

In some cases, this visibility surfaces insights that were previously inaccessible—simply because the data could not be found.

Navigating Mergers and Acquisitions

The challenge becomes even more complex during mergers and acquisitions.

Organizations inherit not just new teams, but entirely new data ecosystems. Multiple ELNs, different chromatography systems, and incompatible formats quickly create operational friction.

Without a unifying approach, companies are forced to maintain multiple systems indefinitely, increasing cost and complexity.

By consolidating data into a single platform, organizations can preserve historical knowledge while enabling new teams to access and build upon it. This creates continuity across the organization, even as systems and structures evolve.

Advancing Research Through Data Reuse

At its core, chromatography is about insight—understanding compounds, validating methods, and supporting discovery.

But that insight is limited when data cannot be easily reused.

With standardized, FAIR-aligned data, researchers can compare results across vendors, overlay chromatograms, and analyze outcomes without returning to original instrument software. Data becomes a foundation for further analysis rather than a static output.

This not only accelerates research but also supports emerging initiatives in AI and machine learning, where accessible, well-structured data is essential.

A Shift Toward Unified Data Platforms

The broader takeaway from the session is a shift in how organizations think about data.

Rather than solving individual use cases in isolation—archiving here, integration there, analysis somewhere else—there is a move toward unified platforms that support the entire data lifecycle.

From collection and integration to preservation and reuse, the goal is to create a single source of truth that can serve multiple stakeholders across the organization.

This approach reduces complexity while increasing the overall value of data.