Chromatography data systems (CDS) and analytical software platforms are rapidly transforming, driven by a shift toward digital labs, automation, and other factors. Once considered isolated data repositories or tools for instrument control, CDS and analytical platforms are now becoming the hub of digital laboratory ecosystems.
In this article, experts from Waters, Shimadzu, and Thermo Fisher Scientific provide insights into the latest trends shaping the evolution of these products and how they can address key laboratory challenges.
Cloud-native, centralized, and scalable analytical data platforms
The three experts we spoke with agree that cloud-backed systems represent a significant shift. Clarence Friedman, portfolio owner, Empower, Waters Corporation, notes that cloud-based architectures for secure, centralized access and global scalability are becoming foundational as CDS evolves from passive repositories into intelligent, integrated platforms at the core of digital laboratories. Friedman explains that this solves key challenges for data integrity and compliance, as “approximately 80 percent of FDA 483s over the last five years flagged data integrity gaps.” Intrinsic platform compliance with FDA 21 CFR Part 11 and EMA Annex 11 helps reduce risks to product quality and patient safety. Friedman also adds that cloud-native CDS provides complete data lineage, enabling some customers to trace data back to the 1990s.
Similarly, Jeff Parish, informatics product manager at Shimadzu Scientific Instruments, highlights the importance of flexible deployment models, particularly regarding where data is stored and how it is accessed. “This might include on-premises or cloud-based options, combined with both physical and virtual environments”, he says. Parish adds that a unified analytical software platform “can be installed across physical and virtual environments and shared in multiple ways to allow access to data from anywhere in the world.”
Dave Abramowitz, senior director, chromatography and mass spectrometry software product management, Thermo Fisher Scientific, frames cloud adoption as a strong industry trend, noting a clear shift toward centralized cloud-backed data platforms that provide secure, global access for analytical data and data management. Abramowitz emphasizes the need for scalable, service-oriented, intelligent CDS and analytical software offerings that can grow seamlessly from a single workstation to a global multi-site enterprise. Centralized deployment ensures everyone runs the same version with consistent performance. Cloud-native enterprise solutions also shift much of the overhead associated with traditional CDS installations (cost and time associated with deploying, validating, upgrading, and maintaining) into centralized management with managed updates, automated deployment tools, and streamlined validation packages.
Takeaway: Cloud-native CDS decisions now affect validation effort, audit readiness, and long-term data traceability—not just IT architecture.
AI-driven automation and the movement toward autonomous analytical workflows
Artificial intelligence (AI) is rapidly transforming how labs process and interpret data. According to Friedman, AI-driven automation accelerates data processing and review, and machine learning enables rapid peak integration and anomaly detection, reducing manual effort and improving consistency. This trend has benefits for workflow efficiency under expanding analytical demands. Friedman explains that pharmaceutical pipelines have doubled from ~12,000 molecules in 2015 to over 23,000 today, and biologics have risen sharply as well. Automation is essential as AI tools have the potential to drastically cut data review times.
Parish identifies AI as a top trend as well. A platform can use included AI models to allow accurate peak integration without any parameters, and can be configured with automated workflows for increased productivity. AI-enhanced workflows span LC, GC, MS, FTIR, UV-Vis, ICP-MS, thermal analysis, particle size, and more. This addresses the challenge of consistency across diverse modalities because all data from these instruments is handled in the same way.
Abramowitz explains the broader vision, with rapid progress toward highly automated, analytics-rich platforms that minimize manual review, standardize interpretation, and employ AI for system monitoring, data flow validation, proactive predictions, and faster decision-making, eventually leading to autonomous use of analytical instrumentation. This helps address long-standing issues such as method variability and analyst-to-analyst differences.
Takeaway: AI-enabled CDS can relieve review bottlenecks and staffing pressure, but only if workflows, oversight, and data governance are clearly defined.
Interoperability, open standards, and unified digital science architectures
Interoperability is now a cornerstone of CDS innovation. Friedman explains that future-ready systems adopt open standards, API (application programming interface)-first designs, and FAIR principles, making data findable, accessible, interoperable, and reusable across diverse instruments.
Parish elaborates on the shift toward seamless data exchange between analytical software packages. Rather than using LIMS to collate and analyze data from multiple software packages by importing data into tables in a database, new approaches convert many types of data into a common format that allows querying all data across sources. Other approaches like Allotrope (a scientific data standards initiative) create a common language (taxonomy) for data.
This trend helps overcome the challenge of interfacing with other software systems, as a consistent data architecture enables multiple export and conversion options, allowing the platform to interact smoothly with a wide range of analytical applications.
Abramowitz emphasizes the operational implications. Labs face growing pressure to connect their people, instruments, and enterprise data through a single interface, reflecting a broader move away from disparate tools and siloed data. “Today’s solutions cannot just control instruments or perform basic data analysis. They must operate as a broader digital science platform that integrates LIMS, analytical applications, chromatography and mass spectrometry (MS) workflows, predictive analytics, proactive maintenance, and partner collaboration.” Adding that “by providing robust APIs, standardized interfaces, and enterprise connectors, integrations with digital science platforms create a consistent, interoperable digital workflow across the entire analytical lifecycle.”
According to Abramowitz, this approach helps overcome the issue of data fragmentation caused by multi-vendor instrument fleets. These instruments each have their own software, file formats, storage locations, and security models. A unified CDS and analytical platform provides an integrated software environment for chromatography and MS with centralized data storage, so scientists no longer hunt across PCs, network drives, or instruments. This also supports enterprise-wide connectivity. Legacy CDS often have limited APIs or fragile custom integrations that block communication between LIMS, ELN, laboratory execution system, enterprise resource planning, and automation platforms. Modern platforms overcome this with standardized, enterprise-grade connectors.
Takeaway: Interoperability determines whether analytical data becomes an enterprise asset or remains locked in instrument-specific silos.
Strengthened data integrity, compliance, and end-to-end auditability
Compliance has shifted from a specialized requirement to a universal CDS design principle. Parish identifies applying the principles of FDA 21 CFR Part 11 beyond chromatography as a major trend. This guidance provides best practices for electronic records and signatures and ensures proper data tracking that enforces good scientific practices.
According to Parish, a unified analytical software platform overcomes the challenge of consistent application of audit trails and security models across diverse instruments. The platform handles logging in, audit trails, electronic signatures, and data management in the same way for all instrument types.
Friedman emphasizes that strengthened compliance features are now fundamental to lab operations. Long-term traceability and complete data lineage are very important, and “CDS platforms enable labs to shift from reactive compliance to predictive, intelligent data stewardship—driving faster decisions, safer therapeutics, and smarter science.”
Abramowitz reinforces that modern regulatory expectations require compliance to be engineered into analytical systems from the beginning. Platforms must be designed so that “compliance, data integrity, and secure global access are core design principles, not optional add-ons…unifying digital science solutions and lab automation under one interconnected, interoperable platform.” This emphasis on compliance-by-design helps avoid the inconsistencies that arise when compliance controls differ across instruments or workflows.
Takeaway: Compliance-by-design CDS platforms reduce audit risk and rework by enforcing consistent controls across instruments, users, and sites.
As CDS platforms evolve, lab managers must evaluate how cloud, AI, interoperability, and compliance work together—not in isolation—to support reliable, scalable operations. The most effective systems are those that reduce complexity over time, align with regulatory expectations, and adapt as labs expand in scope, scale, and connectivity.
