From Fragmentation to Integration: Single Interface Workflows with ZONTAL Operations

Laboratory workflows today are often fragmented across multiple systems, tools, and data formats—creating inefficiencies, increasing risk, and limiting the ability to scale digital transformation. In this webinar, experts from ZONTAL and their partner Syngenta explore how integrated, single-interface workflows can address these challenges.

The session highlights how disconnected planning, execution, and documentation processes introduce manual data movement, poor traceability, and operational inefficiencies. Through real-world examples and demonstrations, the webinar showcases how ZONTAL Operations enables seamless, end-to-end workflows built on standardized, FAIR data principles.

The Challenge: Fragmented Laboratory Workflows

Modern laboratories rely on a wide range of digital tools, including ELNs, LIMS, instrument control software, and analysis platforms. While each system serves a specific purpose, they often operate in isolation, creating significant gaps in interoperability.

As a result, scientists must manually transfer data between systems, navigate multiple user interfaces, and reconcile information stored across decentralized locations. This fragmentation not only slows down workflows but also increases the risk of transcription errors, data loss, and compliance issues.

Additionally, proprietary data formats limit accessibility and reuse, making it difficult to perform advanced analytics or integrate AI-driven capabilities into laboratory operations.

Understanding the Interoperability Problem

At the core of these challenges is the lack of interoperability between digital assets within the lab.

Critical components such as samples, procedures, instruments, results, and analytical insights are often distributed across different platforms, each with its own data structure and interface. This creates a fragmented data landscape where no single system has full visibility into the workflow.

Without a unified framework, laboratories must rely on manual processes or custom-built integrations, both of which are difficult to maintain and scale. These inefficiencies become increasingly problematic as organizations attempt to modernize and automate their operations.

The Role of FAIR Data and Standardization

To address fragmentation, the webinar emphasizes the importance of standardizing data into a vendor-neutral, FAIR (Findable, Accessible, Interoperable, Reusable) format.

By transforming proprietary data into structured, interoperable models, organizations can unlock their data from individual systems and enable seamless integration across platforms. This approach not only improves accessibility but also ensures long-term usability and future-proofing of data assets.

Standardization also lays the foundation for advanced analytics and AI applications, allowing organizations to derive greater value from their data without extensive preprocessing or transformation.

The Solution: ZONTAL Operations

ZONTAL Operations introduces a unified workflow management layer that integrates all digital lab assets into a single, coherent system.

Built on industry standards such as ISA-88 and ISA-95, the platform models laboratory processes in a way that connects samples, procedures, instruments, and results into a fully interoperable workflow. This eliminates the need for manual data movement and reduces reliance on multiple user interfaces.

Instead of switching between systems, scientists can plan, execute, and document experiments within a single interface—often directly embedded within their existing ELN. Behind the scenes, ZONTAL automates data handling, standardizes formats, and ensures that all information is captured with full context and traceability.

From Manual Processes to Automated Workflows

The transition from fragmented workflows to integrated operations delivers immediate and measurable benefits.

Previously, analysts were required to manually transfer data between systems, interpret different formats, and manage multiple tools throughout the experiment lifecycle. With ZONTAL Operations, these steps are automated, enabling a streamlined workflow that can be executed in just a few clicks.

Data is automatically captured, standardized, and returned to the appropriate systems without manual intervention. This not only improves efficiency but also enhances data integrity, reduces errors, and ensures compliance with GxP requirements.

Real-World Application: Syngenta Use Case

The webinar includes a practical example from Syngenta, illustrating how integrated workflows can transform day-to-day laboratory operations.

Initially, Syngenta faced challenges with decentralized data stored across local systems, limited accessibility, and inefficient data sharing processes. By implementing ZONTAL Operations, they were able to centralize their data, standardize formats, and enable seamless integration between systems.

Chemists can now initiate analytical requests directly from their ELN, with data automatically flowing through instruments, analysis tools, and back into the system without manual input. This significantly reduces complexity while improving speed, consistency, and usability across workflows.

Enabling Scalable, AI-Ready Laboratories

Beyond operational efficiency, integrated workflows create a foundation for advanced analytics and AI.

By ensuring that all data is standardized, contextualized, and centrally managed, organizations can more easily apply machine learning models, perform large-scale analyses, and automate decision-making processes. This enables a shift toward more intelligent, data-driven laboratory environments.

The ability to connect systems, automate workflows, and maintain full data lineage positions laboratories to scale innovation while maintaining compliance and control.

The Future of Digital Lab Workflows

As laboratories continue to evolve, the move toward unified, data-centric workflows will become increasingly critical.

Single-interface solutions like ZONTAL Operations not only simplify current processes but also enable future capabilities such as closed-loop experimentation, autonomous workflows, and AI-driven optimization. By reducing complexity and improving interoperability, organizations can accelerate their digital transformation journey and unlock the full value of their data.