How ZONTAL Offers a Scalable Platform for High-Throughput, Complex Scientific Data

According to Building the Next-Gen Pharma Lab: Digitally Connected, Environmentally Sustainable by Capgemini, roughly 93% of science-based organizations identify data volume and complexity as their top data and technology challenges. Managing the data generated during research and development presents significant challenges, which can be viewed through three key lenses: technology, process, and people.

Technology Challenges in Scientific Data Management

A single genomics study or a set of high-resolution images can generate datasets of enormous size. In many cases, the available storage capacity and computational power are quickly overwhelmed by the volume and complexity of data produced in the lab. This data can be structured or unstructured, spanning real-world sources such as imaging and lab data, omics datasets, clinical trial records, research papers, lab notes, and operational data — all of which require effective management. The inclusion of manual records further compounds the challenge.    

• Storage: Conventional on-premise data centers are often costly and lack the flexibility to keep pace with rapidly growing information demands.   
• Computational Power: Advanced analytical methods — such as those used in artificial intelligence for drug discovery — require High-Performance Computing (HPC) environments, which involves significant capital investment.  
• No Unifying Format: Scientific labs collect data from diverse sources — LIMS, ELNs, Scientific Data Management Systems, and numerous instruments from different manufacturers — each producing information in its own proprietary format. Without a unifying standard, data cannot be easily integrated or analyzed, leaving it effectively trapped within technological silos.  

The ZONTAL platform addresses these three challenges as a cloud-based scientific data management system that scales to meet demand by leveraging the cloud’s virtually limitless, elastic storage. It also manages both security and computational loads, enabling organizations to scale their data operations on demand.

Format-agnostic and capable of handling over 400 data types, ZONTAL seamlessly harmonizes data from both legacy and modern systems in alignment with the Allotrope Framework of ontologies and vocabularies. This gives scientists a cohesive, reliable view of their data — and the confidence to trust their findings.

Process Barriers to Scientific Data Integrity and Compliance

Even with advanced technology in place, labs can’t realize the full value of their data if their systems and processes are outdated.  

• Pervasive Data Silos and Data Fragmentation: When outdated systems are in place, data may remain physically isolated within the systems where it is generated. Even when it is not physically isolated, inconsistent formats can prevent the formation of a cohesive picture, making it difficult for scientists to interpret the data or trust in their findings — often forcing them to repeat experiments.  
• The Need for Compliance and Integrity: With public health and trust at stake, science-based companies must navigate a complex regulatory landscape. For example, compliance with GxP (Good Laboratory, Clinical, Manufacturing Practices) and FDA’s 21 CFR Part 11 is mandatory. In environments lacking a unifying format and interoperability, ensuring data integrity across dozens of disconnected systems becomes a formidable task.  

The ZONTAL platform enables organizations to break down data silos and drive digital transformation. By unifying data from multiple sources into universal ontologies, it provides scientists with a complete, trustworthy view of their data. As a GxP-compliant platform, ZONTAL supports adherence to the FDA’s 21 CFR Part 11 and ensures traceability, transparency, and control.

Built on FAIR principles — making data Findable, Accessible, Interoperable, and Reusable — ZONTAL not only accelerates research but also maximizes the return on investment in data generation.  

People and Talent Gaps in Modern Scientific Data Management

With the right technology in place and well-governed processes established, the final element organizations need to maximize the value of data generated during research and development is the right human talent.

• Lack of Specialized Talent: There is a critical shortage of professionals with the necessary hybrid expertise: deep understanding of a scientific domain (such as biology or chemistry) paired with advanced skills in information science, statistics, and machine learning. Without dedicated information specialists, bench scientists face a disproportionate burden, spending most of their time wrangling data instead of focusing on their primary role: experimentation and discovery.
• Lack of Information-Centric Culture: Transitioning from a traditional, experiment-focused culture to one that treats information as a reusable, strategic asset requires a profound organizational shift, led by top management. This shift demands ongoing employee training and the removal of cultural barriers between research, IT, and other business units to foster collaboration.

ZONTAL preempts the need for data wrangling by unifying data into a common standardized format, Allotrope Simple Model (ASM), as a “single source of truth,” eliminating the need for manual data consolidation.

The Way Forward: ZONTAL as a FAIR, GxP-Compliant Data Management Platform  

The ZONTAL platform is a GxP-compliant, cloud-based, advanced data management platform that digitalizes the lab, securely preserves data, and democratizes data analytics. It aligns with the Allotrope Framework, which includes the Allotrope Simple Model (ASM), Allotrope Data Models (ADM), and Allotrope Foundation Ontologies (AFO).

Science-based organizations looking to enhance their operations — or those experiencing growth — must contend with increasing data volumes and complexity. ZONTAL is designed to deliver end-to-end digital transformation, helping organizations tackle these challenges head-on.