Case Study
A global agrochemical leader was losing $50M annually to duplicated research efforts due to siloed data across 300+ labs and 40+ ERP instances.
DataCatalyst implemented a tailored MDM solution with specialized data masters using our Accelerate methodology and lean expert teams.
Eliminated duplicated research costs, reduced time-to-market by a projected 18 months, improved regulatory compliance, and transformed data into a strategic competitive asset.
A $12 billion global agrochemical and biotechnology leader with over 300 laboratories and 40+ ERP instances worldwide was struggling with significant operational inefficiencies.
Despite their market presence and scientific expertise in critical areas such as genome sequencing, enzyme libraries, protein structures, and product formulations, their ability to innovate and compete effectively was being severely impacted by data fragmentation.
The client identified critical inefficiencies costing them approximately $50 million annually in duplicated research efforts, with multiple laboratories unknowingly working on identical projects such as engineering the same drought-resistant gene.
Their fragmented data landscape was causing new synthetic biology products to take 18 months longer than necessary to reach market. Additionally, inconsistent data was creating regulatory compliance risks, hindering submissions to agencies like the EPA and USDA for genetically modified crop approvals.
DataCatalyst deployed a lean team of experts who worked closely with 40 key stakeholders across R&D, Product Development, Laboratory Management, and Regulatory Affairs to diagnose root causes.
Our team mapped current data flows across numerous source systems, including Laboratory Information Management Systems (LIMS), ERP platforms, and phenotypic data warehouses. This comprehensive assessment revealed that the challenge wasn’t just a technology issue but required a holistic approach to data management.
Using our proprietary Accelerate methodology with an Agile-Hybrid approach, we collaborated with the client to develop a solution that would deliver value incrementally while addressing the complex biological data domains that required specialized taxonomy alignment with industry standards.
We implemented a comprehensive Master Data Management (MDM) solution precisely tailored to the client’s scientific research environment. Our solution included specialized data masters designed to unify critical research assets:
DataCatalyst’s expertise in data management for large enterprises, and our understanding of the intersection between business operations and technology, enabled us to deliver this solution efficiently with a purpose-built team of seasoned experts.
Implementing the MDM solution required significant changes in how the client’s research teams worked with data. This wasn’t simply a technological change – it required researchers to adopt new data documentation practices and adhere to consistent taxonomies for biological entities across all laboratories.
We helped reconcile different scientific vocabularies and nomenclatures used across the global research network, guiding researchers who had previously maintained locally optimized datasets to contribute to and trust a centralized data resource.
Our MDM implementation delivered significant projected results:
Enhanced research collaboration across previously siloed laboratories
The MDM implementation produced lasting change beyond immediate efficiency gains. The client now has a unified data foundation enabling more strategic research prioritization and resource allocation. Their researchers build upon each other’s work rather than inadvertently duplicating efforts, accelerating the pace of innovation.
The improved data capabilities position the company to respond more efficiently to evolving regulatory requirements in the biotechnology space. Most importantly, the organization has transitioned from viewing data as a by-product of research to recognizing it as a strategic asset driving competitive advantage.
This foundational shift opens new possibilities for data-driven decision making across their R&D portfolio.