Dotmatics Case Study
Replacing Rigid, Manual Workflows with Agile, Scalable Data Management
A global chemicals and materials company simplifies data processing and enables reuse by standardizing complex test data in Dotmatics Luma.
Background
R&D teams in the chemicals and materials sector face an uphill battle managing the growing volume and complexity of scientific data. While life science vendors often focus on standard formats like NMR or LCMS, materials scientists must contend with bespoke test rigs, multi-day experiments, and highly variable data structures—none of which are well served by rigid or pharmacentric platforms.
At this global specialty chemicals company, decades of macrobased workflows and inconsistent post-processing practices had created a fragmented data landscape. Engineers, chemists, and application teams often relied on siloed, manual processes to analyze results—leading to delays, duplicated effort, and underused insights. The organization needed a future-ready solution that could scale across data types, reduce reliance on custom scripting, and make complex experimental data easier to find, use, and trust.
Challenge
Despite their advanced science, the company struggled with data usability. Scientists were manually updating scripts to handle new test types, patching together macros, and waiting days for results to be analyzed. Large experimental files were cumbersome to process, and poor standardization prevented data from being reused or verified across groups.
Previous vendor attempts fell short. Some solutions couldn't ingest data from non-standard sources. Others lacked cloud support or required costly customization just to get started. What the team
needed was a SaaS-native platform that could adapt to atypical data, automate repeatable workflows, and remove friction from data access and visualization. Visit dotmatics.com to learn more
Challenge Data complexity across characterization and test rig systems made it hard to analyze and share results. Teams used inconsistent macros, had trouble updating scripts for new tests, and faced delays of several days between test completion and analysis.
Solution Ran a three-month Luma proof of concept to automate data ingestion, standardize outputs, and build low-code visual analytics workflows. Focused on typical (e.g., NMR) and atypical (e.g., test rig) data.
Results
Evaluation completed ahead of schedule with consulting time to spar 10% faster decisions in test teams; up to 80% faster for application team Standardized flows improved data reuse and visibility