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.
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
Solution
The te m p rtnered with Dotm tics to r n three-month ev l tion of L m nd L m L b Connect. The project foc sed on two core o ls
Simplifyin the in estion nd processin of both st nd rd nd bespoke experiment l d t
Cre tin ser-friendly vis liz tions nd n lytics workflows witho t he vy developer inp t
Usin L m , the te m q ickly inte r ted diverse d t so rces, pplied consistent c lc l tions, nd b ilt o t vis l d t flows th t m de processes tr nsp rent nd reprod cible. B siness sers were ble to ener te insi hts on their own, nd L m 's S per User model llowed intern l experts to confi re nd evolve Experiences in-ho se—witho t relyin on IT.
Results
L m delivered v l e e rly, helpin the te m move f ster, red ce complexity, nd stren then intern l confidence in d t -driven decisions. Here re the hi hli hts:
Fast deployment, early completion
The POC wr pped p well he d of sched le.
Time savings across teams
Testin te m decision-m kin improved by 10
Applic tion te ms s w n lysis speed incre se by p to 80%
Stronger reuse and quality control
D t st nd rdiz tion en bled bro der re se cross te ms while red cin postprocessin errors.
Enhanced transparency
Vis l, no-code d t flows elimin ted bl ck-box processes nd simplified intern l li nment.
Internal buy in
E se of se nd e rly wins helped ener te stron b siness sentiment nd s pport for rollo t.
Significance
By doptin L m , the or niz tion demonstr ted th t di it l tr nsform tion in chemic ls nd m teri ls R&D doesn't h ve to be p inf l, slow, or limited to st nd rd instr ments. L m proved th t even hi hly speci lized nd complex d t workflows c n be stre mlined with the ri ht pl tform—witho t s crificin scientific ri or.
This c se is proof point th t sc l ble, S S-n tive d t sol tions c n s cceed o tside tr dition l life science settin s, en blin bro der ccess to insi hts nd cceler tin the p th to innov tion cross ind stries.
What s Next?
With the proof of concept exceeding expectations, the team is prioritizing new use cases and initiating a broader push to curate historical data for reuse. Luma is now seen as a key enabler for delivering data that s FAIR (Findable, Accessible, Interoperable, Reusable), bridging lab data complexity with enterprise needs.
Previous Challenge
Manual post-processing using macros and scripts
Inconsistent results from manual workflows
Long delays between test completion and insights
Data reuse limited by lack of standardization
Difficult to update or scale internal tools
Painful vendor experiences and slow rollouts