๐งฌ ๐ง๐ต๐ฒ ๐๐ถ๐ฑ๐ฑ๐ฒ๐ป ๐๐ผ๐๐ป๐ฑ๐ฎ๐๐ถ๐ผ๐ป ๐ผ๐ณ ๐ฆ๐๐ฐ๐ฐ๐ฒ๐๐๐ณ๐๐น ๐ฃ๐ต๐ฎ๐ฟ๐บ๐ฎ ๐ฎ๐ป๐ฑ ๐๐ถ๐ผ๐๐ฒ๐ฐ๐ต ๐ฅ&๐: ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐ถ๐ & ๐ช๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐ ๐ ๐ฎ๐ฝ๐ฝ๐ถ๐ป๐ด.
Pharma and biotech R&D often focus on the scienceโand rightfully so. But there’s a critical discipline that can make or break your data management strategy: business analysis and workflow mapping.
๐ช๐ต๐ ๐ง๐ต๐ถ๐ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐ ๐ ๐ผ๐ฟ๐ฒ ๐ง๐ต๐ฎ๐ป ๐๐๐ฒ๐ฟ?
๐ค๐๐พ๐๐ ๐๐๐พ๐ฟ๐ฟ๐๐ผ๐๐พ๐๐ ๐ฝ๐บ๐๐บ ๐๐บ๐๐ฝ๐๐ฟ๐ฟ ๐ป๐พ๐๐๐พ๐พ๐ ๐ฝ๐๐๐ผ๐๐๐พ๐๐ ๐๐พ๐บ๐๐, ๐๐ ๐ ๐๐๐ ๐๐พ๐๐พ๐บ๐๐ผ๐ ๐๐๐๐๐๐๐ ๐๐ฟ๐๐พ๐, ๐๐๐บ๐ผ๐พ๐ ๐ป๐บ๐ผ๐ ๐๐ ๐๐๐๐๐ ๐ ๐๐๐ฝ๐พ๐๐๐๐๐๐ฝ ๐๐๐๐ผ๐พ๐๐๐พ๐. ๐ถ๐๐พ๐ ๐๐พ ๐ฝ๐๐’๐ ๐๐บ๐ ๐๐๐ ๐ฝ๐บ๐๐บ ๐ฟ๐ ๐๐๐ ๐ฟ๐๐๐ ๐ป๐พ๐๐ผ๐ ๐๐ ๐ฝ๐พ๐ผ๐๐๐๐๐-๐๐บ๐๐๐๐, ๐๐พ’๐๐พ ๐พ๐๐๐พ๐๐๐๐บ๐ ๐ ๐ ๐ฟ๐ ๐๐๐๐ ๐ป๐ ๐๐๐ฝ ๐๐๐๐๐๐๐ ๐๐๐พ ๐๐๐๐ ๐ฝ๐บ๐๐บ-๐๐๐๐พ๐๐๐๐๐พ ๐๐๐บ๐๐พ ๐๐ฟ ๐ฝ๐๐๐ ๐ฝ๐พ๐๐พ๐ ๐๐๐๐พ๐๐.
๐ง๐ต๐ฒ ๐ฅ๐ฒ๐ฎ๐น ๐๐บ๐ฝ๐ฎ๐ฐ๐
- โ ๐ฟ๐๐ฉ๐ ๐๐๐ฅ๐ง๐ค๐๐ช๐๐๐๐๐ก๐๐ฉ๐ฎ: ๐ด๐๐ฝ๐พ๐๐๐๐บ๐๐ฝ๐๐๐ ๐พ๐๐๐พ๐๐๐๐พ๐๐๐บ๐ ๐๐๐๐๐ฟ๐ ๐๐๐ ๐๐๐พ๐๐พ๐๐๐ ๐๐๐พ “๐จ ๐ผ๐บ๐’๐ ๐๐พ๐๐ ๐๐ผ๐บ๐๐พ ๐ ๐บ๐๐ ๐๐พ๐บ๐’๐ ๐๐พ๐๐๐ ๐๐” ๐ผ๐๐๐๐๐ ๐๐๐บ๐ ๐ผ๐๐๐๐ ๐ฝ๐๐๐ผ๐๐๐พ๐๐ ๐๐พ๐บ๐๐ ๐๐๐๐๐๐
- โ ๐พ๐ง๐ค๐จ๐จ-๐๐๐ ๐พ๐ค๐ก๐ก๐๐๐ค๐ง๐๐ฉ๐๐ค๐ฃ: ๐ฌ๐บ๐๐๐๐๐ ๐๐พ๐๐พ๐บ๐ ๐ ๐๐๐พ๐๐พ ๐ผ๐๐พ๐๐๐๐๐๐, ๐ป๐๐๐ ๐๐๐, ๐บ๐๐บ๐ ๐๐๐๐ ๐บ๐๐ฝ ๐ผ๐๐๐๐๐๐บ๐๐๐๐๐บ๐ ๐๐พ๐บ๐๐ ๐๐๐๐พ๐๐๐พ๐ผ๐โ๐บ๐๐ฝ ๐๐๐พ๐๐พ ๐ฝ๐บ๐๐บ ๐๐พ๐๐ ๐ ๐๐๐ ๐๐ ๐๐๐บ๐๐๐ ๐บ๐๐๐๐
- โ ๐๐ก๐๐ฉ๐๐ค๐ง๐ข ๐๐ฃ๐ฉ๐๐๐ง๐๐ฉ๐๐ค๐ฃ: ๐ธ๐๐ ๐ผ๐บ๐’๐ ๐ผ๐๐๐๐พ๐ผ๐ ๐ ๐บ๐ป ๐๐๐๐๐๐๐๐พ๐๐๐, ๐ค๐ซ๐ญ๐, ๐บ๐๐ฝ ๐ฝ๐บ๐๐บ ๐ ๐บ๐๐พ๐ ๐๐๐๐๐๐๐ ๐๐๐ฝ๐พ๐๐๐๐บ๐๐ฝ๐๐๐ ๐๐๐พ ๐๐๐ฝ๐พ๐๐ ๐๐๐๐ ๐๐พ๐๐พ๐บ๐๐ผ๐ ๐๐๐๐ผ๐พ๐๐๐พ๐
- โ ๐๐๐จ๐๐๐ง๐๐ ๐พ๐ค๐ฃ๐ฉ๐๐ฃ๐ช๐๐ฉ๐ฎ: ๐ข๐ ๐พ๐บ๐ ๐๐๐๐๐ฟ๐ ๐๐ ๐ฝ๐๐ผ๐๐๐พ๐๐๐บ๐๐๐๐ ๐พ๐๐๐๐๐พ๐ ๐๐๐๐๐ ๐พ๐ฝ๐๐พ ๐๐๐บ๐๐๐ฟ๐พ๐ ๐๐๐พ๐ ๐๐ผ๐๐พ๐๐๐๐๐๐ ๐๐๐๐พ ๐ป๐พ๐๐๐พ๐พ๐ ๐๐๐๐๐พ๐ผ๐๐ ๐๐ ๐ ๐พ๐บ๐๐พ ๐๐๐พ ๐๐๐๐บ๐๐๐๐บ๐๐๐๐
๐๐ฎ๐น๐ฎ๐ป๐ฐ๐ถ๐ป๐ด ๐๐น๐ฒ๐ ๐ถ๐ฏ๐ถ๐น๐ถ๐๐ ๐๐ถ๐๐ต ๐๐ฎ๐๐ฎ ๐๐ป๐๐ฒ๐ด๐ฟ๐ถ๐๐
Early research thrives on agilityโhypotheses pivot, experimental designs evolve, and new technologies emerge constantly. Yet this flexibility can’t come at the expense of data quality and lineage. The challenge is designing workflows that allow scientists to adapt quickly while maintaining clear data traceability from raw instrument data through analysis to decision-making. When data flows seamlessly between systems, researchers can focus on science instead of hunting down datasets, metadata and context and samples.
The Business Analyst as Strategic Partner
The best pharma business analysts don’t just document processesโthey become translators between discovery scientists and data infrastructure. They ask the critical questions: “How does this assay data inform compound selection?” and “What happens when the mass spec goes down during a critical experiment?”. They help finding more efficient solutions that will advance growth and progress.
In early-stage drug discovery, where a missed insight or broken data pipeline can mean years of wasted research, this work isn’t just operationalโit’s the foundation of innovation.
What’s been your experience with workflow mapping in discovery research? How has business analysis helped your team move from hypothesis to lead compound more efficiently?
Contact us to discuss how the Spotty Alicorn can help your digital data journey.
#DrugDiscovery #EarlyResearch #pharma #Biotech #BusinessAnalysis #DataManagement #ProcessImprovement #scientificworkflows #scientificdata
This article was originally published on LinkedIn by Nathalie Batoux.
Leave a Reply