A MedTech group's analytics ran on siloed data and Excel, producing late, two-number reports. Trexin re-architected it into a reusable 'analytics factory': MVP in 5 months, with data-prep effort down 60%.
The cardiac-diagnostics group of a prominent international MedTech company wanted to grow into predictive analytics and ML, but their environment had drifted into data silos and Excel-based reporting that was error-prone and slow to change. Business users waited weeks or months for updates, and ad hoc analysis was nearly impossible. The marketing leader who championed analytics asked Trexin to re-architect the environment and ship an MVP within six months.
A root-cause analysis found the real problem (beyond sub-optimal tooling): every report pulled from the same sources but with its own acquisition and prep code: mass duplication and “two-number” reporting. We designed a reusable BI architecture that pushed work upstream: generalized Python data pipelines feeding a dimensional model with “one-number” inputs on Tableau Server, serving standardized measures to Tableau Workbooks. A single master table per dimension, built by a single script, with all measures managed in a “develop once” environment.
The MVP shipped in 5 months: a shift from an analytics “job shop” to an efficient “analytics factory” embedded in the operating model. Product managers gained new insight and could run ad hoc analysis without analytics-staff help, and data-acquisition/prep effort dropped 60%, freeing the team to focus on actionable issues and faster speed-to-insight.
We fixed the architecture, not just the reports, so the gains compound instead of decay.
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