A mid-market health division within a global pharma organization unified its data with LakeStack, saving over $400K annually by replacing months of engineering with a two-week deployment.
Customer overview
Industry and customer challenges
Commercial and patient-engagement teams often rely on disconnected systems - CRM, engagement platforms, lab outputs, and outcomes datasets. Without unification, cohort analysis becomes slow, inconsistent, and analyst-dependent. This division faced the same issues:
1. Multiple legacy systems across workflows
CRM, lab systems, and patient engagement platforms each operated independently with no unified structure.
2. Heavy reliance on a small analytics team
Analysts became a bottleneck, leading to long queues and delayed insights.
3. Limited visibility into cohorts and adherence
Leadership had no near-real-time insight into patient cohorts, behavioral trends, or commercial performance.
4. Failed data engineering attempts
Past efforts to build pipelines were costly, slow, and never fully operationalized.
How LakeStack helped
LakeStack was deployed in a matter of days, delivering a complete data foundation across ingestion, governance, analytics, and AI, without requiring additional engineering resources.
LakeStack harmonized CRM activity, patient engagement logs, and outcomes datasets into structured Data Spaces.
PHI masking, audit logs, RBAC, and governed access were preconfigured for HIPAA-aligned operations.
Dashboards provided immediate visibility into adherence trends, prescriber patterns, cohort performance, and patient-support metrics.
Models identified risk segments, intervention triggers, drop-off patterns, and population-level trends.
Workflows were deployed without the need to hire engineers or build custom pipelines.
Success metrics
- $400K+ in annual engineering savings.
- 6-9 months of engineering work replaced by a 2-week deployment.
- Real-time cohort intelligence available for the first time.
- 75% reduction in reporting backlog.
- Division now operating on a controlled, AI-ready, governed AWS foundation.
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