Use cases — stage 01
Design & Stress-Test
Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.
120 stories across 8 vendors
Collibra38
- 01Centralized AI governance framework with risk-tiered workflows for safety-critical mining operations
- 02Data governance + AI governance frameworks across 95 markets
- 03European DIY/home-improvement retailer building organization-wide data governance
- 04Integrated Collibra Business Glossary into Tableau/workflows for standardized term definitions
- 05Data fabric implementation to increase data transparency and value
- 06Unified data governance for GDPR/BCBS 239 compliance, bridging risk & compliance silos
- 07Built a cloud-based "Data Governance Center" unifying metadata, glossary, and quality metrics
- 08Uses data to improve efficiency, regulatory compliance, and customer service
- 09Data governance/quality program establishing data ownership and automated quality checks
- 10Built an internal data marketplace for employees to share/access data and build new products
- 11Data management to build personalized insurance products (life, property, casualty, borrower
- 12Data governance to shift from retrospective reporting to predictive insight across 15 country affiliates, deployed in <2 months
- 13Norway's largest bank consolidated 900+ data silos, enabled churn prediction and benchmark reporting
- 14Data governance supporting renewable energy production and carbon-neutrality goals
- 15Reduced insurance claims-denial anomaly detection from six weeks to one day via governance/quality (30M patients/yr, 1,000+ sites
- 16Centralized data platform to optimize pipelines and speed new data-product delivery
- 17Eliminated duplicate data spend and improved quality by cataloging master data sources
- 18Centralized governance platform unifying mainframes, warehouses, Hadoop, and cloud for 2,000+ data users
- 19Unified clinical/business metrics (45+ DBs, 850+ metrics), cut financial analysis time 50%
- 20First end-to-end data governance program at scale in Portugal (with Deloitte
- 21Unified governance platform for a single source of truth across 80+ operating companies
- 22Becoming a data-driven organization via consistent, trusted data availability
- 23Unified 40 siloed glossaries into one business glossary (4,000+ terms/KPIs) across European DIY retail brands
- 24Using data as a core asset to accelerate digital transformation
- 25Expedites product time-to-market through improved data accessibility/governance
- 26Modernizing data architecture with Collibra + Databricks, governance by design
- 27Business-led governance program consolidating fragmented data dictionaries across 160+ stewards
- 28Data Quality & Observability plus Data Catalog for better decision-making
- 29Built "Advana," a centralized platform to govern enterprise data across military services
- 30Shifting from one-off data projects to reusable, governed data products
- 31Unified siloed data across on-prem/cloud for a German insurer
- 32End-to-end view of technical data for regulatory compliance
- 33Data-quality "system of engagement" to reduce risk
- 34Centralized data catalog cut data-search time from hours to minutes
- 35Democratized data access to find/trust/use data across the org
- 36Building a UC-wide medical/healthcare research data warehouse
- 37Cut data curation/governance time from weeks to seconds for personalized learning
- 38Enterprise data governance/metadata management across legacy systems
Domino Data Lab17
- 01Accelerated time to regulatory submission by modernizing vaccine and pharmaceutical development processes on one platform
- 02Presented on using Domino for multi-GPU deep-learning (computer-vision/histopathology) models for precision oncology, 10x faster model development
- 03Deployed Domino's Enterprise MLOps platform as an "FDA-qualified research system of record," letting researchers test tens of thousands of hypotheses with full reproducibility
- 04Health and life sciences customer story (page exists; specific metrics not extracted
- 05Improved trustworthiness of mine-detection intelligence models
- 06Used data science to transform value delivery to advertisers, viewers and distribution partners
- 07Faster claims processing and faster model development
- 08Financial-services data science use case (not industry-relevant to pharma
- 09Insurance data science use case (not pharma-relevant
- 10Financial-services use case (not pharma-relevant
- 11Building a unified Scientific Compute Environment (SCE) at scale
- 12Scaling enterprise data science across drug development
- 13Reproducible AI as a property of the platform
- 14Standardizing on one SCE
- 15How UCB modernized its SCE
- 16Data science innovation across healthcare (webinar
- 17Non-pharma video sessions also present in the resource library (governance, data collaboration, agentic AI architecture) — not detailed here as out of scope
Snorkel AI16
- 01Built a document-classification system to identify HER2 protein status in patient records, 93% accuracy, automating clinical-trial screening
- 02Enhanced decision-making resilience for Indo-Pacific operations tracking
- 03Improved customer-support agent response times to under 3 seconds
- 04Achieved 99% accuracy with an AI-powered sales-productivity platform
- 05Deployed production AI in under 60 days, accelerating claims/subrogation review 67%
- 06Automated CLO contract review, 94% end-user acceptance, hours-to-seconds
- 07Generated decision-grade responses in 15 seconds across data sources
- 08Went from a stalled pilot to $43M annual ROI and 95% accuracy on a customer-facing chatbot
- 09Scaled an agentic AI personal assistant using high-quality synthetic data
- 10Measured and improved virtual-assistant customer experience
- 11Programmatic data-labeling for product-catalog tagging; 99% category win rate, +7pt clickthrough, 10x faster model dev
- 12Built labeling functions that cut manual dataset labeling from months to minutes; 52% performance improvement labeling 6.5M data points for content classifiers
- 13Automated extraction of geological entities from unstructured field reports, hours to seconds
- 14Extracted 50+ attributes from 10-K filings for KYC verification, saved 10,000+ analyst hours
- 15Automated labeling of 10,000+ news articles (hours vs. ~1 year) for an NLP auditing application
- 16Extracted inclusion/exclusion criteria and Schedule of Assessments data from clinical trial protocol PDFs with 95–99% model accuracy, built in weeks instead of months
Dataiku15
- 01Moved from manual spreadsheet-based calculations to Dataiku-driven decision-making; used Dataiku LLM Mesh for healthcare market research, 90% reduction in time-to-insight on GenAI use cases, 600% faster data ingestion on spreadsheet use cases
- 02Used Dataiku to redesign patent/legal workflows with agentic AI; reduced attorney time and cut GenAI project build cycles from months to days
- 03Ran a generative AI training/hackathon with Dataiku that produced working prototypes in under 2 days
- 04GxP-compliant pharmaceutical AI collaboration ("Driving AI Innovation in Life Sciences"
- 05Saved roughly 40 hours/month versus its prior manual process using Dataiku (dedicated story page not directly confirmed live in this pass; reported via Dataiku's own marketing copy)
- 06Transformed data organization and clarity (non-pharma; food/agriculture
- 07Deployed an AI-agent-powered sales model saving 250,000 hours annually (non-pharma
- 08Accelerated analytics delivery 60% and scaled GenAI (non-pharma
- 09Drove business outcomes through data initiatives (non-pharma
- 10Data/AI solutions for energy operations (non-pharma
- 11Market-share insights using AI agents (non-pharma
- 12Unified siloed data into a global AI infrastructure (non-pharma
- 13Improved IT support efficiency using AI agents (non-pharma
- 14Operational excellence via Dataiku (non-pharma
- 15Insurance operations strengthened with data/ML (non-pharma)
Alation13
- 01Used Alation's data-catalog/AI-analytics platform to break down cross-divisional data silos and help identify/treat rare diseases (cited example: identifying patients with transthyretin cardiomyopathy, often misdiagnosed
- 02Democratized biopharmaceutical data access, breaking down silos and increasing discoverability/collaboration with Alation Data Catalog (published as a webinar, not a written case study
- 03Built an "AI-ready" healthcare organization by establishing trust in its data
- 04Reduced risk by improving data consistency
- 05Built a "front door to data intelligence" via comprehensive data governance (non-health; financial services
- 062,500+ users use Alation to find data (non-health
- 07Rebuilt its data foundation across 32,000 employees/150+ facilities (non-health; manufacturing
- 08Built an "AI-ready" environment for autonomous agents via metadata management (non-health
- 09Implemented agentic AI governance, prototypes built in days (non-health
- 10Governed Critical Data Elements across its global supply chain (non-health
- 11Created a single source of truth for supply-chain decisions (non-health
- 12Got AI into production using governance as the foundation (non-health
- 132025 Data Radicals Award winners, cited for metadata strategy (non-health
Databricks11
- 01Pharmaceutical company using Agent Bricks for clinical and pharmacovigilance workflows
- 02Runs its platform on Databricks/AWS to accelerate drug discovery; the Regeneron Genetics Center built one of the largest genetics databases pairing sequenced exomes with EHRs for 400,000+ people
- 03Built a knowledge graph and recommendation engine on Databricks to help scientists mine millions of data points across thousands of sources and generate novel drug-target hypotheses; separately used Agent Bricks to parse 400,000+ clinical trial documents into structured data in under 60 minutes with no code
- 04Transformed its data ecosystem (data silos/redundancy) using Databricks and Unity Catalog; Consumer Health division built reusable core data assets/data products for dashboarding, ad hoc analytics, ML and AI
- 05Replaced an aging data warehouse with Databricks; cut data-processing time 69%, data-availability lag 52%, and operating costs 45%
- 06Healthcare organization using Lakebase for patient, provider and member applications
- 07Healthcare performance-improvement company using Genie for AI-driven analytics/BI
- 08Healthcare story listed under Databricks' healthcare & life sciences customers
- 09Australian public health provider, healthcare story
- 10National digital health service, healthcare story
- 11Described as a Databricks/AWS customer using the Lakehouse Platform in Databricks newsroom/partner material; no dedicated `/customers/amgen` story page could be found live (404) — flagged as unconfirmed/no story page, not fabricated as a full case study
MDClone6
- 01Case study on driving healthcare transformation (data/pharmacy and antimicrobial-stewardship context
- 02Used MDClone's synthetic data to speed research turnaround (same-day answers, 2-3x research output); Sheba is a Newsweek top-10 global hospital 3 years running
- 03Ran 3 pilot projects validating synthetic-data accuracy while preserving patient privacy
- 04Engaged 200+ users with the data platform in under 6 months
- 05Used high-quality synthetic data to support rapid healthcare-innovation initiatives
- 06Generated valid, complete, accurate standardized data from 2.5M unique patient records
Gretel4
- 01Used Gretel's synthetic-data platform to create safe synthetic genomic datasets, enabling genomics research and safe/private data sharing between researchers, healthcare providers and industry
- 02Generated 16M+ synthetic patient records for labor-and-delivery data to train ML forecasting models while preserving privacy
- 03Turned synthetic data into a strategic-growth capability (finance; non-pharma) — referenced on Gretel's finance solutions page, dedicated case-study URL not directly resolved in this pass
- 04Synthetic time-series data for cross-institution use (finance; non-pharma) — referenced on Gretel's finance solutions page, dedicated case-study URL not directly resolved in this pass