Healthcare & life sciences · benchmark
Data platform modernization in healthcare & life sciences: published results
Healthcare and life sciences organizations modernize their data platforms under tighter constraints than most: protected health information, validated systems and research data that has to stay traceable. We collected 27 public case studies from Databricks and Snowflake customers in the sector and extracted every outcome they report.
- median cost reduction (9 results)
- 50%
- median speed-up where reported as a multiplier (9 results)
- 10x
- of case studies report a cost outcome
- 37%
- published case studies (6 Snowflake and 21 Databricks)
- 27
Analysis
What the results have in common
Our reading of the 27 published outcomes, and what it means if you're planning a program.
The biggest speed-ups are in research and analytics workloads
The largest multipliers in this set come from research data, genomics and claims analytics, where queries over very large datasets moved from hours to minutes. Operational reporting improves too, but by smaller factors.
Cost results come from consolidation and PHI handling
Reported savings come from consolidating separate platforms and from cheaper ways to process and de-identify protected data. Programs that kept PHI in several copies tend not to report cost results at all.
Clinical and revenue-cycle outcomes are the strongest business cases
Where organizations report business impact, it's in measurable operational terms: fewer coding denials, lower readmissions, faster reimbursement. These are easier to defend to a CFO than general "insight" claims.
Cost
Largest reported cost reductions
Median 50% across 9 results. Each bar is one published result.
| Company | Reduction | What was reduced |
|---|---|---|
| OM1 | 75% | savings in processing costs using Snowpark Container Services to redact PII data |
| Marketscan | 60% | Cost savings for clients accessing MarketScan real-world data via Snowflake |
| Hinge Health | ~50% | Reduced costs |
| Syngenta | 50% | Decrease in engineering costs |
| Tata 1mg | 50% | reduction in ETL costs since migrating to Databricks |
| Integra Life Sciences | 45% | Reduction in overall operating costs |
| Bayada | ~30% | Better workload performance and cost efficiency compared to Snowflake |
| CodaMetrix | 30% | Reduction in AWS compute costs |
| Duly Health and Care | 30% | lower data platform costs vs. SQL Server |
Speed
Biggest speed-ups
- 600xRegeneronImprovement in query runtime on entire data sets
- 35xMilliman MedInsightFaster data pipelines
- 12xInsuletFaster data processing speeds
- 10XMarketscanFaster access to research-ready data for clients
- 10xPremier IncFaster SQL creation with Genie compared to manual coding
Business outcomes
Revenue and risk results
- 75%Austin HealthPatient engagement success rate
- 46%Flo HealthIncrease in overall employee engagement on Databricks
- 60%CodaMetrixDecrease in coding denials
- 29%Duly Health and Carereduction in hospital readmission rates, saving $4.5M annually
Method
How we built this benchmark
We collected every public healthcare and life sciences customer story from Databricks and Snowflake that reports at least two numbers, 27 in total, on 27 October 2026. Each headline number was recorded exactly as published and classified as a cost, speed, revenue, risk or scale outcome. Medians use only results that are directly comparable: percentages up to 100% and "N×" multipliers.
Limits: Numbers are self-reported by the customer and published by the platform vendor. Healthcare organizations also publish fewer case studies than other sectors, so this sample is smaller and skews toward large research and health-tech organizations.
These are not Metric Oak engagements. See every source in the library, or read how we approach healthcare and life sciences.
Case studies in this benchmark: AMN Healthcare, Austin Health, Bayada, Bupa, CodaMetrix, Duly Health and Care, Ensemble, Ensemble Health Partners, Flo Health, Healthdirect Australia, Hinge Health, Insulet, Integra Life Sciences, Marketscan, Milliman MedInsight, NYC Health + Hospitals, OM1, Orizon, Premier, Premier Inc, PTHB, Regeneron, Sanofi, SCAN Health, Syngenta, Tata 1mg, VGZ
Frequently asked questions
What cost reduction do healthcare and life sciences companies report after modernizing their data platform?
Across 9 cost results published by healthcare and life sciences organizations in this set, the median reported reduction is 50%, ranging from 30% to 75%. 37% of the 27 case studies report a cost outcome at all.
How much faster do data workloads get after modernization in healthcare and life sciences?
Where results are reported as a multiplier, the median is 10x faster across 9 results. Where they're reported as a percentage, the median is 60% faster across 6 results. The largest gains are in batch processing and getting models into production.
Databricks or Snowflake: which gets better results in healthcare and life sciences?
This set includes 6 Snowflake and 21 Databricks case studies, with results across a similar range on both. Published case studies can't settle the question, because each vendor publishes its own best results. The right choice depends on your workloads, your team's skills and your existing contracts. The Data Platform Assessment ends with a written recommendation.
Are these numbers realistic for my organization?
Treat them as an upper-quartile view. Numbers are self-reported by the customer and published by the platform vendor. Healthcare organizations also publish fewer case studies than other sectors, so this sample is smaller and skews toward large research and health-tech organizations. Your own baseline (current cost, batch windows and delivery times) is what a business case should be built on.
Where does this data come from?
From 27 public customer case studies published by Databricks and Snowflake, collected on 27 October 2026. We record each headline number exactly as published and link to every source in our case-study library.
How long does a data platform modernization take?
Published case studies report duration inconsistently, so we don't benchmark it here. As a guide, Metric Oak's Data Platform Assessment takes 3 weeks, and the first migrated domain typically reaches production 4–6 weeks after that.
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