Databricks Healthcare Wales

Powys Teaching Health Board

Enterprise Knowledge Search · Real-time Analytics

The demand for PTHB’s services has increased significantly over the years as they’ve dealt with evolving healthcare needs and population growth. As new patients enter the national healthcare system, so does the rise in data captured about the patient, hospital operations and more.

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40% Decrease in time to insight

65% More productive with Databricks Assistant

<1 year To modernize data infrastructure

Hammer’s team estimated that it would take five to 10 years to modernize their tech stack in this way if they were to follow their own processes and tech stack.

Reported impact

Business outcome

With the Databricks Data + AI Platform, PTHB has taken their first step toward modernization by moving to the cloud in less than a year — a much quicker timeline than their 10-year estimate — and providing a federated lakehouse to unify all their data. Through the lakehouse, they are able to seamlessly connect to their on-premises SQL warehouse and remote BigQuery environment at NHS Wales to create a single view of t

Solution

Powys Teaching Health Board built its Databricks implementation around Unity Catalog, Databricks Data + AI Platform, Databricks Assistant, Lakehouse Federation. The demand for PTHB’s services has increased significantly over the years as they’ve dealt with evolving healthcare needs and population growth. As new patients enter the national healthcare system, so does the rise in data captured about the patient, hospital operations and more. Unity Catalog applies centralized governance, lineage, auditing, and access control across shared data assets. Databricks Data + AI Platform provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workflows in this deployment. Databricks Assistant helps practitioners accelerate code, query, and troubleshooting work inside the Databricks workspace. Lakehouse Federation lets teams query remote systems alongside lakehouse data without copying all source data first.

Data flow

Source data and events are ingested through managed connectors, files, or streaming feeds into databricks, curated in shared lakehouse tables, processed with Databricks compute and orchestration services, and exposed to downstream analytics and users. It connects with Power BI, Power Apps, BigQuery.

Solution architecture

6 components · 4 layers

Ingest
- Lakehouse Federation Lets teams query remote systems alongside lakehouse data without copying all source data first.
- Power Apps Captures operational inputs from low-code applications that feed the platform.
Serving
- Databricks Assistant Helps practitioners accelerate code, query, and troubleshooting work inside the Databricks workspace.
- Power BI Presents dashboards and reports to business users and planners.
Governance
- Unity Catalog Applies centralized governance, lineage, auditing, and access control across shared data assets.
Uncategorized
- Databricks Data + AI Platform Provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workflows in this deployment.

Architecture clues

Databricks Assistant
Databricks Data + AI Platform
Lakehouse Federation
Unity Catalog
federated lakehouse
single source of truth

Evidence from the source

40% Decrease in time to insight

<1 year To modernize data infrastructure

Data governance is also critical, but creating standard processes was difficult before transitioning to Databricks as their core platform. With Unity Catalog, PTHB has a model where all of their security and governance is done only once at

Deriving actionable insights on data through numerous Power BI dashboards with ease is something PTHB could not do before. “Now HR has the data they need to improve operational efficiency while protecting the bottom line,” said Hammer.

With the Databricks Data + AI Platform, PTHB has taken their first step toward modernization by moving to the cloud in less than a year — a much quicker timeline than their 10-year estimate — and providing a federated lakehouse to unify all

“Our data sat in so many places that it caused major frustrations. Our on-premises SQL warehouse couldn’t cope with the scale of our growing data estate,” explained Jake Hammer, Chief Data Officer at PTHB.

Transparency Evidence & methodology 96/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value96/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity16/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
3 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence89%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
6 direct source quotes retained.
Date provenance10/10
The publication date comes from the source page or publisher.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Databricks
Powys Teaching Health Board customer story

First-party
https://www.databricks.com/customers/pthb

Source type
Vendor-published customer story

Published
Feb 5, 2024
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date

Date evidence

Published on source page Feb 5, 2024 · Databricks · First-party
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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