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Understanding the Real Benefit of Data Modeling & Medallion Architecture in Healthcare
- Christian Steinert
- Jun 24
- 5 min read
What is the business outcome of data modeling and technical architecture?
A quick note on this week's focus: While I originally planned to dive into cost-effective data infrastructure implementation, I'm still working through some Microsoft Fabric pipeline optimizations with current engagements. Instead, let's focus on something equally critical - how to set the framework for a proper data model and introduce medallion architecture foundations that keeps healthcare clients focused on business outcomes.
When Things Get Technical, Too Fast
At Steinert Analytics, we typically work with IT and data executives. On one hand, it’s extremely helpful because they know their data well. If we have questions, they’re not afraid to dive into the weeds.
However, this also creates a data product management challenge: shifting the focus from technical details to the business outcome of what we’re building.
I've experienced this with several healthcare clients who became intensely focused on platform-specific details: capacity planning discussions, monitoring dashboard configurations, and pipeline troubleshooting sessions that consumed entire working meetings.
While these technical considerations matter, they shouldn't dictate where time is spent during client to consultant interactions.
The Reset Framework: Getting Back to Business Value
When I encounter this dynamic, I implement what I call the "reset framework" - a structured approach that redirects focus from technical minutiae back to business outcomes.
Step 1: Call for Strategic Refocus - When working sessions become troubleshooting calls rather than HOW the product is progressing and moving towards goal, pause and reestablish the project's true objectives.
Step 2: Return to Traditional Data Modeling Fundamentals - Walk healthcare clients through the traditional progression:
Conceptual Data Model: Start with the "what" and "why" - what business processes are we modeling and why does this matter for patient outcomes, operational efficiency, or financial performance?
This is an in-depth exercise that can span one or several meetings. Keboola has great documentation on this.
Logical Data Model: Move into entity relationship diagrams, attributes, and relationships that define business rules and data relationships. What will the tables and relationships actually look like when built in the database?
Physical Data Model: This is when it’s okay to get into the nitty gritty hands-on code. Cut the engineers loose and build the data model aligned to your logical model. The heavy lifting done with the conceptual model ensures a product built for business outcomes.
Step 3: Bridge to Modern Architecture - Once you’ve finished the data modeling framework, position medallion architecture as the implementation strategy for the physical model, showing how the pattern serves specific business and technical needs (ie. incremental updates multiple times per day).
Step 4: Tie back to business value - Data modeling & architecture creates timely and quality information, which delivers trust. That trust drives confident decision making that increases a company’s bottom line faster and at less cost.
The Trust Foundation: How Data Modeling Drives Healthcare Decision-Making
Let’s dive into the trust data modeling creates a little further…
Trust Through Accuracy: Proper data modeling creates standardized definitions ensuring stakeholders across the healthcare organization work with consistent, accurate datasets. When a CFO and Chief Medical Officer both look at "patient volume" metrics, they see the same numbers calculated the same way.
Trust Enables Speed: When healthcare stakeholders trust their data, they make decisions faster. Instead of spending weeks validating numbers, clinical and operational teams can access pre-validated datasets and move directly to analysis and action.
Trust Drives Efficiency: Trusted data eliminates multiple versions of truth, reduces time spent in "data reconciliation" meetings, and prevents costly mistakes from decisions made on inconsistent information.
Measurable Business Impact: This trust foundation directly impacts the bottom line through faster revenue cycle management, more efficient resource allocation, improved patient outcomes, and reduced operational waste - all because stakeholders can make confident decisions on reliable data.
Why Medallion Architecture in Microsoft Fabric for Healthcare
I’d also like to touch a little more on Microsoft Fabric architecture. Admittedly, I am not an expert (yet) in Fabric - I’ve left that job to my team of engineers. :)
However, given Microsoft’s dominance in healthcare, accounting for over 79% of all org’s main infrastructure (https://echeloncyber.com/intelligence/entry/why-every-healthcare-organization-should-assess-their-microsoft-365-environment), I predict that Fabric usage will continue to grow in this space.
Its native integration with Office 365 and Power BI makes it a dangerous contender for the de facto data infrastructure for healthcare in the Age of AI. Steinert Analytics is taking this seriously.
When implementing the physical data model in Microsoft Fabric, medallion architecture provides specific advantages for healthcare organizations - especially those that come from a legacy tech stack like SQL Server.
Bronze Layer (Raw Data Ingestion): Healthcare organizations deal with data from EHRs, medical devices, billing systems, and external partners. The bronze layer provides a landing zone for all raw data while preserving data lineage and ensuring compliance with audit requirements.
Silver Layer (Cleaned and Standardized): Healthcare-specific data standardization happens here - converting date formats, standardizing patient identifiers, and applying consistent business rules while maintaining detailed transformation history for compliance.
Gold Layer (Business-Ready Analytics): Purpose-built datasets for specific healthcare use cases - patient outcomes analysis, financial reporting, operational dashboards. Each dataset is optimized for its intended business function while maintaining full traceability back to source systems.
Microsoft Fabric's native capabilities for data lineage tracking, security governance, and compute optimization make it particularly well-suited for healthcare's medallion architecture implementation.
Furthermore, having the gold layer sit in a Fabric Warehouse, it places T-SQL and traditional SQL Server functionalities like stored procedures at the front and center. The barrier to entry is familiar and lower because of this, allowing healthcare IT teams to ramp up quickly in using Fabric and getting immediate ROI.
Practical Lessons for Fellow Consultants
As a consultant who is getting baptized by fire, let’s recap some learnings when leading these large healthcare implementation projects…
Establish Technical Boundaries Early: Strategic data architecture consulting is different from hands-on platform administration. Your value is in methodology, business alignment, and architectural decision-making.
Use Traditional Frameworks as Reset Tools: When clients get too technical too fast, traditional data modeling frameworks provide a structured way to refocus on business requirements. This ensures what you build is directly tied to outcomes.
Position Modern Architecture as Implementation Strategy: Lead with business requirements identified through proper data modeling, then use medallion architecture as the solution.
Focus on Trust: If there is no trust, no decisions can be made. Lean into this as a core point for why a healthcare company would want to build robust data modeling & architecture in the first place.
Looking Ahead
Next week, we’ll be looking to discuss tips on ensuring the traditional data modeling framework goes well. I can’t wait to share with you the wins and how we achieved them along the way!
Remember: healthcare clients need strategic guidance first, technical implementation second. When you successfully refocus client conversations on business value rather than technical weeds, the client is actually able to learn something.
Furthermore, this creates a strong focus on the future vision of growth for the company and the outcome of all this technical implementation.
Christian Steinert is the founder of Steinert Analytics, helping healthcare & roofing organizations turn data into actionable insights. Subscribe to Rooftop Insights for weekly perspectives on analytics and business intelligence in these industries.
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