For healthcare leaders juggling the demands of operational efficiency and patient care, health data is increasingly the lifeblood of decision-making. However, with data originating from diverse sources – from clinical systems to financial records – providers must navigate complex data ecosystems. This challenge is further complicated by cloud computing, distributed systems, and multiple data platforms. As organizations seek solutions, it’s crucial to understand the differences between six key evolutions in data architecture: data lake, data lakehouse, data model, data platform, data fabric, and the health intelligence platform.  

Data Lake: Storing Raw Data 

A data lake serves as a central repository for raw data from multiple sources, stored in its native format. It offers significant scalability, making it ideal for health systems managing large amounts of clinical, operational, and patient-generated data. However, while data lakes centralize data, they don’t eliminate the effort it takes to extract it’s value. That’s because data lakes lack inherent organization and structure, leading to inefficiencies in retrieving actionable insights. Without proper governance or metadata management, data lakes risk becoming “data swamps”, where useful data is buried and difficult to access. 

Data Lakehouse: Combining Lakes and Warehouses 

A data lakehouse merges the advantages of a data lake with the structure of a traditional data warehouse. It allows organizations to store raw data while supporting the structured, organized approach of a warehouse, giving the ability for ‘Just-in-time’ warehousing. Because it accommodates both clinical data analysis and financial reporting without duplicating data across systems, it provides better governance and data quality than a traditional data lake, ensuring quicker, more reliable data access and analysis. However, lakehouses and data lakes are limited in their ability to provide organizations with a full 360-degree view of a patient. Furthermore lakehouses are not architecturally designed for serving real-time requests from end-user applications, meaning the data still needs to be processed into a form that makes that possible. They still require a data model to enable integration with third-party applications.  

Data Model: Structuring the Data 

A data model defines the relationships between various data elements, structuring the data to make it more usable. In healthcare, this may involve mapping how patient records relate to clinical workflows or how financial data links to operational metrics. While critical for organizing complex datasets, a data model is not a storage solution but rather a blueprint that guides how data should be structured within storage systems. A robust data model ensures that the right data is available for healthcare decision-making. 

Data Platform: Managing the Data Ecosystem 

A domain agnostic data platform refers to the broader infrastructure that encompasses the tools, technologies, and environments used to collect, store, and process data. It supports data ingestion, integration, storage, and analysis. While a data platform is foundational to all data architectures, it lacks the advanced integration and automation capabilities of a data fabric or health intelligence platform. For healthcare systems, a data platform might offer the necessary infrastructure but may require additional solutions to provide the insights and governance offered by more specialized architectures. 

Data Fabric: Weaving Everything Together 

A data fabric offers a set of capabilities on top of a model. It is a holistic solution for integrating, accessing, and governing data across various environments. It provides an overarching framework that connects data sources, tools, and processes within a health system, enabling seamless access and governance. In healthcare, a data fabric ensures that clinical, operational, and financial data can be accessed from a unified interface, irrespective of where the data is physically stored, improving decision-making and operational efficiency. However, generic data fabrics still require data teams to manually integrate with their existing data infrastructure.  

Health Intelligence Platform: Amplifying Data’s Value.  

A health intelligence platform takes a step beyond traditional architecture by offering a comprehensive, healthcare-specific architecture. Built on a health data supermodel and leveraging a data fabric structure, it enables deep interoperability and integration of clinical, operational, and financial data while ensuring compliance with healthcare regulations, empowering organizations with previously unachievable insights, improving both business and patient care outcomes. Already speaking the language of health means easy deployment and low cost of ownership, as data teams don’t have to learn the process and fix the tooling.  Health specific data fabrics are domain-specific, giving you seamless access, integration and governance of data, regardless of its location or format. Essentially, the health intelligence platform adds behaviours around the data that make it domain specific. They also power machine learning and prepare health organisations for advanced AI. While data lakehouses and models focus on data’s organization and storage, a health intelligence platform transforms data into real-world value through decision support, automated reporting, and actionable clinical insights. 

Comparing the Architectures 

  • Data Lake: Ideal for raw data storage but lacks structure and governance, limiting its usability. 
  • Data Lakehouse: Combines the flexibility of data lakes with the structure of warehouses, balancing analytics and operational reporting. 
  • Data Model: Provides a framework for organizing data, essential for efficient querying and reporting. 
  • Data Platform: Provides data modelling capabilities and the underlying infrastructure for data storage, management, and analysis. Requires additional layers for advanced integration and insight generation.  
  • Data Fabric: Offers broader architecture that integrates, governs, and provides seamless access to data across systems, regardless of storage location. 
  • Health Intelligence Platform: Combines data fabric architecture with a health data supermodel to generate actionable insights, directly supporting healthcare decision-making and patient care. The data elements alone are not sufficient, a health intelligence platform adds behaviours around the data that make it domain specific.  

How the terms relate to each other  

While data lakes and lakehouses handle large volumes of data storage, and data models structure the data, a data fabric provides a unified architecture that connects these elements, ensuring healthcare systems can make informed, data-driven decisions efficiently. A health intelligence platform extends these capabilities further by integrating data environments, including those from external non-health third parties, offering a seamless framework for data access and governance, and transforming data into actionable insights that enhance both patient care and operational efficiency.  

Learn more about Orchestral Health Intelligence Platform