Strategic Data Warehouse Transformation Roadmap for Healthcare

12+

Stakeholder interviews conducted to define goals and requirements

100%

Assessment of existing data sources, ETL workflows, and reporting assets across the legacy environment

9 Week

Structured discovery completed with full architectural recommendations delivered

Background

Our client is a large public healthcare organization operating within a complex data ecosystem supporting clinical, operational, and administrative reporting. Over time, multiple systems and reporting platforms were introduced, resulting in a fragmented data landscape supported by legacy technologies and siloed data structures. The organization relies heavily on data for performance monitoring, regulatory reporting, and strategic planning, making the modernization of its data warehouse a critical priority for long-term sustainability and analytics maturity.

Challenge

The client faced increasing limitations from a legacy data warehouse and ETL environment that constrained scalability, flexibility, and reporting efficiency. Existing systems struggled to support evolving business requirements, modern analytics demands, and expanding data volumes. Additionally, inconsistent data models, outdated transformation logic, and fragmented reporting layers created operational risk, reduced trust in data, and limited the organization’s ability to implement future digital initiatives. The organization required a structured discovery effort to assess its current state, uncover gaps, define an optimized target architecture, and establish a clear transformation roadmap that would reduce risk while enabling a modern, scalable analytics foundation.

Approach

Keyrus followed a structured multi-phase discovery methodology focused on aligning business objectives with technical modernization strategies: Conducted comprehensive stakeholder interviews to capture business needs, reporting expectations, and operational pain points Analyzed existing data sources, ETL workflows, transformation logic, and reporting structures Performed gap analysis between current capabilities and future-state requirements Designed a high-level target data model and architecture to support scalability and performance Developed future-state architecture and technology recommendations Delivered a phased implementation roadmap outlining risks, resources, and execution strategy This approach ensured the client could move forward with confidence and clarity toward a modernized data warehousing solution.

Key results

Clear Modernization Roadmap Established: The client received a structured transformation plan defining roles, milestones, technical architecture, and sequencing for data warehouse modernization.

Future-State Architecture Defined: A scalable, governed, and analytics-ready architecture was designed to replace legacy systems and improve long-term performance and flexibility.

'Improved Alignment Between Business and Technology: Business requirements were directly mapped to technical capabilities, improving transparency and alignment across departments.

Benefits

The discovery engagement provided the client with a precise, low-risk pathway to modernize its data warehouse ecosystem. By identifying structural gaps, clarifying future-state architecture, and producing a clear transformation roadmap, the client gained confidence in its ability to evolve toward a more scalable, secure, and performance-driven analytics environment. This work strengthened strategic planning, minimized implementation risk, enhanced data governance readiness, and positioned the organization for future reporting innovation, improved decision-making, and advanced analytics capabilities.