Fragmented sources
Critical information is distributed across systems without a reliable integration model.
Sources
← Expertise
Data Architecture
Reliable decisions start with reliable data architecture.
Reformeta designs data foundations that make integration, analytics and future change easier to understand—not harder to maintain.
Problem signals
Data architecture problems usually appear somewhere else first.
A dashboard may be wrong because the metric is inconsistent. Integration may be slow because ownership is unclear. A warehouse may become fragile because it grew without an explicit model. We start with the symptom and trace it back to structure.
Conflicting definitions
Teams calculate the same business concept differently and trust erodes downstream.
DefinitionsBrittle pipelines
Changes in one source repeatedly break hand-built or tightly coupled integrations.
PipelinesDuplicated logic
Business rules are repeated across ETL, reports and spreadsheets with no clear authority.
LogicUnclear ownership
No one can easily answer who owns a data domain, definition or source of truth.
OwnershipWarehouse growth without structure
New tables and pipelines accumulate faster than the architecture can explain them.
StructureWhat we do
Build the data foundation the rest of the system can depend on.
We focus on architecture that remains understandable as sources, reporting needs and technology choices change.
Data warehouse architecture
Logical and physical structures aligned to analytical needs and operating constraints.
Dimensional modeling
Reusable fact, dimension and semantic structures designed around business meaning.
Data integration architecture
Clear movement, transformation and ownership patterns across source and target systems.
Database architecture & design
Structures that support performance, integrity, maintainability and future change.
Enterprise data platforms
Architecture decisions spanning warehouses, analytical stores, integration and delivery layers.
Data management
Practical stewardship, ownership and design conventions that keep data usable over time.
Approach
Understand → Model → Integrate → Govern → Deliver
Architecture is useful when each decision makes the next one easier to reason about.
01Understand
→
Map sources, users, decisions, constraints and failure points.
02Model
→
Define business meaning, grain, relationships and durable structures.
03Integrate
→
Design how data moves, changes and reconciles across boundaries.
04Govern
→
Clarify authority, ownership, definitions and change expectations.
05Deliver
Expose data in forms analytics and operational systems can trust.
Typical outcomes
Less ambiguity downstream.
The goal is not a prettier architecture diagram. It is a data environment where people can understand what a number means, where it came from and how safely the system can change.
More reliable analytics
Reporting and decision layers inherit clearer, more stable foundations.
Less duplicated logic
Shared structures reduce competing calculations and one-off transformations.
Clearer ownership
Teams can identify the authority for important sources and definitions.
Reusable models
New reporting needs build on governed structures rather than restart from scratch.
Safer change
Dependencies and interfaces are easier to understand before modifying the system.
Your data environment doesn’t need another layer of complexity.
Tell us where it’s breaking down. We’ll start there.