Connecting data across the enterprise to create one accessible and trusted data foundation
A data fabric transformation that connected fragmented data sources, platforms and business domains through a common architecture for discovery, integration, governance and analytics.
Data was distributed across systems, but the organization had no common way to discover and connect it.
Different business functions had built their own data pipelines, repositories and integration patterns. Analysts often depended on engineering teams to find the right source, understand its meaning and prepare it for use.
What we found
- More than 60 enterprise data sources across operational and analytical environments.
- Multiple integration patterns for similar source systems.
- Data duplicated across warehouses, lakes, reporting stores and departmental environments.
- Limited visibility into where trusted data was located.
- Business definitions differed between domains.
- Data consumers frequently created local extracts for recurring analysis.
- Adding a new source required significant engineering effort and coordination.
What the business needed
- A common architecture for connecting distributed data.
- Faster discovery of trusted enterprise data.
- Reusable integration and access patterns.
- Governed data products that could be consumed by multiple teams.
- Reduced duplication across analytical environments.
- Clear lineage and ownership across connected data.
- A scalable foundation across Databricks, Fabric and Snowflake environments.
We created a connected data fabric that made distributed data easier to find, access, govern and reuse.
The solution did not require every source to move into one platform. Instead, we established common integration, metadata, governance and access patterns across the existing data landscape.
Data fabric transformation approach
The team created a common operating layer across data sources and platforms while allowing each business domain to retain the technology best suited to its workloads.
- Mapped enterprise data sources, domains, owners and consumers.
- Created common ingestion and integration patterns for batch and near real time workloads.
- Established a metadata and catalog layer to improve data discovery.
- Connected data quality, lineage and ownership information to critical datasets.
- Created reusable domain data products for analytics consumption.
- Reduced unnecessary replication by enabling controlled access to data across platforms.
A connected data layer across enterprise sources, modern platforms and business consumers
The architecture allowed data to remain distributed while creating common controls for integration, metadata, governance and consumption.
A six stage model for connecting distributed enterprise data
The implementation started with priority data domains and expanded as reusable patterns were established.
Map
Inventory sources, platforms, domains, owners, consumers and integration dependencies.
Prioritize
Select high value data domains and use cases for the first implementation waves.
Connect
Implement reusable ingestion and integration patterns across source environments.
Organize
Create domain data products and a common metadata and discovery layer.
Govern
Apply quality, ownership, lineage, security and lifecycle controls.
Scale
Extend the architecture to additional domains, sources and analytics workloads.
The data fabric reduced integration effort and made enterprise data easier to discover and reuse.
Faster source onboarding
Reusable integration patterns reduced the effort required to connect new enterprise data sources.
Faster data discovery
Cataloging, metadata and ownership information improved the time required to locate trusted datasets.
Less duplicated movement
Controlled access and reusable data products reduced unnecessary copying and repeated integration work.
Sources connected
Operational and analytical sources were brought into a common integration and governance framework.
Lower integration effort
Standard patterns reduced repeated engineering work across business domains.
Higher data reuse
Domain data products allowed multiple analytics teams to consume common trusted datasets.
The organization moved from disconnected data environments to a connected enterprise data foundation.
The data fabric improved how teams discovered, accessed and reused information without requiring a single platform to replace every existing system.
Faster Access to Data
Analysts and data teams could locate trusted datasets faster through common metadata, ownership and discovery patterns.
Lower Integration Effort
Reusable patterns reduced the need to build separate integration approaches for every source and business function.
Better Data Reuse
Domain data products reduced repeated preparation and allowed the same trusted data to support multiple use cases.
Scalable Enterprise Architecture
The architecture could expand across Databricks, Microsoft Fabric and Snowflake as new workloads and business domains were introduced.
Connect enterprise data without creating another silo.
From data landscape assessment and integration architecture to metadata, governance and domain data products, a data fabric can make distributed enterprise data easier to use and scale.
Discuss your data fabric program