Trusted data consumption
Curated Gold-layer data products replace manually exported CSVs as the default source for approved internal applications.
- Managed ingestion
- Shared business definitions
- Curated schemas
Case study · Interlinked
Scaling internal AI innovation on a governed data foundation
Interlinked connected home-grown AI applications to curated Microsoft Fabric data, keeping the pace of experimentation while adding the architecture, access and ownership needed to scale safely.
- Client
- Interlinked
- Industry
- Technology and managed services
- Engagement
- Enterprise AI architecture · Fabric enablement
- Context
- Existing Microsoft Fabric foundation
FoundationMicrosoft FabricMicrosoft identityCurated Gold data products
Outcomes
What changed
Curated
Gold-layer data replaces manual CSV exports
Shared
Business definitions are applied once and reused
Defined
Experiments have a clear path toward production
Governed
Controls increase with value, risk and audience
Enabled
Internal champions receive patterns and guardrails
Extended
Fabric investment supports reporting, automation and AI
The challenge
Useful AI experiments were starting to depend on fragile data preparation
Interlinked had strong internal AI momentum. Teams were building AI-assisted workflows, automations and lightweight business tools with local files, manually exported CSVs and desktop environments.
That approach worked for experimentation, but not for applications the wider organisation might rely on. Exports became outdated, different applications used different versions of the same information, business rules were prepared repeatedly and successful tools depended on the person who built them.
Interlinked did not need less experimentation. It needed a practical way for the right experiments to mature into governed business applications.
The approach
Connect experimentation to the Fabric foundation already in place
DataMust had previously helped Interlinked establish a Microsoft Fabric foundation. This engagement defined how internal AI applications should consume that data and what must change as a tool moves from personal experiment to shared capability.
- Review the internal AI portfolio and where data enters each workflow
- Use curated Gold-layer data products as the default consumption point
- Define consistent authentication, access and write-back principles
- Give approved internal champions reusable patterns and guardrails
- Progress applications according to their value, risk and audience
Microsoft FabricGoverned data backbone
Connects to
Operational systems
Curated Gold data products
Internal AI applications
Workflow automation
Reporting and interfaces
The target model
AI applications consume curated data products, not exports
The target model separates fast experimentation from the controls required when an application becomes shared, relied upon or operationally important.
Proportionate governance
Controls increase with the application: a personal experiment stays lightweight, while a shared or enterprise capability gains review, ownership and support.
- Personal experiment
- Team productivity tool
- Enterprise capability
A path to production
Reusable integration patterns, access principles and internal champion guidance help the right applications move into reliable business use.
- Authentication and access
- Write-back principles
- Defined ownership
The model protects the speed of internal innovation without allowing every successful experiment to become a new shadow system.
Strategic impact
Experimentation kept moving, with clearer boundaries for production
Interlinked established a clear model for consuming curated Fabric data instead of preparing separate copies for each application. Business definitions can be applied once in the platform and reused across reporting, automation and AI.
The maturity model also gave teams a shared understanding of what an operational application requires. Personal experiments can remain fast and flexible, while shared capabilities gain proportionate review, ownership, security and support.
Before“Manual exports, local preparation and person-dependent tools.”
After“Curated data products, reusable patterns and defined ownership.”
The existing Fabric investment now supports internal AI innovation as well as reporting, without creating another generation of disconnected systems.
Why it matters
AI rarely fails for lack of tools
The harder constraints are fragmented data, unclear ownership and the gap between a useful prototype and a production application.
Eliminating experimentation slows innovation. Letting every experiment evolve independently creates new silos. Interlinked chose a middle path: trusted data, clear architecture and governance proportionate to how each application is used.
Similar priority?
Are useful AI experiments becoming difficult to govern or scale?
Book a 30-minute fit call to decide whether you need an AI readiness roadmap or are ready to connect a defined use case to governed business data.