Data, analytics and Microsoft Fabric

AI is only as good as the data it can trust

Fabric readiness, data platform modernisation, data engineering, Power BI and governance — AI-ready data foundations.

Request a data readiness assessmentSee the approach

The challenge

Data spread across silos, without ownership or classification, limits both analytics and AI. Fabric can unify it — if the platform is designed and governed well.

What you get

  • One governed analytics platform
  • Data products with owners and quality checks
  • Self-service analytics that stays secure
  • Data ready to ground AI

Reference topology

Microsoft Fabric data platform reference topology

ERP, CRM, files and operational databases land in OneLake through Data Factory pipelines and mirroring. Lakehouse, warehouse, real-time and data-science workloads share one copy of data; semantic models in Direct Lake mode feed Power BI and data agents, with Purview cataloguing and lineage.

Microsoft Fabric data platform reference topologyERP, CRM, files and operational databases land in OneLake through Data Factory pipelines and mirroring. Lakehouse, warehouse, real-time and data-science workloads share one copy of data; semantic models in Direct Lake mode feed Power BI and data agents, with Purview cataloguing and lineage.MICROSOFT FABRICERP · financeSAP · DynamicsCRM · SaaSAPIs · exportsFiles & IoTCSV · logs · eventsOperational DBsSQL · Cosmos DBData FactorypipelinesMirroringnear real-timeOneLakeone copy of dataPurviewcatalog · lineageLakehousebronze→silver→goldWarehouseT-SQL · martsReal-timeeventstream · KQLData sciencenotebooks · MLSemantic modelDirect LakePower BIreports · CopilotData agentsask your data
Data / request in flightAgent / workload runningIllustrative reference topology

What we deliver

  • 01Fabric readiness
  • 02Data platform modernisation
  • 03Data engineering
  • 04Power BI
  • 05Data governance
  • 06AI-ready data foundations
  • 07Enterprise analytics

Architecture layers

Ingest
  • Pipelines
  • Mirroring and shortcuts
  • Streaming
Store and model
  • Lakehouse
  • Warehouse
  • Semantic models
Use
  • Power BI
  • AI and agents
  • Power Platform apps and flows
Govern
  • Purview catalogue and lineage
  • Sensitivity labels
  • Access control
Data → AI → apps → automation

Technologies and availability

Also used

  • Power BI
  • Power Platform
  • Microsoft Purview

Availability checked against official vendor sources on 11 October 2026. Features, licensing and regional availability change — we confirm them for your tenant during assessment.

How we work

  1. Stage 1

    Assess

    Data maturity and AI-readiness mapping.

  2. Stage 2

    Design

    Workspace, capacity, lakehouse and governance design.

  3. Stage 3

    Build

    Ingestion, models and reports.

  4. Stage 4

    Enable

    Self-service and data-product ownership.

Security and governance, built in

  • Classification before AI grounding
  • Lineage for critical reports
  • Row-level and object-level security
  • Capacity and cost monitoring

Ways to engage

Assessment
A fixed-scope review with findings, priorities and a costed roadmap.
Pilot
A time-boxed build with agreed success criteria and a go/no-go decision.
Delivery
Full implementation by a Techcerv team working alongside yours.
Managed
Ongoing operation and improvement under a contracted service scope.

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