Microsoft FabricBring data integration, storage, engineering and analytics into a shared platform. We implement Microsoft Fabric around trusted sources, useful models and the operational questions your teams need to answer.
Select the workloads your use case needs, then agree ownership, freshness and access for the resulting data products.
Use supported pipelines and dataflows to ingest and transform source data.
Organise the shared data estate around Fabric items and the selected storage approach.
Build and operate transformations in lakehouses and notebooks.
Prepare structured analytical data in a warehouse for agreed business queries.
Work with supported event and streaming data when operational freshness matters.
Serve analytical models and reports with configured access, ownership and capacity.
Choose a useful role for AI, then define the information, access and review needed for that role.
Use workload-specific Copilot assistance for supported authoring and analysis tasks. Review generated queries, transformations and interpretations.
Build conversational, read-only access to selected analytical data with instructions and tested questions. The core data agent is documented as generally available; some extensions remain previews.
Develop and evaluate prediction models in the data-science workload when the use case needs them. Agree input quality, evaluation and monitoring separately from reporting.
Combine the agreed product configuration with the systems and development that make the complete experience work.
Combine payment-provider activity, ERP postings and commercial records into reconciled operational reporting.
Discuss Payments, Refunds & Revenue in One ViewJoin equipment or service events with asset and customer data to identify actionable operational conditions.
Discuss Live Service & Equipment SignalsLet authorised users ask supported questions about sales, service or operational measures.
Discuss a Governed Business Data AgentChoose an example to see the work, decision, handover and completion point.
Follow the work through the decision, handover and confirmed result.
Load provider events, ERP entries and relevant commercial records.
Apply transaction references and explicit exception rules.
Define revenue, refunds and outstanding amounts with finance.
Publish reporting and monitor refresh or reconciliation failures.
Measures to Agree
Transactions matched across the required sources.
Time since each required source update.
Transactions awaiting an agreed reconciliation.
Scheduled data updates completed successfully.
Follow the work through the decision, handover and confirmed result.
Receive the supported service or equipment stream.
Join signals to the relevant asset, location or customer.
Apply the agreed threshold or analysis rule.
Send the actionable result to the system responsible for intervention.
Measures to Agree
Delay between source event and available analytical signal.
Detected conditions accepted for intervention.
Time from accepted signal to owned action.
Recurring conditions after the recorded intervention.
Follow the work through the decision, handover and confirmed result.
Prepare the selected tables and business definitions.
Provide instructions and limit the available analytical sources.
Test supported questions, permissions and uncertain responses.
Monitor use and assign source or instruction gaps to an owner.
Measures to Agree
Evaluated answers matching the expected business result.
Supported questions with an appropriate data source.
Permission tests passing the intended boundaries.
Authorised users returning to the data-agent experience.
Use the application that owns each record and make the handover between systems explicit.
Agree the business outcome, prepare the foundation and validate the complete experience before expanding it.
Choose the first process, its owner, completion criteria and baseline measures.
Capacity, source access, data ownership, refresh commitments, semantic definitions and the selected AI prerequisites determine the design.
Configure or develop the agreed experience, permissions and system handovers.
Check realistic data, permissions, exceptions and confirmed outcomes. Prepare the team for a controlled launch.
Review source-confirmed results, operational issues and adoption before extending the next use case.
The starting point depends on the process and the systems you already use.
Power BI is the reporting and analytical experience within the broader Fabric platform. Fabric also includes ingestion, engineering, warehousing and other data workloads. A reporting-only need may not require a wider data-platform implementation.
Start with a specific task and the evidence needed to evaluate it. We check current feature status, environment prerequisites, data quality, permissions and cost. Preview capabilities are assessed separately from the agreed production scope.
The examples describe configured or custom solutions. The feature cards explain the product or delivery foundation; each use case and process identifies the connections, development and related services it needs.
Capacity, source access, data ownership, refresh commitments, semantic definitions and the selected AI prerequisites determine the design. We also agree ownership, acceptance criteria, operating responsibility and the first measurable outcome.
Explore the applications, data platforms, and development capabilities that can support your requirements.
Browse practical use cases for payments, subscriptions, portals, approvals, customer communications, and connected operations.
Tell us where customers or teams lose time, context or a clear next action. We’ll help identify a practical starting point for Microsoft Fabric.
