Auto Dashboard with MS Fabric Metric Intelligence Layer- KHantix 02-13-2026 01:44 AM

[duongthuyhoang]

Metric Intelligence Layer for Microsoft Fabric- KHantix

Problem Solved

Pain Point

Small and medium-sized businesses struggle not because they lack data — but because they lack clarity on what metrics to measure and how to calculate them correctly.

Current BI workflows create several challenges:

• Metric definitions are inconsistent across teams
• Users must manually determine which KPIs are relevant
• DAX and calculation logic require technical expertise
• AI tools (e.g., Copilot) cannot reliably recommend metrics without structured metadata
• Dashboard creation is time-consuming and error-prone

  Even when semantic models exist, they do not guide users toward validated, business-aligned metrics. The result is duplicated effort, incorrect KPI logic, and low trust in analytics.

  Solution

  Metric Intelligence Layer introduces a metrics-first automation approach inside Microsoft Fabric.

  The workload:

  • Automatically scans uploaded datasets to detect business entities and structures
  • Matches detected entities against curated, industry-standard metric libraries
  • Validates data feasibility before recommending metrics
  • Programmatically generates DAX measures
  • Assembles structured, ready-to-use dashboard templates
  • Stores metric metadata and artifacts in OneLake for governance and reuse

    All of this occurs within Fabric, enabling teams to move from raw data to trusted dashboards in a guided, reproducible workflow.

    How It Works

    Create

    Add a new Metric Intelligence Layer item in Fabric and open the Setup tab.

    Configure

    Select:

    • Workspace
    • Lakehouse or Warehouse
    • Target dataset or data source

      All selections are made through controlled drop-down menus — no manual ID inputs required.

      Scan

      Click “Analyze Dataset”.

      The workload programmatically creates and executes a Fabric Notebook that:

      • Extracts schema and metadata
      • Identifies business entities (customers, transactions, campaigns, revenue, etc.)
      • Validates available fields and date granularity
      • Prepares structured metadata artifacts

        The process leverages Fabric-native APIs and notebook orchestration — no repeated manual token handling required.

        Recommend

        Based on detected entities, the system:

        • Matches the dataset against industry metric libraries (Sales, Marketing, SaaS, Finance, E-commerce)
        • Recommends relevant KPIs
        • Explains why each metric matters
        • Flags feasibility constraints if required fields are missing

          Only validated metrics are suggested.

          Generate

          When the user confirms selected metrics:

          • DAX formulas are automatically generated
          • Measures are structured for semantic model integration
          • A dashboard scaffold is assembled programmatically
          • Structured metric metadata is stored in OneLake (Delta tables + JSON packages)

            Review & Refine

            Users can:

            • Inspect generated metric definitions
            • Modify descriptions or tags
            • Adjust metric parameters
            • Export metric metadata packages for AI/Copilot workflows

              Key Features

              AI-Powered Metric Detection

              • Automatically detects business entities and recommends KPIs aligned with industry best practices.

                Data Feasibility Validation

                • Ensures required fields and granularity exist before proposing a metric.

                  Automated DAX Generation

                  • Produces validated, standardized DAX measures ready for semantic model integration.

                    Dashboard Scaffolding

                    • Creates aligned dashboard templates based on selected metrics.

                      Governed Metric Library

                      • Stores metric definitions as certified Delta tables in OneLake with version tracking.

                        AI-Ready Metadata

                        • Generates structured metadata packages optimized for Copilot and agent retrieval scenarios.

                          Fabric-Native Orchestration

                          • Uses Fabric notebooks, OneLake storage, and semantic model integration through platform APIs.

                            Limitations

                            The current prototype focuses on demonstrating the core value proposition and workflow:

                            • Automated push of measures directly into existing semantic models may require additional permissions or refinement
                            • Industry metric libraries are extensible but currently limited to predefined domains
                            • Advanced Copilot integration is demonstrated conceptually rather than fully automated
                            • UI refinement and configuration improvements are ongoing

                              The workload is functional and demonstrates the end-to-end automation flow, with minor enhancements planned post-contest.

                              Why It Matters

                              Time Savings

                              • Reduces dashboard creation time from weeks to hours by eliminating manual metric definition and validation.

                                Increased Accuracy

                                • Standardized, validated metric logic improves consistency and trust across teams.

                                  Governance-Ready

                                  • Metric definitions, validation artifacts, and metadata are stored in OneLake for auditability and reuse.

                                    AI Enablement

                                    • Structured metric metadata enhances Copilot’s ability to recommend and explain KPIs correctly.

                                      Scalable Pattern

                                      The metrics-first architecture can be extended to:

                                      • Industry-specific KPI packs
                                      • Automated metric quality scoring
                                      • Cross-model metric comparison
                                      • Agent-driven analytics workflow

                                        Metric Intelligence Layer transforms Fabric from a powerful BI platform into a guided analytics engine — enabling organizations to build trusted dashboards without expanding technical headcount.

                                        • Demo Video: https://share.synthesia.io/f746cf1f-fd37-4b92-9b08-a66c1733f345 
                                        • Figma mockups: https://www.figma.com/make/FpbYvb5wEM5HmvoeaIN7y9/AI-Metrics-Dashboard-UI?p=f&t=tv7gplCe4mof9jlx-0&…...