The Modern Microsoft Analytics Platform: Enterprise Migration to AI-Augmented Intelligence

The Platform Problem: Why Most Analytics Investments Underperform

After over 200 enterprise analytics implementations, I have noticed a clear pattern. Organizations that use Power BI as a standalone tool achieve limited results. In contrast, those that develop a complete Microsoft analytics platform see greater benefits.

This comprehensive platform includes:

• Data engineering
• AI-augmented decision-making

These organizations experience compound returns that grow over time.

The Microsoft analytics stack in 2026 is the most powerful enterprise analytics platform ever assembled. It includes:

• Azure Data Factory for ingestion
• Fabric OneLake for storage
• Power BI for visualization
• Purview for governance
• Copilot for AI-augmented insights

However, most organizations are only using about 20% of this platform.

In many enterprise engagements, I observe several common issues:

• Power BI is used solely as a reporting tool, lacking a governance framework.
• Data is stored in 4-6 disconnected systems without a unified catalog.
• Analytics teams spend 60% of their time on data plumbing instead of analysis.
• There is no semantic model strategy, leading to multiple report authors creating their own version of "revenue."
• AI augmentation is absent because the data foundation is not ready.

The cost of fragmentation in analytics is substantial. A Fortune 500 client we onboarded last year spent $47,000 per month on seven different analytics tools.

Despite this investment, they faced challenges in answering basic cross-departmental questions. Each inquiry required a two-week data integration project.

After building a unified platform, their analytics infrastructure cost dropped to $28,000 per month. Additionally, their time-to-insight improved from weeks to hours.

This guide is your blueprint for success. It details the five-stage journey from legacy BI migration to AI-augmented intelligence.

• Each stage features specific Microsoft technologies.
• Implementation patterns are clearly defined.
• ROI benchmarks are provided for each stage.

This is not just theory. It is the playbook we have refined through over 200 enterprise implementations.

The 5-Stage Microsoft Analytics Platform Journey

Every successful analytics modernization follows a clear sequence. Skipping a stage can lead to problems later on. When each stage is completed in order, it increases the ROI of the following stages. Below is the framework:

Stage 1: MIGRATE — Legacy BI to Azure and Fabric

Migration is the starting point for analytics modernization. However, many organizations make a key mistake here. They migrate reports one-to-one without reconsidering the data architecture that supports them.

A legacy SSRS environment with 500 reports does not need 500 Power BI reports. It requires a strong semantic model layer for self-service analytics. This approach usually reduces those 500 reports to:

• 50 to 100 Power BI reports
• Improved data accuracy
• Faster report generation

• 30-50 well-designed Power BI datasets
• Accessible for business users to explore directly

The migration offers a chance to streamline processes, not just move them.

Migration Paths by Source Platform

SSRS to Power BI

SQL Server Reporting Services is the most common legacy BI platform we see in enterprise environments. The migration process includes several key steps:

• Catalog all SSRS reports and identify which are active versus dormant. Typically, 40-60% of SSRS reports haven't been viewed in 12 months.
• Map SSRS shared datasets to Power BI semantic models.
• Convert RDL report definitions to Power BI paginated reports for pixel-perfect needs or to interactive Power BI reports for analytical use.
• Migrate SSRS subscriptions to Power BI scheduled delivery and Power Automate notifications.
• Decommission the SSRS server infrastructure.

For organizations with heavy paginated report requirements — healthcare claim forms, financial statements, regulatory filings — Power BI paginated reports provide pixel-perfect rendering with the same RDL format, hosted in the Power BI service with no on-premises server required.

Cognos to Power BI

IBM Cognos migrations are complex due to the close connection between Cognos Framework Manager models and the reporting layer. This process requires rebuilding the semantic layer in Power BI instead of simply converting it.

Our approach includes several key steps:

• Reverse-engineering the Cognos Framework Manager model to document business logic.
• Recreating that logic as Power BI semantic models with DAX measures.
• Rebuilding reports against the new semantic models.
• Validating data accuracy with automated comparison testing.
• Running parallel environments for 30-60 days before cutover.

Cognos migrations typically take 50% longer than SSRS migrations. This is mainly because the semantic layer needs to be rebuilt.

Despite the longer duration, the result is a much more flexible analytics environment.

Cognos users often find Power BI's self-service features surprisingly beneficial. These features were theoretically possible in Cognos but were mostly out of reach for many business users.

Tableau to Power BI

Tableau migrations are technically straightforward but can be politically difficult. Users often feel strongly about their tool. To ensure a successful migration, it is crucial to demonstrate that Power BI can match or exceed Tableau's visualization features.

Additionally, Power BI offers:

• Enterprise governance
• Microsoft 365 integration
• AI features that Tableau lacks

The technical migration includes several key steps:

• Converting Tableau data sources to Power BI semantic models.
• Mapping Tableau calculated fields to DAX measures.
• Recreating dashboards using Power BI's visualization library, along with custom visuals when necessary.
• Migrating Tableau Server or Online security and distribution to Power BI workspaces and apps.
• Retraining Tableau users on Power BI Desktop and the web experience.

Cost savings often make migration worthwhile. Here are the key pricing details:

• Tableau Creator licenses: $75/user/month
• Power BI Pro: $10/user/month
• Fabric F64: Offers Pro-equivalent capabilities for all capacity users

On-Premises Data Warehouse to Fabric

Organizations using SQL Server Analysis Services (SSAS), SQL Server Integration Services (SSIS), and SQL Server Data Warehouse on-premises can modernize their infrastructure by migrating to Fabric. This migration offers several benefits:

• Improved performance and scalability
• Enhanced data integration capabilities
• Access to advanced analytics tools

• SSIS packages mapping to Data Factory pipelines in Fabric.
• SSAS tabular models transforming into Power BI semantic models, often with Direct Lake connectivity to Fabric Lakehouse.
• SQL Server data warehouses moving to Fabric Warehouse for T-SQL compatibility or Fabric Lakehouse for a modern medallion architecture approach.

Migration ROI Benchmarks

Stage 2: GOVERN — Microsoft Purview, Security, and Data Catalog

Governance begins on day one, right alongside migration. It is called Stage 2 because it becomes the main focus after the initial migration wave stabilizes.

Organizations that treat governance as an afterthought may end up with a Power BI environment that is just as chaotic as the legacy system it replaced. The main difference is that this disorder now exists in the cloud.

The governance layer turns a set of reports into a complete enterprise analytics platform. It creates a distinction between:

• “We have Power BI”
• “We have a governed, trusted, auditable analytics environment.”

Microsoft Purview: The Governance Hub

Microsoft Purview serves as the unified governance layer across the entire analytics platform. For enterprise analytics, the critical Purview capabilities include a data catalog that automatically discovers and classifies data assets across Fabric, Azure SQL, and other sources, sensitivity labels that flow from data source through transformation to Power BI report (a financial dataset labeled "Confidential" carries that classification all the way to the dashboard), data lineage that traces every report back to its source system through every transformation step, and access policies that enforce who can see what data across all platform components.

The sensitivity label integration is especially useful in regulated industries. For example, when a healthcare organization labels patient data as "HIPAA Protected" in Purview, it triggers several automatic actions:

• Restricts export capabilities in Power BI
• Prevents sharing outside the organization
• Generates audit events when the data is accessed

This approach ensures governance operates automatically, reducing reliance on individual users to follow policy.

Row-Level Security and Object-Level Security

Enterprise Power BI deployments require security that goes beyond workspace-level access. Row-level security (RLS) restricts the data a user can see based on their identity.

• A regional sales manager can only view data for their specific region.
• The VP of Sales has access to all data across regions.

Object-level security (OLS) restricts access to specific tables or columns for certain users. For instance, salary data is available to HR and finance teams. However, this information is not visible to other departments.

The effective implementation pattern for enterprise scale is role-based Row-Level Security (RLS) using security groups in Microsoft Entra ID. You should:

• Define security roles in the Power BI semantic model.
• Map those roles to Entra ID security groups.
• Manage membership through your existing identity governance process.

This method can scale to thousands of users without requiring per-user configuration.

Deployment Pipelines: Governed Promotion Workflows

Production analytics environments need a careful development-to-production process, just like production software. Power BI deployment pipelines include three stages:

• Development
• Test
• Production

Each stage has controlled promotion to ensure quality.

• Report developers work in Development workspaces.
• Validated changes move to Test for User Acceptance Testing (UAT) and data validation.
• Only approved changes reach Production, where business users access them.

For enterprise organizations, we enhance this with Azure DevOps or GitHub integration. This allows for version control of Power BI artifacts. We also provide:

• Automated testing that compares Development and Production data outputs.
• Approval gates that require sign-off before promoting to Production.
• Rollback capability for issues discovered after deployment.

Data Catalog and Discovery

A governed analytics platform allows users to find trusted data assets independently, without needing to consult the analytics team. Purview's data catalog offers:

• Enterprise search across all data assets
• Business glossary terms that standardize definitions (e.g., "revenue" means the same thing everywhere)
• Endorsement labels: "Certified" for IT-validated datasets and "Promoted" for business-unit-validated datasets
• Domain organization that groups assets by business function

The practical impact is considerable. Without a data catalog, each new analytics project starts with the question, "Where is the data and can I trust it?"

This question can take days to resolve.

In contrast, a well-maintained catalog allows you to find that answer in minutes.

Stage 3: ANALYZE — Power BI Semantic Models, DAX, and DirectLake

The analytics platform starts to deliver significant returns at this stage. With data migrated to Fabric and governance in place, Stage 3 emphasizes building the semantic model layer.

This layer converts raw data into valuable business intelligence.

The Enterprise Semantic Model Strategy

The semantic model is frequently the least funded aspect of enterprise Power BI deployments. Yet, it represents the most valuable investment you can make. A well-structured semantic model:

• Captures business logic, such as the definitions of "revenue" and "active customer."
• Stores these definitions in one central location.
• Ensures that every report uses the same definitions.

Without a semantic model, each report author uses their own definitions. This can lead to confusion. For instance, the CFO might see three different revenue figures based on the dashboard she accesses.

We implement an enterprise semantic model architecture that uses a hub-and-spoke pattern. The hub contains shared enterprise semantic models with core business entities, including:

• Customers
• Products
• Transactions
• Financials

These models feature standardized DAX measures. Each department has specific spokes that add unique calculations for their areas.

All models connect to Fabric Lakehouse or Warehouse using Direct Lake or DirectQuery. This setup ensures a single version of truth.

DAX: The Analytics Logic Layer

DAX (Data Analysis Expressions) is the formula language that powers Power BI's analytical capabilities. At the enterprise level, DAX mastery is what separates a reporting tool from an analytics platform. Advanced DAX patterns that enterprise organizations should invest in include time intelligence calculations (year-over-year, moving averages, same-period-last-year comparisons), semi-additive measures for snapshot data (inventory levels, account balances), calculation groups that apply common transformations (currency conversion, year-to-date) across all measures, and dynamic security implementation using DAX for row-level and column-level restrictions.

The DAX layer is crucial for improving AI capabilities. Well-structured measures with clear names and descriptions enable Copilot to produce accurate natural language insights. On the other hand, poorly structured models can result in hallucinated or misleading outputs.

For this reason, we view semantic model quality as essential for Stage 5:

• Enhances AI capabilities
• Enables accurate insights from Copilot
• Avoids misleading outputs

Composite Models and DirectLake: The Performance Architecture

Enterprise datasets that span billions of rows need careful mode selection. There are three connectivity modes, each serving different use cases:

• Import mode: Loads data into Power BI's in-memory engine for maximum query performance. This mode is suitable for datasets up to 10-25GB.
• DirectQuery: Sends queries to the source database at report time. It provides real-time data, but performance depends on the speed of the source system.
• Direct Lake: Available exclusively in Fabric, this mode loads Delta table column chunks directly into memory from OneLake. It combines import-mode performance with near-real-time freshness and requires no scheduled refresh.

Composite models enable the integration of different data modes within a single semantic model. We often use the following pattern:

• Core dimension tables in import mode for quick filtering and slicing.
• Large fact tables in Direct Lake mode for up-to-date data without refresh delays.
• Real-time operational tables in DirectQuery mode for live dashboards.

This hybrid approach provides sub-second query performance on datasets with billions of rows. It also keeps data current without the need for scheduled refreshes.

Microsoft Fabric Deep Dive: OneLake and Medallion Architecture

The data architecture underlying the semantic model layer is critical. Microsoft Fabric's OneLake provides a single data lake for all analytics workloads, and the medallion architecture pattern — Bronze, Silver, Gold layers — provides the structure for organizing data within OneLake.

The Bronze layer stores raw, unprocessed data exactly as it is received from source systems. There are no transformations or cleaning—just a faithful copy for auditability and reprocessing.

The Silver layer applies data quality rules. It standardizes formats, deduplicates records, and resolves entity references. Most of the data engineering effort is focused here.

The Gold layer contains business-ready, aggregated, and optimized datasets designed specifically for analytics consumption. Power BI semantic models connect to Gold layer tables through Direct Lake mode.

This architecture offers several key features:

• Reprocessing capability: You can rerun Silver and Gold from Bronze when business logic changes.
• Clear data quality boundaries: Bronze is raw, Silver is clean, and Gold is trusted.
• Performance optimization: Gold tables are pre-aggregated for faster queries.
• Governance clarity: Sensitivity labels and access policies are defined at each layer.

Stage 4: AUTOMATE — Power Automate, Alerts, and Operational Analytics

Stage 4 transforms analytics from a pull activity to a push activity. Instead of simply opening the dashboard for insights, the system now alerts you when something needs attention. This change enables analytics to drive action rather than just inform awareness.

Power BI Data-Driven Alerts

Power BI alerts trigger notifications when a metric crosses a threshold. At the enterprise level, these become operational intelligence — inventory drops below reorder point, customer churn probability exceeds 70%, revenue variance exceeds 5% from forecast. The alert configuration is straightforward: set a threshold on any gauge, card, or KPI visual and specify email or Teams notification. The enterprise value comes from connecting these alerts to Power Automate workflows that trigger downstream actions.

Power Automate Integration Patterns

We implement several impactful automation patterns to enhance efficiency:

• Alert-to-ticket workflows: A Power BI alert for SLA breaches automatically creates a ServiceNow or Jira ticket assigned to the responsible team.
• Executive summary distribution: Weekly Power Automate flows export Power BI report pages as PDFs and send them via email or Teams to executives who prefer static reports.
• Data quality monitoring: Automated flows check for data freshness, null rates, and schema changes. They alert the data engineering team before bad data reaches reports.
• Approval workflows: Anomalous data changes, such as a customer order 10x larger than average, trigger approval workflows before being reflected in production dashboards.

Scheduled Refresh and Dataflow Orchestration

Dataflows provide a managed ETL experience for Power BI-centric data transformation. In the context of a Fabric-based analytics platform, dataflows serve a specific niche: enabling business analysts to perform data preparation without requiring data engineering resources. Enterprise dataflow patterns include shared dataflows that centralize common transformations (currency conversion, date enrichment) used by multiple semantic models, incremental refresh that processes only changed data to reduce refresh times from hours to minutes, and linked entities that reference dataflows from other workspaces to promote reuse without duplication.

For organizations using Fabric, a more effective method is Gen2 dataflows. These dataflows write directly to OneLake. This makes the transformed data accessible to all Fabric workloads, not just Power BI.

Stage 5: AI-AUGMENT — Copilot, Azure OpenAI, and Decision Intelligence

The platform investment provides substantial returns. AI augmentation is not merely an additional feature; it is a capability. This capability arises from a well-governed, semantically rich, automated analytics platform.

Skipping Stages 1-4 leads to unreliable results. However, if you execute Stages 1-4 effectively, AI augmentation can be transformative.

Copilot in Power BI

Copilot in Power BI allows you to interact with your analytics platform using natural language. Its production-ready features include:

• Creating report pages from natural language prompts. Just describe the analysis you need, and Copilot generates the visuals.
• Providing narrative summaries that automatically explain what a report page displays. These summaries update dynamically as filters change.
• Generating DAX formulas from business language descriptions.
• Enhancing Q&A capabilities by using semantic model metadata for more accurate natural language queries.

The key to Copilot's success is the quality of the semantic model. A well-structured model includes:

• Well-named tables and columns
• Defined relationships
• Descriptive measure descriptions
• A clean data model

Organizations that invested in Stage 3 semantic model design achieve significantly better AI augmentation results than those that did not. This demonstrates the power of compound returns in action.

Azure OpenAI Integration for Custom Analytics AI

Beyond Copilot's built-in capabilities, Azure OpenAI integration enables custom AI-augmented analytics scenarios. Anomaly narratives: when Power BI detects an anomaly, Azure OpenAI generates a contextual explanation drawing from historical patterns and external factors. Insight generation: automated daily or weekly insight reports that identify the most significant changes, trends, and outliers across the analytics platform and explain them in business language. Conversational analytics: custom chat interfaces built on Azure OpenAI that allow users to query the analytics platform in natural language, with responses grounded in governed Power BI semantic models. Recommendation engines: prescriptive analytics that recommend actions based on predictive models and historical decision outcomes.

The governance layer from Stage 2 is essential. Azure OpenAI queries must follow the same row-level security, sensitivity labels, and access policies as direct report access.

We use a method that channels all AI queries through the Power BI REST API. This approach keeps the existing security model intact. It also prevents direct database access, which helps maintain governance.

Machine Learning Integration: The PREDICT Function

Organizations with data science capabilities can benefit from Fabric's PREDICT function. This feature links machine learning (ML) models to business analytics.

Data scientists can:

• Train models in Fabric notebooks using Python or PySpark.
• Register these models in the MLflow model registry.

Business analysts can easily use these models in:

• SQL
• Power BI

Common use cases we implement include:

• Customer churn prediction scores in CRM dashboards
• Demand forecasting in supply chain Power BI reports
• Credit risk scoring in financial reporting workflows
• Equipment failure prediction in operational monitoring dashboards

The PREDICT function allows business users to access machine learning (ML) easily. They do not need to understand the complex models behind it.

Instead, users will see a new column in their Power BI report that displays:

• Prediction score
• Probability
• Classification

EPC Group's Decision Intelligence Framework

The Decision Intelligence Framework is our unique method. It improves the 5-stage platform into a full decision system. Traditional analytics tells you what happened. In contrast, Decision Intelligence guides you on the actions to take and assesses their effectiveness.

The Decision Feedback Layer distinguishes this framework from a simple stack diagram. When the system suggests an action, such as providing a 15% discount to retain an at-risk customer, Power Automate monitors whether the action was executed. It also evaluates if the action led to the intended result.

This data is fed back into the predictive models. This process improves future recommendations. Over time, the system becomes smarter by learning from its own decisions.

This closed-loop pattern is what separates enterprise analytics platforms from enterprise decision systems. The platform informs. The decision system acts, tracks, learns, and improves.

Technology Stack: The Complete Microsoft Analytics Platform

ROI at Each Stage: The Compound Return Model

CTOs and CFOs often ask, "What's the ROI?" The honest answer is that ROI is cumulative and compounding. Each stage provides its own returns.

However, the true value arises from the interaction between these stages.

The compound effect is an important concept. AI augmentation (Stage 5) applied to ungoverned data results in unreliable outcomes. As a result, users cannot trust these results. In contrast, AI augmentation that utilizes governed, well-modeled, and automated data produces actionable intelligence.

This intelligence can significantly influence business decisions. The ROI of Stage 5 depends entirely on the quality of Stages 1-4.

Point Solution vs. Platform Approach: 3-Year TCO Comparison

The financial benefits of a platform approach become clear after three years. Here is a typical profile for a Fortune 500 organization:

• 5,000 analytics users
• 500 report authors

• Analytics users: 5,000
• Report authors: 500

The platform approach can save about $3M over three years. This method also provides much greater capability. The savings result from:

• Licensing consolidation
• Eliminated integration overhead
• Reduced FTE time spent on data plumbing

These figures are based on actual client engagements, not theoretical projections.

Analytics Center of Excellence (CoE) Playbook

A Microsoft analytics platform needs organizational support to avoid becoming shelfware. The Analytics Center of Excellence provides this support. It ensures the platform delivers ongoing value.

• Establish a clear vision for analytics.
• Develop a skilled team to manage the platform.
• Implement best practices for data governance.

CoE Structure: The Federated Model

The ideal Center of Excellence (CoE) model is federated. It consists of a small central team of 5 to 8 people. This model is best for organizations with over 5,000 employees.

• The central team sets standards.
• It maintains shared assets.
• It offers expertise.

Additionally, each business unit should have embedded analytics champions. These champions apply the standards locally and act as the first line of support.

Central team roles include:

• CoE Director: Owns the analytics strategy, KPIs, and executive communication.
• Platform Architect: Designs and maintains the Fabric workspace structure, semantic model architecture, and security model.
• Senior Analysts (2-3): Build and maintain shared enterprise semantic models and complex DAX logic.
• Governance Lead: Manages the Purview data catalog, sensitivity labels, and compliance.
• Training Lead: Develops and delivers role-based training programs.

CoE Operating Cadence

Weekly activities include:

• Report certification review: promote, certify, or request changes for submitted reports.
• Data quality standup: review automated quality monitoring alerts.
• User support queue: handle escalated questions from business unit champions.

Monthly activities consist of:

• Adoption metrics review: active users, self-service report creation rates, and report usage patterns.
• Platform health check: capacity utilization, refresh success rates, and query performance.
• Champion community meeting: share best practices, announce new features, and collect feedback.

Quarterly activities include:

• Strategy review with executive sponsors.
• Platform roadmap update.
• Training curriculum refresh based on adoption patterns and skill gaps.

CoE Success Metrics

For a mature Center of Excellence (CoE), the key metrics include:

• Self-service ratio: Aim for 80% of new reports created by business users, not IT.
• Report certification rate: Target 90% of production reports to be endorsed as Certified or Promoted.
• Time-to-insight: Strive for less than 4 hours from question to answered report.
• Data quality score: Keep the null rate in the Gold layer below 2% and known data quality issues under 0.1%.
• Adoption depth: Ensure 60% of licensed users actively use analytics weekly.
• Platform cost per user: Aim for decreasing costs quarterly as adoption and usage increase.

When to Use What: Synapse vs. Fabric vs. Databricks

This is the most common architecture question we get from enterprise organizations evaluating their analytics platform strategy. The answer is nuanced but the decision framework is clear.

Many enterprise organizations prefer using Fabric for the analytics platform and Databricks for advanced data science. These platforms allow for easy data sharing through OneLake shortcuts.

Also, think of Synapse as a migration source instead of a destination for new investments.

Implementation Roadmap: 18-Month Enterprise Deployment

Here is the phased approach we use for enterprise analytics platform deployments:

Phase 1: Foundation (Months 1-3)

We focus on several key areas to ensure effective data management and reporting. These include:

• Fabric capacity provisioning and workspace architecture design
• Purview configuration and initial data catalog population
• First migration wave: top 20 most-used reports from legacy BI
• Governance framework: RLS model, deployment pipelines, naming conventions
• CoE charter and initial team hiring

Phase 2: Scale (Months 4-6)

The design and implementation of the enterprise semantic model layer are essential. This includes the second migration wave, which targets department-specific reports and datasets. Key initiatives include:

• Enhancing data accessibility for various departments
• Improving report accuracy and relevance
• Streamlining data integration processes

• Medallion architecture implementation in Fabric Lakehouse
• Launch of a self-service training program
• Power Automate integration for initial automation use cases

Phase 3: Optimize (Months 7-12)

We offer a complete decommissioning of legacy BI systems. Our services include:

• Advanced DAX patterns and calculation groups
• Composite model optimization for large-scale datasets
• Copilot rollout with semantic model preparation
• ML model integration using the PREDICT function (first use cases)
• Full automation layer: alert workflows, distribution, and data quality monitoring

Phase 4: Augment (Months 12-18)

Azure OpenAI offers custom integration for generating insights. We implement the Decision Intelligence Framework to enhance decision-making.

• Establish decision feedback loops and track outcomes.
• Manage advanced Center of Excellence (CoE) operations, including certification workflows and adoption optimization.
• Focus on continuous improvement through quarterly platform reviews, capacity optimization, and feature adoption.

How EPC Group Delivers Analytics Platform Modernization

EPC Group has successfully completed over 200 enterprise analytics implementations. Our work spans various sectors, including healthcare, finance, education, and government.

We combine:

• Deep Microsoft technology expertise
• Understanding of regulated industries
• Knowledge of compliance requirements

Many analytics consultancies do not offer this level of insight.

• Platform assessment and roadmap — Evaluate your current analytics estate, identify quick wins and migration priorities, and develop a phased implementation roadmap with ROI milestones at each checkpoint
• Migration execution — Hands-on migration from SSRS, Cognos, Tableau, or on-premises data warehouses to Fabric and Power BI, including data validation, performance testing, and parallel running
• Governance framework design — Purview configuration, sensitivity labels, RLS/OLS implementation, deployment pipelines, and compliance documentation for HIPAA, SOC 2, and FedRAMP requirements
• Semantic model architecture — Design and build enterprise semantic models with DAX measures, composite model strategy, and Direct Lake optimization for your specific data volumes and query patterns
• CoE establishment — Define the organizational structure, hire or train the team, build the training curriculum, and operationalize the analytics platform with measurable KPIs
• AI augmentation — Copilot enablement, Azure OpenAI integration, ML model deployment, and Decision Intelligence Framework implementation
• Power BI training and adoption — Role-based training programs for executives, analysts, data engineers, and administrators, with hands-on labs using your organization's actual data and reports

Frequently Asked Questions