Fictional demo company • Synthetic data • Public portfolio project

Equipment Rental Executive Dashboard

A Power BI engineering case study for NorthPeak Equipment Rentals, a fictional branch based rental business. Built to demonstrate semantic modeling, governed DAX, dynamic RLS, performance review, Power BI Service publishing, and AI ready analytics.

View Live Dashboard Explore Source Data Review Technical Build
Power BI Semantic Model Star Schema DAX Dynamic RLS Performance Analyzer AI Readiness Power BI Service Production Architecture
Total Revenue $0.0K
Gross Margin $0.0K
Gross Margin % 0.0%
Rental Count 0
Utilization % 0.0%

Executive Summary

Beyond the Dashboard

This case study was designed to show the difference between building a dashboard and engineering a trusted BI product.

The visible Power BI report is only one layer. Behind it are modeling decisions, reusable KPI definitions, role based security logic, performance validation, and AI ready semantic metadata. The project demonstrates BI engineering thinking across semantic modeling, DAX engineering, dynamic RLS, Power BI Service publishing, performance analysis, and production grade BI architecture.

Live Report and Data Source

Live Report and Data Source

Explore the published Power BI artifact and the synthetic source data used to build the semantic model.

Executive dashboard embedded from Power BI Service.

This is a public demo embed. Public publish to web is anonymous, so user specific RLS is not enforced in the embedded view.

Open in New Tab

Report Visuals

Equipment Category Performance matrix, Revenue Trend by Month line chart, Revenue by Branch bar chart, KPI cards, and business slicers.

Interactive Filters

Slicers for YearMonth, Region, Category, and Segment support executive exploration without exposing technical model fields.

Technical Architecture

Semantic Model Architecture

A clean star schema keeps business slicing predictable while centralizing calculations through a governed measures layer.

DimDateTable
DimCustomerTable
DimBranchTable
DimEquipmentTable
DimSalesRepTable
SecurityUsersTable
Central Fact FactRentalsTable
  • FactRentalsTable is the transaction grain.
  • Dimensions support business slicing by date, customer, branch, equipment, and sales rep.
  • Relationships are one to many from dimensions into the fact table.
  • Single direction filtering keeps behavior predictable.
  • SecurityUsersTable supports dynamic RLS.
  • Technical fields are hidden from report users.
  • Certified measures are exposed through _Measures.

Implemented Scope

Implemented in This Demo

These are the actual Power BI implementation items completed for the public portfolio project.

Power Query import from Excel based source data
Data type validation
Clean star schema semantic model
FactRentalsTable as the central fact table
Dimension tables for date, customer, branch, equipment, and sales rep
Relationship cleanup after import
Single direction filtering model
Marked DimDateTable as the official date table
Created dedicated _Measures table
Created reusable DAX measures
Created financial, operational, and time intelligence KPIs
Hidden technical keys and raw fact fields from report view
Business friendly fields exposed for users
Dynamic region based RLS using SecurityUsersTable
RLS testing in Power BI Desktop using View As
Power BI Service publishing
Public Power BI web embed for demo viewing
Performance Analyzer review
Semantic model descriptions and synonyms for AI readiness

DAX and KPI Engineering

DAX and KPI Engineering

The measures were centralized in a dedicated _Measures table to support consistency, governance, and reuse across visuals.

Financial KPIs

Total Revenue, Gross Margin, Gross Margin %, and revenue trend measures are defined once and reused throughout the report.

Operational KPIs

Rental Count and Utilization % translate transaction and availability data into decision ready metrics.

Time Intelligence

Month over month logic is anchored on DimDateTable so trend calculations remain consistent with slicers and reporting periods.

Total Revenue

Total Revenue =
CALCULATE(
    SUM(FactRentalsTable[TotalRevenue]),
    FactRentalsTable[IsCancelled] = FALSE()
)

Gross Margin %

Gross Margin % =
DIVIDE(
    [Gross Margin],
    [Total Revenue]
)

Utilization %

Utilization % =
DIVIDE(
    [Total Utilization Hours],
    [Total Available Hours]
)

Revenue MoM %

Revenue MoM % =
DIVIDE(
    [Revenue MoM Change],
    [Previous Month Revenue]
)

Dynamic Row Level Security

Dynamic Region Based RLS

Security is modeled as a data driven access pattern instead of hardcoded filters per user.

User opens report USERPRINCIPALNAME captures email SecurityUsersTable maps email to region DimBranchTable filters allowed branches FactRentalsTable returns permitted rental transactions

RLS DAX

VAR CurrentUser =
    USERPRINCIPALNAME()

VAR AllowedRegion =
    LOOKUPVALUE(
        SecurityUsersTable[Region],
        SecurityUsersTable[Email], CurrentUser
    )

RETURN
    AllowedRegion = "All"
        || DimBranchTable[Region] = AllowedRegion
Executive User $224.7K

Sees all regions and full demo revenue.

Central Manager About $88.3K

Sees Central region only.

West Manager About $61.4K

Sees West region only.

For this public portfolio embed, RLS is not enforced because publish to web is anonymous. In production, authenticated Power BI Apps, secure embed, or Power BI Embedded would be used to enforce RLS.

Performance Review

Performance Analyzer Review

Performance work starts with evidence: visual timing, query behavior, and model design signals.

No major DAX bottleneck found.

Query execution was lightweight for the current demo size and metric complexity.

Matrix visual was the heaviest.

Equipment Category Performance had the largest visual cost, so optimization would start with visual simplification.

KPI cards were acceptable.

KPI cards generate separate visual queries, which is acceptable for this demo and easy to revisit at scale.

Model cleanup supports performance.

Hidden technical fields, single direction filtering, and reusable measures reduce ambiguity and report sprawl.

Production tuning checklist

DAX Studio VertiPaq Analyzer Column cardinality review Remove unused columns Aggregations Incremental refresh Calculation groups Field parameters Report page optimization Upstream SQL or dbt optimization

AI Readiness

AI Ready Semantic Model

AI readiness is not just about clicking Copilot. It starts with semantic model quality.

Model Preparation

  • Hidden technical fields
  • Certified measures
  • Measure descriptions
  • Synonyms for business language
  • Clean star schema
  • Explicit KPI definitions
  • Natural language question patterns
  • Preparedness for Copilot, Q&A, and Fabric data agents

Semantic metadata examples

Total Revenue

Description: Total revenue from non cancelled rental transactions, including rental, service, and damage waiver revenue.

Synonyms: revenue, sales, total sales, rental sales

Gross Margin %

Description: Gross margin divided by total revenue, used to evaluate profitability.

Synonyms: margin percent, profitability, profit rate

BranchName

Synonyms: branch, location, depot, rental location

Category

Synonyms: equipment category, fleet category, equipment type

CustomerName

Synonyms: customer, account, client

YearMonth

Synonyms: reporting month, period, month period

Natural language questions the model is prepared to answer

What is total revenue by branch? Show gross margin percent by equipment category. Show utilization by equipment category. Show rental count by month. Show revenue by region.

Production Architecture

Production Extensions and Architecture Possibilities

The current demo intentionally focuses on the Power BI semantic and reporting layer. In a real enterprise implementation, the same model could be extended with a scalable cloud data platform, curated gold layer, deployment pipelines, monitoring, and AI enabled analytics.

This demo does not implement Direct Lake, but the semantic model design is compatible with a Fabric gold layer feeding a Direct Lake model. This demo uses a public embed, but production sharing would use authenticated Power BI Apps or secure embedding so RLS is enforced.

01

Source Ingestion

In a production version, this could be extended by landing operational rental, branch, customer, fleet, and finance data into Fabric Data Factory, Dataflows Gen2, Azure Data Factory, SSIS, dbt, or Spark notebooks depending on platform strategy.

02

Bronze Layer

For enterprise scale, raw ingested data could land in a Microsoft Fabric lakehouse as the bronze layer, with OneLake acting as the unified storage layer for analytics data products.

03

Silver Standardization

Silver tables could standardize types, naming, conformed keys, deduplication rules, and data quality expectations before BI developers consume the data.

04

Gold Dimensional Model

The gold layer could expose curated dimensional tables and facts for Power BI semantic models. Data engineers and BI developers would collaborate on curated tables, SQL endpoints, data quality rules, and gold layer schemas.

05

Power BI Semantic Model

The same star schema, governed measures, business friendly fields, certified semantic models, descriptions, synonyms, and verified answers could become the governed reporting contract.

06

Direct Lake or Import Decision

Direct Lake could be considered when the model sits on Fabric lakehouse or warehouse data and the goal is high performance without traditional import refresh. Import, Direct Lake, or Composite model decisions should be based on data size, latency needs, licensing, security, and performance.

07

Governed Reports and Apps

Production sharing would use authenticated Power BI Apps, secure embedding, deployment pipelines, dev/test/prod workspaces, RLS through Entra ID or Microsoft 365 security groups, and OLS where sensitive columns or tables need to be hidden.

08

Monitoring and Adoption

Monitoring Hub, refresh history, usage metrics, capacity metrics, service diagnostics, and Power BI REST API automation could support health checks, deployment validation, metadata extraction, refresh automation, and governance reporting.

09

AI Ready Experience

Copilot and Fabric data agents could answer from governed definitions when the semantic model includes descriptions, synonyms, certified measures, verified answers, and AI instructions.

Platform Patterns

Microsoft Fabric lakehouse, Fabric Warehouse, OneLake, SQL endpoint, medallion architecture, and a curated gold layer could be implemented as production extensions.

Cloud Data Warehousing

A similar semantic model could be built on top of curated analytical data from Redshift, Snowflake, BigQuery, Databricks, Azure Synapse, PostgreSQL, SQL Server, or Fabric Warehouse.

Transformation Governance

For production, dbt could define tested, documented, version controlled transformation models feeding a gold layer. Spark notebooks, Fabric Data Factory, Dataflows Gen2, ADF, or SSIS could also support scalable ELT or ETL pipelines.

Semantic Model Standards

Incremental refresh could reduce refresh time for large rental history. Aggregation tables could speed executive visuals. Calculation groups could centralize YTD, MTD, YoY, and MoM logic. Field parameters could let users switch metrics or dimensions.

Lifecycle Discipline

Tabular Editor could help manage calculation groups, display folders, measure formatting, descriptions, and advanced semantic model standards. TMDL and Git integration could support source control, code review, and deployment discipline.

Enterprise Security

For enterprise scale, I would consider Entra ID group based RLS assignment, OLS for sensitive data, workspace discipline, certified semantic models, deployment pipelines, and operational support standards.

Roadmap

What I Bring to the Team

A practical 90 day roadmap for turning reports into a trusted, governed, AI ready analytics platform.

This roadmap shows how I would approach a senior Power BI role in a branch based, fleet driven equipment rental business. The focus is not just on creating reports, but on building the semantic layer, governance standards, Power BI Service discipline, and AI ready foundation that make reporting trustworthy at scale.

Semantic Modeling DAX Governance Fabric Readiness AI Ready Analytics Power BI Service Governance

Why This Matters

From Dashboards to Decision Infrastructure

Most organizations already have reports. The real challenge is whether leaders trust the numbers, whether branches use the same KPI definitions, whether security is consistent, whether refresh failures are visible, and whether future AI assistants can answer questions from governed data instead of raw tables.

30 / 60 / 90 Day Roadmap

Practical steps from discovery to trusted delivery.

30

30 Days, Discover and Diagnose

Goal: Understand the current BI landscape before changing anything.

Activities

  • Audit reports, workspaces, semantic models, refresh schedules, data sources, and ownership.
  • Meet with users across leadership, finance, operations, sales, branch teams, and fleet teams.
  • Document trusted reports, broken reports, duplicate logic, manual Excel workarounds, and KPI confusion.
  • Review security model and RLS patterns.

Deliverables

BI inventory KPI definition log Data source map Pain point register Semantic model maturity assessment

Impact: Start from facts, avoid rework, and quickly identify the highest value fixes.

60

60 Days, Standardize and Stabilize

Goal: Create one trusted foundation for high value reporting.

Activities

  • Refactor the core rental or fleet model into a clean star schema.
  • Create reusable DAX patterns and calculation groups.
  • Document measures with business definitions.
  • Apply dynamic RLS aligned to branch or region hierarchy.
  • Set up deployment discipline from development to production.

Deliverables

Certified semantic model Reusable DAX library RLS design document Report template standards Deployment checklist

Impact: Reports become more consistent, faster, easier to support, and easier for business users to trust.

90

90 Days, Deliver and Future Proof

Goal: Ship visible business value and prepare the model for AI enabled analytics.

Activities

  • Deliver executive dashboard improvements.
  • Configure model metadata, synonyms, measure descriptions, and verified Q&A examples.
  • Create a Power BI monitoring dashboard for refresh health, usage, and report adoption.
  • Build a practical Copilot or data agent style proof of concept using the certified model.
  • Deliver self service reporting guidance for business users.

Deliverables

Executive KPI package AI ready semantic model checklist Monitoring dashboard Governance guide 6 month analytics roadmap

Impact: Leadership gets trusted answers faster, the BI team becomes proactive, and the analytics platform becomes ready for AI assisted reporting.

Semantic Model Maturity

From raw reports to governed self service.

Level 1

Raw Reports

Reports connect directly to source tables. KPI logic is repeated across files.

Risk: High inconsistency.
Level 2

Shared Dataset

A common dataset exists, but measures and definitions may not be fully documented.

Risk: Medium inconsistency.
Level 3

Certified Semantic Model

Star schema, documented measures, tested relationships, dynamic RLS, certified ownership, and clear promotion rules.

Risk: Low.
Level 4

AI Ready Semantic Model

Business friendly names, measure descriptions, synonyms, verified Q&A examples, AI instructions, and governance rules.

Risk: Controlled.
Level 5

Governed Self Service Platform

Multiple certified domains, sandbox and production separation, monitoring dashboard, usage tracking, and clear ownership.

Risk: Managed at scale.

Governed AI

Practical AI Integration Opportunities

AI should act as a governed assistant on top of trusted data. It should help people ask better questions, spot exceptions faster, and explain performance patterns using certified definitions.

Leadership

Executive KPI assistant, weekly performance summary, branch and region variance explanation, risk and exception summary.

Business problem
Leaders need fast, consistent explanations of performance movement.
Data needed
Revenue, margin, utilization, branch, region, customer, and period data.
Power BI or Fabric connection
Certified model, executive dashboard, Copilot style Q&A.
Governance requirement
Certified KPIs, RLS, verified prompts, and approved metric definitions.
Expected impact
Faster decision cycles and fewer conflicting performance narratives.

Sales

Customer account research assistant, quote to rental conversion insights, cross sell and upsell opportunity signals, customer segment analysis.

Business problem
Sales teams need account context without stitching together multiple reports.
Data needed
Customers, rentals, quotes, segments, branch coverage, equipment categories.
Power BI or Fabric connection
Sales semantic model, account level report pages, curated data products.
Governance requirement
Customer access rules, approved definitions, and documented lineage.
Expected impact
Better account planning and more relevant customer conversations.

Branch Operations

Branch KPI assistant, equipment availability assistant, utilization exception alerts, demand forecasting support.

Business problem
Branches need operational signals before issues become month end surprises.
Data needed
Inventory, availability, utilization, open rentals, returns, demand patterns.
Power BI or Fabric connection
Operational scorecards, alerts, Fabric data agent style assistant.
Governance requirement
Branch hierarchy security, refresh monitoring, and consistent availability logic.
Expected impact
Earlier exceptions, better fleet positioning, and stronger branch accountability.

Fleet and Maintenance

Fleet utilization analysis, downtime pattern detection, repair cost trend analysis, asset lifecycle decision support.

Business problem
Asset decisions require a joined view of revenue, usage, downtime, and cost.
Data needed
Fleet master, rental history, maintenance events, repair cost, age, category.
Power BI or Fabric connection
Fleet gold tables, certified utilization measures, lifecycle dashboards.
Governance requirement
Validated asset grain, data quality checks, and documented calculation rules.
Expected impact
Sharper buy, hold, transfer, repair, and retire decisions.

Finance

Revenue and margin analysis, AR aging assistant, budget versus actual reporting, month end reporting support.

Business problem
Finance needs reconciled numbers and explainable variance analysis.
Data needed
Revenue, cost, margin, AR aging, budget, forecast, period close data.
Power BI or Fabric connection
Finance semantic model, paginated summaries, governed financial KPIs.
Governance requirement
Access control, audit trail, certified measures, and close period discipline.
Expected impact
Less manual reconciliation and faster month end insight.

Data and Analytics Team

Semantic model documentation assistant, DAX measure explainer, refresh failure diagnosis, report inventory assistant, data quality rule assistant.

Business problem
BI teams need to reduce support load while improving standards.
Data needed
Metadata, lineage, refresh history, usage metrics, measure definitions.
Power BI or Fabric connection
Monitoring Hub, REST API metadata, model descriptions, Git artifacts.
Governance requirement
Workspace standards, source control, deployment checks, and ownership rules.
Expected impact
More proactive support and clearer BI engineering standards.

AI Ready Semantic Layer

How I Would Make the Analytics Layer AI Ready

AI is only as reliable as the semantic layer it reads from. My focus would be to make the model understandable to humans first, then structured enough for AI assistants to answer business questions safely.

Business friendly table and column names Explicit DAX measures instead of implicit measures Measure descriptions with business definitions Synonyms for business terms Certified semantic models Verified Q&A examples Dynamic RLS and OLS where needed Clear workspace governance Lineage documentation Deployment pipeline Monitoring Hub and refresh health reporting Copilot or data agent readiness testing

Platform Architecture

A practical architecture for trusted analytics.

Source Systems

Rental transactions, fleet data, maintenance data, finance data, sales data, safety and training data

Curated Data Layer

Lakehouse or warehouse tables, validated business grain, clean dimensions and facts

Semantic Layer

Certified Power BI model, star schema, reusable DAX, calculation groups, RLS and OLS

Consumption Layer

Executive dashboards, branch reports, finance reports, operational scorecards, self service analytics

AI Layer

Copilot style Q&A, data agent style assistant, verified answers, governed natural language insights

Governance Layer

Git, deployment pipeline, monitoring, data quality checks, documentation, access control

Success Metrics

Measured by trust, speed, and adoption.

Success is not measured by the number of reports created. Success is measured by whether the business can make decisions faster with numbers they trust.

Reduction in duplicate reports
Reduction in manual Excel reconciliation
Semantic models certified
Refresh failures monitored
KPI definitions documented
Report adoption improved
RLS coverage validated
Measures documented
AI ready questions verified
Stakeholder trust improved

The Value I Bring

I bring the mindset of a Senior Power BI Developer who thinks like a semantic model architect, platform owner, and business partner. I can help turn scattered reporting into a governed analytics foundation that supports executive reporting, branch operations, finance, fleet decisions, and future AI assisted analytics.

Back to Live Report Explore Architecture Contact Mihir

What This Case Study Demonstrates

What This Case Study Demonstrates

Senior BI capability across model, report, service, and story.

Semantic Model Engineering

Dimensional design, relationship cleanup, field hygiene, and model behavior that is predictable for users.

Governed KPI Design

Reusable DAX measures, explicit definitions, and metric logic that can be audited and trusted.

Secure Analytics

Dynamic RLS design and production awareness around authenticated sharing, group based access, and OLS.

Performance Awareness

Performance Analyzer review, visual level tuning priorities, and future scale patterns.

AI Ready Modeling

Semantic descriptions, synonyms, certified measures, and natural language readiness for Copilot and data agents.

Executive Storytelling

A focused executive view that links revenue, profitability, utilization, branch performance, and fleet category patterns.

Production Thinking

Honest extension path across Fabric, OneLake, medallion architecture, deployment pipelines, monitoring, and governance.

Stakeholder Communication

A portfolio case study that translates technical implementation into business value without burying the reader.

About

Built by Mihir Trivedi

Mihir Trivedi is a BI Developer and senior Power BI candidate with experience across Power BI, SQL, semantic modeling, data warehousing, Tableau, PostgreSQL, Power Platform, stakeholder reporting, and executive analytics. This project was created as a public technical case study to demonstrate how dashboards can be backed by governed semantic models, reusable metrics, secure access patterns, performance validation, and AI ready metadata.

Portfolio LinkedIn Email