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Power BI

Dashboards people
actually use

Self-service analytics with the guardrails that keep Finance from losing sleep. Semantic models that encode your business logic. 100+ dashboards delivered, built for decisions, not decoration.

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Engagement overview
Power BI Implementation
Timeline8 weeks
DeliverableProduction dashboards
Phases4 phases
Deployments100+
IncludesSemantic model + training
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The problem

The reporting
reality

Most organizations have Power BI licenses. Very few have Power BI working.

The endless backlog
"Can you add customer segment to this report?" That request joins the queue behind 47 others. Two weeks later, the requester has moved on. Meanwhile, they built their own spreadsheet because they couldn't wait.
Fifty dashboards, zero accountability
Everyone has "their" dashboard. Marketing's version. Sales's version. The CFO's "real" numbers. When leadership meets, the first ten minutes are always "let me explain where my data comes from." Nobody knows which one is right.
Beautiful charts, ugly data
The visualizations look professional. But underneath: direct connections to production databases that time out, imported data that's three days stale, measures that calculate differently depending on which filter you apply. The dashboard is a facade over chaos.

What we build

Six capabilities.
One governed analytics layer.

Semantic Model Design
Star schemas, calculated measures, display folders, documented relationships. Business logic encoded once. Every report calculates consistently, no more "my dashboard shows $12M, yours shows $11.7M."
Executive Dashboards
Relevant KPIs without clutter. Drill-through for the details when needed. Mobile views for executives who check numbers from the airport. Designed for decisions, not decoration.
Migration & Optimization
We migrate legacy reports from SSRS, Crystal Reports, or "that Excel file nobody can explain." We fix slow reports: inefficient DAX, bloated models, refresh timeouts. Existing investments rescued.
Row-Level Security
Sales reps see their territory. Managers see their team. Executives see everything. One report, appropriate access for everyone. No separate versions to maintain for each stakeholder group.
Deployment Pipelines
Changes move through dev → test → production. Validated before end users see them. Version control. Rollback capability. CI/CD thinking applied to analytics.
Analyst Training
DAX fundamentals. Visualization best practices. How to use the semantic models we built. Self-service that actually works; analysts build reports confidently without waiting for IT.

How we deliver

Eight weeks to real analytics

Four phases. Defined outputs. No ambiguous scope.

1
Week 1
Assessment
Inventory existing reports and data sources. Identify what's working and what's trusted. Define success metrics and prioritize dashboards.
Prioritized dashboard roadmap
2
Weeks 2–3
Data Foundation
Connect to source systems. Build or refine the semantic model. Configure refresh schedules and row-level security.
Certified semantic model
3
Weeks 4–6
Dashboard Development
Design and build priority dashboards. Implement interactivity and drill-through. Optimize for performance and mobile viewing.
Dashboards deployed to production
4
Weeks 7–8
Enablement
Train analysts on self-service. Document the semantic model. Set up deployment pipelines. Establish governance for ongoing health.
Analysts building their own reports

Real example

$80M distribution company,
847 Excel files → same-day reporting

Distribution company
Finance team of three. Reporting infrastructure: a network folder with 847 Excel files and a 12-year-old Access database nobody dares to touch. Month-end took five days of manual reconciliation.
What we built
  • Semantic model connecting SAP (financials), GP (legacy history), and WMS (inventory)
  • Executive dashboard: revenue, margin, inventory turns, cash position
  • Automated daily refresh replacing manual monthly exports
  • Row-level security: regional managers see their region, CFO sees everything
  • Self-service workspace for finance team exploration
Before the engagement
  • Manual exports from SAP, GP, and WMS every month-end
  • Five days of reconciliation before numbers could be shared
  • CFO built her own "trust but verify" spreadsheet alongside official reports
  • Different stakeholders received different versions depending on export timing
5 days → same dayMonthly reporting cycle
RetiredCFO's verify spreadsheet
One sourceOf truth for all stakeholders

Common questions

What people
usually ask

We have Power BI licenses already. Why do we need help?
Most organizations have licenses but haven't built the foundation. Without a proper semantic model, governance, and training, Power BI becomes another collection of ungoverned files, just with better visualizations. We build the foundation that makes self-service work.
Can Power BI handle large datasets?
Yes. Billions of rows with proper design: incremental refresh, aggregations, efficient DAX. When Power BI feels slow, it's usually model design, not platform limits. We optimize for scale.
How do we prevent everyone from creating their own dashboards?
You don't prevent creation; you establish ownership. Workspaces with clear owners. Certification labels for trusted datasets. Training on when to reuse vs. create. Governance that enables responsible self-service.
We already have Fabric. What does Power BI add?
Power BI is included in Fabric as the BI workload. Direct Lake mode lets Power BI query OneLake directly without importing data: faster refresh, less duplication, same governance. The semantic model work we do applies to both.
What's the ongoing maintenance?
Semantic models need attention when source systems change. Refresh schedules need monitoring. New reports get created. We train your team to handle routine maintenance and establish a governance process for ongoing health.

Ready for dashboards
people trust?

We'll assess your current reporting, identify quick wins, and map out what governed self-service looks like for your organization.

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