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Free Power BI & Fabric Checklists, Guides & Quality Framework | BiMasters
Free tools & frameworks

Build Things That Actually Work.

After 18 years and 30+ enterprise engagements, the patterns are clear. The same quality failures appear in nearly every organisation. These resources help you avoid them — before they cost you users, trust, or your weekend.

  • Microsoft Certified Trainer (MCT)
  • PL-300, DP-600/700 certified
  • 18+ yrs in BI
  • 200+ trained
  • 30+ enterprises advised
The quality framework

What a Production-Ready Solution Looks Like

Most quality conversations in Power BI start and end with data quality. That’s too narrow. A production-ready analytics solution has to be solid end-to-end — from the first pipeline run to the report in a board pack.

Speed without quality isn’t speed — it’s technical debt with a deadline. I’ve watched organisations rush reports into production, skip testing, and spend three months unpicking the consequences. The six dimensions below define what “done” actually means.

R
Reliable
Returns accurate results consistently and performs well under load. Users trust it without second-guessing the numbers.
  • Refresh schedules that complete without manual intervention
  • DAX measures validated against a known baseline
  • Alerts when refresh fails — not silence until someone notices
E
Efficient
Achieves its purpose without waste — in query time, model size, refresh duration, or analyst effort to maintain it.
  • No calculated columns where measures would do the job
  • Import vs DirectLake decided deliberately, not by default
  • Spark pipelines that don’t thrash shared capacity
A
Automated
Routine tasks don’t depend on one person staying late. Deployment, testing, and monitoring run on schedule — not on heroics.
  • Dev → Test → Prod deployment pipelines
  • Automated regression tests before every promotion
  • Refresh history monitored by notebook, not inbox
P
Polished
The model and report are organised, named, and documented as if the next person to open them is a stranger — because one day they will be.
  • Measures in display folders with clear descriptions
  • Format strings applied consistently across every figure
  • Hidden fields hidden; visible fields purposeful
E
Easy to Use
The intended audience gets value without a tutorial. Business-friendly naming, sensible defaults, and focused design do the work.
  • No internal IDs exposed to report users
  • Tooltips and descriptions for non-obvious measures
  • Reports that answer a specific question, not every possible one
R
Robust
New data, schema changes, and code updates don’t silently break working solutions. Error handling exists.
  • Power Query error handling for bad source data
  • Limited cross-dependencies between measures
  • Regression tests before every production deployment
Deployment checklists

Before You Ship, Check the List

The exact checks I run — or recommend teams run — before promoting any Power BI or Fabric artefact to production. Minimum bar, not a ceiling.

🧱
Semantic Model Checklist
Protect users from wrong numbers and developers from 2am calls.
  • Star schema confirmed — no flat table anti-patterns
  • All relationships set to correct cardinality and direction
  • Calculated columns replaced with measures where possible
  • DAX measures tested against a known baseline
  • Refresh schedule configured and tested end-to-end
  • Row-level security tested with representative accounts
  • Sensitivity labels applied and documented
  • Model description updated with owner and date
8 checks
📊
Report Checklist
A report that works in Desktop doesn’t always behave the same in the Service.
  • All visuals load without errors
  • Layout tested at 1366×768 and 1920×1080
  • Slicer and cross-filter interactions behave as intended
  • Bookmarks and navigation tested in reading view
  • Alt text added to all non-decorative visuals
  • Page titles use field values, not hardcoded text
  • Published to correct workspace with correct permissions
7 checks
🔁
Dataflow Checklist
Dataflows are often the least-documented part of the stack.
  • Source connections use service accounts, not personal creds
  • Refresh schedule configured — not left on manual
  • Incremental refresh set where data volume warrants it
  • Column types explicitly set — no auto-detection reliance
  • Null and error handling applied to critical columns
  • Output columns use business-friendly naming
6 checks
🏢
Workspace Checklist
Prevent permission sprawl and the “I don’t know who owns this” problem.
  • Workspace has a documented Owner and Contributor
  • Access roles assigned to groups, not individuals
  • Workspace licence mode confirmed
  • Git integration configured for Fabric workspaces
  • Deployment pipeline connected to Dev/Test/Prod
  • Workspace contact details updated in settings
6 checks
🚀
Deployment Pipeline Checklist
The pipeline is the guardrail. These checks ensure it actually guards something.
  • Dev → Test → Prod stages assigned to correct workspaces
  • Deployment rules configured for connection strings
  • Automated tests run before each Test → Prod promotion
  • Only designated approvers can deploy to Prod
  • Post-deployment validation documented
  • Rollback procedure documented for failed deployments
6 checks
📱
Power BI App Checklist
By the time content reaches an app it should be production quality — but things slip through.
  • App audience aligned with intended users — not “Everyone”
  • Navigation tested by a non-developer user
  • App description and contact information updated
  • Default filter state reviewed for relevance
  • App update process documented and tested
5 checks
The analytics lifecycle

Quality Doesn’t Live in One Place

The most common mistake is treating quality as a final-step review. It needs to be built in at every stage — a flawed foundation always surfaces at the worst possible moment.

Stage 01
Design & Requirements
Define the actual question before building anything. The most expensive bugs are built-in ones.
Stage 02
Build & Test
Automated tests during development, not after. Each iteration should be reliable enough to ship.
Stage 03
Deploy & Validate
Structured promotion through Dev → Test → Prod. UAT with real users, not developers acting as users.
Stage 04
Monitor & Maintain
Quality degrades without attention. Scheduled monitoring notebooks catch regressions before users do.
The methodology

Why DataOps Changes the Equation

DataOps is a set of habits that make your analytics pipeline reliable enough to sleep through the night.

It borrows from DevOps and Agile — separate environments, automated testing, version control, monitored deployments — and applies them to the full analytics stack. Not just ETL, not just code. Reports, models, pipelines. All of it.

The organisations that struggle most with quality rely on individual heroes rather than systematic processes. DataOps replaces heroics with structure. Start with one principle today — separate Dev and Prod workspaces. That alone changes everything.

Separate Environments for Every Stage
Publishing changes directly to a production workspace real users depend on is how you create emergencies. Dev, Test, and Prod are the minimum.
Automate Testing Before You Promote
Logic tests, accuracy tests, regression tests, performance tests — all running automatically before any promotion to production.
Monitor After Release, Not Just Before
A notebook that runs nightly, checks key measures against a baseline, and fires an alert when something drifts is worth more than any manual review.
Version Control Is Not Optional
You can’t roll back what you can’t track. Fabric’s Git integration makes semantic model version control accessible. Use it from day one.