What Data Quality Really Means in Power BI

A decision guide for the questions that actually show up in Power BI projects. Use the FAQs to align stakeholders, avoid misleading insights, and decide when quality is “good enough” for a specific decision.

Decision protection snapshot

Decision: Not selected Persona: General Quality bar: Medium
Start with the decision.
Data quality is not a checkbox. It is a safety rail to protect a decision from being wrong for avoidable reasons. Pick the decision you need to protect and this app will adjust the quality bar and the checks you should care about.
Tip: If you are unsure, start with Operational action vs Financial reporting. Those two usually have very different tolerances.
Quick interpretation
The goal is not perfect data. The goal is to reduce the chance of making a confident decision with a wrong picture. In Power BI this usually means controlling:
  • Source issues (missing, late, duplicated, invalid values)
  • Transformation issues (Power Query logic, join errors, type conversion)
  • Model issues (relationships, granularity, ambiguous paths)
  • Measure issues (context, filters, totals, time intelligence)
  • Presentation issues (misleading visuals, wrong level of detail, unlabelled assumptions)

What “good” means

Fit-for-purpose

Good quality means the data is reliable enough for the decision, at the right grain, with known limits, and consistent definitions. It also means the report behaves predictably when users filter, slice, and drill.

What quality protects

Decision safety

Quality protects against decisions that are confident and wrong: wrong trend, wrong cohort, wrong total, wrong comparison period, or wrong interpretation because assumptions were hidden.

When it becomes urgent

Blocker

Quality becomes a blocker when the report cannot be trusted to answer a simple question consistently, or when the business cost of a wrong decision is higher than the cost of improving the data.

10 FAQs Tip open multiple questions
Fit-for-purpose scorecard
Tick what is true. The score helps you decide if quality is blocking, acceptable, or risky for your decision.
0% complete
Rating Not started Risk Unknown
How to use this with Power BI:
Run source and transformation checks in Power Query, validate model grain and relationships, then validate measures in a small table visual. If any check fails, decide if it is a blocker based on the decision you selected, not based on preferences.

Quality dimensions, explained in Power BI terms

Goal align language

Accuracy

Correct values

Are values correct compared to an agreed source of truth? In Power BI, accuracy fails when business rules are wrong, currency conversions are inconsistent, or measures apply the wrong context.

Completeness

Nothing important missing

Are required rows and fields present for the decision window? In Power BI, this often shows up as missing dates, missing entities after joins, or partial refresh windows.

Consistency

Same definition everywhere

Do KPIs and labels mean the same thing across reports and over time? In Power BI, consistency improves when you reuse semantic models and enforce naming, measures, and filter conventions.

Timeliness

Fresh enough

Is the data recent enough for the decision? In Power BI, timeliness depends on refresh schedule, latency in the source, and whether users understand “last updated”.

Validity

Values make sense

Do values obey rules (types, ranges, codes)? In Power BI, validity fails when data types are wrong, IDs are malformed, or a date column contains text or blanks.

Uniqueness

No unintended duplicates

Are keys unique where they must be? In Power BI, duplicates can silently multiply totals through relationships or many-to-many patterns.

Power BI shortcut: if totals look inflated, check uniqueness and relationships first. If trends look wrong, check completeness and timeliness. If different reports disagree, check consistency and definition ownership.

What this app is

A practical FAQ and scorecard you can use to explain data quality in Power BI without getting stuck in abstract definitions. It focuses on decision-making and the common failure points across source, Power Query, model, DAX, and visuals.

How to use it in a project

1) Pick the decision you need to protect.
2) Walk through the FAQs with stakeholders.
3) Run the scorecard and agree what is acceptable.
4) Document what is out of scope, and what must be fixed before release.
Disclaimer: This guide supports discussion and prioritization. It does not replace governance policies, audit requirements, or formal data quality monitoring. Always validate critical financial or compliance reporting with approved controls.