Power BI Data Modeling, DAX and Dashboard Guide
A Chinese Power BI study archive covering data import, fact and dimension modeling, relationships, DAX measures, dashboards, refresh and access boundaries.
What this guide is for
This archive explains a practical path from a business question to a reviewable Power BI report. Its central lesson is that a dashboard depends on data grain, relationships and metric definitions before it depends on visual polish. Use a small anonymized dataset to test each decision before extending a model.
Modeling and measures
Start by identifying what one row represents, the keys that identify it and the date range covered. Separate fact tables from dimensions, choose relationship cardinality and direction deliberately, then write measures for totals, counts, rates and time comparisons. Validate DAX with hand-calculated examples so filter context, dates, blanks and duplicated keys are visible.
Dashboard and governance boundaries
Each report page should answer one question and label units, period, source and refresh time. Refresh credentials, gateways, workspaces, row-level security and export permissions are part of report quality. A template or sample file does not authorize access to real business data, and a visually convincing chart can still express the wrong population.
Maintenance note
This page was reviewed on 2026-08-23 against the catalog metadata, modeling workflow and reporting governance notes. Confirm the installed Power BI version and organizational policy before applying an example to production data.
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Power BI modeling, DAX and dashboard practice
Define the business question and row grain first, then build relationships, measures and visuals in a sequence that can be checked.
Before you start
- Prepare a ZIP extractor and a compatible Power BI Desktop or learning environment.
- Understand tables, keys, dates, aggregation and basic descriptive statistics.
- Use fictional or anonymized data with one clearly stated business question.
Quick start
- 01
Check data grain
Confirm what each row represents, inspect field types and duplicates and establish a date table before importing the model.
- 02
Build relationships deliberately
Separate facts and dimensions, choose cardinality and filter direction and test that a small slice returns the expected rows.
- 03
Write and verify DAX measures
Start with totals, counts and ratios, then compare a hand-calculated sample with the measure under several filter contexts.
- 04
Design and review the dashboard
Place visuals around one question and label units, period, source and refresh time before sharing the report.
Usage tips
- Fix grain and relationships before optimizing charts; a polished page cannot repair a duplicated model.
- Check DAX with small samples, especially when dates, blanks or filter context change the result.
- Review workspace, row-level security and export rights before publishing sensitive fields.
Troubleshooting and uninstall
Why does a measure disagree with a spreadsheet calculation?
Inspect grain, relationships, date filters, filter context and duplicate records, then reproduce the result on one small slice.
Why did refresh fail?
Check credentials, changed fields, gateway status and refresh scope, record the error time and recheck key metrics after the fix.
Frequently asked questions
What should be checked first in a Power BI model?
Check row grain, keys, fact and dimension roles, relationship cardinality, field types and the covered date range.
Why validate DAX on a small sample?
A small sample makes filter context, dates, blanks and expected totals easy to calculate by hand before scaling up.
How can a dashboard avoid misleading readers?
State units, period, source and refresh time, use a chart suited to the comparison and document metric definitions.
What should be reviewed before sharing?
Check workspace membership, row-level security, export permissions and whether sensitive fields can be inferred from the report.