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Keep VertiPaq small: five Import modeling cuts that actually matter

Most “we need Premium because the model is huge” tickets are unused columns and string abuse — fix modeling before buying capacity.

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June 21, 20261 min readBy Power BI Analytics Team

VertiPaq compresses repeated values. High-cardinality strings (GUIDs, free-text comments) crush compression. Open VertiPaq Analyzer (or DAX Studio Metrics) and sort tables by size before blaming licensing.

Cuts that move the needle

  • Delete columns not referenced by any measure/visual — especially wide VARCHAR dumps from ERP “just in case.”
  • Split DateTime into Date + Time (or drop Time) so the date dictionary stays small; mark a proper Date table with Mark as date table.
  • Replace text status codes with integer keys + dimension attributes; keep display names on the dimension, not the fact.

Relationship hygiene

Prefer single-direction many-to-one star schemas. Bidirectional “to make the slicer work” often forces larger query plans and hides model errors. Avoid both-direction on large facts. If a flat Excel export is the source, shape to star in Power Query (or warehouse) before Import — a 40-column denormalized fact rarely compresses or filters cleanly.

Re-measure after each cut: Dataset size in the service and local PBIX file size. If Cardinality of a column stays near row count, that column is not dimensional — reconsider keeping it in Import at all.

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Illustrative KPI view

Sample model

Revenue

$2.1M

-1.0% vs LY

Gross margin

32.4%

+0.9 pts

Decision cycle time

4.9 days

+0.3 days

Open exceptions

19

+4 vs last week

Trailing 12 periodsvs plan
On track
75%
Watch
24%
Critical
6%

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