Consistency — Semantic Stability in the Tabular Engine

1. Definition — What CONSISTENCY means in Power BI

In Power BI, consistency is the ability of the data model to maintain semantic, structural, and logical coherence across all its layers:

• Data sources
• Power Query transformations
• Tabular model
• Relationships
• DAX measures
• Visualizations and filters
• Refresh pipelines

A model is consistent when the same question produces the same answer, regardless of the visual, filter context, or aggregation level.

Example (EXPLANATORY EXAMPLE)

A KPI such as Total Sales must show the same value:

• In a card
• In a table
• In a matrix
• In a chart
• In a tooltip
• In a drill-down

If one visual shows: 1,250,000
and another shows: 1,180,000
under the same filters, a contextual or semantic inconsistency exists.

Sample (IMPLEMENTATION SAMPLE)

1. Correctly defined measure (context-robust):

Total Sales = SUMX('Sales', 'Sales'[Amount])

2. Consistency validation using a Truth Row:

EVALUATE
ROW(
   "Value_Card", [Total Sales],
   "Value_Table", CALCULATE([Total Sales], ALL('Sales')),
   "Value_Filtered", CALCULATE([Total Sales], KEEPFILTERS('Products'))
)

Interpretation of the sample:
If Value_Card = Value_Table under the same filters → consistent
If any differ without business justification → inconsistency detected

2. Nature — Why CONSISTENCY is essential in Power BI

Consistency in Power BI arises from the need of the tabular engine (VertiPaq + DAX engine) to maintain a stable, unified, and non-contradictory representation of the business.
This property ensures that:

• Entities maintain a unique identity
• Relationships follow coherent semantics
• Measures produce reproducible results
• Transformations do not alter business logic
• Metrics operate under uniform definitions across the model

Consistency has an ontological nature within BI:
it is the force that maintains the integrity of the “world” represented by the model.
Without it, the system fragments into contradictory versions of the truth.

Example (EXPLANATORY EXAMPLE)

A customer must be interpreted the same way across all sources.

Inconsistent case:

• In Sales → CustomerID = 123
• In CRM → Customer_ID = "CUS123"
• In Support → IdCustomer = "00123"

Power BI interprets three different entities:

• It cannot join the tables correctly
• Customer totals differ by source
• DAX models return divergent values
• Analyses become fragmented

Result:
Customer identity is not stable → semantic inconsistency.

Sample (IMPLEMENTATION SAMPLE)

1) Normalize all CustomerID values to a uniform format:

CustomerID_Normalized =
Text.PadStart(
    Text.Select([CustomerID], {"0".."9"}),
    6,
    "0"
)

2) Result:

Sales    → 123     → 000123
CRM      → CUS123  → 000123
Support  → 00123   → 000123

3) Impact:

- The tabular engine recognizes a single entity
- Relationships become stable
- DAX calculations return consistent values

3. Function — How CONSISTENCY operates in the Power BI pipeline

A) Structural Consistency (Power Query)

Stable and deterministic transformations.

Example

Dates stored in different formats across tables.

Sample

Date = Date.FromText([RawDate])

B) Relational Consistency (Tabular Model)

Relationships must reflect business logic.

Example

Duplicated OrderIDs generate inflated totals.

Sample

Duplicates = Table.DuplicateRows(Sales, {"OrderID"})

C) Calculated Consistency (DAX)

Measures must behave the same across valid contexts.

Example

The measure works in a Card but fails in a Matrix.

Sample

EVALUATE
SUMMARIZE(
  'Sales',
  "Card", [Total Sales],
  "All", CALCULATE([Total Sales], ALL('Sales')),
  "RowContext", SUM('Sales'[Amount])
)

4. Consequences — What happens when consistency fails

A) KPIs differ depending on the visual

CALCULATE([Total Sales], ALLSELECTED())

B) Granularity conflicts

Daily Budget = 
DIVIDE(
   [Monthly Budget],
   COUNTROWS(VALUES('Calendar'[Date]))
)

C) Measures break across hierarchies

Sales YTD =
CALCULATE([Total Sales], DATESYTD('Calendar'[Date]))

D) Duplicates break key integrity

Group = Table.Group(DimCustomer, {"CustomerID"}, {{"Count", each Table.RowCount(_), Int64.Type}})

5. Interactions — Related properties

Integrity

MissingProducts =
EXCEPT(
  VALUES(FactSales[ProductID]),
  VALUES(DimProduct[ProductID])
)

Freshness

MinDate = MIN('Sales'[Date])
MaxDate = MAX('Calendar'[Date])

Lineage

#"Renamed Columns" = Table.RenameColumns(Source, {{"Date", "TransactionDate"}})

6. Evaluation Methods — How to verify CONSISTENCY

Stress Test Visual

EVALUATE ROW(
   "Card", [Total Sales],
   "Table", CALCULATE([Total Sales], ALL('Sales')),
   "Filtered", CALCULATE([Total Sales], KEEPFILTERS('Products'))
)

Relationship Audit

Model.Relationships | summarize Count=count() by FromTable, ToTable

Measure Validation

CALCULATE([KPI], REMOVEFILTERS())

7. Applicable Models — Consistency Frameworks

Model 1 — Semantic Consistency Matrix

| Entity | KPI | Definition | Formula |
| Sales  | Total Sales | Billed amount | SUMX(Sales, Amount) |
| Finance| Total Sales | Net revenue   | SUMX(Sales, Amount - Taxes) |

Model 2 — DAX Truth Table

EVALUATE
ADDCOLUMNS(
   VALUES('Products'[Category]),
   "Expected", [Expected Total Sales],
   "Actual", [Total Sales],
   "Consistent", IF([Expected Total Sales] = [Total Sales], TRUE(), FALSE())
)

Model 3 — Granularity Alignment

Daily Budget =
DIVIDE(
   SUM(Budget[Amount]),
   COUNTROWS(VALUES('Calendar'[Date]))
)