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Physically impossible values should not enter summary statistics unexamined. That targets investigate impossible sensor readings, not assess impact of extreme but possibly valid values.

Legitimate rare business events are meaningful and should not be deleted solely for extremity. It may suit preserve valid exceptional transactions, not assess impact of extreme but possibly valid values.

Date constraints can identify records inconsistent with known business time boundaries. It is useful for detecting impossible chronology, not for assessing impact of extreme but possibly valid values.

Referential checks expose missing dimensions, latency, or deleted source records. It is useful for resolving orphan transactional references, not for diagnosing unexpected merged row multiplication.

Field-level and timing comparisons can reveal source lag or transformation error. That is meant for reconcile mismatched ledger totals, not diagnose unexpected merged row multiplication.

Joining exchange rates at the wrong grain distorts converted amounts. It would address correct currency conversion lookup, not diagnose unexpected merged row multiplication.

Source manifest comparison exposes absent expected partitions. It would address restore weekend extract completeness, not validate required invoice-detail coverage.

Privacy-driven redaction is a required transformation rather than a quality defect. That targets distinguish deliberate redaction from missingness, not validate required invoice-detail coverage.

Coverage validation detects missing segments that are not visible in raw row counts. It is useful for detecting missing geographic coverage, not for validating required invoice-detail coverage.

Targeted textual transformations avoid destroying identifier semantics. It is useful for clean descriptions without changing keys, not for avoiding collapsing separate historical entities.

Unicode normalization prevents byte-form differences from appearing as distinct text values. Its scope is unify canonically equivalent character strings, not avoid collapsing separate historical entities.

Unconditional lowercasing would merge distinct source-defined categories. That is meant for retain legitimately case-sensitive codes, not avoid collapsing separate historical entities.

Blank and zero convey different data completeness and performance meanings. That targets distinguish no measurement from measured zero, not quantify missing customer identifiers.

Empty textual values and numeric zero are different states. It may suit normalize absent text while preserving zero, not quantify missing customer identifiers.

Treating missing as zero would bias the mean downward. It is useful for unbiased average across measured observations, not for quantify missing customer identifiers.

Unit-of-measure reconciliation prevents thousandfold numeric misstatement. It is useful for consistent numeric scale and units, not for avoiding percent double-scaling errors.

Proper monetary typing reduces imprecise floating-point calculations for currency. That is meant for reliable cents-level financial totals, not avoid percent double-scaling errors.

Converting a noninteger to whole number without review can change the underlying business event. It would address enforce whole-package quantity integrity, not avoid percent double-scaling errors.

Distribution summaries distinguish number of categories from once-only entries. It would address quantify vendor category variety, not assess field-level validity proportions.

Numeric summaries reveal surprising range and central tendency. That targets diagnose unusual age distributions, not assess field-level validity proportions.

Full data profiling observes rows absent from default preview samples. It is useful for inspecting errors hidden outside preview sample, not for assessing field-level validity proportions.

Timestamps can collide and lack stable identity guarantees. Its scope is retain stable event identifiers, not avoid cross-company product-key collision.

OrderID is unique in the parent but repeated at transaction-line grain. That is meant for correct order header and line cardinality, not avoid cross-company product-key collision.

Surrogate keys isolate identity from mutable natural attributes. It would address preserve historical employee entity identity, not avoid cross-company product-key collision.

Left outer join retains left-side customers even without matching orders. It would address preserve customers absent from purchase fact, not concatenate time-partitioned transactions.

Merge joins columns from matching rows across two tables. It may suit enrich fact rows with dimension attributes, not concatenate time-partitioned transactions.

A left anti join returns unmatched rows from the left input. It is useful for find orphan sales account references, not for concatenate time-partitioned transactions.

Transpose exchanges rows and columns to repair rotated structure. It is useful for restoring orientation of a rotated table, not for normalize wide month columns.

Aggregated pivoting creates cross-tab columns from long fact data. Its scope is produce summarized region cross-tab, not normalize wide month columns.

Pivot turns distinct row attribute values into separate columns. It would address create one column per survey question, not normalize wide month columns.

Text combination produces a single presentation field without changing the original keys. It would address create formatted location descriptions, not separate structured identifier components.

Text.Start extracts a fixed-length prefix without altering the source field. That targets derive product category prefix, not separate structured identifier components.

Key normalization reduces false nonmatches due to controlled formatting differences. It may suit standardize textual join keys, not separate structured identifier components.