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Data Cleansing & Migration Strategy Flashcards

7 cards from real ERP practice questions. Tap to flip, then mark Knew It or Still Learning โ€” missed cards come back until you master them.

Read the first 7 Data Cleansing & Migration Strategy flashcards as text
  1. Which data profiling technique identifies the percentage of records with missing values in a field?

    Answer: Completeness analysis

    Completeness analysis measures what proportion of records have populated values for each field.

  2. During ERP migration, a 'delta load' refers to:

    Answer: Migrating only records changed since the last extract

    A delta load captures and migrates only the records that have been added or modified since the previous data extraction.

  3. What is the primary purpose of a data steward during an ERP migration project?

    Answer: Owning accountability for data quality within a business domain

    A data steward is a business-side role responsible for defining data rules, resolving quality issues, and ensuring domain data is fit for purpose.

  4. Which approach best reduces cutover risk when migrating a high-volume ERP general ledger?

    Answer: Migrating only open balances and archiving history separately

    Migrating open balances and archiving historical GL data reduces cutover window duration while preserving compliance access.

  5. A 'freeze period' in ERP migration is best described as:

    Answer: A window where source system transactions are halted to create a stable migration snapshot

    A freeze period halts or minimizes source system activity so that the extracted dataset is stable and consistent for final migration.

  6. Master data deduplication during ERP migration most commonly uses which technique to identify potential matches?

    Answer: Fuzzy matching algorithms on name and address fields

    Fuzzy matching detects near-duplicate records where names or addresses differ slightly due to abbreviations, typos, or formatting inconsistencies.

  7. In the context of ERP data migration, 'data harmonization' means:

    Answer: Standardizing data from multiple source systems into a unified format and taxonomy

    Harmonization resolves differences in codes, units, and naming conventions across disparate source systems so all data conforms to a single target model.