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Common errors in data modeling include:

  1. Incomplete requirements: Missing essential requirements can lead to inaccurate data models.

  2. Overlooking Relationships: Neglecting to define or represent relationships between entities and cause data inconsistencies.

  3. Normalization issues: Failing to properly normalize data can lead to insertion, update, or deletion irregularity.

  4. Lack of flexibility: Data models may struggle to remodel, to future changes or new requirements.

  5. Scalability changes: Data models should consider scalability to contain future growth.

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