10 Remarkable Data Management Mistakes to Avoid

1. Overlooking Data Governance

Failure to develop data access, quality, and compliance policies.

2. Inadequate Data Security Measures

Missing encryption, multi-factor authentication, and role-based access control.

3. Not Developing a Backup and Recovery Plan

Risking losing data without regular backups or recovery process testing.

4. Using Outdated Tools and Technology

Using legacy systems that can't keep up with the demands of modern data.

5. Poor Data Integration

Allowing siloed systems to result in disconnected and inconsistent datasets.

6. Data Overcollection

Data collection without clear objectives, and hence the storage and processing become inefficient.

7. Lack of Real-Time Monitoring

Data is not tracked and analyzed, and thus critical insights are missed.

8. Overlooking Data Quality Assurance

Data validation, deduplication, and standardization processes are ignored.

9. Metadata Management

Documentation of data origin, definition, and usage guidelines is not done.

10. Staff Not Trained on Best Practices for Data

Human errors and inconsistencies occur because staff is not educated enough about best practices.

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