birchwoodu
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 Using Outdated Tools and Technology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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