The General Data Protection Regulation (GDPR) has significantly impacted data science projects by introducing strict rules for data collection, processing, and storage. Here’s how GDPR affects data science:
1. Data Collection and Consent
- What It Means:
- Data must be collected transparently, with explicit consent from individuals.
- Impact:
- Data scientists must ensure that data collection methods comply with GDPR.
- Consent forms must clearly explain how data will be used.
2. Data Minimization
- What It Means:
- Only collect data that is necessary for the specified purpose.
- Impact:
- Data scientists must carefully select relevant data and avoid unnecessary collection.
3. Data Anonymization and Pseudonymization
- What It Means:
- Personal data must be anonymized or pseudonymized to protect privacy.
- Impact:
- Data scientists must implement techniques to anonymize data while preserving its utility for analysis.
4. Data Security
- What It Means:
- Implement robust security measures to protect data.
- Impact:
- Data scientists must ensure data is encrypted and stored securely.
5. Right to Access and Erasure
- What It Means:
- Individuals have the right to access their data and request its deletion.
- Impact:
- Data scientists must design systems that allow for easy data access and deletion.
6. Data Breach Notification
- What It Means:
- Notify authorities and affected individuals of data breaches within 72 hours.
- Impact:
- Data scientists must implement monitoring systems to detect breaches promptly.
7. Accountability and Documentation
- What It Means:
- Maintain records of data processing activities and demonstrate compliance.
- Impact:
- Data scientists must document their processes and ensure transparency.