Data Strategy
Build a robust data foundation that powers your AI product while respecting privacy and ensuring quality.
AI products are only as good as their data. Whether you're using pre-trained models or building your own, your data strategy determines performance, accuracy, and user trust.
This section covers how to collect, process, store, and protect the data that powers your AI product.
Data Collection Strategy
Data Sources
- User-generated data: Inputs, outputs, interactions, feedback within your product
- Public datasets: Kaggle, HuggingFace, academic datasets, open-source corpora
- Web scraping: Legal, ethical web data collection with proper robots.txt compliance
- Third-party APIs: Integrate external data sources (social, analytics, CRM)
- Synthetic data: AI-generated training data to supplement real data
- Manual labeling: Human-annotated data for training and evaluation
Data Collection Checklist
- □Identify minimum data requirements for MVP
- □Ensure legal rights to use all data sources
- □Get user consent for data collection where required
- □Document data lineage (where it came from, how it was processed)
- □Set up automated collection pipelines
- □Implement versioning for datasets
Data Quality & Processing
Data Quality Criteria
Accuracy
Data correctly represents the real-world phenomena it describes. Verify samples, cross-reference sources, validate against ground truth.
Completeness
No critical missing values or gaps. Handle missing data through imputation, exclusion, or collection of additional data.
Consistency
Data is uniform across sources and formats. Standardize units, formats, naming conventions, and schemas.
Timeliness
Data is current and relevant. Establish refresh cadence, archive old data, update regularly.
Relevance
Data is useful for your specific AI use case. Remove irrelevant fields, focus on signal over noise.
Data Processing Pipeline
1. Ingestion
Collect raw data from sources, handle different formats, manage API rate limits
2. Validation
Check for errors, missing values, outliers, format issues
3. Cleaning
Remove duplicates, fix errors, standardize formats, handle missing values
4. Transformation
Normalize, aggregate, derive features, encode categorical variables
5. Enrichment
Add metadata, join with other datasets, compute derived metrics
6. Storage
Save processed data in optimized format (Parquet, HDF5) for fast access
Privacy & Compliance
Privacy Requirements
- GDPR (Europe): Right to access, right to deletion, consent, data portability, data minimization
- EU AI Act (2025): Risk-based regulation for AI systems, transparency requirements for high-risk models, extends GDPR with AI-specific compliance
- CCPA (California): Right to know, right to delete, right to opt-out of data sales
- HIPAA (Healthcare): Protected health information (PHI) must be encrypted, access controlled, audited
- SOC 2: Security, availability, processing integrity, confidentiality, privacy controls
Privacy Best Practices
- □Collect only data you actually need (data minimization)
- □Anonymize or pseudonymize personal data where possible
- □Encrypt data at rest and in transit
- □Implement data retention policies (auto-delete after X days/months)
- □Provide user data export and deletion capabilities
- □Don't send user data to third-party AI APIs without consent
- □Document all data flows and processing activities
- □Have clear privacy policy and terms of service
Data Management & Governance
Ongoing Data Management
Versioning
Track versions of datasets, models trained on each version. Use tools like DVC (Data Version Control) or LakeFS.
Monitoring
Watch for data drift (distribution changes over time), schema changes, data quality degradation. Alert when issues detected.
Feedback Loops
Collect user corrections, ratings, and implicit feedback to improve datasets continuously.
Access Control
Limit data access to authorized personnel only. Use role-based access control (RBAC), audit all data access.
Documentation
Maintain data dictionaries, schemas, lineage documentation. Document assumptions, limitations, known biases.
Key Takeaways
- Start with minimum viable data—collect only what you need for MVP
- Prioritize data quality over quantity—clean, accurate data beats large noisy datasets
- Build automated data pipelines early to scale collection and processing
- Privacy is non-negotiable—comply with regulations, respect user data, be transparent
- Version your datasets and document data lineage for reproducibility
- Monitor for data drift and quality degradation post-launch