Data Strategy Fundamentals
A data strategy defines how an organisation collects, stores, manages, governs, and derives value from data. In the modern business environment — where competitive advantage increasingly comes from data-driven decision-making, personalisation, and AI — a coherent data strategy is a business imperative, not a technical nice-to-have.
Data Strategy Components
- Data collection: What data is captured, from which sources, at what granularity? Event tracking, operational data, third-party data.
- Data infrastructure: Data warehouse, data lake, data lakehouse — the storage and processing architecture for analytical data
- Data governance: Ownership, quality standards, lineage, access control, retention policies
- Data culture: Making data accessible to decision-makers through self-service analytics, data literacy programmes, and embedding analysts in teams
- Data products: The outputs of the data platform — dashboards, reports, ML models, data APIs
Common Data Architecture Patterns
- Data warehouse (Snowflake, BigQuery, Redshift): Structured, optimised for analytical queries. Best for BI and reporting.
- Data lake (S3, GCS, ADLS): Raw data storage at scale — flexible schema, multiple formats. Good for ML and data science.
- Lakehouse (Databricks, Delta Lake): Combines data lake flexibility with data warehouse performance
- Data mesh: Federated architecture where domain teams own their data products. Scales better than centralised approaches for large organisations.