# Data Strategy Frameworks

## Data Maturity Assessment

| Level | Characteristics | Typical Actions |
|-------|-----------------|-----------------|
| **Reactive** | Data silos, manual reporting, no ownership | Inventory data assets, assign owners, consolidate basics |
| **Managed** | Central warehouse, basic governance, BI tools | Quality programs, self-service, standardize metrics |
| **Optimized** | Trusted data products, ML in production, data-driven culture | Monetization, advanced analytics, real-time capabilities |

## Strategy Components

### 1. Vision Statement
- 3-year aspiration for data capabilities
- Tie to business strategy explicitly
- Example: "Enable real-time decisions at every customer touchpoint"

### 2. Current State Assessment
- Data asset inventory
- Technology landscape
- Skills gap analysis
- Pain points from business stakeholders

### 3. Target State
- Capabilities needed
- Technology architecture
- Org structure
- Governance model

### 4. Roadmap
- Phased initiatives
- Quick wins vs foundational investments
- Dependencies mapped
- Resource requirements

## Data Strategy Frameworks

### DAMA-DMBOK Framework
- Data governance, architecture, modeling, storage, security, integration, quality, metadata, master data, warehousing, BI, documentation

### Data Mesh (for large orgs)
- Domain ownership of data
- Data as product thinking
- Self-serve data platform
- Federated computational governance

### Modern Data Stack
- Ingestion (Fivetran, Airbyte)
- Storage (Snowflake, Databricks, BigQuery)
- Transform (dbt)
- BI (Looker, Tableau, Metabase)
- Orchestration (Airflow, Dagster)

## Success Metrics

| Metric | Target |
|--------|--------|
| Data downtime | < 4 hours/quarter |
| Query time (p95) | < 10 seconds |
| Self-service adoption | > 60% of reports |
| Data quality score | > 95% |
| Time to new data source | < 2 weeks |