# 🗄️ Data Readiness Assessment for GenAI

**Version:** 1.0
**Date:** ___________________________
**Organization:** ___________________________
**Assessor:** ___________________________
**Department/Business Unit:** ___________________________

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## 📋 Assessment Overview

This assessment evaluates your organization's data readiness for GenAI initiatives across five key areas: Data Inventory, Data Quality, Data Architecture, Data Governance, and AI-Specific Readiness.

**Scoring Guide:**
- **1 - Not Started:** No capabilities or processes in place
- **2 - Initial:** Ad-hoc processes, minimal documentation
- **3 - Developing:** Basic processes defined, some consistency
- **4 - Established:** Documented processes, consistent execution
- **5 - Optimized:** Automated, continuously improved, best-in-class

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## 📊 Section 1: Data Inventory & Classification

### 1.1 Data Asset Inventory

| Assessment Item | Score (1-5) | Evidence/Notes |
|-----------------|-------------|----------------|
| Complete inventory of data assets exists | ___/5 | _________________________ |
| Data assets are cataloged with metadata | ___/5 | _________________________ |
| Data lineage is documented | ___/5 | _________________________ |
| Data ownership is clearly assigned | ___/5 | _________________________ |
| Inventory is regularly updated | ___/5 | _________________________ |

**Section Score:** ___/25

### 1.2 Data Classification

| Assessment Item | Score (1-5) | Evidence/Notes |
|-----------------|-------------|----------------|
| Data classification scheme exists | ___/5 | _________________________ |
| All data is classified by sensitivity | ___/5 | _________________________ |
| Classification drives access controls | ___/5 | _________________________ |
| PII/PHI/PCI data is identified | ___/5 | _________________________ |
| Classification is enforced automatically | ___/5 | _________________________ |

**Section Score:** ___/25

### Data Inventory Checklist

**Critical Data Sources for GenAI:**
- [ ] Customer/client data (structured)
- [ ] Transactional data
- [ ] Product/service data
- [ ] Employee/HR data
- [ ] Financial data
- [ ] Operational data
- [ ] Unstructured documents (contracts, policies, manuals)
- [ ] Communication data (emails, chat logs, tickets)
- [ ] External/third-party data
- [ ] IoT/sensor data
- [ ] Other: _________________________

**Data Locations:**
- [ ] On-premises databases
- [ ] Cloud data warehouses
- [ ] Data lakes
- [ ] SaaS applications
- [ ] File shares/document repositories
- [ ] Legacy systems
- [ ] Other: _________________________

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## 📈 Section 2: Data Quality Assessment

### 2.1 Data Quality Dimensions

_Rate each dimension for your primary data sources:_

| Dimension | Definition | Score (1-5) | Key Issues |
|-----------|------------|-------------|------------|
| **Completeness** | Required data fields are populated | ___/5 | _________________________ |
| **Accuracy** | Data correctly represents reality | ___/5 | _________________________ |
| **Consistency** | Same data across systems matches | ___/5 | _________________________ |
| **Timeliness** | Data is current and available when needed | ___/5 | _________________________ |
| **Validity** | Data conforms to defined formats/rules | ___/5 | _________________________ |
| **Uniqueness** | No unintended duplicates exist | ___/5 | _________________________ |

**Section Score:** ___/30

### 2.2 Data Quality by Source

_Rate data quality for each critical source:_

| Data Source | Completeness | Accuracy | Consistency | Timeliness | Overall |
|-------------|--------------|----------|-------------|------------|---------|
| _________________ | ___/5 | ___/5 | ___/5 | ___/5 | ___/20 |
| _________________ | ___/5 | ___/5 | ___/5 | ___/5 | ___/20 |
| _________________ | ___/5 | ___/5 | ___/5 | ___/5 | ___/20 |
| _________________ | ___/5 | ___/5 | ___/5 | ___/5 | ___/20 |
| _________________ | ___/5 | ___/5 | ___/5 | ___/5 | ___/20 |

### 2.3 Data Quality Processes

| Assessment Item | Score (1-5) | Evidence/Notes |
|-----------------|-------------|----------------|
| Data quality metrics are defined | ___/5 | _________________________ |
| Quality is measured regularly | ___/5 | _________________________ |
| Quality issues are tracked/resolved | ___/5 | _________________________ |
| Data profiling tools are used | ___/5 | _________________________ |
| Quality SLAs exist with owners | ___/5 | _________________________ |

**Section Score:** ___/25

---

## 🏗️ Section 3: Data Architecture Evaluation

### 3.1 Current Architecture Assessment

| Component | Status | Technology/Platform | GenAI Ready? |
|-----------|--------|---------------------|--------------|
| **Data Warehouse** | ⬜ None ⬜ Basic ⬜ Modern | _________________ | Yes / Partial / No |
| **Data Lake** | ⬜ None ⬜ Basic ⬜ Modern | _________________ | Yes / Partial / No |
| **Lakehouse** | ⬜ None ⬜ Basic ⬜ Modern | _________________ | Yes / Partial / No |
| **Streaming Platform** | ⬜ None ⬜ Basic ⬜ Modern | _________________ | Yes / Partial / No |
| **Vector Database** | ⬜ None ⬜ Basic ⬜ Modern | _________________ | Yes / Partial / No |
| **Knowledge Graph** | ⬜ None ⬜ Basic ⬜ Modern | _________________ | Yes / Partial / No |

### 3.2 Integration Capabilities

| Assessment Item | Score (1-5) | Evidence/Notes |
|-----------------|-------------|----------------|
| APIs available for data access | ___/5 | _________________________ |
| Real-time data streaming capability | ___/5 | _________________________ |
| ETL/ELT pipelines are automated | ___/5 | _________________________ |
| Data can be exported in AI-ready formats | ___/5 | _________________________ |
| Integration with AI/ML platforms exists | ___/5 | _________________________ |

**Section Score:** ___/25

### 3.3 Scalability & Performance

| Assessment Item | Score (1-5) | Evidence/Notes |
|-----------------|-------------|----------------|
| Architecture can scale for AI workloads | ___/5 | _________________________ |
| Query performance meets requirements | ___/5 | _________________________ |
| Storage can handle growth projections | ___/5 | _________________________ |
| Compute resources are elastic | ___/5 | _________________________ |
| Cost optimization mechanisms exist | ___/5 | _________________________ |

**Section Score:** ___/25

### 3.4 Architecture Maturity Checklist

**Essential for GenAI:**
- [ ] Centralized data platform (warehouse/lake/lakehouse)
- [ ] API-first data access
- [ ] Support for unstructured data (documents, images, etc.)
- [ ] Vector storage for embeddings
- [ ] Real-time or near-real-time data availability
- [ ] Cloud-native or hybrid cloud architecture
- [ ] Data versioning capability
- [ ] Metadata management system
- [ ] Data observability/monitoring

---

## 🔐 Section 4: Data Governance Framework

### 4.1 Governance Structure

| Assessment Item | Score (1-5) | Evidence/Notes |
|-----------------|-------------|----------------|
| Data governance council/committee exists | ___/5 | _________________________ |
| Data stewardship roles are defined | ___/5 | _________________________ |
| Data policies are documented | ___/5 | _________________________ |
| Governance processes are enforced | ___/5 | _________________________ |
| Regular governance reviews occur | ___/5 | _________________________ |

**Section Score:** ___/25

### 4.2 Access Control & Security

| Assessment Item | Score (1-5) | Evidence/Notes |
|-----------------|-------------|----------------|
| Role-based access control implemented | ___/5 | _________________________ |
| Sensitive data is encrypted at rest | ___/5 | _________________________ |
| Data is encrypted in transit | ___/5 | _________________________ |
| Access logging and auditing exists | ___/5 | _________________________ |
| Data masking/anonymization available | ___/5 | _________________________ |

**Section Score:** ___/25

### 4.3 Privacy & Compliance

| Assessment Item | Score (1-5) | Evidence/Notes |
|-----------------|-------------|----------------|
| Privacy impact assessments performed | ___/5 | _________________________ |
| Consent management in place | ___/5 | _________________________ |
| Data retention policies enforced | ___/5 | _________________________ |
| Right to deletion supported | ___/5 | _________________________ |
| Regulatory compliance documented | ___/5 | _________________________ |

**Section Score:** ___/25

### 4.4 Regulatory Compliance Checklist

_Check all applicable regulations:_

- [ ] **GDPR** (EU General Data Protection Regulation)
- [ ] **CCPA/CPRA** (California Consumer Privacy Act)
- [ ] **HIPAA** (Health Insurance Portability and Accountability Act)
- [ ] **SOX** (Sarbanes-Oxley Act)
- [ ] **PCI-DSS** (Payment Card Industry Data Security Standard)
- [ ] **GLBA** (Gramm-Leach-Bliley Act)
- [ ] **FERPA** (Family Educational Rights and Privacy Act)
- [ ] **FISMA** (Federal Information Security Management Act)
- [ ] **Industry-specific:** _________________________
- [ ] **Regional/local:** _________________________

---

## 🤖 Section 5: AI-Specific Data Readiness

### 5.1 Training Data Availability

| Assessment Item | Score (1-5) | Evidence/Notes |
|-----------------|-------------|----------------|
| Sufficient volume of relevant data exists | ___/5 | _________________________ |
| Historical data is accessible | ___/5 | _________________________ |
| Data represents target use cases | ___/5 | _________________________ |
| Diverse/representative data available | ___/5 | _________________________ |
| Data can be refreshed for retraining | ___/5 | _________________________ |

**Section Score:** ___/25

### 5.2 Data Labeling & Annotation

| Assessment Item | Score (1-5) | Evidence/Notes |
|-----------------|-------------|----------------|
| Labeled datasets exist for key use cases | ___/5 | _________________________ |
| Labeling quality standards defined | ___/5 | _________________________ |
| Labeling process/tools available | ___/5 | _________________________ |
| Subject matter experts accessible | ___/5 | _________________________ |
| Label versioning/tracking in place | ___/5 | _________________________ |

**Section Score:** ___/25

### 5.3 Bias & Fairness Assessment

| Assessment Item | Score (1-5) | Evidence/Notes |
|-----------------|-------------|----------------|
| Data bias assessment performed | ___/5 | _________________________ |
| Demographic representation analyzed | ___/5 | _________________________ |
| Historical bias in data identified | ___/5 | _________________________ |
| Mitigation strategies documented | ___/5 | _________________________ |
| Ongoing bias monitoring planned | ___/5 | _________________________ |

**Section Score:** ___/25

### 5.4 RAG & Retrieval Readiness

| Assessment Item | Score (1-5) | Evidence/Notes |
|-----------------|-------------|----------------|
| Document corpus is organized | ___/5 | _________________________ |
| Documents can be chunked/processed | ___/5 | _________________________ |
| Embedding infrastructure exists | ___/5 | _________________________ |
| Search/retrieval mechanisms available | ___/5 | _________________________ |
| Document updates can be indexed | ___/5 | _________________________ |

**Section Score:** ___/25

### 5.5 AI Data Requirements Checklist

**For Fine-tuning/Training:**
- [ ] Minimum data volume available (typically 1000+ examples)
- [ ] Data formats compatible with AI platforms
- [ ] Training/validation/test split strategy defined
- [ ] Data augmentation options identified
- [ ] Synthetic data generation considered (if needed)

**For RAG/Retrieval:**
- [ ] Knowledge base documents identified
- [ ] Document preprocessing pipeline exists
- [ ] Chunking strategy defined
- [ ] Vector database selected/implemented
- [ ] Update/refresh mechanism designed

**For Agents/Automation:**
- [ ] APIs available for data access
- [ ] Real-time data feeds accessible
- [ ] Write-back capabilities exist (if needed)
- [ ] Audit trail for data changes

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## 📊 Assessment Summary

### Section Scores

| Section | Max Score | Your Score | Percentage |
|---------|-----------|------------|------------|
| 1. Data Inventory & Classification | 50 | ___/50 | ___% |
| 2. Data Quality | 55 | ___/55 | ___% |
| 3. Data Architecture | 50 | ___/50 | ___% |
| 4. Data Governance | 75 | ___/75 | ___% |
| 5. AI-Specific Readiness | 100 | ___/100 | ___% |
| **Total** | **330** | **___/330** | **___%** |

### Readiness Level

| Score Range | Readiness Level | Interpretation |
|-------------|-----------------|----------------|
| 264-330 (80-100%) | **Ready** | Data foundation supports advanced GenAI initiatives |
| 198-263 (60-79%) | **Partially Ready** | Foundation exists, targeted improvements needed |
| 132-197 (40-59%) | **Developing** | Significant gaps require attention before scaling |
| 66-131 (20-39%) | **Early Stage** | Major data initiatives needed before GenAI |
| 0-65 (0-19%) | **Not Ready** | Foundational data strategy required |

**Your Readiness Level:** _________________________

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## 🎯 Recommendations by Maturity Level

### If Score 0-19% (Not Ready)

**Immediate Priorities:**
1. [ ] Establish data governance committee
2. [ ] Create data inventory of critical sources
3. [ ] Implement basic data quality monitoring
4. [ ] Define data classification scheme
5. [ ] Identify quick-win data sources for initial GenAI pilot

### If Score 20-39% (Early Stage)

**Key Actions:**
1. [ ] Complete data asset inventory
2. [ ] Implement data quality remediation for top sources
3. [ ] Establish data stewardship program
4. [ ] Modernize integration capabilities (APIs)
5. [ ] Pilot data labeling for one use case

### If Score 40-59% (Developing)

**Focus Areas:**
1. [ ] Automate data quality monitoring
2. [ ] Implement vector database for RAG
3. [ ] Establish bias assessment process
4. [ ] Create self-service data access layer
5. [ ] Build document processing pipeline

### If Score 60-79% (Partially Ready)

**Enhancement Priorities:**
1. [ ] Scale RAG infrastructure
2. [ ] Implement advanced data governance automation
3. [ ] Establish data marketplace for AI
4. [ ] Create synthetic data capabilities
5. [ ] Build real-time data streaming for agents

### If Score 80-100% (Ready)

**Optimization Focus:**
1. [ ] Continuous data quality improvement
2. [ ] Advanced bias detection and mitigation
3. [ ] Multi-modal data support
4. [ ] Knowledge graph implementation
5. [ ] Data product management

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## 📋 Action Plan

### Immediate Actions (Next 30 Days)

| Priority | Action | Owner | Due Date | Status |
|----------|--------|-------|----------|--------|
| 1 | _____________________________ | _______ | ___/___/___ | ⬜ |
| 2 | _____________________________ | _______ | ___/___/___ | ⬜ |
| 3 | _____________________________ | _______ | ___/___/___ | ⬜ |

### Short-term Actions (30-90 Days)

| Priority | Action | Owner | Due Date | Status |
|----------|--------|-------|----------|--------|
| 1 | _____________________________ | _______ | ___/___/___ | ⬜ |
| 2 | _____________________________ | _______ | ___/___/___ | ⬜ |
| 3 | _____________________________ | _______ | ___/___/___ | ⬜ |

### Medium-term Actions (90-180 Days)

| Priority | Action | Owner | Due Date | Status |
|----------|--------|-------|----------|--------|
| 1 | _____________________________ | _______ | ___/___/___ | ⬜ |
| 2 | _____________________________ | _______ | ___/___/___ | ⬜ |
| 3 | _____________________________ | _______ | ___/___/___ | ⬜ |

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## ✍️ Sign-Off

| Role | Name | Signature | Date |
|------|------|-----------|------|
| Data Assessment Lead | _____________________ | _____________________ | ___/___/______ |
| Chief Data Officer | _____________________ | _____________________ | ___/___/______ |
| AI Program Lead | _____________________ | _____________________ | ___/___/______ |

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## 📞 Support & Resources

**GenAI Maturity Portal:** https://genaimaturity.net
**Assessment Tools:** https://genaimaturity.net/assessment
**Implementation Resources:** https://genaimaturity.net/implementation

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_This assessment should be completed annually or before major GenAI initiatives to ensure data foundations support AI success._

**Document Version:** 1.0
**Last Updated:** ___________________________
**Next Assessment Date:** ___________________________