data-strategy

name

data-strategy

description

Comprehensive data strategy covering governance, architecture, quality, analytics, and organizational culture. USE THIS SKILL when the user asks about data governance frameworks, data architecture design, analytics roadmaps, data platform selection, data quality improvement, data mesh vs data warehouse decisions, data catalog implementation, data monetization, or building a data-driven organization. Covers the full stack from governance to culture with actionable maturity assessments.

Data Strategy

Required Inputs

• Organization: Company name and industry.
• Current State: Brief description of existing data landscape (tools, team, maturity).
• Strategic Objectives: Business goals the data strategy must support.
• Scope: Enterprise-wide, business unit, or specific domain.
• Time Horizon: Strategy period (default: 3 years with 90-day quick wins).
• Constraints: Budget range, regulatory requirements, technology preferences.

Execution Steps

1. Data Maturity Assessment

Assess maturity across five pillars using the standard 1-5 maturity model. Score each sub-dimension and calculate pillar averages.

Pillar 1: Data Governance (Weight: 25%)

Sub-Dimension	Level 1: Ad Hoc	Level 3: Defined	Level 5: Optimizing	Score
Ownership & Accountability	No data owners	Domain owners assigned	Federated ownership with stewardship council
Policies & Standards	No policies	Documented policies for key domains	Automated policy enforcement, continuous review
Metadata Management	No metadata catalog	Central catalog with manual updates	Automated metadata harvesting, lineage, active catalog
Data Lifecycle Management	No retention rules	Defined retention policies	Automated lifecycle with archival and purging
Change Management	Ad hoc schema changes	Change request process	Versioned schemas, backward compatibility enforcement
Pillar Average				/5.0

Pillar 2: Data Architecture (Weight: 25%)

Sub-Dimension	Level 1: Ad Hoc	Level 3: Defined	Level 5: Optimizing	Score
Integration Patterns	Point-to-point, manual ETL	Central ETL/ELT, some real-time	Event-driven, streaming, self-serve integration
Storage Architecture	Siloed databases, spreadsheets	Central warehouse or lake	Lakehouse or mesh with governed self-serve access
Data Modeling	No standards	Dimensional models, documented schemas	Domain-driven design, semantic layer, versioned models
API & Access Layer	Direct database access	REST APIs for some systems	GraphQL/REST APIs, data products, self-serve portal
Scalability	Manual scaling, capacity limits	Auto-scaling for some workloads	Elastic, serverless, cost-optimized at scale
Pillar Average				/5.0

Pillar 3: Data Quality (Weight: 20%)

Sub-Dimension	Level 1: Ad Hoc	Level 3: Defined	Level 5: Optimizing	Score
Quality Monitoring	No monitoring	Scheduled quality checks on key tables	Real-time quality monitoring with anomaly detection
Quality Rules	No rules defined	Rules for critical data elements	Comprehensive rules, automated remediation
Data Profiling	None	Periodic profiling	Continuous profiling with trend analysis
Issue Resolution	Reactive, manual fixes	Defined escalation process	Automated root cause analysis and remediation
Quality Metrics	No measurement	DQ dashboards for key domains	SLA-based quality targets with executive visibility
Pillar Average				/5.0

Pillar 4: Analytics & Insights (Weight: 20%)

Sub-Dimension	Level 1: Ad Hoc	Level 3: Defined	Level 5: Optimizing	Score
Reporting	Spreadsheet-based, manual	BI platform, standard dashboards	Self-serve analytics, embedded analytics
Analytics Depth	Descriptive only	Diagnostic analytics, root cause	Predictive and prescriptive analytics at scale
Data Science Capability	None	Small team, ad hoc projects	Industrialized ML, MLOps, model governance
Decision Integration	Reports ignored or delayed	Data informs some decisions	Data-driven culture, real-time decision support
Experimentation	No A/B testing	Some experimentation	Systematic experimentation, feature flagging
Pillar Average				/5.0

Pillar 5: Data Culture & Literacy (Weight: 10%)

Sub-Dimension	Level 1: Ad Hoc	Level 3: Defined	Level 5: Optimizing	Score
Data Literacy	Low; spreadsheet-only skills	Training programs, data champions	Organization-wide fluency, data in every role
Self-Service Access	IT bottleneck for all requests	Business users access standard reports	Governed self-serve with guardrails
Data-Driven Decisions	Gut-feel dominant	Data referenced in some decisions	Data required for all major decisions
Community of Practice	None	Informal knowledge sharing	Formal CoP, internal conferences, shared best practices
Executive Sponsorship	No data executive	CDO or equivalent exists	Data strategy integrated with business strategy
Pillar Average				/5.0

Overall Data Maturity Score:

Overall Score = (Governance x 0.25) + (Architecture x 0.25) + (Quality x 0.20) +
                (Analytics x 0.20) + (Culture x 0.10)

2. Data Governance Framework Design

Governance Operating Model:

Role	Responsibility	Accountability Level
Data Governance Council	Strategic direction, policy approval, dispute resolution	Executive (CDO, CIO, business leaders)
Data Domain Owners	Domain-level data strategy, quality targets, access decisions	VP / Director per business domain
Data Stewards	Day-to-day quality, metadata, issue resolution	Manager / Senior IC per domain
Data Engineers	Pipeline quality, schema management, technical implementation	Engineering team
Data Consumers	Adherence to policies, issue reporting, feedback	All data users

Core Governance Policies (prioritize by maturity level):

Policy	Priority at Level 1-2	Priority at Level 3-4	Priority at Level 5
Data classification (public, internal, confidential, restricted)	Critical	Maintain	Optimize
Access control and provisioning	Critical	Maintain	Automate
Data retention and deletion	High	Maintain	Automate
Data quality standards per domain	High	Critical	Optimize
Metadata and documentation standards	Medium	Critical	Automate
Master data management	Medium	High	Optimize
Data sharing and external data use	Medium	High	Critical
Privacy and consent management	Critical (if regulated)	Maintain	Automate

Metadata Management Framework:

• Business metadata: Definitions, ownership, classifications, business rules
• Technical metadata: Schemas, lineage, transformations, freshness, row counts
• Operational metadata: Job runs, SLAs, quality scores, access logs
• Tool selection: Evaluate data catalog solutions (see System Selection skill for vendor evaluation)

3. Data Architecture Design

Architecture Pattern Selection:

Pattern	Best For	Avoid When	Complexity	Cost Profile
Data Warehouse	Structured analytics, BI reporting, known query patterns	Unstructured data, ML workloads, schema evolution	Low-Medium	Predictable
Data Lake	Raw data storage, ML, diverse data types	Governance-heavy environments (risk of data swamp)	Medium	Storage-cheap, compute-variable
Data Lakehouse	Unified analytics + ML, ACID transactions on lake	Very small data volumes, simple BI-only needs	Medium-High	Moderate
Data Mesh	Large orgs with autonomous domains, federated ownership	Small teams, centralized culture, early maturity	High	Distributed

Architecture Decision Framework:

Factor	Weight	Warehouse	Lake	Lakehouse	Mesh	Weighted Score
Data variety (structured vs. unstructured)	15%
Analytics workload types (BI vs. ML vs. both)	15%
Team size and autonomy	15%
Governance requirements	15%
Real-time requirements	10%
Cost sensitivity	10%
Existing technology stack fit	10%
Scalability needs (data volume growth)	10%
Weighted Total	100%

Reference Architecture Components:

Data Sources → Ingestion → Storage → Processing → Serving → Consumption
    |              |           |          |            |           |
  APIs         Batch ETL   Raw Zone    Transform    Data API    BI Tools
  Databases    Streaming   Curated     ML Training  Semantic    Notebooks
  SaaS Apps    CDC         Archive     Feature Eng  Cache       Applications
  Files/IoT    Event Bus               Aggregation  Search      Dashboards

4. Data Quality Framework

Six Dimensions of Data Quality:

Dimension	Definition	Measurement Method	Target Threshold
Accuracy	Data correctly reflects the real-world entity	Validation against source/reference data	>99% for critical, >95% for standard
Completeness	Required fields populated, no missing records	Null/empty checks, referential integrity	>98% for critical, >90% for standard
Consistency	Same data yields same value across systems	Cross-system reconciliation	>99% for master data
Timeliness	Data available when needed, within freshness SLA	Freshness monitoring, lag measurement	Per-domain SLA (e.g., <1hr, <24hr)
Validity	Data conforms to defined formats and rules	Schema validation, business rule checks	>99.5% for critical
Uniqueness	No unintended duplicate records	Deduplication analysis, entity resolution	>99% for master data

Data Quality Scoring Formula:

Domain Quality Score = Sum of (Dimension Score x Dimension Weight) for each dimension
Overall DQ Score = Sum of (Domain Score x Domain Criticality Weight)

Quality Level:
  95-100%: Excellent -- maintain and optimize
  85-94%:  Good -- targeted improvements
  70-84%:  Fair -- dedicated remediation program needed
  <70%:    Poor -- critical investment required

Data Quality Implementation Roadmap:

Phase	Timeline	Activities	Expected Outcome
Foundation	Months 1-3	Profile critical data, define rules, establish baselines	DQ baselines for top 10 data domains
Monitoring	Months 3-6	Deploy automated monitoring, alert on anomalies	Real-time quality visibility
Remediation	Months 6-12	Root cause analysis, upstream fixes, process changes	15-25% improvement in DQ scores
Optimization	Months 12-18	Predictive quality, automated remediation	Self-healing data pipelines

5. Analytics Capability Roadmap

Analytics Maturity Progression:

Stage	Capability	Key Question Answered	Technologies	Typical Timeline
Descriptive	Reporting, dashboards, KPI tracking	"What happened?"	BI tools, SQL, data warehouse	Months 0-6
Diagnostic	Drill-down, root cause, cohort analysis	"Why did it happen?"	Advanced BI, statistical tools	Months 3-12
Predictive	Forecasting, propensity models, risk scoring	"What will happen?"	ML platforms, Python/R, feature stores	Months 6-18
Prescriptive	Optimization, recommendation, automation	"What should we do?"	Decision engines, reinforcement learning, real-time ML	Months 12-36

Analytics Use Case Prioritization:

Use Case	Business Impact (1-5)	Data Readiness (1-5)	Technical Feasibility (1-5)	Priority Score	Stage
				Impact x Readiness x Feasibility

6. Data Lineage and Impact Analysis

Lineage Coverage Requirements:

Level	Scope	Purpose
Source-to-target	End-to-end data flow from origin to consumption	Impact analysis for source changes
Column-level	Field-level transformations and derivations	Root cause analysis for quality issues
Business-level	Business process to report/KPI mapping	Business impact assessment

7. Privacy and Compliance Integration

Privacy Framework Mapping:

Requirement	GDPR	CCPA/CPRA	HIPAA	Industry-Specific	Implementation
Data inventory/mapping	Required	Required	Required	Varies	Automated discovery tools
Consent management	Required	Required	N/A	Varies	Consent platform
Right to deletion	Required	Required	Limited	Varies	Deletion pipeline
Data minimization	Required	Recommended	Required	Varies	Retention automation
Breach notification	72 hours	"Without unreasonable delay"	60 days	Varies	Incident response plan
Cross-border transfer	Restricted	Limited	Restricted	Varies	Data residency controls
Privacy impact assessment	Required for high risk	Recommended	Required	Varies	PIA process

8. Data Monetization Opportunity Assessment

Monetization Models:

Model	Description	Revenue Type	Risk Level	Data Maturity Required
Internal optimization	Better decisions from better data	Cost savings, efficiency	Low	Level 2+
Product enhancement	Data-powered features for customers	Product differentiation	Low-Medium	Level 3+
Data-as-a-Service	Sell data or insights to third parties	New revenue stream	Medium-High	Level 4+
Data marketplace	Platform for data exchange	Platform revenue	High	Level 5
Embedded analytics	Analytics built into customer-facing products	Premium pricing	Medium	Level 3+

9. Data Team Organizational Model

Model Comparison:

Model	Description	Best For	Risks
Centralized	Single data team serves all domains	Early maturity (Level 1-2), small orgs	Bottleneck, disconnected from business
Embedded	Data people sit within business units	Strong domain alignment needed	Inconsistent standards, duplication
Hub-and-Spoke	Central CoE + embedded analysts	Medium maturity (Level 3), growing orgs	Coordination overhead
Data Mesh	Domain-owned data products, platform team	High maturity (Level 4-5), large orgs	Requires mature governance, skilled domains

Recommended Team Sizing (by data maturity):

Maturity Level	Data Team : Total Employee Ratio	Key Roles to Hire First
Level 1-2	1:200 - 1:100	Data engineer, analytics engineer, data analyst
Level 3	1:80 - 1:50	+ Data governance lead, data platform engineer
Level 4	1:40 - 1:25	+ Data scientists, ML engineers, data product managers
Level 5	1:20 - 1:15	+ AI/ML specialists, data mesh platform team

10. Build vs. Buy Decision Framework for Data Platforms

Evaluation Criteria:

Factor	Weight	Build	Buy (Commercial)	Buy (Open Source + Managed)	Score
Time to value	20%	Slow (6-18 months)	Fast (1-3 months)	Medium (2-6 months)
Customization	15%	Full control	Limited to vendor roadmap	Moderate flexibility
Total cost (3 year)	20%	High labor, low license	Predictable, potentially high	Moderate
Operational burden	15%	High	Low (SaaS)	Medium
Talent availability	10%	Specialized skills needed	Lower barrier	Community skills available
Vendor lock-in risk	10%	None	High	Low-Medium
Innovation pace	10%	Depends on team	Vendor roadmap	Community velocity
Weighted Total	100%

Output Template

# Data Strategy: [Organization Name]

**Prepared for**: [Stakeholder] | **Date**: [Date] | **Horizon**: [X]-Year Strategy

## Executive Summary

**Overall Data Maturity Score**: [X.X] / 5.0 -- Level [N]: [Ad Hoc / Managed / Defined / Quantified / Optimizing]

[3-5 sentence synthesis: where the organization is today, where it needs to be, and
the critical investments required to close the gap.]

### Maturity Snapshot
| Pillar | Current Score | Target Score (Year 1) | Target Score (Year 3) |
|---|---|---|---|
| Governance | X.X | X.X | X.X |
| Architecture | X.X | X.X | X.X |
| Quality | X.X | X.X | X.X |
| Analytics | X.X | X.X | X.X |
| Culture | X.X | X.X | X.X |
| **Overall** | **X.X** | **X.X** | **X.X** |

### Estimated Investment: $[X]M - $[Y]M over [N] years

## 1. Data Governance Framework
[Governance operating model, roles, priority policies]

## 2. Data Architecture Recommendation
**Recommended Pattern**: [Warehouse / Lake / Lakehouse / Mesh]
[Architecture design, technology selections, migration plan]

## 3. Data Quality Program
**Current Overall DQ Score**: [X]%
[Quality framework, priority dimensions, improvement targets]

## 4. Analytics Roadmap
**Current Analytics Stage**: [Descriptive / Diagnostic / Predictive / Prescriptive]
[Capability build plan, prioritized use cases, technology needs]

## 5. Privacy & Compliance
[Applicable regulations, gap analysis, remediation plan]

## 6. Data Organization & Talent
**Recommended Model**: [Centralized / Hub-and-Spoke / Embedded / Mesh]
[Team structure, hiring plan, capability development]

## 7. Data Monetization Opportunities
[Assessed opportunities with feasibility and timeline]

## Implementation Roadmap

### Phase 1: Foundation (Months 1-6)
- [Key initiative with owner, cost, and success metric]

### Phase 2: Scale (Months 7-18)
- [Key initiative with owner, cost, and success metric]

### Phase 3: Optimize (Months 19-36)
- [Key initiative with owner, cost, and success metric]

### Quick Wins (First 90 Days)
1. [Quick win with expected impact]
2. [Quick win with expected impact]
3. [Quick win with expected impact]

## Investment Summary

| Category | Year 1 | Year 2 | Year 3 | Total |
|---|---|---|---|---|
| Technology (platforms, tools) | | | | |
| People (hiring, training) | | | | |
| Process (governance, quality) | | | | |
| External (consultants, vendors) | | | | |
| **Total** | | | | |

## Risk & Dependencies
| Risk | Likelihood | Impact | Mitigation |
|---|---|---|---|
| [Risk description] | H/M/L | H/M/L | [Mitigation action] |

## Success Metrics
| Metric | Baseline | Year 1 Target | Year 3 Target |
|---|---|---|---|
| Data maturity score | X.X | X.X | X.X |
| Data quality score (overall) | X% | X% | X% |
| Time to insight (new data request) | X days | X days | X hours |
| Self-service adoption rate | X% | X% | X% |
| Analytics ROI (documented value) | $0 | $XM | $XM |

Quality Checks

• All five maturity pillars assessed with sub-dimension scoring and evidence.
• Overall maturity score calculated using weighted formula.
• Data governance framework includes roles, policies, and metadata management.
• Architecture recommendation justified with decision framework scoring.
• Data quality framework covers all six dimensions with measurement methods.
• Analytics roadmap maps progression from descriptive to prescriptive.
• Privacy and compliance requirements mapped to specific regulations.
• Data team model recommendation aligned with current and target maturity.
• Build vs. buy analysis completed for major platform decisions.
• Investment estimate provided as a range with year-by-year breakdown.
• Implementation roadmap includes quick wins (90 days) and phased execution.
• Success metrics defined with baselines and targets.
• All estimates carry confidence levels (High/Medium/Low).
• Strategy aligns data investments to stated business objectives.