Data & Analytics Strategy

Data infrastructure, analytics capabilities, data governance, and AI/ML readiness assessment

Data & AI Package

Deliverables

Data Architecture Assessment

Review of data infrastructure, pipelines, and storage with modernization recommendations

-Data sources: Databases, APIs, event streams, third-party integrations
-Data pipelines: ETL/ELT processes, orchestration, data quality
-Data warehouse/lake: Architecture, partitioning, query performance
-Real-time analytics: Streaming architecture, latency requirements

Analytics Maturity Evaluation

Assessment of analytics capabilities and self-service BI adoption

-BI tools: Dashboard adoption, self-service capabilities, data literacy
-Metrics framework: KPIs, data definitions, single source of truth
-Reporting: Automated reports, ad-hoc analysis capabilities
-Data democratization: Access controls, training, documentation

Data Governance Framework

Policies and procedures for data quality, security, and compliance

-Data quality: Validation rules, monitoring, remediation processes
-Data catalog: Metadata management, lineage tracking, discovery
-Access controls: Role-based access, PII handling, audit logging
-Compliance: GDPR, CCPA, industry-specific regulations

AI/ML Readiness Assessment

Evaluation of data foundation for AI/ML initiatives

-Data availability: Volume, variety, velocity for ML use cases
-Feature engineering: Data preparation, feature stores, labeling
-Infrastructure: Compute resources, experimentation platforms
-Team capabilities: Data science skills, ML engineering, MLOps

Key Questions

(10 questions)

Is there a centralized data warehouse or data lake for analytics?

Are data pipelines automated and monitored for failures?

Is data quality measured and maintained with clear ownership?

Can business users access and analyze data through self-service BI tools?

Are key metrics defined with consistent business logic across reports?

Is there a data catalog with documentation and lineage tracking?

Are data access controls aligned with business roles and compliance requirements?

Is the organization ready to leverage AI/ML (data, skills, infrastructure)?

Are data privacy regulations (GDPR, CCPA) being followed?

Is there a data retention policy aligned with business and legal requirements?

Artifacts To Review

Data architecture diagrams

ETL/ELT pipeline configurations

Data warehouse schema documentation

BI tool dashboards and reports

Data quality monitoring setup

Data catalog or metadata management tools

Access control policies

AI/ML project documentation

Sample Outputs

Data Architecture Roadmap

Modernization plan for data infrastructure with specific tools, migrations, and timeline

Format: PDF with architecture diagrams and implementation phases

Analytics Playbook

Framework for metrics definition, dashboard design, and self-service analytics adoption

Format: Markdown guide with templates and examples

Data Governance Policy

Comprehensive policies for data quality, access, retention, and compliance

Format: PDF policy document with implementation checklist

AI/ML Readiness Scorecard

Assessment of data and organizational readiness for AI/ML initiatives with gap analysis

Format: Interactive scorecard with recommended next steps

Maturity Levels

Emerging

Siloed data, manual reporting, no data governance, reactive analytics

Developing

Basic data warehouse, some BI adoption, informal data quality, ad-hoc governance

Defined

Modern data stack, self-service analytics, data quality monitoring, documented governance, AI/ML exploration

Advanced

Real-time analytics, data mesh architecture, ML in production, data-driven culture, automated governance

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