Data Quality Infrastructure Governance Team
Category 1 of 4

Data Quality & Management

How well does your organization collect, store, and maintain data?

01 How would you describe the quality of your core business data?
Mostly inconsistent or unreliable, with frequent errors
Some data is clean, but many sources have quality issues
Generally reliable, but lacks standardized quality processes
Good quality with regular audits and defined standards
Excellent: automated validation, profiling, and continuous monitoring
02 How accessible is your data for analysis and reporting?
Data lives in disconnected silos; access requires manual effort
Some systems are connected, but many require manual extraction
Most data is accessible through a central repository or warehouse
Well-integrated with a modern data warehouse and self-service tools
Fully integrated data platform with real-time pipelines and cataloging
03 Do you have documented data dictionaries or metadata management?
No documentation exists for our data
Some informal documentation, mostly tribal knowledge
Partial documentation for key datasets
Comprehensive data dictionary covering most critical systems
Automated metadata management with lineage tracking
04 How often is your data refreshed or updated?
Sporadic, manual updates with no defined schedule
Monthly or quarterly batch updates
Weekly automated refreshes for most data sources
Daily automated pipelines with monitoring
Real-time or near-real-time streaming with event-driven updates
05 Do you have labeled training data for potential AI use cases?
No labeled data exists; we haven't identified AI use cases
We have raw data but no labeling or annotation process
Some labeled data for a few use cases, but inconsistent quality
Defined labeling processes with quality controls for key use cases
Mature annotation pipelines with active learning and quality assurance
Category 2 of 4

Infrastructure & Technology

Can your tech stack support AI workloads and scale?

06 What best describes your current compute infrastructure?
On-premise servers only, limited capacity
Basic cloud usage, mostly for hosting applications
Cloud-first with some ML-ready resources (GPUs on demand)
Scalable cloud infrastructure with dedicated ML pipelines
MLOps platform with automated training, serving, and monitoring
07 How mature are your data pipelines and ETL processes?
No defined pipelines; data is moved manually (CSV, email)
Basic scripts or cron jobs with no monitoring
Scheduled ETL jobs with some orchestration (Airflow, dbt)
Robust pipeline architecture with testing and monitoring
Modern data stack with CI/CD, data contracts, and observability
08 Do you use APIs to integrate systems and share data?
No API usage; integrations are manual or file-based
A few point-to-point integrations, mostly vendor-provided
Internal APIs for key systems with basic documentation
API-first architecture with versioning and access controls
Comprehensive API platform with gateway, rate limiting, and analytics
09 How do you handle version control and experiment tracking?
No version control for code or data
Basic Git for application code only
Git for code, some data versioning for key datasets
Full code versioning with experiment tracking tools (MLflow, W&B)
Complete ML lifecycle management: code, data, models, and experiments
Category 3 of 4

Governance & Compliance

Are the guardrails in place for responsible AI deployment?

10 Does your organization have a data governance policy?
No formal data governance exists
Informal guidelines, but no enforced policies
Documented policies for data access and privacy (GDPR/CCPA aware)
Active governance program with data stewards and auditing
Mature governance with automated compliance monitoring and reporting
11 How does your organization approach AI ethics and bias?
AI ethics hasn't been discussed
Aware of AI bias risks but no formal approach
Ethical guidelines exist but lack systematic implementation
AI ethics framework with bias detection and fairness metrics
Ethics review board with mandatory impact assessments for AI projects
12 Do you have a process for AI model explainability and transparency?
Never considered model explainability
Understand the concept but haven't implemented anything
Some ad-hoc explainability for specific models
Standard explainability tools (SHAP, LIME) used consistently
Comprehensive transparency framework with audit trails for all models
13 How does your organization handle data security and access control?
Minimal security; most employees can access most data
Basic access controls (passwords, firewalls) but inconsistent
Role-based access control with regular security reviews
Comprehensive IAM with encryption, logging, and incident response
Zero-trust architecture with continuous monitoring and automated threat detection
Category 4 of 4

Team & Culture Readiness

Is your organization equipped to adopt and scale AI?

14 What AI/ML talent exists in your organization today?
No dedicated AI/ML talent
A few individuals exploring AI tools (ChatGPT, Copilot) informally
Some data analysts or engineers with basic ML experience
Dedicated data science team with ML engineering capabilities
Cross-functional AI team with data scientists, ML engineers, and domain experts
15 Does leadership actively champion AI adoption?
Leadership is skeptical or unaware of AI potential
Some interest, but AI is not a strategic priority
AI is mentioned in strategy, with some budget allocated
Active executive sponsor with a defined AI strategy and investment
AI is a core strategic pillar with C-level ownership and dedicated budget
16 How does your organization handle change management for new technology?
Technology changes are met with resistance; no change management
Ad-hoc change management; some teams adapt faster than others
Defined change management process with training programs
Strong change culture with continuous learning and upskilling programs
Innovation-first culture with dedicated R&D time and rapid experimentation
17 Have you identified specific business problems where AI could add value?
No specific use cases identified
Vague ideas ("we should use AI") without specific business cases
A few identified use cases with rough feasibility estimates
Prioritized use case pipeline with ROI estimates and data requirements
Strategic AI roadmap tied to business KPIs with proven POCs