Data Maturity Assessment
Where Does Your Enterprise Stand on the Data Maturity Matrix?
Answer 20 technical questions to get your Data Maturity Score + personalized roadmap.
Question 1 of 20
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Data Architecture & Engineering
How would you describe your current data architecture?
No formal architecture
Multiple disconnected systems
Central data warehouse/lake
Unified governed data layer (semantic + metadata + access)
Data Architecture & Engineering
How reliable is your data ingestion framework?
Mostly manual imports
Some scheduled jobs
Automated pipelines with monitoring
Fully orchestrated, versioned, auto-healing pipelines
Data Architecture & Engineering
How do you manage schema evolution and source system changes?
Manual remediation
Script-based
Tool-assisted
Automated schema governance
Data Architecture & Engineering
How frequently do engineering teams spend time on fixes or reconciliation?
Daily
Weekly
Monthly
Rarely (automated + governed)
Integration & Connectivity
How many core systems feed your analytics environment?
1–4
5–9
10–19
20+
Integration & Connectivity
How integrated is your data landscape?
Mostly isolated
Some systems integrated
Most systems integrated
Fully integrated through a unified layer
Integration & Connectivity
How real-time is the movement of data between your systems?
Batch (daily/weekly)
Near real-time for some data
Mostly near real-time
Real-time data streaming
Data Quality, Governance & Compliance
How would you rate the maturity of your data quality program?
No defined process
Basic rule-based checks
Mostly automated checks
Fully automated + anomaly detection + remediation
Data Quality, Governance & Compliance
Do you maintain end-to-end lineage?
No lineage
Partial lineage
Operational lineage
Full lineage (field → table → pipeline → dashboard)
Data Quality, Governance & Compliance
How standardized is your KPI taxonomy across teams?
No standardization
Partial/departmental
Mostly standardized
Fully governed cross-enterprise taxonomy
Data Quality, Governance & Compliance
How robust is your data access and security model?
Table-level access
Role-based security
Granular attribute-based
Automated + policy-driven + audited
Analytics, Automation & Self-Service
How automated is your data preparation pipeline?
Manual transformations
Some scripts
Automated pipelines
Fully automated with governance & monitoring
Analytics, Automation & Self-Service
How scalable is your analytics environment today?
Struggles with load
Moderate scalability
Good scalability
Enterprise-grade & highly scalable
Analytics, Automation & Self-Service
How self-service is your analytics ecosystem?
No self-service
Limited to power users
Self-service for BI users
Fully self-service (NLQ, exploration, AI-assisted)
Analytics, Automation & Self-Service
How real-time are your reporting and dashboards?
Static, batch-based
Partially refreshed
Frequently refreshed
Real-time or near real-time
AI, ML & GenAI Readiness
How prepared is your data foundation for AI/ML workloads?
Not ready
Some structured datasets
Mostly AI-ready
Fully AI-ready unified semantic + governed layer
AI, ML & GenAI Readiness
How would you describe your current AI/ML adoption?
None
Experimenting / pilots
Adopted in select functions
Actively scaling across units
AI, ML & GenAI Readiness
Do you use GenAI for analytics or operational decision-making?
Not yet
Early experiments
Limited adoption
Enterprise-wide GenAI adoption & agents
Organizational Alignment & Intent
How aligned are business and technical teams on data priorities?
Very misaligned
Partially aligned
Mostly aligned
Fully aligned with shared models/governance
Organizational Alignment & Intent
What is your modernization timeline?
No plans
6–12 months
3–6 months
0–3 months (active priority)
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