Benchmark your organization’s data capabilities across 6 dimensions. Each dimension is scored 1-5. This is the same framework Valiotti Data uses in Phase 1 diagnostics with clients.
How to Use This Assessment
- Score each dimension honestly (1 = ad-hoc, 5 = optimized)
- Calculate your total and maturity level
- Identify the 2-3 lowest-scoring areas as priority focus
- Re-assess quarterly to track progress
Dimension 1: Data Strategy & Leadership
| Score | Description |
|---|---|
| 1 | No data strategy. Decisions made by gut. No data leadership role. |
| 2 | Informal data goals. Data managed by IT or engineering as side task. |
| 3 | Documented data strategy exists. Part-time data lead or fractional CDO. |
| 4 | Data strategy aligned with business OKRs. Dedicated data team with clear roadmap. |
| 5 | Data is a board-level topic. CDO reports to CEO. Data strategy reviewed quarterly. |
Dimension 2: Data Infrastructure
| Score | Description |
|---|---|
| 1 | Data lives in spreadsheets and email. No centralized storage. |
| 2 | Basic database exists but no warehouse. Manual data exports. |
| 3 | Cloud data warehouse deployed. Automated ingestion for key sources. |
| 4 | Modern data stack (warehouse + ELT + transformation). All sources integrated. |
| 5 | Real-time and batch pipelines. Data mesh or federated architecture. Cost-optimized. |
Dimension 3: Data Governance
| Score | Description |
|---|---|
| 1 | No documentation. No data ownership. No quality checks. |
| 2 | Some documentation exists but outdated. Ad-hoc quality fixes. |
| 3 | Data catalog started. PII classified. Basic quality monitoring. |
| 4 | Active data catalog. Automated quality checks. Clear ownership matrix. |
| 5 | Full data lineage. Automated compliance. Data contracts between teams. |
Dimension 4: Analytics & BI
| Score | Description |
|---|---|
| 1 | No BI tool. Reports generated manually via spreadsheets. |
| 2 | BI tool exists but only analysts use it. Reports are request-based. |
| 3 | Self-serve dashboards available. Key metrics tracked consistently. |
| 4 | Embedded analytics in workflows. AB testing framework. Attribution model. |
| 5 | Predictive analytics and ML models in production. Real-time dashboards. |
Dimension 5: Data Culture
| Score | Description |
|---|---|
| 1 | Decisions are opinion-based. “I think” dominates meetings. |
| 2 | Data referenced occasionally. Some people look at dashboards. |
| 3 | Key decisions require data backing. Exec team reviews metrics weekly. |
| 4 | Data literacy training for all teams. Data-driven OKRs at every level. |
| 5 | Experimentation culture. Everyone can query data. Data democratized. |
Dimension 6: Data Team & Skills
| Score | Description |
|---|---|
| 1 | No data team. Engineering handles everything ad-hoc. |
| 2 | 1 analyst or data engineer. No formal data team structure. |
| 3 | Small data team (2-4). Mix of analytics and engineering skills. |
| 4 | Structured data team with clear roles. Career paths defined. |
| 5 | Full data org with specializations. Data engineers, analysts, scientists, governance. |
Maturity Levels
| Total Score | Level | Recommendation |
|---|---|---|
| 6-10 | Ad-Hoc | Start with a data diagnostic and strategy roadmap |
| 11-15 | Emerging | Hire a Fractional CDO to accelerate foundation building |
| 16-20 | Defined | Focus on governance, self-serve analytics, and team growth |
| 21-25 | Managed | Optimize costs, implement advanced analytics and ML |
| 26-30 | Optimized | Scale innovation, data mesh, real-time capabilities |
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