Raeburn Research · Methodology note · 2026
Digital Maturity Measurement Framework
A transparent framework for assessing digital capability across ten dimensions without pretending a self-score is an external benchmark or certification.
Scoring principle
Each dimension is scored 0–10. A high score should require observable capability and evidence, not aspiration. The public self-assessment is directional. Consulting use should validate answers against systems, operating data, documents and process observation. Scores are summed to 100 for communication, but decisions should focus on dimension-level evidence and dependencies rather than the headline number alone.
Technology
System ownership, lifecycle, reliability, technical debt and architecture visibility.
AI
Governed use cases, evaluation, human oversight, data boundaries and operational integration.
Automation
Repeatable workflow design, exception handling, control, observability and measured outcomes.
Data
Ownership, definitions, quality, accessibility, security and decision usefulness.
Cybersecurity
Identity, least privilege, vulnerability hygiene, recoverability and incident readiness.
Integration
Systems of record, API/event reliability, duplicate entry, interoperability and exit risk.
Processes
Process clarity, handoffs, rework, waiting time, standardisation and outcome measurement.
People
Ownership, skills, adoption, training, decision rights and change capacity.
Governance
Risk thresholds, evidence requirements, accountability, change control and supplier oversight.
Customer Experience
Customer effort, response time, consistency, failure recovery and cross-channel continuity.
Interpretation bands
These bands are descriptive labels created for this framework; they are not percentile rankings and do not currently represent a statistically validated population benchmark.
Research integrity
Future benchmark publications should state sample source, inclusion criteria, sample size, field dates, missing-data treatment, weighting if any, version changes and conflicts. Client-specific scores should only enter aggregate research where consent, privacy and anonymisation requirements are satisfied.