Measure data maturity and focus the next 90 days

1. Our data priorities are explicitly linked to business goals. *

2. Senior leaders actively sponsor the data agenda. *

3. We maintain a documented, funded data roadmap. *

4. Data investment decisions use defined value and risk criteria. *

5. Progress against data objectives is reviewed on a regular cadence. *

1. Data ownership and stewardship responsibilities are formally assigned. *

2. Critical data policies and standards are documented and accessible. *

3. Business terms and key data elements have agreed definitions. *

4. Governance decisions follow a clear escalation and approval process. *

5. Compliance obligations are mapped to data controls and evidence. *

1. Our target data architecture is documented and aligned to business needs. *

2. Core data flows, interfaces, and dependencies are understood. *

3. Integration patterns reduce duplication and manual reconciliation. *

4. Platforms are scalable, supportable, and governed through lifecycle standards. *

5. Metadata and lineage are captured for priority data products. *

1. Critical data has measurable quality rules and thresholds. *

2. Quality issues are logged, owned, prioritised, and resolved systematically. *

3. Root-cause analysis is used instead of repeated manual correction. *

4. Quality performance is monitored and reported to accountable owners. *

5. Preventive controls are embedded at key data capture and processing points. *

1. Teams can access trusted reporting without excessive manual effort. *

2. Metrics and KPIs have consistent definitions across functions. *

3. Advanced analytics or AI use cases follow a repeatable delivery process. *

4. Models and analytical products are monitored after deployment. *

5. Self-service analytics is supported by governed datasets and guidance. *

1. Data roles, skills, and capacity needs are understood. *

2. Staff receive role-appropriate data literacy and technical development. *

3. Communities of practice share methods, standards, and reusable assets. *

4. Recruitment and workforce plans address critical data capability gaps. *

5. Leaders reinforce evidence-based decision making and responsible data use. *

1. The division of responsibilities between central and domain teams is clear. *

2. Data work is prioritised through a transparent portfolio process. *

3. Delivery teams use repeatable product, project, or service management practices. *

4. Service levels and operational responsibilities are defined for key data products. *

5. Dependencies, decisions, risks, and benefits are governed across functions. *

1. Data is classified according to sensitivity and business criticality. *

2. Access is granted on least-privilege principles and reviewed regularly. *

3. Privacy requirements are designed into data processes and analytical use cases. *

4. Retention, deletion, backup, and recovery controls are consistently applied. *

5. Security and privacy incidents are detected, investigated, and learned from. *

1. Data initiatives have named outcomes, baselines, targets, and accountable owners. *

2. Benefits are measured after delivery rather than only forecast beforehand. *

3. Adoption and behavioural change are tracked for data products and insights. *

4. Low-value or duplicative data activities are stopped or redesigned. *

5. Lessons from realised benefits inform future investment decisions. *