Data
Data your leaders can rely on, owned by someone.
At a glance
- Best for
- Organisations where reports disagree, ownership is unclear, or regulators ask questions the data cannot answer
- Typical engagement
- Fixed-scope or ongoing, agreed to fit the work.
- Typical duration
- Varies by scope; agreed at the start of the engagement.
- Team
- Sized to the scope, drawn from our delivery team.
The problem
Where this costs you time.
Conflicting definitions
The same metric means different things to different teams.
No clear owner
When a figure is wrong, nobody is accountable for fixing it.
Policies on paper only
Rules exist, but they are not applied where the data is created.
What we do
Capabilities
Data strategy
A direction for how data will support decisions and operations.
Ownership model
Named data owners and stewards for each important data set.
Definitions and glossary
Agreed meanings for core metrics, shared across teams.
Quality rules
Standards for completeness, accuracy and timeliness, with checks.
Access and privacy policies
Rules for who may see and change data, applied in the systems.
Governance operations
Forums and processes that keep definitions and quality current.
Benefits
What changes when this works.
Outcomes we design for. Measured results appear only after a client confirms them.
- One agreed meaning for each key metric
- Clear accountability for data quality
- Policies applied in the systems themselves
- Confidence in reports and audits
Process
How data strategy & governance engagements run.
Stage 1
Discover and Diagnose
Business, workforce and compliance analysis.
Tech deliverablesProcess map, data-flow map, risk registerStage 2
Design and Align
Solution architecture, operating models, integration planning.
Tech deliverablesArchitecture, prototype, test planStage 3
Implement and Enable
Deployment, onboarding, system validation, change management.
Tech deliverablesBuild, integrate, test, trainStage 4
Govern and Optimize
Continuous compliance monitoring, reporting, automation and enhancement.
Tech deliverablesMonitor, support, improve
Technologies
Tools we would use.
Governance
- Data catalogues
- Business glossaries
- Ownership registers
- Data stewardship
- Data lineage
Quality
- Data profiling
- Quality dashboards
- Validation rules
- Reconciliation checks
- Issue tracking
Policy
- Access policies
- Retention schedules
- Audit logging
- Privacy impact assessments
- Classification standards
Industries
Where this matters most.
Oil and gas
Field, contractor and production data defined and owned across site and head office.
Pharmaceuticals
Data integrity and traceability set out for regulated records and audits.
Chemicals and fertilizers
Plant measurements and cost data defined so operations and finance agree.
Related
Where this connects.
- Data EngineeringReliable pipelines that move and prepare data from source to use.
- Pipelines from source to warehouse
- Data models built for analysis
- Monitoring for freshness and quality
- Data AggregationBring data from many sources into one consistent, trusted view.
- Consolidation across sources
- Consistent, reconciled figures
- One view for leaders and teams
- Our platformStellar Core connects people, payroll and compliance.Explore Stellar Core
FAQ
Questions we hear.
Answers are drafted for review. Anything that needs a fact from Stellar is marked.
Have a business challenge worth solving?
Tell us what you're trying to fix. An engineer will reply, not a form robot.
