SERVICES
Every important output traceable to source, rule, version and review.
Every important output traceable to source, rule, version and review through a controlled, source-linked workflow.
Overview
Data lineage records where data originated, how it was transformed, which rules were applied and which version/reviewer produced the final output; data quality monitors completeness, timeliness and consistency.
A horizontal specialist module across Data, Risk & Performance and AI-enabled services.
Why it matters
Without lineage, automated reporting/AI can produce convincing numbers that cannot be explained or audited.
Common operating challenges
- Unknown source of final number.
- Transformations undocumented.
- Stale/missing data not visible.
- Rule changes break comparability.
What we deliver
| Capability | What it covers |
|---|---|
| Lineage | Track source → transformation → output. |
| Quality rules | Completeness/timeliness/format checks. |
| Definition registry | Maintain field meaning/owner. |
| Change history | Version mappings/rules. |
| Quality dashboard | Expose exceptions and confidence. |
Our role can be configured around a defined operating mandate: a recurring managed service, a technology-enabled client workflow, or a co-sourced model in which execution and review are split between Kelton, the client and appointed providers.
Inputs, workflow and outputs
Typical inputs
- Source metadata
- Mappings
- Transformation rules
- Output fields
- Review history
Controlled workflow
| Step | Activity | What happens |
|---|---|---|
| 1 | Register | Catalogue source/definition. |
| 2 | Track | Record transformations. |
| 3 | Test | Run quality checks. |
| 4 | Flag | Create exceptions. |
| 5 | Review | Approve changes. |
| 6 | Publish | Expose lineage with output. |
Typical outputs
- Lineage graph/register
- Quality score/exception report
- Definition dictionary
- Change log
Controls and review
The workflow is designed so that automation does not obscure responsibility. Routine processing can be standardised; exceptions, material judgements and formal approvals remain visible and attributable.
- No silent transformations.
- Version/effective date.
- Source ownership.
- Quality exception escalation.
Responsibility boundary
Lineage supports explainability but does not independently validate the truth of third-party source data.
Technology & expertise
Our operating model combines specialist knowledge with controlled technology. Domain experts define the rules, review logic and exception criteria; the technology layer makes the workflow repeatable, traceable and scalable.
Technology
- Metadata/lineage store.
- Quality engine.
- Rule/version registry.
Expertise
- Data governance.
- Financial operations controls.
Expert knowledge is converted into controlled rules, SOPs, checklists, validation tests and exception criteria so that the operating standard is embedded in the workflow rather than dependent on one individual.
Delivery models
| Model | How it works |
|---|---|
| Managed Operations | We execute the agreed recurring workflow. Client and appointed-provider approvals remain explicit. |
| Technology Enablement | We implement the data, workflow and control layer for the client team to operate. |
| Hybrid / Co-sourced | Execution and review are shared through a documented responsibility and escalation model. |
| Transition & Implementation | We mobilise the workflow during a launch, provider change or target-operating-model transition. |
Frequently asked questions
Why is lineage important for AI?
It allows users/reviewers to trace an output to approved sources and rules instead of trusting opaque model text.
Is a quality score enough?
No; material exceptions and source limitations should be visible and reviewable.
Discuss Data Lineage & Quality
Start with one workflow, one operating issue or one provider transition. We will map the current process, responsibility boundaries, required data and a practical first engagement.