SERVICES
Build once, validate once, reuse across operations and reporting.
Build once, validate once, reuse across operations and reporting through a controlled, source-linked workflow.
Overview
Reusable controlled datasets are governed data products designed to feed multiple workflows—NAV oversight, regulatory reporting, risk, performance, management reporting—without repeated manual reconstruction.
Below Data Aggregation / Portfolio Data Management.
Why it matters
Repeated spreadsheet rebuilds create inconsistent definitions and duplicated validation.
Common operating challenges
- Same data reassembled by different teams.
- Different calculations from same source.
- No reusable approved snapshot.
- Reporting periods not reproducible.
What we deliver
| Capability | What it covers |
|---|---|
| Dataset specification | Define fields/owner/use. |
| Approved snapshot | Create controlled period dataset. |
| Reuse interfaces | Serve multiple workflows. |
| Version/history | Retain period/rule versions. |
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
- Validated source data
- Definitions/mappings
- Approval status
Controlled workflow
| Step | Activity | What happens |
|---|---|---|
| 1 | Design | Define dataset. |
| 2 | Build | Transform approved sources. |
| 3 | Validate | Run quality/reconciliation. |
| 4 | Approve | Lock version. |
| 5 | Reuse | Feed downstream services. |
| 6 | Archive | Retain reproducibility. |
Typical outputs
- Controlled dataset
- Data dictionary
- Version history
- Downstream usage map
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.
- Immutable approved version.
- Lineage.
- Access permissions.
- No downstream silent mutation.
Responsibility boundary
A controlled dataset is a workflow artefact, not a claim that Kelton is the legal/official book of record.
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
- Data product layer.
- Versioned storage.
- API/report interfaces.
Expertise
- Data governance.
- Cross-service operating model.
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 this a capability?
It is the mechanism that allows AI/automation and reporting to scale without rebuilding data every time.
Can different teams use the same data?
Yes with role-based access and purpose-specific views while preserving the core approved dataset.
Discuss Reusable Controlled Datasets
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.