Pipelines and operations
Review failures, refresh windows, observability, lineage, recovery, release practices and concentration of knowledge.
Data-platform audit
We assess the platform from source to decision, separate verified evidence from assumptions and turn the findings into a sequenced modernisation roadmap.
Audit coverage
A platform can look healthy in one layer while failures, access risk or duplicated logic accumulate elsewhere.
Review failures, refresh windows, observability, lineage, recovery, release practices and concentration of knowledge.
Trace important metrics through transformation and semantic layers to find duplication, ambiguity and control gaps.
Assess access boundaries, sensitive data handling, capacity or compute use, environments and operating responsibilities.
Audit method
The report shows what was inspected and how strongly each conclusion is supported.
Agree business outcomes, critical workloads, known incidents, scope boundaries and access.
Review architecture, configurations, code or jobs, models, reports and operational records.
Test important claims with owners and distinguish verified evidence, gaps and unresolved questions.
Prioritise containment, repair and modernisation with dependencies, owners and decision gates.
Qualification
Executive sponsorship and safe read-only access make the assessment materially stronger.
FAQ
Architecture, sources, pipelines, storage, models, reporting, refresh and reliability, access, costs, governance, deployment and ownership.
Read-only access to representative environments and operational evidence is preferable. Where access is constrained, the report labels those limitations.
Only when evidence supports it. The roadmap separates immediate risk reduction, targeted repair and longer-term modernisation.
A current-state map, evidence register, prioritised risks, target principles and a sequenced roadmap with owners, dependencies and decision points.
Scope the audit
Tell us the stack, symptoms, critical workloads and upcoming decisions. We’ll propose an evidence-gathering scope.
Prefer email? info@datagrape.ai