Quality framework
Freshness, consistency, uniqueness, and anomaly checks for critical metrics.
Data engineering
Data governance, quality, privacy, and observability for systems people trust
Governance, quality frameworks, privacy controls, lineage, access design, and observability that keep analytics from drifting and teams from guessing what is true.
Inspectable delivery artifact
A buyer should see the operating evidence, not only read the promise.
This service is evaluated through source contracts, quality checks, owners, failure states, and handover evidence. The preview shows the kind of artifact that belongs in the delivery packet.
Data governance, quality, privacy, and observability for systems people trust
Sample acceptance matrix
customer_email
Check
PII masked
State
pass
Owner
Security
metric_grain
Check
owner approved
State
pass
Owner
Analytics
retention_rule
Check
90-day review
State
watch
Owner
Ops
| Artifact | Check | State | Owner |
|---|---|---|---|
| customer_email | PII masked | pass | Security |
| metric_grain | owner approved | pass | Analytics |
| retention_rule | 90-day review | watch | Ops |
Privacy controls
PII handling, masking, role-based access, and evidence for retention and deletion expectations.
Operational visibility
Monitoring, alerting, and issue workflows for production data quality.
Where this fits
Use this service when a business process already depends on data, but the current path from source system to accepted metric is fragile, undocumented, or too dependent on one person.
DataDost AI provides practical data governance, quality rules, privacy controls, lineage, retention, ownership, and observability for startups and growing companies.
What we build
▾
Metric owners, certified tables, quality tests, lineage notes, access controls, retention rules, and incident workflows are implemented together.
Governance operating modelMetric owner registerData quality frameworkAccess matrixRetention notesLineage documentationIssue and exception workflow
Engineering decisions we make explicit
▾
We define sensitive field handling, retention, ownership, access, change approval, quality thresholds, and escalation routes as part of design governance.
Controls and quality checks
▾
Controls include freshness checks, volume checks, duplicate checks, row consistency checks, reconciliations, and access reviews by cadence.
Critical metrics have ownersSensitive fields are identifiedAccess is role-basedQuality tests are definedData issues have a workflowCertified tables are markedGovernance cadence is agreed
How we work with client data
▾
All governance work is anchored to minimum necessary access and explicit approval workflows for each system and role.
Pilot-first access modelLeast privilege across roles and systemsClient ownership of primary workspacesData minimization and field-level scopeRevocation and audit trail at handoff
What handover looks like
▾
Handover includes the governance charter, owner lists, quality catalog, access matrix, lineage notes, and recurring review cadence.
Architecture summary with assumptions and trade-offsSource inventory and ownership notesRunbook for routine operation and failure handlingKnown limitations, backlog, and scale-up recommendationsUser-facing explanation for business stakeholders
How we demonstrate this service
▾
The demonstration starts with a disputed metric and proves that definitions, owners, controls, and escalation are not guesswork.
Metric dictionary with owner, grain, inclusion rules, and last review dateQuality checks with a deliberate failure routed through issue workflowAccess request, grant, modification, and revoke with audit trailLineage trace for finance and product KPI to source fieldsPeriodic governance review cadence and change-control process
What you can review in the demo
▾
The output is evidence-first and designed for teams that need compliance-ready visibility.
Metric dictionary table with definitions and exceptionsQuality check report with impact notesIssue lifecycle showing assignment and closureAccess and retention/audit trail logsGovernance cadence plan with review owners
Why this matters▾
Governance is the operating discipline that tells a team where reliable data starts and where unsafe assumptions end.
Security and data handling
Access is role-based, production changes are documented with rollback notes, and handover includes access assumptions and evidence.
Working surface
Map the first reliable data path.
Bring the source exports, dashboards, and decisions that currently create friction. We return a scoped pilot path with owners, evidence, and handover expectations.