Monitoring + incident response (SLA-based)
Pipeline failures, refresh issues, integration breaks—triaged and resolved with clear escalation.
Includes
Data quality operations (freshness + anomalies)
Freshness/completeness checks, anomaly alerts, and root-cause workflows to protect trust.
Semantic model + KPI governance
Controlled changes, measure reviews, RLS/access updates, and prevention of KPI drift.
Release management + change control
Testing, regression checks, versioning, and safe deployments—so changes don’t break reporting.
Performance + cost optimization (Fabric/Azure FinOps)
Capacity tuning, query optimization, cost visibility, and guardrails.
Continuous improvement backlog + adoption support
Enhancement pipeline for new KPIs/sources + adoption cadence so usage grows.
Deliverables
SLA model + severity matrix + response targets
Monitoring dashboards + alerting + runbooks
KPI governance workflows + change approvals
Monthly reliability + usage report (what broke, what improved, what’s next)
FAQ
What does “managed services” cover in analytics and data platforms?
Ongoing operations after go-live: monitoring, incident response, data quality checks, access support, KPI/semantic governance, and a structured enhancement backlog.
Do you offer SLAs and defined support hours?
Yes. Response and resolution targets by severity, coverage windows, and escalation paths.
How do you prevent KPI drift and breaking changes?
Semantic governance, documented definitions, controlled deployments, regression testing, and approvals.
Do you provide staff augmentation or only managed pods?
Both. We can embed engineers/BI developers, but recommend outcome-based pods with SLAs and cadence.
How do you operate AI copilots/agents safely in production?
Monitoring + evaluation, role-based permissions, audit logs, and approved knowledge sources—so outputs and actions remain controlled and reviewable.