Your certified metric layer does not stay certified on its own. We own it: new metrics get defined and approved, changed definitions get versioned, and drift gets caught before it reaches a dashboard or an AI model.
We run the freshness, volume, schema, and distribution monitoring and respond to the alerts, so a broken pipeline is caught and fixed before it becomes a wrong number in a board meeting.
Ownership stays assigned, access stays correct, lineage stays current, and audit evidence stays ready. The governance you built does not decay the moment the project ends.
As you ship new AI and agent workloads, we keep the inputs certified, the controls mapped to NIST AI RMF and ISO 42001, and the data ready for the next use case without a rebuild.
You invested in a metric layer and a catalog, then six months of turnover and new pipelines eroded it. Nobody owned keeping it true, so trust slid back to where it started.
The first sign of a broken pipeline is an executive asking why a number looks wrong. By then it has already spread, because nothing was watching the data continuously.
Each agent or model project re-litigates data readiness because the foundation was never maintained as a living system between projects.
Managed data readiness is an ongoing service that keeps your data continuously AI-ready instead of letting a one-time project decay. It covers continuous metric certification, managed data observability, governance operations (ownership, access, lineage, audit evidence), and AI-readiness maintenance, run as a retainer so the foundation stays true between and during AI projects.
A project delivers a certified foundation on a fixed date. Managed data readiness keeps it true after that date. Turnover, new pipelines, new tools, and new AI workloads all erode a static foundation within a couple of quarters. The managed service owns the metric layer, the monitoring, and the governance as a living system, so trust does not slide back.
Our managed offering bundles the ongoing work that keeps analytics trustworthy: metric certification, data observability monitoring and response, governance operations, and AI-readiness upkeep. You get a named senior owner, a defined scope, and a monthly cadence rather than a queue of ad-hoc tickets.
Teams that have built (or want to build) a certified data foundation and need it to stay true without hiring a full internal governance and reliability team. It fits organizations shipping multiple AI or agent workloads, regulated industries that need continuous audit readiness, and lean data teams that cannot absorb the maintenance load on top of delivery.
It is a monthly retainer scoped to your environment: the number of certified metrics, the pipelines under monitoring, the governance surface, and the AI workloads in flight. We price by a defined scope and service level, not by an hours bucket, so the cost and the coverage are clear up front.
Yes. We start with a short readiness assessment of what exists, document the gaps, certify what can be certified, and then operate it. We do not require that we built the original foundation, only that there is something defensible to maintain or a plan to get there.
Managed data readiness and analytics-as-a-service: continuous metric certification, managed data observability, governance operations, and AI-readiness upkeep as a monthly retainer. Senior-led.
Managed data readiness is an ongoing service that keeps your data continuously AI-ready instead of letting a one-time project decay. It covers continuous metric certification, managed data observability, governance operations, and AI-readiness upkeep, run as a retainer with a named senior owner.
Continuous metric certification so the definitions stay true as things change.
Managed data observability: we run the monitoring and respond to the alerts.
AI-readiness maintenance so each new agent or model does not start from scratch.
A one-time project. The point is what happens after the project, on an ongoing basis.
A staff-augmentation queue. It is a scoped service with a named owner, not loose hours.
A tool license. The tooling is yours; we operate the practice around it.
This is the run-it-for-you tier on top of a build. These are the factors that move the monthly effort.
The number of pipelines, datasets, and reports under the retainer sets the monthly effort.
Faster response and higher uptime guarantees take more to staff.
More sources and tools mean more surface to monitor and maintain.
Ongoing readiness differs from one-time remediation; the retainer is the run-it tier on top of a build.
You want senior data expertise on a monthly retainer, not a one-off project.
Dashboard requests, pipeline fixes, and governance keep piling up with no owner.
You want a partner who keeps the data AI-ready over time, not just once.
You need a fixed-scope assessment first: see AI Readiness Assessment.
The project that builds the certified foundation we then keep true for you.
The monitoring layer we operate and respond to as part of the retainer.
The assessment that scopes what readiness means before we maintain it.
Managed data readiness keeps your data continuously AI-ready instead of letting a one-time project erode. We own the metric layer, the observability, and the governance as a living system, run as a retainer with a named senior owner.
Managed data readiness is an ongoing service that keeps your data continuously AI-ready instead of letting a one-time governance project decay. Thinklytics owns the metric layer, the observability, and the governance operations as a living system, run as a retainer with a named senior owner, so readiness holds instead of eroding after go-live.
Start with a readiness assessment. We map what exists, certify what we can, and propose a monthly scope to keep it true.