The data team’s side of the platform.
Model once, slice honestly
Governed source models and datasets with dimensions, measures and filters — built star-schema so every tool downstream agrees on the numbers. Row-level security travels with the dataset.
Warehouse-grade analytics without running a warehouse
Datasets materialize into a managed cloud data warehouse on schedules you set, with validate-and-preview before publish.
Old data moves out of the app, not out of reach
Rule-based archival shifts aged records into warehouse tables — the app stays fast, history stays reportable.
The star schema, literally.
The capabilities, stated plainly.
Every row ships today and traces to a capability described on this page.
| Capability | xMatix Data Studio |
|---|---|
| Governed, reusable data models | Native |
| Dataset builder | Native |
| Star-schema datasets | Native |
| Row-level security in the dataset | Native |
| Managed cloud warehouse | Native |
| Validate and preview before publish | Native |
| Rule-based archival | Native |
| Scheduled refresh | Native |
| Feeds banded reports, pivots and dashboards | Native |
| Governed embedded BI | Native |
Everything in xMatix Data Studio.
The questions evaluators ask.
Do I need a data warehouse to report on this?
No — the warehouse comes managed. Your operational data reshapes into clean, analysis-ready star-schema datasets that materialize into a managed cloud warehouse, provisioned and refreshed on schedule for you. There are no pipelines to build and no servers to babysit; dashboards, pivots and banded reports all draw from the same datasets, so everyone argues about the decision instead of the numbers.
What is the difference between Analytics and Data Studio?
Analytics is where numbers are consumed: dashboards and KPI widgets, print-perfect banded reports, pivots, scheduled email subscriptions. Data Studio is the modelling layer beneath it: governed data models, dataset building, refresh schedules and archival rules that feed everything above. Define revenue once in the studio and every report downstream means the same thing by it. Explore xMatix Data Studio.
Can reports respect the user's record permissions?
Yes — row-level security lives with the data, not with each report. The same dashboard shows a branch manager their branch and a region head their region, automatically; each user queries the same dataset and sees only their slice. Because the rules travel with the dataset, a new report cannot accidentally widen access.
Can I export or connect BI tools?
Approved dashboards publish into other apps and portals through governed embedded BI — what is shared is allowlisted, so nothing leaks that should not. Scheduled subscriptions deliver any report to the team's inbox on a cadence. For ad-hoc slicing, the pivot designer answers most spreadsheet-export impulses in place: drag dimensions and measures until the answer appears, then save the view and it stays live.
The same governed data, everywhere people look.
Datasets built here feed everything downstream — with the same row-level security.
