Power BI Services

Building a Power BI report is easy. Getting one revenue number that finance, sales and operations all recognize is the hard part, and that is a modeling and governance problem before it is a visualization one. Iseyon works on the layer underneath the reports: the semantic model, the refresh path, the access rules, and a release process that stops a change from breaking the report someone presents on Monday.

We are brought in to build a reporting estate from scratch, to consolidate one that has sprawled into hundreds of near-duplicate reports, or to fix models that are slow and expensive to refresh.

The model comes first

Semantic model design

We build star schema models with explicit relationships, a date dimension that owns time intelligence, and a clean separation between facts and dimensions. Column types and cardinality are chosen for compression, because model size drives both refresh duration and query response. Measures are defined once in the model, so a KPI has one definition instead of one per report.

Measure logic and DAX

We write measures that are readable and testable, keep filter context explicit, and avoid the calculated-column habits that quietly bloat a model. The business definition is documented next to the measure, so the meaning of a number survives the person who wrote it.

Connectivity and refresh

We choose storage mode per table rather than per project, configure gateways for on-premises sources, and set up incremental refresh so a daily load stops reprocessing history. Query folding is verified rather than assumed: a transformation that folds back to the source is often the difference between a refresh that finishes and one that times out.

Reporting people use

Report and dashboard design

We settle the question a page answers before placing a visual. Layout and interaction patterns stay consistent across the estate, so a user who learns one report can read the rest, and drill paths, filters and cross-highlighting are designed rather than left at defaults. Accessibility, print and mobile layouts are handled during the build.

Self-service with guardrails

We publish certified datasets that analysts build on, so business teams answer their own questions without each one inventing a definition. Workspace roles, endorsement and sensitivity labels make it obvious which content is trustworthy and which is somebody's draft.

Running it in production

Workspaces, deployment and change control

Development, test and production workspaces are separated, and content moves through deployment pipelines rather than by hand. Source control and parameterized connections make a release repeatable and reversible.

Access and row-level security

We implement row-level security in the model and drive it from the organization's own directory groups, so a regional manager sees their own scope without a separate report per region. Access reviews then operate on groups instead of a list of individuals.

Performance and capacity

We size capacity against observed refresh and query load, stagger refreshes so they stop colliding, and reduce model footprint before recommending more compute. Usage monitoring is left in place so the client can see which datasets and reports are actually opened, and retire the ones that are not.

Handover and enablement

We train the people who will own the estate: report authors on the model and its measures, administrators on tenant and workspace settings, and analysts on building from certified datasets. Documentation covers the model, the refresh dependencies and the release path, so the next change does not need us.