Reporting layer

Operational Dashboards

Dashboards wired directly into your modeled data so every view reflects the same definitions, not another rewrite.

Outcome

Dashboards that mirror your core metrics with consistent filters, drill paths, and logic across teams.

Typical timeline

2–4 weeks for the first dashboard that doesn’t collapse when the business changes.

Best for

Operators and leadership who need the same numbers every day without wondering which view is 'right'.

What you actually get

You get dashboards that sit on top of your warehouse instead of reinventing logic in every chart.

Metrics are defined once in the model and reused across every surface so teams stop maintaining their own versions.

Views are built around real decisions, not a wall of KPIs nobody opens.

  • A semantic layer that feeds Looker Studio, Sheets, or your chosen BI tool.
  • Reusable tiles and reports so new views don’t mean new SQL.
  • Filters, drilldowns, and permissions wired in from the start.
  • Consistent metric definitions across every dashboard, team, and tool.

You're here when

  • Leadership screenshots dashboards before edits because they’re afraid something will break.
  • Teams rebuild the same charts in different tools every quarter with slightly different numbers.
  • Reporting day burns half the week just to recreate known metrics.
  • Two dashboards for the same metric disagree and nobody can explain why.

How it works under the hood

The BI layer sits on top of your modeled data. One set of definitions flows into every chart, filter, and drill path so the reporting layer stops drifting.

  1. 01

    Connect directly to modeled tables or APIs that already hold your source of truth.

  2. 02

    Shape semantic layers for each tool: dimensions, metrics, filters, and permissions.

  3. 03

    Build layouts with a consistent structure so teams interpret views the same way.

  4. 04

    Wire scheduled sends or notifications so people see data when it matters, not when they remember.

  5. 05

    Document how to clone, fork, or extend dashboards without breaking shared metric definitions.

What the project looks like

First version in 2–4 weeks, depending on toolchain, access, and the readiness of your data.

Phase 1 – Inventory & intent

Map the reports people actually use and the decisions they support before building anything new.

  • Audit existing reports, shadow spreadsheets, and ad-hoc requests.
  • Prioritize decision moments that need stable, shared visibility.

Phase 2 – Build & iterate

Build the core dashboards, then run tight feedback loops with real users.

  • Configure datasets, semantic models, and initial layouts.
  • Ship first versions to pilot groups and refine based on how they actually work.

Phase 3 – Rollout & coaching

Roll out to teams with enough clarity that dashboards become part of their routine.

  • Document filters, drill paths, and metric caveats in one place.
  • Run short sessions so teams adopt the new surfaces instead of reverting to exports.

What becomes possible after this

Once the reporting layer is stable, it becomes a shared interface — decisions and systems both pull from the same definitions instead of improvising.

  • Dashboards feed workflows and internal tools without re-implementing logic.
  • Product, finance, and operations operate from the same model instead of conflicting versions.
  • Stakeholders ask better questions because the underlying metrics stop shifting.
Structured alerts that run off the same definitions Long-term support that keeps the layer stable

This is overkill if

Your data still moves through simple CSV exports and that process works reliably for now.

You only need a single chart to answer a one-off question.

Your organization hasn’t defined who owns decisions that rely on metrics.

Need eyes on your reporting?

Share your dashboards, exports, and the workflows they support — Anriku will tell you whether an operational reporting layer is worth building.