When every dashboard tells a slightly different story, the fix isn't a better chart. It's a shared definition of what the numbers mean - enforced in the data model, not resolved differently in each report.

The dashboard exists. It has been built, deployed, and linked from the internal wiki. And yet the weekly leadership meeting still starts with someone pulling numbers from a spreadsheet, because nobody is sure the dashboard is right. A different team has a different figure for the same metric. The data team spends two days a week explaining discrepancies rather than fixing them.

This is a data modelling problem wearing a reporting costume. If business logic is defined differently across systems, or transformation rules live inside individual report queries rather than in a shared layer, every dashboard tells a slightly different story. Fixing it requires going below the chart.

Start With The Metric Definition, Not The Chart

The most common failure in BI projects is building dashboards before agreeing on what the numbers mean. What counts as a completed order? When does a customer become active? Which revenue figure is the one the CFO signs off on?

These questions have organisational answers that need to be documented and enforced in the data model - not resolved differently in each report.

Before we build anything, we sit with the teams that own each domain and agree on the definitions. Those definitions go into a semantic layer - dbt metrics, LookML, or equivalent - so that every dashboard, every report, and every ad-hoc query draws from the same source.

When the number is the same everywhere, the conversation in the leadership meeting shifts from "which figure is right" to "what do we do about it."

How We Engage

We work with your existing stack or recommend based on the use case. The discipline matters more than the tooling.

What We Build

Operational Dashboards

High-frequency views for teams making shift-level, daily, or hourly decisions - logistics, manufacturing, e-commerce operations. Built around the decisions the team needs to make, not the data that happens to be available. Replaces the workaround, not supplements it.

Tools: Grafana, Metabase, Apache Superset

Executive & Strategic Reporting

Role-based dashboards for leadership and board audiences. Aligned to OKR frameworks, integrated with the data warehouse, with KPIs selected because they drive action. Row-level security and audit trails for financial and HR data.

Tools: Tableau, Power BI, Looker

Self-service Analytics

Governed data catalogues and access tiers that let business users answer their own questions. The semantic layer ensures every self-service query draws from the same definitions as the executive dashboard - no divergent numbers, no shadow reports.

Embedded Analytics

BI built directly into SaaS products and customer portals. Governed by the same semantic layer as the rest of the stack. Your customers see data they trust because it passes through the same quality controls as your internal reporting.

Data Governance & Access Control

Dashboards showing financial results, HR data, or operational metrics carry access control requirements beyond "who has the link." We design role-based access, row-level security, and audit trails into the reporting layer from the start - not as a retroactive compliance exercise.

For regulated environments, dashboard access is governed by the same data policies that apply to the underlying warehouse. Who can see what, at what granularity, with what audit trail - these are architectural decisions, not configuration settings.

Proof In Production

Senior Aerospace Thailand - From Google Sheets to Real-time Operations Production data for two manufacturing lines - Aero Structure and Aero Engine - was spread across multiple systems. Teams had defaulted to Google Sheets because the ERP was too slow and too complex to query directly. Gradion built a custom operational dashboard integrated with their Infor Syteline ERP, replacing the spreadsheet workaround entirely. Line managers and the supply chain team now have a live view of production status, efficiency metrics, and output by line. Daily decisions that previously required manual data compilation happen in real time. Operational efficiency moved from 55% to 95%.

Vietnam’s largest coffee chain - 928 Outlets, Same-day Decisions Vietnam's largest coffee chain had campaign performance, store-level sales, and customer acquisition figures sitting across four separate systems, reconciled manually before any analysis could begin. Gradion consolidated the data into a central warehouse and built a reporting layer giving marketing and operations teams real-time visibility across every store. Revenue grew 12% within three months - not because of the dashboard itself, but because decisions that had previously taken a week of data preparation could now be made the same day.

HomeToGo - BI at Marketplace Scale The world's largest vacation rental marketplace runs 15 million+ listings across 100+ partner API integrations in 25 countries. The reporting layer that supports product, commercial, and operations teams draws from a data engineering foundation that maintains 99.99% uptime across 50+ daily deployments. At this scale, dashboard reliability is not a convenience - it is operational infrastructure.

All figures are from live engagements. Additional references available under NDA.

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