Dashboards and Data Systems
A dashboard is only as trustworthy as its slowest, least-documented source system. Most dashboard projects fail on metric definitions and data freshness, not on chart design - two departments both showing 'revenue' with different numbers because no one agreed on what counts.
The Problem
Business data lives scattered across systems with no agreed definition for shared metrics, so two teams can report different numbers for what's supposedly the same thing, and no one notices until it matters.
Our Approach
Before any chart is designed, we document each metric's formula, source, owner, and refresh interval, and resolve conflicting definitions with the people who actually use the numbers.
Data source connectors
Sources are connected either by pulling from an API/database on a schedule or by receiving pushed events, depending on what the source system supports. A connector has to tolerate the source's schema changing without silently corrupting downstream reports - a new column or a renamed field should fail loudly, not get ignored.
Metric definition governance
Each metric gets one documented formula, one owner, and one source system of record. When two departments define 'active customer' differently, that gets resolved explicitly before the dashboard ships - not left ambiguous so both versions quietly coexist.
Freshness and staleness indicators
A dashboard shows when its data was last refreshed, and flags when a source hasn't updated within its expected interval - showing an old number as if it were current is worse than showing no number at all.
Access control
Role-based views and, where needed, row-level filtering (a regional manager sees their region, not every region) are part of the access model, not bolted on by hiding UI elements client-side.
Architecture and Approach
Source connectors feed a validation/transformation layer that normalizes data into the dashboard's own schema; role-based views query that normalized layer rather than each source directly, so access control and metric definitions stay centralized in one place.
What We Build
- Real-time operational dashboards
- Production and shift reporting
- Custom analytics and KPI tracking
- Data visualization and exports
- Role-based access for teams
Use Cases
Common Engineering Challenges
A polished chart cannot repair bad source data. If a source system's numbers are wrong, the dashboard will faithfully display wrong numbers - the fix is upstream, in the source system or its data entry process.
The most common dashboard failure isn't a bug - it's two stakeholders expecting different definitions of the same metric name. This has to be resolved with people, not with a chart configuration.
FAQ
We document one formula and one source of record per metric before building the dashboard - reconciling conflicting definitions is part of the project, not something the dashboard papers over.
The dashboard displays its last refresh time and flags when a source hasn't updated within its expected interval, so stale data is visible rather than mistaken for current.
Yes - role-based and row-level access control (e.g., a manager seeing only their region) is part of the access model, enforced at the data layer rather than just hidden in the UI.