MetadevXInnovate. Code. Create.MetadevXInnovate. Code. Create.

(01) The challenge

Forty systems, one operations room, zero shared picture

Each department ran its own telemetry - traffic, drainage, lighting, waste, permits - and the operations room reconciled them on a whiteboard during incidents. The gap between knowing and seeing was measured in minutes, which is the wrong unit for an emergency.

  • · 40+ feeds across nine departments, no common schema
  • · Incident handovers done by phone and spreadsheet
  • · No historical view to review what actually happened

(02) Approach

A ingestion layer, then a picture worth arguing about

We normalised every feed into one event schema on Google Cloud, so a pump, a camera and a permit application all arrive as comparable observations. On top of that sits a BigQuery model and a Next.js wall view designed to be read from six metres away.

  • · Stream ingestion with per-feed health monitoring
  • · Unified event schema and incident timeline model
  • · Operator mode: keyboard-first, wall-display, dark by default

(03) Outcome

Incident response time cut by more than half

The first live test came during a summer storm season. Departments that had never shared data were looking at the same timeline, and the average time from first signal to a coordinated response dropped by 54%.

40+

Feeds in one view

-54%

Time to coordinated response

9

Departments onboarded

(04) What we did

Schema agreement before software

Half the project was negotiation, not code. We ran joint workshops where each department mapped its own fields onto a shared vocabulary, and we shipped read-only access to that mapping weeks before the dashboard existed.

  • · Cross-department schema workshops and a written data contract
  • · Read-only pilot for three feeds to prove the model
  • · Full build on GCP with BigQuery and streaming pipelines
  • · Ops handover with runbooks and a support escalation path
Next projectEnterprise ERP Automation→