From Excel Chaos to a GIS Dashboard: A Practical Workflow

If you’ve ever received a spreadsheet that looks fine but breaks the moment you try to map it, this is for you.

This mini case study shows a simple workflow we use at TFixLab to go from:

• messy Excel →
• validated table (no duplicates, correct formats) →
• dashboard-ready dataset →
• a shareable map view.

What we start with

Typical problems we see:

• inconsistent column names ("Lat", "latitude", "LATITUDE")
• mixed date formats
• duplicates without an obvious key
• locations missing or swapped (lat/long flipped)

Here’s a visual placeholder of the “before” spreadsheet:

Step 1 — Define the minimum schema

Pick the minimum set of fields you need for a clean, reliable map layer:

• record_id (uuid)
• source_file (text)
• created_at (timestamp)
• name (text)
• category (text)
• latitude / longitude (numeric)
• status (text)

This keeps the pipeline stable even when clients add extra columns.

Step 2 — Add automated validation checks

We run a small validation stage before anything becomes “reportable”:

• required fields present
• valid ranges (lat: -90..90, lon: -180..180)
• duplicates (based on a configurable key)
• warning flags (e.g., missing category)

Step 3 — Publish to a dashboard-friendly structure

Once validated, the dataset is ready to power:

• a table view
• a map view
• filters by category/status
• basic metrics (counts by category)

Here’s a placeholder “dashboard” view:

Step 4 — A simple architecture that scales

This is the basic pattern:

1. Upload (form / admin)
2. Validation job (server action / API route)
3. Store clean rows (Supabase)
4. Render (Next.js pages + components)

Practical tips

• Keep the first version boring. Don’t over-model the database.
• Validate early. It’s easier to fix errors before they are “published”.
• Make “issues” visible. Add a validation_status + validation_notes column.

If you want this exact flow built into your workspace (with your rules + templates), book a free workflow audit.