From Spreadsheets to AI-Ready: A Practical Migration Guide

Every data-driven company started with spreadsheets. That's not a criticism — spreadsheets are the most flexible, accessible data tool ever built. The problem isn't that you used them. It's that you're still using them for things they were never designed to handle.

This guide is for businesses that know they need to level up their data infrastructure but don't want to disrupt the operations that depend on those spreadsheets today.

Phase 1: Audit What You Have

Before you change anything, map your current data landscape. For every spreadsheet that runs a business process, document:

• What data does it contain? (sales, inventory, schedules, etc.)
• Who updates it? (manually or via export from another system)
• How often? (daily, weekly, when someone remembers)
• Who consumes it? (which reports, dashboards, or decisions depend on it)
• What breaks when it's wrong? (the consequence of bad data)

This audit typically reveals two things: you have more spreadsheets than you thought, and fewer of them are actually maintained than you assumed.

Phase 2: Identify Your Source Systems

Most spreadsheets are manual copies of data that already exists in a system somewhere. Your POS has sales data. Your scheduling tool has labor data. Your accounting software has cost data.

For each spreadsheet, ask: where does this data originally come from? If the answer is "someone types it in from another screen," that's your first automation opportunity.

Prioritize by:

1. Frequency — data that's updated daily has the most to gain from automation
2. Impact — data that drives important decisions should be the most trustworthy
3. API availability — check if the source system has an API or webhook capability

Phase 3: Build the First Pipeline

Pick your highest-priority data source and automate the extraction. Don't try to replace the spreadsheet yet — just automate the data getting into it (or into a database alongside it).

A practical first pipeline might look like:

POS API → Cloud Function (scheduled) → Database table
                                                    ↓
                                        Existing spreadsheet
                                        (still works, for now)

The key insight: run both systems in parallel. Your team keeps using the spreadsheet while you verify the automated pipeline produces the same results. When you're confident, you retire the manual process.

Phase 4: Consolidate Into a Central Store

Once you have 2-3 pipelines running, you'll naturally need a central place to join the data together. This is when you set up your data warehouse (BigQuery, Snowflake, or a Supabase PostgreSQL instance).

The warehouse becomes your single source of truth. Every report, every dashboard, every analysis pulls from here — not from individual spreadsheets.

• Star schema — organize your tables around facts (sales, orders, shifts) and dimensions (products, locations, dates). This makes querying intuitive.
• Naming conventions — establish consistent naming from day one. We use source_entity_description (e.g., pos_product_sales, sched_labor_hours).
• Documentation — for every table, document what it contains, how it's populated, and when it refreshes. Future you will thank present you.

Phase 5: Build the Dashboard Layer

With data flowing automatically into a central store, you can build dashboards that actually stay current. No more stale charts. No more "let me update the spreadsheet first."

Start with the reports your team looks at most often. Replicate them as live dashboards. Once people see real-time data, they'll never go back to manual reports.

Phase 6: Add Intelligence

This is where AI enters the picture — and only now, after the foundation is solid. With clean, centralized, automatically updated data, you can:

• Build demand forecasting models that actually have enough historical data
• Deploy monitoring agents that catch anomalies in real-time
• Create recommendation systems that understand your full business context
• Train custom models on data that's consistent and trustworthy

The Migration Mindset

The most important thing about this process: it's incremental, not revolutionary. You don't shut down spreadsheets on day one. You don't need a 6-month planning phase. You pick the most painful manual process, automate it, verify it works, and move to the next one.

Each step delivers standalone value. Each step makes the next one easier. And eventually, you look up and realize you're running an AI-ready data infrastructure — built one pipeline at a time.

The journey from spreadsheets to AI isn't a leap. It's a series of practical steps, each one making your business a little smarter.