The Data Readiness Checklist
Get your data into shape before the build, and save yourself pain later.
Every system we build stands on your data. If that data is messy, duplicated, or incomplete, the best-designed system in the world will still feel broken, because it'll be full of the wrong information. This checklist helps you get your data into reasonable shape before we build on top of it. You don't have to do all of this alone; we'll help. But the more of it you can tackle, the smoother and faster the build.
Know where your data lives
Start by inventorying it. Which systems and spreadsheets hold your contacts, companies, deals, and history? Which is the real source of truth when two of them disagree? You can't clean or migrate data you can't find, so this is always step one.
Deal with duplicates
Duplicate contacts and companies are the single most common data problem we see. The same customer entered three times, three different ways, undermines reporting and confuses automation. Flag known duplication now; it's much cheaper to resolve before a migration than after.
Check completeness and consistency
- Required fields. Which fields actually matter, and how often are they empty?
- Formatting. Are phone numbers, states, and dates entered consistently, or is it a free-for-all?
- Owners. Do records have the right person attached, or are they orphaned?
Identify what to keep, and what to leave behind
Not all data deserves to make the trip. Ancient, dead, or irrelevant records just add noise. Part of readiness is deciding what genuinely needs to come across and what's better archived or left behind. A smaller, cleaner dataset is almost always more valuable than a big, messy one.
Don't try to make it perfect
Data readiness is about "good enough to build on," not perfection. Chasing a flawless dataset before starting is its own kind of stall. Get the big problems handled, duplication, source of truth, obvious gaps, and we'll work with you on the rest as part of the build. The point of this checklist is to catch the issues that are expensive to fix later while they're still cheap to fix now.