If you’re starting to realize your business data is messier than you'd like — good news:

You’re not alone.

Over the past 15 years, working with hundreds of organizations, I’ve seen that almost nobody starts out with perfect data.

The important thing isn’t where you are right now.

It’s where you’re willing to go — one simple, smart step at a time.

This post is the third in the Data First, AI Second series:

• Why Good Data Beats Fancy Algorithms Every Time

• What Every Business Owner Needs to Know About Data Governance

• Simple Ways to Start Cleaning and Organizing Your Business Data Today (you’re here)

• The 5 Golden Rules of Data Collection for Small Businesses

• How Bad Data Can Break Your AI (And How to Fix It Before It’s Too Late)

Want practical, business-first AI insights every week?

Join the Field Guide and build smarter systems, one step at a time.

Why Cleaning Your Data Now Will Save You Later

Here’s what most businesses do:

They wait.

They wait until they need AI.

They wait until there’s a new tool to buy.

They wait until the problems are huge — and expensive to fix.

You don’t have to wait.

Simple moves today will save you time, money, and frustration later.

And you don't need a PhD or a data team to get started.

5 Simple Ways to Start Cleaning Your Data Today

1. Standardize Formats

• Pick one format for things like dates, addresses, phone numbers.

• Apply it everywhere.

Example: Always use YYYY-MM-DD for dates.

Always list U.S. phone numbers as (xxx) xxx-xxxx.

Small format fixes = big clarity gains.

2. Fix Obvious Duplicates

• Customer listed twice?

• Same product with two slightly different names?

Pick a "master version" and merge or remove the extras.

Duplicates confuse humans and AI alike.

3. Label Important Fields Clearly

• Don't just call a column "value."

• Call it "Sales Revenue USD" or "Customer Tenure Months."

Clear labels = faster onboarding for humans and easier learning for AI.

4. Archive What You Don’t Use

Old, irrelevant, or incomplete records?

Move them to an archive.

They don’t have to be deleted — but they shouldn't clutter your daily work.

5. Document as You Go

Every time you make a cleaning decision — write it down.

(Example: “We decided that all customer industries will match the NAICS code standard.”)

This way, you start building your own Data Playbook without even realizing it.

Action Step

Pick one active dataset you rely on — sales, customer contacts, project files.

Spend 15 minutes doing two things:

• Fix obvious format issues or duplicates.

• Write down what you cleaned up.

Field Guide Action Step Template

Dataset: [name]
Quick Fixes Made: [list]
Notes for Future Standards: [list]

You don't have to fix everything today.

Just start. The momentum will build.

Final Thoughts

You don't build strong AI systems by waiting for perfection.

You build them by cleaning up one field, one file, one fix at a time.

Start where you are.

Use what you have.

Do what you can.

In the next post, we'll talk about The 5 Golden Rules of Data Collection for Small Businesses — and how to make sure your future data is even better than what you have now.

Smarter moves, stronger systems — that’s the Field Guide way.

Know a business owner who’s feeling overwhelmed by messy data?

Share this Field Guide with them — and help them get unstuck, one simple move at a time.