Your Data’s a Mess. AI Can Help.

This Week

If you’ve spent any time in marketing, sales, or ops, you know that data is equal parts power and pain.

I’ve spent most of my career knee-deep in it — bloated CRMs, half-broken automations, and Excel sheets so big they took five minutes just to open. I loved finding patterns in the noise, seeing the story behind the numbers. But the fiddling, the formulas, the endless “why won’t this pivot table work” moments? 

Not so much.

If I’d had today’s AI tools back then, my life would have been a lot easier.

We’ve now got Copilot and Gemini built right into Excel and Google Sheets. We can ask our data questions in plain language. Tools like Rows analyze it. Clay enriches it. Dedupely keeps it clean. It’s never been easier to turn messy information into something meaningful.

That said, AI can’t do much with bad inputs. If you want accurate answers, you need the right foundation. So this week, I’m walking through the full data stack, from cleaning and enrichment to analysis and automation.

Here’s what we’ve got:

• If you can’t trust your data, you can’t trust your decisions

• Tools that make data less painful

• The AI data analyst you didn’t know you already had

• How to analyze spreadsheets in Claude

Read on.

You Can’t Make Good Decisions with Bad Data

I’ve worked with teams across industries, and the thing they all struggle with is the same — their data looks complete on the surface but falls apart the moment you try to use it.

One system uses abbreviations, another spells out every field. File names change from quarter to quarter. Nobody’s sure what’s current, and privacy policies live in a folder no one’s opened since last year.

AI can’t solve that. It just amplifies the confusion.

Your data is ready when it’s centralized, consistent, and easy to update. That means:

• It’s stored in one place and accessible to both people and integrated systems.

• Every naming convention follows the same taxonomy, no matter where the data sits.

• You can audit, refresh, and track it without pulling an all-nighter.

When your data meets those standards, you can start treating it like an engine by feeding in clean inputs and collecting meaningful outputs.

To get there, I start with five steps:

1. Audit every data source and record ownership in a simple inventory.

2. Clean and map fields, fix missing values, and align formats.

3. Enrich with external context such as events, timing, or signals that add depth.

4. Build compliance from the start with consent tracking, retention rules and privacy.

5. Automate uploads with APIs or ETL tools so clean data keeps flowing.
   

Once your data foundation is solid, AI can finally do what you hired it to do.

Tools That Make Data Management a Breeze

#1 Clay

You can’t personalize anything if your data is outdated.

People change jobs, companies get acquired, new funding rounds happen, and your data just sits there, frozen in time.

Clay enriches your existing data with up to date context from across the web, including company news, role changes, funding updates, and social profiles. All you need to do is upload a spreadsheet of your contacts or domains, and Clay turns it into a living map of your network.

It plugs straight into tools you already use (Airtable, Notion, HubSpot, Google Sheets) and automates the enrichment process so your CRM never goes stale.

Clay starts with a free plan, with paid options beginning around $134/month depending on team size and data volume.

Learn more at Clay.com

#2 Dedupely

The cornerstone of every company’s sales and marketing engine is the CRM. It’s where everything lives. But if you’ve had it long enough, you know the truth: sooner or later, it turns on you.

I worked for a company where we had more than 150,000 records in Salesforce and 20% of them were duplicates. The same contact listed under multiple emails, slightly different spellings, old job titles. It was chaos.

We talked about fixing it for years, but the options were either a massive software contract or an army of interns clicking “merge.” So, like most teams, we learned to live with it. Reports didn’t match, campaigns misfired, and every data-driven decision came with an asterisk.

I wish we’d had Dedupely

It connects directly to CRMs like Salesforce, HubSpot, and Pipedrive, scans for duplicates automatically, and merges them cleanly. There’s no manual cleanup or weekend data purges required. You can define your own matching rules (by name, email, or company), and it keeps notes, activity, and history intact.

Plans start around $32 per month for 30,000 records.

Learn more at Dedupe.ly

#3 Rows

I’ve tested a lot of tools that promise to “make data easy,” and most of them get the analysis wrong or are so difficult to master, I give up. Rows is one of the few that don’t make me want to tear my hair out.

It’s a spreadsheet with superpowers. You can connect your CRM, marketing data, or product metrics, then use AI to ask questions in plain language: Which campaign had the best ROI? What products are trending by region? Which hotels offer the best rate in our top visited cities?

You don’t have to create pivot tables or manually create charts. Rows does everything for you and if you don’t like the output, you can just ask for edits directly in the chat.

Here’s why I like it:

• It helps you audit, clean, and analyze in one place

• You can automate imports and updates

• The AI assistant can summarize patterns, spot outliers, and generate visualizations

• It’s easy enough for anyone to use, but powerful enough for serious analysis

Pricing starts with a free plan, and the paid tiers scale with usage — around $8 per user per month for expanded automations and AI tasks. For most small teams, the free version is enough to test and prove the concept before rolling it out across a workspace.

Learn more at Rows.com

The AI Data Analyst Hiding in Your Workspace

Every week there’s a new AI platform promising to revolutionize how we work, but before you rush out and buy another license, open the tools you already use, because AI is already hiding inside them.

If you’re in Excel or Google Sheets, you’ve now got built-in intelligence that can clean, analyze, and visualize data faster than most standalone products.

Here’s a quick breakdown of each:

Microsoft Copilot now lives inside Excel and Power BI. You can ask questions in plain language (“Which campaigns grew fastest last quarter?”), and it builds charts, finds anomalies, and explains trends without formulas or macros. It can even suggest new ways to visualize your data, making reporting almost effortless.

If you want to see what it looks like in action, fellow Australian Lisa Crosbie has an excellent walkthrough that shows every new Copilot feature step by step, including how to ask questions and interpret the insights. Watch her tutorial here.

Google’s Gemini brings some of the same features to Sheets and Workspace. It can analyze data, summarize information, and generate pivot tables. I’ve used Gemini to generate keywords from a video description, create one-sentence summaries and analyze sentiment from survey results. You can also refresh the results to get variations and apply prompts across multiple rows at once.

Want to see it in action? This Gemini for Sheets demo walks you through how to invoke Gemini inside a spreadsheet.

How to Analyze Spreadsheets in Claude

Ever stared at a messy spreadsheet and wished an AI could just handle it? In this video, I put Anthorpic's Claude to the test for data analysis. From cleaning up data files to analyzing anonymized records, we see how far it can really go.

The results? 

Well, let's just say it nailed some of my requests, but things got interesting as we went along.

If you want to know how AI can help with your spreadsheets (and where it can’t), this one’s for you.

Watch it on YouTube.

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