AI Data Management: Why Clean Data is Crucial for Marketers' Success
- Jennifer Leonard
- May 20
- 4 min read
Updated: Jun 10
AI is everywhere in marketing conversations right now. Tools are popping up by the hundreds. Marketers are being told to adopt them, or risk falling behind.
The promise is clear: real personalization, delivered at scale. But here’s the problem no one wants to admit: AI only works if your data does.
AI Can't Succeed Until We Manage the Data Underneath
I’ve worked in campaign strategy and lifecycle marketing across large tech teams, and I’ve seen the same issues appear repeatedly:
Duplicate contacts in our CRM
Null fields (These may seem harmless, but they're actually not.)
Conflicting signals across systems
Triggers that break when someone changes a naming convention
Here’s a real example that happened with a previous client:
One of their triggered emails was showing customers real-time usage data. One day, a customer reached out saying the numbers didn’t match what they saw in their account dashboard.
After digging in, we discovered that the product team had changed the underlying signal, the data definition itself, without changing the signal name. The new logic wasn’t wrong, but it no longer matched the use case for our email.
And here’s where the deeper challenge came in:
There was no centralized signal directory, so we couldn’t easily check what had changed
No one flagged the update to marketing, so the email kept running with outdated assumptions
The marketing team didn’t have access to fix the signal themselves
There was no defined governance process for data owners to notify comms teams about changes
Long story short: We had to pause the campaign, locate the right signal, and request an engineering resource just to surface it where we could use it.
The fix? Nearly three months because the engineer had to be pulled from another team, and we only got a narrow window to implement.
What broke wasn’t the email. What broke was our shared understanding of the data and how teams were supposed to collaborate around it.
What Happens When You Add AI on Top of That?
It doesn’t fix the mess, it magnifies it.
Without clean, connected, and consistently structured data, AI tools can’t:
Identify real customer signals
Deliver accurate predictions
Improve over time
What's worse, bad inputs lead to bad outputs that erodes trust with both customers and internal teams.
So… Where Should Marketers Start?
This part can be very frustrating.
Here are some common responses marketers hear when they need data:
When marketers need data access or fixes:
"Submit a ticket to the data team and they'll get to it when they can."
"We're prioritizing other requests right now - your project is in the queue."
"Just use what you have for now, we can clean it up after launch."
"That would require joining multiple data sources - it's more complex than you think."
"Our systems don't talk to each other that way."
"The data exists, but not in the format you're looking for."
When seeking insights:
"What exactly do you need to know? Be more specific."
"We need at least two weeks to pull those insights."
"That's not something we track regularly."
"The data is there, but you wouldn't know how to interpret it correctly."
"Why don't you just use Google Analytics?"
Advice marketers typically receive about working with data teams:
"Frame your request in terms of business outcomes, not just data points."
"Give us more lead time - data requests aren't quick fixes."
"Come prepared with exactly what metrics you need."
"Understand that every custom report takes time away from other priorities."
"Try learning some basic SQL so you can pull simple data yourself."
Working with data teams can feel intimidating, opaque, or unproductive—especially when priorities don’t align or the systems feel too technical to navigate.
Even experienced marketers sometimes don’t know:
What to ask for
How to explain what they need
Or how to follow through once they do get access
So we lean on instinct, use whatever metrics are easiest to access, and often send the same message to everyone, not because we want to, but because the data we need to personalize just isn’t there.
I wrote this article and am developing a full series to help change that.
You don’t need to be a data engineer. But if you want to:
Find the "aha" insights in your customer base
Use predictive modeling to identify who’s likely to convert (or churn)
Create smarter, more relevant comms...
…then you do need to understand what makes data usable, how to talk about it, and how to work with cross-functional partners to make it real.
Here's What I Recommend
Start by asking the foundational questions marketing teams should be asking before layering AI into their campaigns or even attempting personalization at scale:
Where does our customer data live?
How consistent is it across platforms?
Do we have shared definitions for key lifecycle stages?
Who owns the data, and who fixes it when things go wrong?
Want to Go Deeper?
I've created a free downloadable checklist:
"Fix the Foundation: The Marketer's Guide to AI-Ready Data"
It’s marketer-friendly, jargon-free, and includes the same questions I use to guide AI data-management conversations around campaign flows and retention strategies.
Grab the checklist here:
Final Thought
AI won't save your campaigns if your foundation is broken. Start with clean, consistent, trusted data, and you'll be miles ahead of teams still stuck in reactive mode.
Next up: This is just the beginning. I'll be sharing a full series that dives deeper into each of the 10 essential steps from the checklist, starting with how marketers can run a smart, non-technical data audit (even if you don't own the tech stack).
The goal is to help marketing teams work smarter with data and AI—without needing a data science degree.
Check back here on NextWise Studio for the next post, or connect with me on LinkedIn to follow the series.
Need help facilitating this process with your team? Fill out the contact form on this site
or connect with me on LinkedIn to chat about identifying gaps, improving data hygiene, and getting AI-powered marketing strategies off the ground.
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