Your AI Is Hungry. 6 Steps to Feed It a Better Data Strategy.

Here’s the thing about AI: it doesn’t magically work because you slapped a few prompts into a chatbox. The real power — the part that gets you personalization, prediction, and performance at scale — comes from what you feed it.

That’s your AI data strategy. And most companies? Still figuring that part out.

According to the IAB State of Data 2025, half the industry is flying blind when it comes to AI strategy. The other half may have a strategy on paper, but many are still stitching together siloed tools and fragmented datasets.

Let’s fix that. Here are 6 steps to help you create a better AI data strategy. 

Step 1: Admit You Don’t Have Enough First-Party Data (Unless You’re Amazon)

If you’re not one of the data titans, odds are your first-party data won’t get you very far on its own. Site behavior, app engagement, CRM data — great. But if your goal is to train smarter models or power personalization that actually feels personal, you’re going to need more.

Think of first-party data as your lead actor — great, but you still need a full cast to tell the full story. That’s where collaboration comes in. Bring in data from publishers, partners, and platforms to expand your reach and deepen your insights.

Step 2: Build an AI Data Strategy with Layers

Here’s what a layered data strategy looks like:

• First-party data: Yours. Accurate. Relevant. Limited in scale.
• Second-party data: From trusted partners — publishers, other brands. Think “I’ll show you mine if…”
• Third-party data: For reach, richer profiles, and the missing puzzle pieces you didn’t know you needed.

Your AI data strategy should consider where the data came from, how it was collected, and what it can realistically do for you. Survey data ≠ purchase data. Contextual ≠ behavioral. And saying someone might be near a store is not the same as knowing what they just bought.

Your AI data strategy should go beyond just collecting data — it needs to evaluate the quality, origin, and intent behind that data. Why? Because not all data types are created equal, and mistaking one for another can tank your AI’s accuracy.

Let’s break it down:

Survey Data ≠ Purchase Data

Someone saying they bought something doesn’t mean they actually did. Surveys are self-reported and subject to human error, memory lapses, or even wishful thinking (“Sure, I always buy organic…”).

Purchase data, on the other hand, is concrete. It’s verified behavior, not claimed intent. If your AI is trained on claimed behavior instead of real transactions, the insights — and your targeting — will be off.

Contextual ≠ Behavioral

Contextual data tells you what someone is looking at — like reading an article about electric cars. Behavioral data tells you what they’ve actually done — like researching EVs across sites, searching for tax credits, and comparing car models.

Context gives you a moment in time. Behavior shows you a pattern. Smart AI needs both, but if you mistake one for the other, you’re guessing instead of predicting.

Step 3: Expanding Globally? Your AI Data Strategy Needs to Travel

If your AI strategy is built in a data-rich market like the U.S., replicating that success globally isn’t as simple as copy and paste. Many regions — especially across parts of Europe, APAC, and LATAM — operate with far more limited data availability and stricter regulations.

This makes it essential to evaluate whether your data strategy can scale internationally.
Ask:

• Do you have access to meaningful, high-quality data in those markets?
• Are your data partners equipped to navigate local compliance and identity challenges?
• Can your collaboration strategy adapt to regions that are more fragmented or privacy-restrictive?

The right data partner — particularly one with coverage, compliance capabilities, and infrastructure across multiple regions — is critical to building a scalable global AI data strategy.

Step 4: Connect the Dots (Across Devices and Domains)

Siloed data is useless data. The more your AI can connect across mobile IDs, CTV, podcasts, desktop, and even digital out of home (DOOH), the more it can reason instead of just react.

Let’s say you’ve got MAIDs tied to great financial insights. Cool. But now imagine extending that to Connected TV to reach your actual human across channels. That’s where things start getting fun (and performant).

Step 5: Choose a Platform That Doesn’t Fight You

A smart AI data strategy needs a smart data collaboration platform. Here’s the checklist:

• Activation: Can you model, segment, and actually do something with the data?
• Compatibility: Does it play nice with your stack?
• Flexibility: Can it handle deterministic and probabilistic data? Or does it get squeamish when things get messy?

Want a version you can actually use in meetings? Download Lotame’s Data Collaboration Platform Evaluation Checklist to help guide your selection process.

Step 6: Ask the Right Questions (Before You Sign Anything)

• Is there enough overlap to make collaboration worthwhile?
• Is the partner’s data additive, not redundant?
• How is the data stored, transferred, and secured?
• What IDs are available for matching — and can they build a graph if needed?

At Lotame, we look for at least 5,000 unique profiles with strong attributes before we say “let’s go.” A good collaboration creates value neither side could get alone. If it doesn’t, it’s not worth doing.

Who Can You Actually Collaborate With?

• Brand + Brand: Cross-sell, co-market, see who your customers have in common.
  Brand + Publisher: Reach the right people, in the right content, in real-time.
• Agency + Data Source: Build networks across clients, surface patterns, and scale targeting with nuance.

Want to go deeper? Check out our Best Practices for Data Collaboration guide to make your next partnership more effective.

TL;DR: Your AI Is Only As Smart As Your Data Strategy

And no — that strategy doesn’t need to rely on owning mountains of first-party data. The right AI data strategy brings together smart partners, flexible platforms, and overlapping insights that actually unlock something new.

If your AI isn’t delivering what you expected, don’t blame the algorithm. Blame the inputs.

Ready to fix that? Let’s talk data collaboration.