INBOX INSIGHTS: Data Governance, Managing AI News
Data Governance: The Step You Can’t Skip
Picture this: Your demand gen team had just finished designing what you thought was the perfect ABM campaign. You’ve spent weeks aligning with sales on target accounts, creating personalized content journeys, and building out sophisticated trigger-based workflows in your marketing automation platform.
The day before launch, you are reviewing the final targeting parameters when you realize something concerning – you were planning to use firmographic data that had been appended by a third-party vendor six months ago, but nobody could tell you if your data processing agreement covered using that information for this specific type of outreach.
“No problem,” you thought. “Legal can give us a quick answer.”
Three weeks and seven email threads later, you discover that not only was your agreement insufficient for the planned usage, but half the data fields you had built your personalization strategy around were flagged as “questionable quality” in a data audit that nobody on the marketing team had seen.
You have to rebuild the entire campaign from scratch using only first-party data for which you have proper permissions. The delay cost you an entire quarter of pipeline generation.
Sound familiar? It happens to the best of us. So, how do we prevent this from happening? With data governance.
What Is Data Governance Anyway?
At its simplest, data governance answers three fundamental questions:
These might seem like basic questions, but you’d be shocked at how many organizations - even sophisticated ones - struggle to answer them clearly.
The Painful Reality of Skipping This Step
Just recently, I watched a project get delayed by nearly four months because these questions weren’t addressed upfront. After extensive planning and statement of work development, the analytics team was ready to dive in—only to discover that the privacy team had concerns about customer data usage that completely contradicted the proposed approach.
Everyone had assumed someone else had checked these boxes. No one had.
The frustrating part? This was entirely preventable.
Why Does This Keep Happening?
I’ve noticed a few patterns that lead to these situations:
Building Your Data Governance Foundation
Before you even think about that fancy machine learning model or dashboard, make sure you:
The Benefits of Getting This Right
When you invest in proper data governance upfront:
Moving Forward
If you’re starting a new analytics initiative, I strongly recommend beginning with a data governance assessment. It might not be the most exciting kickoff activity, but I promise it will save you from that awful moment when someone asks, “Wait, are we even allowed to use this data?” after weeks or months of work.
And if you’re in the middle of a project that’s hitting roadblocks because of unclear data ownership or access issues? It’s never too late to pause and build that foundation properly.
Remember: the best analysis in the world is worthless if you can’t actually implement it because of governance issues that should have been addressed at the start.
Quick plug—if you want help navigating your data governance, give me a shout!
Have you experienced a project derailment due to overlooked data governance? I’d love to hear your stories and how you addressed them.
Reply to this email to tell me, or come join the conversation in our free Slack Group, Analytics for Marketers.
- Katie Robbert, CEO
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Data Diaries: Interesting Data We Found
In this week’s Data Diaries, let’s talk about evaluating AI news. There’s a very nearly literal firehose of AI news spewing information at you every day. Since we last spoke a week ago, these things have happened:
And this is just in the last 7 days.
Each of these is a pretty big deal in some fashion. Each of these contributes to our overall capabilities with generative AI in some way. But the question is, how? And what do you need to pay attention to?
Katie often asks me to “tell her when I should care”, and that’s a useful request from a stakeholder to filter down the news. So, in true data nerd fashion, let’s create a scoring rubric for AI news that can help us filter the firehose. We’ll call this the TellKatie™ rubric:
From there, we can think through more broadly how close a piece of news is to the user:
In your own filtering of AI news, this is a handy rubric. How close is the news to the work you do? And this will vary by person. Katie’s not going to be hunting down iQuants of GGUF models. I am, because I have different use cases.
Once we score and sort the news, this is what we see:
From this, I should definitely let Katie know about the changes to Google Gemini, and how we might be using the new Gemma 3 model in production. I might make mention of the upcoming changes to NotebookLM, but that’s about it. The rest can wait until it’s closer to the user or more impactful to the operations of Trust Insights.
Consider adopting a similar practice. Any time you see a new piece of AI news, ask yourself whether you could use it, whether you have a use case for it, and how impactful the news will be to your work. You’ll find that a lot of the AI news is actually AI noise for you to screen out.
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