INBOX INSIGHTS, March 19, 2025: Data Governance, Managing AI News - Trust Insights Marketing Analytics Consulting

INBOX INSIGHTS: Data Governance, Managing AI News (2025-03-19) :: View in browser

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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:

Who owns and is responsible for the data?
What exactly is the data (and what format is it in)?
How can you properly access and use it?

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:

Siloed organizations: Data, analytics, privacy, and legal teams operating independently without clear communication channels
Excitement over results: Focusing on the exciting insights rather than the foundational elements needed to get there
Assumed knowledge: “Someone must have already figured this out” thinking
Pressure to move quickly: Skipping what seems like bureaucratic steps to show progress

Building Your Data Governance Foundation

Before you even think about that fancy machine learning model or dashboard, make sure you:

Identify your data stewards: Who has ultimate authority over each data source? Get them involved early.
Document your data inventory: What exactly do you have? Where does it live? How is it structured? What are the quality issues?
Establish access protocols: Who can see what data, under what circumstances, and with what approvals?
Clarify usage policies: What can this data be used for? Are there regulatory or privacy restrictions?
Create review processes: How will new data uses be evaluated and approved?

The Benefits of Getting This Right

When you invest in proper data governance upfront:

Projects move faster overall (despite the initial time investment-trust me on this one)
Teams avoid frustrating and expensive mid-project pivots
Data quality and reliability improve
Regulatory risks decrease substantially
Cross-functional collaboration becomes smoother

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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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:

New Alibaba VACE video generation model announced
New Baidu ERNIE 4.5 and X1 models
New Cohere Command A model
New DAPO algorithm
New Google Gemini 2 Flash Image model
New Google Gemini 2 upgrades – audio overviews, canvas, and enhanced Deep Research, with Veo support announced as coming soon
New Google Gemma 3 models
New IBM BeeAI agent framework
New LG Exaone Deep models that beat Deepseek
New MCPs for Claude that allow 3D asset generation in Blender
New Microsoft Phi-4 Multimodal model
New Mistral Small 3.1 Multimodal model
New NotebookLM mind maps announced
New NVIDIA hardware – DGX Spark (formerly Project Digits) – announced
New Perplexity MCP API
New Sesame CSM 1B speech generation model
New SmolDocling OCR model

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:

Could Katie use it today? This is binary. Is it something that, with little to no preparation, Katie could use?
Does Katie have a use case, as a strategic but non-technical user? This is also binary.
Finally, how impactful is it to the work of Trust Insights? Something might not be valuable to Katie but might be valuable to customers and clients, because there are a huge number of different use cases. This is a sliding scale, like 1-5.

From there, we can think through more broadly how close a piece of news is to the user:

An announcement with no product is unusable and furthest away
An algorithm or paper on a methodology is very far away; the non-technical user will not be implementing DAPO or GRPO, for example
A raw model requires lifting to get up and running, especially for a non-technical user
A SaaS model requires much less lifting to get up and running; this is usually a menu item or a dropdown
A fully baked product is the closest to the user; they just log in and start using it

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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