My Top 5 Articles of 2025

Still reeling from the anarchic introduction of generative AI, 2024 saw the beginnings of a tectonic shift in how we manage, enable, and activate our data for business users. And that left a lot of things to write about.

As we round out the year, here are 5 articles from 2024 that I think are worth a second look! Catch up on what you missed or double-click on some of my favorite topics.

The Past, Present, and Future of Data Quality Management

The times, they are a-changin’. And so is how we manage data quality. Whether we’re talking about dashboards or ML models, garbage in always means garbage out. Unfortunately, as your data environment grows, traditional data quality methods become proportionately less effective.?

In the age of AI, data quality isn’t merely a business risk—it’s an existential one. From a lack of necessary automation to a lack of incident management features, traditional data quality methods can’t monitor all the ways your data pipelines can break—or help you resolve it quickly when they do. And that’s a big problem for AI.

In this piece, I compared three popular data quality methodologies—testing, monitoring, and observability—including how they map to the needs of AI and what you’ll need to effectively drive data trust at scale.

The Data ROI Pyramid: A Method for Measuring & Maximizing Your Data Team?

When AI first strolled onto the scene in 2022, it was nearly impossible to navigate the cloud of LinkedIn evangelists. Teams were caught throwing untold levels of resources at the phantom of AI—often without a clear problem for it to solve. But as we enter 2025, cooler heads are starting to prevail.??

Today, maximizing and measuring data team ROI is on every data leader’s agenda. And delivering value from AI investments is right near the top of that list. But delivering value is one thing…proving it is another thing entirely.?

In the months leading up to 2024, my team spoke with a number of data leaders to discover what makes a data investment financially viable. After iterating on a variety of ROI formulas, we arrived at a solution that, if not capturing the exact value of a data team, can at least get us a little closer. Give this one a look and let me know what you think.

5 Hard Truths About Generative AI for Technology Leaders

The truth hurts. And when it came to AI-readiness, there were a lot of organizations that needed a strong dose of reality in 2024.?

For the last 36 months or so, organizations have been scrambling to build and release new AI features that would make them competitive in the AI arms race. But what those teams found out pretty quickly was that it wasn’t building AI features that was the hard part—it was building something that was actually useful. Spoiler alert: it all starts with the data.

In this article, I shared my full breakdown of the 5 hard truths every technology leader needs to understand to deliver production-ready AI in 2025. If that’s you, this one is definitely worth a read (in my admittedly biased opinion).

Most Data Quality Initiatives Fail Before They Start. Here’s Why.?

There’s one topic that gets me out of bed faster than anything else—and that’s operationalizing data quality. Every day I talk to organizations ready to dedicate huge amounts of time and resources to data quality initiatives that are doomed to fail. But why? And what can teams do about it??

Any data quality strategy worth its engineering time will be a mix of tooling and process. It’s how you motivate adoption that makes the difference.

It’s no revelation that incentives and KPIs drive good behavior. Sales teams have been doing it for years. In this article, I asked the simple question: what if we incentivized data quality scorecards instead? Read on to find out.?

When a Data Mesh Doesn’t Make Sense for Your Organization?

If you’re old enough to remember a time before AI, you’ll probably recall another hype train that was making the rounds in the data community in the 2020s: the data mesh. And like every trend that rises the ranks of industry hype, there was a nugget of truth to the data mesh legend.?

As far as enterprise data architecture is concerned, the data mesh is a deeply thoughtful decentralized approach that effectively facilitates the creation of domain-driven, self-service data products. The problem is that not every organization should organize their architecture this way—or have the resources to support it.?

But while the data mesh hype train might have ceased operation in late 2023, the data mesh theory is still alive and well—insofar as it makes sense for the organization.?

When should you leverage a data mesh? When shouldn’t you? Find out here.