Data has always been complicated. But something is different right now,  and most data teams can feel it, even if they can't quite name it.

We recently sat down with Ben Siegel, co-founder and CEO of Matia, and data thought leader, Doug Laney, bestselling author of Infonomics and a former Gartner Distinguished Analyst, to talk about what's actually happening at the intersection of AI and the modern data stack. No vendor pitche, just two guys who've spent a lot of time in the trenches of data thinking out loud about where we are and where we're headed.

Here's what came out of that conversation.

The data stack wasn't designed, it accumulated

This might be the most honest framing of how we got here.

"Organizations didn't really design a data stack," Doug said during the webinar. "They kind of accumulated one." Every new data challenge created a new tool. ETL tools, data warehouses, self-service analytics, observability platforms, each one was bought or built in response to urgency, not architecture. And the result? Stacks that are technically impressive but organizationally incoherent.

Ben knows this firsthand. Before starting Matia, he was head of data at Pangaea, managing five or six vendors simultaneously, meeting with each of them once a year at renewal time, fighting with account executives over pricing, and spending more time managing tools than actually doing anything meaningful with data.

Sound familiar?

The fragmentation was tolerable before. It's becoming untenable now, because AI doesn't solve the underlying mess–it amplifies it. "AI doesn't magically fix a fragmented environment," Doug said. "It can actually amplify the consequences of unclear definitions, weak data lineage, and inconsistent governance."

AI raised the stakes. And the bar.

The pressure on data teams right now is real. AI initiatives are failing left and right,  not because AI doesn't work, but because the data underneath it doesn't.

Doug put it in perspective. He pointed out that the same "most projects fail" headline has been written about every major technology wave, the internet, data warehouses, big data, data science. That's how innovation works. What's different this time is the confusion between experimentation and operational readiness.

Ben added something sharp here: organizations are now using data operationally, not just for reporting. Data isn't just flowing to dashboards anymore. It's powering live products. Driving automated decisions. Feeding AI agents. That means a bad pipeline isn't just an inconvenience that gets fixed next morning, it can break something a user is actively depending on right now.

The stakes got bigger. The tolerance for bad data got smaller.

The real differentiator isn't the model, it's the data

This came up more than once, from both directions of the conversation.

The AI models themselves are rapidly becoming commodities. What makes one organization's AI better than another's isn't which model they're running,  it's the quality, context, lineage, and governance of the data they're feeding it.

"If data is treated like an asset, and managed and measured like one, that's where organizations are going to win," Doug said.

Ben echoed it from a different angle: data teams are increasingly becoming full engineering teams. The data they manage isn't background infrastructure anymore,  it's a live operational function. Which means reliability, observability, and governance aren't nice to haves. They're table stakes.

What the best data organizations actually do differently

We asked both of them what separates leading data organizations from everyone else. Their answers were refreshingly practical.

Doug's take: The best organizations don't treat data as an IT function. They treat it as an enterprise capability with real business accountability. He also made a distinction worth holding onto: rather than "data owners," a term that tends to produce hoarding and silos. He prefers "data trustees." People who are ethically and legally responsible for the quality, meaning, use, and value of data within their domains.

Leading organizations also think about data products, not just data projects. They measure data's condition and its contribution to business value. And, critically, they don't confuse data governance with bureaucracy. Good governance makes data easier to find, trust, and use. Bad governance just creates meetings.

Ben's take: From working closely with companies like Ramp, he's noticed a few recurring patterns in high performing data teams. First, the most effective data leaders are relentless about showing ROI. They frame data work in dollar terms, not technical terms, and that makes it much harder for anyone to cut their budget. Second, they build strong relationships across the business, so when something goes wrong (and something always eventually goes wrong), there's goodwill already in the bank. Third, they're early adopters. They move to Snowflake, ClickHouse, and vector databases before everyone else does. They don't spend their energy saving 5% on tool costs — they spend it positioning their teams to move faster.

And one more thing Ben mentioned: the best data leaders aren't isolated. They're connected. They talk to other data leaders. They share what's working. That network is underrated.

The case for a unified DataOps platform

Doug had a phrase for what happens when you build a data stack out of best in class point solutions: "Best of breed begets worst of bleed."

The math isn't complicated. More tools means more handoffs. More handoffs means more points of failure. More points of failure means nobody is actually accountable for the whole system,  everyone owns a piece of reliability, and nobody owns all of it.

That's the problem Matia was built to solve. When ingestion, reverse ETL, observability, and catalog all live in one platform, you get something that fragmented stacks can't offer: full end to end visibility into where data came from, whether it's been changed, whether it's complete, who uses it, and what decisions depend on it.

One of the most compelling moments in the webinar was Ben describing Matia's MCP integration, currently in private preview. A customer used it to ask: "I want to deprecate this column, what will the downstream impact be, and what monitors should I add or remove?" The answer came back in 30 seconds. Something that used to take weeks.

That's what unified looks like in practice.

What's next for data leaders, and a word on the CDO role

Both Ben and Doug see the CDO role evolving significantly. Data environments are going to become more self-governing. Metadata will stop being passive and start being active,  routing data, classifying it, monitoring it, flagging it when it's no longer fit for purpose. Data quality will become more self-diagnosing.

Doug's framing for the destination: the self-driving organization. An enterprise where data, AI, rules, agents, sensors, and process management are integrated tightly enough that many functions can monitor, decide, and act with limited human intervention.

Ben added a more grounded note. He thinks CDOs will start looking more like CISOs — carrying real ownership of risk, not just oversight. "Things are gonna be automated, you'll have less control. It will work beautifully when it works. But the day it doesn't work, you need to be ready to catch it." The CDO who thrives in that world will be both strategic and hands on. Close to the data, not just managing from a distance.

If you were a CDO today, what would you prioritize?

We asked both of them directly. Here's what they said.

Doug: Automate everything you reasonably can, right now. Mandate– don't just recommend – that data be treated with the same discipline as any other company asset. And think seriously about redesigning your operating model for an AI-enabled enterprise, from the ground up if necessary. The models humans built for businesses were designed around human limitations. That's changing.

Ben: Make sure you're on the right tools, and actually using the right features within those tools, because the pace of change is fast enough that what was true 12 months ago may not be true now. And invest in building AI skills across your team, not just at the top.

One thing that's overhyped

We couldn't resist asking.

Doug's answer: AI as a copilot or assistant. Useful starting point, but not the real prize. The real prize is AI-orchestrated work,  agents working in swarms to decide what needs doing, gather data, analyze it, and act. That's the destination.

Ben's answer was more technical, but equally interesting: he thinks the way we write code and logic today, structured for human readability, will look very different in a few years. Agents don't need code to be human-readable. They need it to be fast and efficient. "We'll see huge difference in the way code and logic are written–way more efficient for agents than for humans." Think less SQL, more something closer to protobuf.

The bottom line

The companies that are going to win in this next era aren't necessarily the ones with the biggest AI budgets or the most advanced models. They're the ones taking their data seriously, including managing it as an actual asset, unifying it so it can be governed and trusted end to end, and building the infrastructure that lets AI actually do something meaningful on top of it.

The data stack got complicated by accident. Fixing it on purpose is the work.

‍

Watch our webinar, The Next Era of the Data Stack: Unified DataOps, featuring Doug Laney and Ben Segal, where they dive deeper into the future of data infrastructure, AI readiness, and why today's data stacks are becoming increasingly difficult to manage.

Follow Doug Laney on LinkedIn:

Follow Ben Segal on LinkedIn:

‍

‍

‍