At dbSeer, we’ve spent years building data platforms for organizations that are serious about what they can do with their data. And repeatedly, we’ve seen the same pattern: companies invest in getting their data infrastructure right, only to discover that the quality of data flowing through it quietly degrades over time. Systems run perfectly. No alarms go off. But the numbers don’t add up; the reports contradict each other, and nobody can pinpoint why.
What’s missing isn’t more technology. It’s a practice — specifically, a DataOps practice. When Gartner dedicated significant airtime to DataOps at their most recent Data & Analytics Summit, it confirmed something we’ve believed for a while: this isn’t a niche engineering concern. It’s becoming a baseline expectation. And for organizations deploying AI, it’s critical.
So, let’s define it clearly, and explain why it matters even if you’re not the one touching a line of code for your business.
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Companies Protect What They Can See
Think about how enterprise technology investment has evolved over the past two decades. First, companies invested heavily in network infrastructure, routers, switches, monitoring tools, to ensure systems stayed connected and communication remained reliable.
Then security became the board-level priority. The wave of high-profile breaches prompted a sustained and dramatic shift in spending. Organizations built dedicated security teams, compliance frameworks, audit requirements, and insurance programs around cyber risk. The market responded accordingly.
Expected global cybersecurity spending hit roughly $212 billion in 2025 — about double what it was less than a decade ago. Security went from an IT concern to a strategic imperative for companies.
And yet, through all of this, most organizations have never established a formal practice around the quality of the data they’re protecting. The data quality management market, the tools and services dedicated to ensuring data accuracy, consistency, and reliability, was valued at approximately $4 billion in 2024. That’s roughly 50x smaller than what the world now spends on cybersecurity. The information companies use every day to make decisions, run operations, and increasingly to train and run their AI is largely unmonitored. Data is arguably the most underused asset in most organizations. And the cost of ignoring it is measurable.
According to Gartner, poor data quality costs organizations an average of $12.9 million per year. And yet many don’t even measure the annual financial cost of their data quality problems, meaning the damage accumulates invisibly. When it does surface, the cost compounds fast. Research from Dataversity documents what’s known as the 1x10x100 rule. A data quality issue caught at the source costs 1x to fix. If it goes undetected and propagates through the system, the cost rises to 10x. If it reaches the decision-making stage — informing a report, driving a forecast, or feeding an AI model — the cost can reach 100x from the initial. It’s meant to demonstrate the compounding effect bad data can have on a business.
This is the gap DataOps addresses. Not a gap in technology, but a gap in practice. Network monitoring has become standard. Security monitoring has become mandatory. Data quality monitoring is the next frontier, and right now, most organizations are operating without it. dbSeer wants to help usher the change.
What Is DataOps?
Most technology leaders are familiar with DevOps, the engineering discipline that governs how code moves from development into production. DevOps is the practice that ensures your systems are built correctly, deployed reliably, and maintained over time. Rather than letting developers manually push changes to production environments (with all the risk that entails), DevOps embeds deployment logic directly into the code and process, so releases are automated, consistent, and safe.
DataOps is the parallel discipline but instead of governing how code gets delivered, it governs how data gets maintained.
The simplest way to think about the relationship: DevOps builds the pipes. DataOps keeps the water clean.
Good plumbing means nothing if the water isn’t safe to drink.
Your systems can be running perfectly: no errors, no outages, nothing technically broken- but the data flowing through them can still be bad. An outside system quietly changes how it formats a field. A feed that normally delivers thousands of records delivers a hundred, and no one notices. A number that should never be negative shows up negative in a report. Your team is making decisions based on information that isn’t accurate.
DataOps is the discipline that catches this. It’s a layer of checks, monitors, and automated validations — built into the platform from day one — that keeps your data honest over time, not just at launch.
Why This Problem Hasn’t Been Solved Yet
There are tools that claim to handle data quality. So why is this still such a persistent problem?
Because DataOps isn’t just a tool problem. It’s a tool, process, and ownership problem, and most organizations only address one of the three.
Here’s the core challenge: your business changes over time. Teams grow, shrink, and reorganize. The people responsible for maintaining data quality move on. Business logic evolves. And naturally, data quality degrades along with those changes, not because of a system failure, but because the human and process layer around the system has shifted.
There’s also a structural disconnect that most organizations don’t address. Data engineers are responsible for how data moves through a platform, but they have no control over what goes into the source systems. If the data coming from your CRM or ERP is wrong, the data engineer can’t fix it. That fix requires someone on the business side who owns that system. But in most organizations, the data engineer and the business data owner operate in separate worlds, with no formal process connecting them.
That’s where the process breaks down — and that’s exactly where DataOps needs to operate.
What DataOps Looks Like in Practice
In practical terms, DataOps means your data platform is designed to be self-aware — and increasingly, AI is what makes that possible at scale. Rather than relying on a team of engineers to manually catch and diagnose every anomaly, a modern DataOps program uses AI-driven monitoring to surface problems automatically, with the right context already attached. It knows when something looks wrong, flags it before it becomes a business problem, and maintains a clear record of where data came from and how it moved through your systems.
The classic failure mode DataOps prevents is familiar to most organizations: a data feed breaks overnight, no one knows until a dashboard is empty the next morning, and by the time someone investigates, decisions have already been made on incomplete information. With proper DataOps practices in place, the platform surfaces the problem automatically, often before your team starts their day.
It’s a pattern we’re actively working through with our clients. Take a software platform that is integrated across hundreds of locations, pulling data nightly from dozens of different management systems through API calls. Each system stores data differently, so every run involves standardization, sanitization, and loading into a central warehouse — with dashboards, reporting, and customer-facing applications all depending on that pipeline working correctly, every night, for every location.
At that scale, manual monitoring isn’t realistic. What DataOps makes possible is pattern awareness: the system knows that a given location typically syncs thirty appointments per day, so when it sees one, it flags it. It knows that a handful of records might update on any given night, so when it sees ten thousand, it holds for review before those changes propagate downstream. Both scenarios — missing data and mass updates — can have serious consequences if they reach production unchecked. Missing data means staff are working with incomplete information. Mass updates can cascade through every downstream system at once, triggering hours of re-sync work and potentially corrupting records that teams depend on.
DataOps doesn’t just catch these issues. It routes them to the right people with enough context that someone can make an informed decision before anything breaks. That’s the difference between a monitoring tool and a DataOps practice: one tells you something went wrong; the other tells you what, where, and what to do about it.
Beyond catching failures, DataOps builds something harder to measure but more valuable over time: trust in your data.
Why This Matters Most for AI — and Why the Window Is Now
Most organizations are racing to deploy AI. The assumption embedded in that race is that the data feeding these systems is reliable enough to act on. The research suggests otherwise, and the consequences of that gap are not theoretical.
A study published in 2022 examined 32 real-world datasets across industries. Their findings: 91% of machine learning models degrade over time. Not because the code breaks. Not because anyone changes anything. They degrade because the world changes, and the data flowing into the model no longer reflects the reality the model was trained on.
This is what makes AI different from traditional software. When traditional software breaks, it throws an error. When an AI model drifts, it keeps producing outputs — confidently — even as its accuracy quietly erodes.
Gartner is direct about it: 63% of organizations either do not have or are unsure they have the right data management practices for AI. Their prediction: through 2026, organizations will abandon 60% of AI projects due to insufficient data quality.
McKinsey’s 2025 State of AI report puts the performance gap in stark terms: 88% of organizations report using AI in at least one function, but only 39% have achieved any measurable impact on earnings before interest and taxes. The majority of AI investment across most organizations is producing activity without results. Yet, the collected data tells a consistent story about what separates the 39% from everyone else. Companies with strong data integration achieve 10.3x ROI from AI initiatives, versus 3.7x for those with poor data connectivity — nearly a threefold difference. Currently, 75% of business leaders report they don’t trust their own data enough to make decisions from it. And 85% of AI projects fail because of poor data quality or lack of relevant data.
None of this is a technology problem. Every major AI platform available today is capable of producing valuable outputs: when the data behind it is trustworthy. The differentiator isn’t the model. It’s the foundation.
DataOps is what makes that foundation trustworthy over time and it’s not a one-time build. Think of it the way you’d think about maintaining any high-performance system: you don’t stop servicing a car because you’ve already bought it. DataOps protects the investment you’ve already made in your data infrastructure, and it compounds in value as your AI systems grow more dependent on that data being right.
DevOps builds the pipes. DataOps keeps the water clean. For AI to deliver on its promise, the water has to be clean before it reaches the model: and it has to stay clean as the business evolves around it.
Ready to Talk About Your Data Environment?
If you’re wondering whether your organization has the right DataOps practices in place — or what it would take to build them — we’d be glad to have that conversation. Reach out to the dbSeer team to get started.
