Why data quality matters more in the AI era, not less

What you need to know

AI doesn’t fix bad data. It amplifies it. Every company racing to adopt AI needs to understand that the technology makes a lot of really good decisions really fast, or a lot of really bad decisions really fast, depending on what you feed it. At Data Axle, we’ve spent decades building the verification infrastructure that makes AI useful instead of dangerous. The companies winning with AI aren’t the ones with the most sophisticated models. They’re the ones with the cleanest, most connected data foundations.

• AI amplifies data quality problems at machine speed, making verification and maintenance more important than ever before
• A “data fabric,” a standard set of data your business operates on, requires quality inputs to produce reliable AI-driven outputs
• Data Axle’s AI-human verification model combines predictive scoring with 25 million phone verification calls per year across 90+ million business profiles
• B2B-to-B2C identity linkage (150 million linkages rebuilt using AI and digital signals) turns data quality into a competitive advantage
• Smaller, high-quality datasets consistently outperform larger, unreliable ones, especially when the cost of a single sales call includes a 15-20 minute conversation

Introduction

I’ve been building and running data companies for more than 30 years. In that time, I’ve watched the industry cycle through plenty of trends. Big data. Cloud migration. Real-time everything. Most of those shifts changed how we stored or moved data. AI is different. It changes how we act on data, and that distinction matters enormously.

When your AI model is making targeting decisions, scoring leads, or personalizing content across millions of touchpoints, the margin for error shrinks. A flawed record in a spreadsheet is a minor nuisance. A flawed record feeding an automated decisioning engine is an expensive mistake repeated thousands of times before anyone notices.

That’s why I’ve been telling our clients and partners the same thing for the past two years: data quality isn’t a prerequisite you check off before your AI project. It’s the ongoing operational discipline that determines whether AI creates value or destroys it.

What is a data fabric and why does it matter for AI?

A data fabric is the standard set of data your business operates on across every function, from marketing and sales to customer service and finance. It’s the connective layer that ensures everyone is working from the same foundation.

Here’s why that concept becomes critical in an AI context:

• Consistency: AI models trained on fragmented, contradictory data produce fragmented, contradictory outputs
• Speed: Automated systems act on data faster than humans can catch errors, so the data needs to be right before the model touches it
• Scale: When you’re making decisions across millions of records, even a 2-3% error rate compounds into significant waste

At Data Axle, we maintain over 90 million business profiles and hundreds of millions of consumer records. That scale only works because we’ve invested in verification infrastructure that most companies treat as optional. It isn’t.

How does bad data actually break AI?

This is the question I don’t hear enough people asking. The AI conversation is dominated by model architecture and prompt engineering. But the failure mode I see most often in the field is much simpler: garbage in, garbage out, just faster.

Consider a B2B sales team using AI-powered lead scoring. If 15% of the business records in your CRM contain outdated phone numbers, wrong contacts, or closed locations, your AI model learns to score based on noise. It doesn’t know that record is bad. It just patterns off whatever you give it.

Now multiply that by the velocity AI operates at. A human sales rep might call 40 prospects a day and notice something feels off. An AI-driven outbound system touches thousands. The cost of a single sales call, the connect, the 15-20 minute conversation, makes bad data expensive at any scale. At AI scale, it’s a budget problem.

I’d rather have a small set of data that’s high quality than a big set of data that’s not reliable. That’s not a philosophical position. It’s an operational one, backed by decades of watching companies waste money on volume over accuracy.

What does an AI-human verification model look like?

This is where our approach at Data Axle gets specific. We don’t rely on AI alone to maintain data quality, and we don’t rely on manual processes alone either. We’ve built a hybrid model that uses each where it’s strongest.

The AI layer identifies which business records are most likely to have changed, which contacts are most likely to still be in their roles, and which phone numbers are most likely to connect. It’s a predictive filter that focuses human effort where it will have the highest impact.

The human layer then makes 25 million verification calls per year. Real people, calling real businesses, confirming that the data is accurate. That’s not a token effort. That’s an industrial-scale verification operation running continuously across our database of 90+ million business profiles.

This AI-human assist model is how we maintain quality at scale. The AI makes the process efficient. The humans make the output trustworthy. Neither alone would be sufficient.

Why does identity linkage change the game for B2B marketers?

Here’s a point that connects data quality directly to marketing performance: B2B-to-B2C identity linkage.

At Data Axle, we’ve built 150 million identity linkages that connect a professional identity (your work email, your business profile) to a personal identity (your personal email, your consumer profile). We rebuilt all of those linkages using AI and added digital signal data to improve accuracy.

Why does that matter? Because B2B buyers don’t stop being consumers when they leave the office. If I know that a CTO at a mid-market SaaS company also happens to be an avid golfer who watches the Masters, I can reach that person on connected TV with a relevant message during programming they’re already watching.

That kind of targeting is only possible when your identity data is clean, linked, and verified. One bad linkage doesn’t just waste an ad impression. It creates a dissonant experience that damages trust.

The quality of identity linkage is directly proportional to its value. And in a world where AI is automating the activation of those linkages across channels, the tolerance for error approaches zero.

What data quality mistakes do companies make with AI?

After three decades in this industry, I see the same patterns repeat:

Treating data quality as a one-time project. Companies clean their data before an AI initiative, then assume it stays clean. Data decays. People change jobs, businesses close, phone numbers change. Verification needs to be continuous, not episodic.

Prioritizing volume over accuracy. There’s a persistent belief that more data is always better. It isn’t. AI models trained on large but unreliable datasets produce confident but wrong outputs. That’s arguably worse than having no model at all, because it creates a false sense of precision.

Ignoring the human verification step. AI is excellent at pattern recognition and prediction. It’s not excellent at calling a business and confirming that the person listed as the operations manager is still in that role. Some verification tasks require human judgment and human conversation.

Siloing data quality from AI strategy. I see organizations where the data quality team and the AI team report to different leaders, operate on different timelines, and have different priorities. That’s a structural problem. Your AI outputs are bounded by your data inputs. Those teams need to be connected.

How should you think about data quality investment?

Here’s my recommendation, and it’s grounded in what we practice at Data Axle, not just what we advise:

1. Audit your data foundation before scaling AI. Understand your error rates, your decay rates, and your verification gaps. You can’t improve what you haven’t measured.
2. Build continuous verification into your operating model. Not annual cleanses. Not pre-campaign hygiene. Ongoing, systematic verification that keeps pace with data decay.
3. Invest in identity resolution. The ability to link records across contexts (B2B to B2C, online to offline, first-party to third-party) is a competitive differentiator. But only if the linkages are accurate. Products like ourProfileFuse and Audience360 platforms are designed to help connect those dots with deterministic matching rather than probabilistic guesswork.
4. Adopt a hybrid AI-human approach. Use AI to prioritize and predict. Use humans to verify and validate. Neither approach works optimally in isolation.
5. Measure the cost of bad data, not just the cost of good data. When you calculate the fully loaded cost of a wasted sales call, a misrouted direct mail piece, or a poorly targeted ad campaign, the ROI on data quality becomes clear.

Your AI is only as smart as your data

The companies that will win in the AI era aren’t necessarily the ones with the most advanced models or the biggest engineering teams. They’re the ones who’ve done the unglamorous, essential work of building and maintaining a trustworthy data foundation.

At Data Axle, we’ve been doing that work for decades, maintaining over 90 million business profiles, making 25 million verification calls per year, and building 150 million identity linkages. We’re applying AI to make that work more efficient and more precise. But we haven’t forgotten that the value of any AI system is bounded by the quality of what you put into it.

That’s not a new insight. It’s the oldest truth in data, and it’s never been more relevant.

Want to learn more? Listen to Andy’s podcast with Jay Schwedelson.