Data quality has always been a hurdle in go-to-market (GTM).
For years, many companies were forced to work around this issue, thinking of bad data like sand in the gears — not ideal, but operating models could be built to account for it. A good example of this is forecasting: we started with a terrible data set, but created motions, tools, systems, and processes that made it work relatively effectively.
Directionally, we got it right. But that’s not the case anymore.
All of us now face intense pressure to drive more productivity with fewer people, and automation has gone from desirable to inevitable. Widespread access to Large Language Models (LLMs) quickly seemed like a superpower: the ability to automate without needing an engineering team. Simply feed data into models, and get high-quality answers back.
But when companies started running their GTM processes through these AI systems, they realized an uncomfortable truth: the data in their CRM simply isn’t good enough to power meaningful automation of customer experiences.
AI models are becoming cheaper and more capable faster than we can actually scale their use. This trend is only going to accelerate.
But as models become less expensive and better at reasoning through complex tasks, their limiting factor remains: high-quality, relevant data. That’s why focusing on building the best data asset possible is now a strategic function, and why mastering your data has become mission-critical for every GTM team.
Not because clean data is nice to have, but because it’s the difference between leading the market and falling behind.
The Old World vs. the New Reality
In the old world of GTM operations, data quality issues were manageable. When data wasn’t perfect in your CRM, you could still operate. We built processes around the limitations and, while it wasn’t great, it did the job.
But the AI age has fundamentally changed the equation. When you try to automate customer interactions or scale personalization using AI, those same data limitations that humans can identify and work around become critical failures. Comparing what’s possible with AI using good data versus what a human can do with typical CRM data shows a painful difference in capabilities.
The data you need to power meaningful automation doesn’t live in your CRM alone. It’s in your emails, phone calls, transcripts, Zoom meetings, DocuSign agreements, cloud systems, product data in Snowflake, support tickets in Zendesk, sales engagement tools, and marketing automation platforms, which is an exhausting list to simply type. Imagine the effort it would take to sort through all of this data manually, and more importantly, present a cleaned-up aggregation of this information, including context and nuance so that it’s useful to AI.
And not just for a single customer — but for your entire customer and prospect base.
The companies that are winning in this new reality aren’t necessarily the ones with the most sophisticated AI implementations. They’re the teams that have figured out how to unify their customer data across silos while maintaining data quality, and have made it accessible to their AI systems. They’re the ones treating data management not as an IT project, but as a core GTM capability.
The Cost of Inaction
Companies estimate that about a third of their CRM data is inaccurate, and 55% of corporate leaders distrust their own data assets. Experts even suggest that bad data can cost companies up to 25% of their potential revenue.
But the real cost of getting this wrong is simple: falling behind. The companies that figure out how to actually drive automation will grow faster, build better customer experiences, and drive higher conversion rates.
This may seem like a daunting undertaking, but it can be done incrementally. The important thing is getting started now. In many cases, you can get to a 90% solution with reasonable assumptions really quickly. Then you can go in and optimize for the high-value 10%.
Why Traditional Master Data Management Won’t Cut It
Master Data Management (MDM) as a term has a terrible reputation, and for good reason. It’s associated with 18-to-36-month IT-driven projects that, even when they work, create questionable value.
Here’s a stark reality: the time it typically takes to implement traditional MDM is usually longer than the average CRO tenure. And if a CRO can’t use incomplete data as an excuse for missing revenue numbers, prioritizing this installation has been historically difficult.
MDM was originally designed as a heavy, involved process with specific tools built around structured data and classic B2B data application. Only teams where the scale and complexity made it absolutely necessary would embark on these types of projects.
But in an AI-enabled world, mastering your data becomes mission-critical because it directly enables you to automate customer experiences. The good news is that it doesn’t require an old-school MDM project because the focus is tighter: running specific automations that deliver superior conversions, customer experiences, and deal velocity.
The key is figuring out how to unify siloed data from different parts of your business and get to a relevant, “good enough” data asset.
Think of modern data management not as an IT project, but as a revenue-driven initiative. You need IT and data experts as voices in the room. But if you understand a decent amount of your customer journey and you have the ability to identify those touch points and connect the data, you’re 80% of the way there.
All that’s left is enlisting a technology to help you organize and make sense of that data very quickly.
A New Approach to GTM Data Management
While setting your company’s ICP and mapping out meaningful interactions is relatively basic, it’s often still a struggle. That’s because in order to do this right, you need to know your ideal customers, including anyone that’s already a customer, the main decision makers in their buying group, the last touch point you had with them, and the next step, at minimum. This basic table doesn’t exist for most companies.
If you ask sellers on the front line about their target accounts and where they manage them, they likely won’t tell you it’s in Salesforce or HubSpot. They’ll show you a spreadsheet or other type of list because they’ve found a function that works for them better than these legacy products. But unfortunately, that erodes the ability for scale.
The good news is that AI isn’t just creating the imperative for better data — it’s also helping us solve the problem. Modern AI can help us organize and make sense of CRM data very quickly, without requiring a massive IT project.
And while you’ll never achieve perfect data, that’s not the goal. Your sights should be set on enabling better customer experiences in well defined areas and iterating when necessary. That starts with creating automation, then auditing or benchmarking it against what a human would do, and looking at the conversion rate. If you get better data, even the most basic automation is going to create a better customer experience.
It’s interesting to think about how with the leap forward in technology advancements because of AI, we’re really going back to basics. That is, we’re working toward understanding customers more deeply through data we previously couldn’t access or interpret. By mastering that data and leveraging improvements in AI, we’ll get a much better understanding and a much more personalized, sophisticated buyer journey.
While the undertaking of cleansing your data might sound like a huge project, the benefits will outweigh the obstacles.. The technology exists to help you, the imperative is clear, and the cost of waiting is only growing and enriching.
ZoomInfo’s GTM Intelligence platform can help make this process much smoother and help you turn your data challenges into competitive advantages. Find out how in this article.