Good data doesn't fix GTM fragmentation on its own. When marketing, sales, SDRs and RevOps work from different definitions of the ICP, sales-ready and target accounts, even accurate data gets lost in the handoff. James Bradbury, VP of Performance Marketing at Cognism, argues that GTM alignment, not data volume, is what turns intent signals into pipeline. AI only widens the gap: it makes fragmented teams scale inconsistency faster, not perform better.
More data won't save a broken go-to-market (GTM) motion. In my experience across revenue teams, the pattern that holds performance back is often not a data quality problem, but rather fragmentation: marketing, SDRs, AEs and RevOps all working from a different picture of the market, with no shared language to close the gap.
That's a hard thing to hear if you've just signed off on another data or tooling budget. But it's also the more useful truth. Here’s what I mean by it, and what a genuinely unified GTM operating model looks like in practice.
My starting point is that most GTM performance issues are more often a sales and marketing alignment problem wearing a data-quality disguise. Before you can fix what the data says, you have to fix who's using it, and why.
Fragmentation starts small. Marketing runs campaigns the SDR team isn't tracking. SDRs work a patch of the market that AEs don't recognise. RevOps ends up stitching the results together after the fact, reporting on a motion it never actually shaped.
I'd put it plainly: there's no value in good data unless everybody knows what it's for. Data only pays off when marketing, sales and RevOps are pointed in the same direction, working from the same account list and the same ideal customer profile (ICP) definition.
When a fragmented motion underperforms, the instinct is to blame the obvious things: the budget, the channel mix, the number of leads coming through. In my experience, that's usually the wrong diagnosis. The actual cause is more systemic – teams aren't operating as one coordinated GTM motion, so effort doesn't compound.
Fix the alignment problem, and the “performance problem” often resolves itself. Fix the data alone, and you're polishing an input that nobody's using consistently.
I separate two things that get conflated constantly: having good data and getting good outcomes from it. The first is the minimum bar. The second depends entirely on what the team does next.
Good data has a specific job. It helps revenue teams understand the market, understand the audience, and work out which accounts are actually ready to buy. It points teams towards the right individuals to reach out to and gives them the context to do it well.
That's where CRM Enrichment becomes more than a hygiene exercise. If your CRM is missing key fields, carrying stale records or forcing RevOps into manual fixes, the issue isn't data quality alone. The real problem is whether the CRM can be trusted as the operating layer for routing, segmentation, reporting and forecasting.
What it doesn't do is deliver the outcome itself. Data is the input teams use to drive results, not the result.
The delivery happens through the team, not the dataset. A demand generation function that's tightly coordinated but out of step with SDRs and AEs will still get a low payoff, because the teams downstream are acting on something different.
That's the gap between having good data and getting good outcomes from it: a team that knows how to use it – together – in the same direction.
If fragmentation is the disease, the handoff between teams is where the symptoms show up first.
If marketing, SDRs, AEs and RevOps each define the ICP, sales-ready and intent signals differently, the signal itself still arrives. What breaks down is what happens next.
Without a shared definition of what “sales-ready” actually means, ownership of the next step becomes unclear. The value of good data is usually lost in exactly that space, between the signal coming in and someone acting on it.
Data shouldn't just sit in a system waiting to be interpreted. It should tell a team what to do next, and who's doing it.
In practice, that means CRM Enrichment needs governance: control over what gets updated, when it updates, and whether fields are overwritten or only filled when blank. Without that, even good enrichment can create more inconsistency at the handoff, not less.
AI has removed a lot of the technical bottleneck that used to slow GTM teams down. That's mostly good news, but it comes with a catch.
AI has changed what non-technical revenue teams can do. Work that used to sit with engineers and operators (i.e. building complex workflows, automating outreach at scale) is now available to marketing and sales teams directly, with more speed and more autonomy than ever before.
That speed only helps if there's a consistent, shared layer of data underneath it. This data layer provides meaningful context for AI that lets you operate with confidence. Without one, teams don't scale inconsistently, they scale inconsistency. Speed alone can't tell you whether your underlying data is actually AI-ready. Delivering on departmentally aligned goals can.
For teams building AI models, scoring workflows or centralised data operations, this is where Data-as-a-Service (DaaS) becomes relevant. You’ve got to ask whether the CRM is clean and whether the broader data stack can receive accurate, compliant data in a way that supports modelling, targeting and always-on data freshness.
Good data by itself won't fix a fragmented GTM motion, and AI won't either. That's why I always stress that data quality and GTM alignment must move together, not as separate workstreams.
A unified operating model isn't the same thing as a centralised one. There is a clear line to be drawn between the two: what needs to stay consistent everywhere, what can flex by market, and who actually holds the model together day to day.
A genuinely unified operating model isn't one rigid standard imposed from the centre. It's shared foundations, with room for local execution layered on top. The ICP definition, the target account list and the meaning of “sales-ready” need to be consistent everywhere. How that gets executed locally can then flex.
DaaS can help provide that shared foundation at scale, giving teams consistent firmographic and technographic data to build ICPs and support regional segmentation, wherever those teams sit.
This matters more in European go-to-market than most other regions. Job title norms shift by country. Compliance requirements vary by market. Outreach norms that work in one country can misfire in another.
A fully decentralised model, where every local team runs its own way, makes it nearly impossible to operate consistently. A single rigid model, dictated entirely by RevOps with no room for local nuance, doesn't hold up either. The answer is a single operating model built to flex: shared foundations with local intelligence layered in.
In a high-efficiency operating model, RevOps isn't there to report on the motion after the fact. It's there to help the different parts of the motion work together as it happens, cutting the friction that builds up at every handoff.
My framing is that the goal for organisations was never specialist teams working in isolation. It's specialists whose expertise compounds because they're part of one coordinated GTM motion, not five teams pulling in different directions.
This is the shift I believe revenue leaders need to make: stop treating data quality as the finish line, and start treating coordination as the ongoing work.
My advice to leaders is to stop asking only “do we have good data?” and start asking “are we set up to make the most of it?” That means agreeing on what good data is, where it comes from, and how it gets used – in enrichment, in outreach, in the handoff between sales and marketing.
Every week, that means asking:
Answer those consistently, and the compounding effect shows up in how fast a revenue team can get in front of the right customer.
Data quality and GTM alignment are never separate conversations for me. Treat them that way, and even accurate, compliant data will keep getting stuck in the same handoff gaps. Treat them together, and a revenue team can act on a signal at the speed the market now demands.
If your team has the data but not the alignment to act on it consistently, that's worth solving before the next tooling or data investment.
See how Cognism's CRM Enrichment and DaaS can help turn trusted data into a unified GTM operating model – from governed CRM enrichment to data delivery across the systems your revenue team already relies on.