Article
Intent Data Should Work Harder
Remember – a good lead at a bad company is ultimately a bad lead.
Everyone benefits from having a list of all the people and companies that have been actively searching for your product or service this week, right? That’s been the promise of Intent data. GTM teams rely on that Intent signal and often waste even more of their valuable time and resources chasing down what amounts to bad leads. Clearly, intent models don’t work very well – they should’ve just trusted their gut, right?
Wrong. They should’ve trusted the data. As in, all the data. Not just intent. Relying on intent data alone is the error that pushes some many sales and marketing teams away from it. You need to remember, intent alone doesn’t paint the full picture necessary to target deals in the B2B world. In fact, intent data can even point us in the wrong direction and encourage our sales people to confidently jump into a rabbit hole that goes nowhere fast. Let’s explore how to use intent data effectively and avoid diving head-first into a pit of bad leads.
The cost of getting it wrong.
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The Hidden Revenue Tax: 10 Ways Enterprise GTM Teams Lose with Disconnected Data
Enterprise B2B go-to-market (GTM) teams don’t struggle because they lack tools. They struggle because their customer data lives everywhere, but doesn’t work correctly anywhere.
CRM. Marketing automation. Enrichment vendors. Intent platforms. Sales engagement. Data warehouses. Acquired company databases. Regional instances.
Each system holds a piece of the puzzle but none of them independently point towards the same truth. When customer data is fragmented, siloed, and static, the consequences surface exponentially.
Here’s what that really looks like inside an enterprise GTM organization.
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How GTM Teams Can Future-Proof Their Data Architecture for 2026 – 2030
B2B go-to-market teams are entering a new era defined by AI-driven execution, buying group complexity, and real-time buyer signals. Yet most GTM data architectures still rely on fragmented systems, static enrichment, and lead-centric models that cannot support modern revenue operations.
How GTM Teams Can Future-Proof Their Data Architecture for 2026–2030 explores the structural shift reshaping B2B GTM and outlines the data architecture required to support identity resolution, buying group intelligence, AI-ready workflows, and real-time signal activation.
This eBook provides a practical roadmap for building a resilient, enterprise-grade GTM data foundation without disrupting your existing CRM, MAP, ABM, or analytics stack.
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Why Data Governance Is Now a Revenue Function
For a long time, data governance lived in the background of the business.
It sat inside IT. Sometimes legal. Occasionally security... It was something you needed for compliance audits, privacy policies, and system hygiene, but it rarely gets associated with pipeline creation or revenue performance. If anything, governance was seen as something that slowed go-to-market teams down. It was an approval layer or process hurdle that prevented a campaign from launching this week.
But that mental model was built for a very different GTM environment than the one enterprise revenue teams are operating in right now.
