The new B2B data stack: From fragmented records to unified infrastructure

What you need to know

• Data quality—not data volume—is the biggest barrier to successful AI adoption
• Traditional enrichment tools create more data but fail to resolve fragmentation and identity gaps
• Modern B2B strategies require unified data infrastructure across CRM, marketing, and analytics systems
• Identity resolution and data unification are essential to understanding buying groups and account relationships
• Organizations that treat data as infrastructure gain more accurate insights, better targeting, and stronger AI outcomes

Data quality is the biggest barrier to AI

78 percent of organizations say data quality is the biggest barrier to successful AI adoption, according to MIT Sloan research. This finding explains why many marketing and revenue teams struggle to operationalize AI. Over the past decade, organizations invested heavily in data enrichment tools to expand prospect coverage and fill gaps in CRM systems. These tools added contacts, firmographic attributes, and technographic signals. But AI initiatives are exposing a deeper problem. More data does not automatically produce better insights.

What organizations actually need is a structured, unified, and governed data infrastructure that connects data across systems and supports analytics, automation, and AI.

Why the enrichment model is breaking down

Traditional B2B data enrichment focused on adding more records to marketing databases. The assumption was simple. More contacts meant more opportunities. However, modern revenue teams face challenges that enrichment alone cannot solve. Common problems include:

• Duplicate identities across platforms
• Disconnected customer data across CRM and marketing tools
• Inconsistent account hierarchies
• Incomplete visibility into buying groups
• Data that cannot easily be used by analytics or AI systems

These issues prevent organizations from generating reliable insights.

As companies attempt to deploy AI and advanced analytics, these limitations become much more visible.

The rise of data infrastructure

The next phase of the B2B data market focuses on data infrastructure rather than enrichment. Infrastructure connects datasets across systems and organizes information so it can be used consistently across marketing, sales, analytics, and AI platforms. Modern data infrastructure includes several key capabilities:

• Identity resolution that connects individuals to companies and locations
• Data unification across CRM platforms, marketing systems, analytics warehouses, and external datasets
• Semantic structure that helps AI systems interpret relationships between entities
• Validation and governance processes that maintain accuracy

Organizations that invest in this type of infrastructure can generate deeper insights and execute campaigns more effectively.

What modern revenue teams should do next

Marketing and revenue leaders should begin treating data infrastructure as a strategic capability.

Here are four practical steps organizations can take.

1. Audit your current data environment

Start by mapping where customer and prospect data currently exists.

Common locations include:

• CRM systems
• Marketing automation platforms
• Customer data platforms
• Analytics warehouses
• Third-party data providers

Many organizations discover that data about the same accounts and contacts exists in multiple systems with conflicting information. Understanding this fragmentation is the first step toward solving it.

2. Identify identity resolution gaps

Next, evaluate how identities are connected across your systems. Questions to ask include:

• Can we connect contacts to their companies accurately?
• Can we identify relationships between multiple stakeholders in a buying group?
• Do we understand the hierarchy between corporate headquarters, regional offices, and local locations?

Without identity resolution, organizations cannot build reliable account intelligence.

3. Prioritize data unification

Once fragmentation and identity gaps are understood, the next step is data unification.

Data unification connects datasets across systems and creates consistent account and contact profiles.

• Unified data enables organizations to:
• Understand buying group dynamics
• Improve targeting and segmentation
• Coordinate marketing and sales outreach
• Generate more reliable analytics

4. Evaluate data providers differently

Organizations evaluating B2B data providers should expand their criteria beyond record volume. Instead of asking how many contacts a provider offers, leaders should ask:

• Does the provider support identity resolution across accounts and contacts?
• Can the provider unify data across systems?
• Is the dataset structured for analytics and AI?
• Does the provider support complex enterprise environments?

These questions reflect the reality that the B2B data market is evolving. The future of the category is infrastructure.

Why this shift matters

Organizations that treat data as infrastructure gain a significant competitive advantage. They can execute campaigns more precisely, generate deeper insights into customer behavior, and deploy AI systems that produce reliable intelligence.

Companies that continue relying on fragmented enrichment tools will struggle to operationalize modern data strategies.

Download the Forrester Wave Report

To see how leading vendors are evaluated across these capabilities, download a complimentary copy of The Forrester Wave™: Marketing and Sales Data Providers for B2B, Q1 2026.