Neo4j Brings High Speed Identity Resolution to the Snowflake AI Data Cloud

Converting Fragmented Records into Unified Identity Intelligence

For any company that works with consumer data at scale, identity resolution is one of the hardest problems to solve. It sounds simple: take fragmented data points (such as email addresses, device IDs, and physical addresses) and connect them into a single, accurate profile for each individual. In practice, however, the sheer volume of data presents a significant business challenge.

Using Neo4j Graph Analytics for Snowflake, identity services provider Audience Acuity successfully cracked the code on large-scale identity resolution. By processing 3.8 billion records across 24 distinct sources and 91 individual data feeds, they can create a comprehensive, unified view of the consumer with speed and precision. At this volume, maintaining accuracy is a critical competitive advantage.

Why Traditional Databases Hit a Wall

The core challenge of identity resolution is that it’s fundamentally a relationship problem.

A single consumer doesn’t exist as a neat row in a spreadsheet; they’re a web of connections. They may have three emails, two home addresses, and four devices across various platforms. In a traditional relational database, connecting these dots requires “JOIN” operations across massive tables.

“Before Neo4j, running graph-scale identity clustering was slow, expensive, and operationally heavy. Now, we’re processing billions of relationships in under 24 hours with a fraction of the infrastructure.”

Benjamin Squire, Data Scientist, Audience Acuity

Audience Acuity previously relied on third-party vendors for this data stitching work. The process was slow and costly, and turnaround on changes was measured in weeks. At a scale of billions, these operations often lead to system timeouts and “data gravity” issues where moving the data is harder than analyzing it.

Running Graph Analytics Where the Data Lives

The breakthrough occurred when Audience Acuity integrated Neo4j Graph Analytics directly with their Snowflake AI Data Cloud environment.

Instead of moving sensitive data out of their secure Snowflake environment into a separate silo, they brought the analytics to the data. This architecture eliminates the need for duplicate infrastructure and fragile data pipelines.

To solve the identity puzzle, Audience Acuity leveraged the Weakly Connected Components (WCC) algorithm. This graph-native approach allows the system to automatically cluster related records into a single identity, even when identifiers (such as names or birthdates) are inconsistent or fuzzy across sources. Graph technology is uniquely engineered for traversing complex, many-to-many relationships at lightning speed.

From Weeks to Hours

By shifting to a graph-native identity strategy, Audience Acuity transformed its operational efficiency:

• Massive Scale: 3.8 billion raw records processed into a unified identity graph.
• Deep Connectivity: 2 billion edges (relationships) mapped between data signals.
• Precision: ~500 million unique, validated identities produced.
• Speed: End-to-end processing completed in under 24 hours.
• Agility: Change requests that once took weeks are now finalized in 2–3 days.
• Cost Efficiency: Reduced overhead by leveraging existing Snowflake compute rather than maintaining dedicated, disparate systems.

Scaling Predictive Identity Intelligence

With a high-performance foundation in place, Audience Acuity is moving beyond simple resolution. They’re now exploring lookalike modeling and advanced audience segmentation. By using their identity graph to surface latent interests and behavioral patterns, they can reveal the underlying motivations of the individuals in their data, not just who they are.

You don’t need a massive migration to achieve the same results. This level of graph-powered precision is available to your team today, running natively where your data already lives.

Get started for free on the Snowflake Marketplace.