Global Partnership Enables Data Profiling, Transformation, and Validation Without Moving Data from Snowflake’s Secure Perimeter
AI systems fail when training data contains errors, inconsistencies, or governance gaps—a reality enterprises are confronting as they scale generative AI and machine learning deployments beyond proof-of-concept stages. Experian, the global data and technology company, today announced integration of its Aperture Data Studio platform with Snowflake’s AI Data Cloud, enabling organizations to profile, transform, and validate data directly within Snowflake without moving it to external systems. The integration addresses a persistent challenge: how to apply enterprise-grade data quality controls at the scale and speed AI workloads demand while maintaining security, compliance, and governance requirements.
The combined solution, now available globally, allows joint Snowflake and Aperture Data Studio customers to execute data quality workflows within Snowflake’s secure perimeter, reducing transfer risk while accelerating data management operations. By keeping data in place and pushing data quality processing to where data resides, the integration eliminates latency, compliance exposure, and operational complexity associated with moving datasets between platforms for quality remediation.
Why AI Adoption Amplifies Data Quality Requirements
Traditional business intelligence tolerates a certain level of data inconsistency—analysts can identify outliers, apply judgment to anomalies, and contextualize results based on domain knowledge. AI systems lack this flexibility. Models trained on datasets containing missing values, duplicate records, schema drift, or inconsistent categorical encodings produce unreliable outputs without signaling degradation. As enterprises move from experimental AI projects to production deployments affecting customer experiences, financial decisions, and operational automation, data quality shifts from a best practice to a prerequisite.
The challenge intensifies at scale. Organizations running AI workloads on Snowflake may manage petabytes of data across hundreds of schemas, with continuous ingestion from diverse sources operating under different quality standards. Manually inspecting data quality across this complexity doesn’t scale; point solutions that extract data for quality assessment introduce security risks and performance bottlenecks.
“Data is the foundation of every transformation, yet many businesses struggle to turn it into real business value,” said Andrew Abraham, Global Managing Director of Data Quality at Experian. “With the rapid emergence of AI technologies, quality, accurate data is fundamental to its success. Our collaboration with Snowflake brings together Experian’s expertise in data quality and governance with the scale, performance and flexibility of Snowflake’s platform.”
Key Insights at a Glance
- Integration architecture: Aperture Data Studio executes data profiling, transformation, and validation workflows directly within Snowflake’s platform without data movement
- Security model: Data remains within Snowflake’s secure perimeter, reducing transfer risk and simplifying compliance with data residency requirements
- Performance advantage: Workflows created in Aperture Data Studio’s interface execute near-instantly in Snowflake by leveraging native compute resources
- Unified governance: Cataloging, management, and control capabilities operate across data quality and governance domains within single platform
- Global availability: Solution available to joint Snowflake and Aperture Data Studio customers worldwide through Snowflake’s AI Data Cloud partnership program
Data Quality Where Data Lives: Architecture Implications
The integration reflects a broader shift in enterprise data architecture: moving processing to data rather than moving data to processing. Traditional data quality workflows extract datasets from operational systems, apply transformations and validations in specialized tools, then load cleaned data back to target platforms. This extract-transform-load pattern introduces failure points, increases storage costs, creates data synchronization challenges, and complicates security controls by multiplying the locations where sensitive data exists.
Executing Aperture Data Studio workflows within Snowflake inverts this model. Users design data quality processes in Aperture’s interface—defining profiling rules, transformation logic, validation criteria—then execute these workflows using Snowflake’s compute resources against data that never leaves Snowflake’s environment. This architecture delivers performance benefits (eliminating network transfer overhead), security advantages (reducing data exposure surface area), and compliance simplification (maintaining single-location data residency).
For organizations subject to regulations like GDPR, HIPAA, or financial services data protection requirements, keeping data within a certified platform’s perimeter while still applying comprehensive quality controls addresses a persistent tension between data governance mandates and operational efficiency needs.
Unifying Quality and Governance Across Data, Models, and AI Agents
Experian positions Aperture Data Studio as addressing data quality, model quality, and AI agent governance within a unified platform—a scope that extends beyond traditional data profiling tools. As enterprises deploy AI agents that autonomously access data, generate insights, and initiate actions, governance requirements expand from “is the data accurate?” to “is the agent using accurate data appropriately, with proper authorization, in alignment with organizational policies?”
This expanded governance scope matters as AI agents move from experimental chatbots to operational systems managing customer interactions, financial transactions, or supply chain decisions. An AI agent with perfect model performance but access to inaccurate customer data produces wrong outcomes. An agent with accurate data but insufficient governance controls may expose sensitive information inappropriately or make decisions outside authorized boundaries.
“This integration enables customers to build a trusted, compliant data foundation that reduces risk, accelerates AI adoption and supports smarter decision‑making,” said Rinesh Patel, Global Head of Financial Services at Snowflake.
Partnership Model and Market Positioning
Snowflake’s AI Data Cloud Product Partners program, which this integration leverages, aims to maximize Snowflake’s flexibility and performance by connecting specialized capabilities from domain leaders. For Experian, the partnership extends the addressable market for Aperture Data Studio to Snowflake’s enterprise customer base managing AI and analytics workloads at scale.
The integration specifically targets organizations facing the transition from experimental AI to production deployment—the stage where data quality issues that were tolerable in pilots become operational blockers. Financial services institutions running fraud detection models, healthcare organizations deploying clinical decision support systems, and retailers operating recommendation engines all share this challenge: AI effectiveness depends on data quality at a level traditional BI workflows didn’t require.
Whether the integration delivers sustained competitive advantage depends on execution details the announcement doesn’t fully specify: how seamlessly workflows designed in Aperture translate to Snowflake’s execution environment, how performance scales with dataset size and complexity, how version control and change management operate across the two platforms, and how pricing models align when compute occurs in Snowflake but workflow design happens in Aperture.
What’s clear is the strategic alignment: as AI workloads migrate from experimentation to production, the organizations that solve data quality at scale—without introducing new security risks or operational bottlenecks—gain deployment velocity advantages. Whether Experian and Snowflake’s combined solution achieves this will become evident as joint customers report production results beyond the initial announcement.
About Experian
Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help to redefine lending practices, uncover and prevent fraud, simplify healthcare, deliver digital marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics and software. We also assist millions of people to realise their financial goals and help them to save time and money.
We operate across a range of markets, from financial services to healthcare, automotive, agrifinance, insurance, and many more industry segments.
We invest in talented people and new advanced technologies to unlock the power of data and to innovate. A FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 25,200 people across 33 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at experianplc.com.
