Building a Centralized Data Quality Foundation Across a Multi-Cloud Enterprise

The Challenge

One of the world’s most trusted providers of financial data, analytics, and benchmarks had a data quality problem at scale. With thousands of data assets spread across a highly distributed organizational structure, the company lacked a unified, enterprise-wide view of data health. Individual business units had built their own monitoring approaches. They used a mix of rules-based tools and manual checks, but coverage was inconsistent, maintenance was expensive, and scaling the existing model was increasingly untenable.

A major organizational shift accelerated the urgency. The company moved from a highly distributed data ownership model to a centralized approach under a newly formed Enterprise Data Office (EDO), with an executive mandate to bring significant data assets under centralized governance within the year. They needed a single, scalable data quality foundation, one that could span legacy warehouse infrastructure and modern cloud platforms simultaneously.

The Evaluation

The data engineering team ran a structured Proof of Value (POV) against multiple vendors, including their existing rules-based tooling and internal build options. Anomalo was evaluated head-to-head. Several factors distinguished Anomalo during the evaluation:

• AI-based detection that required no manual rule-writing. This is critical for a team that recognized rules-based approaches wouldn’t scale to their data estate.
• Native support for both legacy data warehouses such as Teradata and modern platforms including Google BigQuery, Snowflake, Databricks, Microsoft Azure, Amazon Redshift. Anomalo was the only solution that could serve both their current state and long-term migration roadmap.
• Cross-system reconciliation capabilities provided the ability to monitor data consistency as it moves across platforms and cloud environments.
• Deep integration with their data catalog partner, which meaningfully strengthened Anomalo’s case during the evaluation.

Anomalo replaced their existing rules-based vendor (SODA) and beat the alternative of building their own in-house solution.

The Solution

The company deployed Anomalo using a phased implementation strategy designed to deliver immediate value while building toward enterprise-wide autonomous data operations.

Phase 1: Centralized Quick Wins

Implementation began with the company’s Google BigQuery environment, which houses customer-facing datasets. Starting here established a trusted quality baseline on the most visible, highest-stakes data before expanding coverage.

Phase 2: Shift-Left Quality

With the initial layer operational, the team progressively moved data quality checks earlier in their pipelines, catching issues before they propagated downstream and reducing the cost of remediation.

Phase 3: Federated Ownership at Scale

Anomalo’s natural-language interface for defining quality rules enabled a federated model: data stewards across business divisions could own their own monitoring without requiring deep engineering involvement. Centralized governance could now scale without centralized headcount.

Cross-Platform Reconciliation

A core use case was ensuring data integrity as records move across the company’s multi-cloud environment between Google BigQuery, Snowflake, Databricks, Microsoft Fabric, Amazon Redshift.

Anomalo’s ability to monitor data consistency across all of these platforms from a single interface was foundational to the enterprise quality vision.

Platform Integration

Anomalo was integrated with the company’s full tooling ecosystem: their data catalog for lineage and context, Power BI for downstream reporting health, and workflow tools including ServiceNow and Microsoft Teams for incident management and stakeholder communication.

Results and Business Impact

• ~60% estimated reduction in manual data quality work by the engineering team’s own calculation
• Unified quality monitoring across a multi-cloud environment that previously had fragmented, platform-specific coverage
• A scalable path from centralized governance to federated ownership without requiring linear headcount growth
• Replaced an existing rules-based vendor and outperformed the internal build alternative
• Positioned the Enterprise Data Office to fulfill its executive mandate with a production-ready foundation, not a multi-year build

Why This Matters

For enterprises navigating centralization mandates, the challenge is finding a solution that can serve where you are today and where you’re going. Legacy infrastructure doesn’t disappear overnight. Modern platforms don’t replace everything at once.

Anomalo’s AI-based detection learns what normal looks like across billions of rows without requiring manual rule configuration. When combined with native support for every major data platform, Anomalo became the only credible answer to both requirements simultaneously.

The shift from rules-based data quality to autonomous, AI-driven monitoring isn’t incremental. It’s a fundamentally different operating model. And for a company whose business depends on the integrity of financial data at global scale, getting that foundation right is what makes everything downstream trustworthy.

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