How a Global Energy Company Scaled Data Quality Monitoring for Agentic AI

One of the world’s largest energy companies faced significant challenges with their legacy data quality tools and processes. After implementing Anomalo, the company achieved measurable improvements in data accuracy, operational efficiency, and team productivity to successfully scale data quality monitoring across multiple business units while empowering business users to take ownership of data quality rules without heavy reliance on technical teams.

What Data Quality Challenges Did the Global Energy Company Face?

The company was operating with low data quality maturity across their enterprise and struggling with their existing infrastructure.

• Legacy tool limitations: Their teams were using a legacy data quality tool they found overly complicated to configure and maintain. It required extensive technical expertise and, despite the overhead, still failed to catch critical data quality issues.
• Data trust issues: Reports were regularly reaching business stakeholders with incorrect numbers due to replication failures, surfacing only after business teams flagged obvious discrepancies, by which point trust had already eroded.
• Technical bottlenecks: Business teams had no ability to create or modify data quality rules independently. Every change required a data engineer, leaving teams constrained by technology and unable to respond quickly to emerging data quality needs.
• Enterprise-wide transformation: Rolling out data quality across a global enterprise with low existing maturity represented a significant transformation challenge, one that their legacy tooling made harder, not easier.

How Did Anomalo Provide a Data Quality Solution for the Energy Company?

The company implemented Anomalo as their modern data quality platform, focusing on democratizing data quality and enabling business ownership.

• Out-of-the-box monitoring with AI: Automated checks delivered immediate value without extensive configuration, giving teams instant visibility into table freshness, schema changes, and data anomalies from day one.
• No thresholds, no rules required: Anomalo’s user-friendly interface enabled business users to create data quality rules with minimal technical expertise, reducing dependence on data engineers for routine monitoring tasks.
• Scalable architecture: The platform was deployed across three business units, with a clear path to expand across the full enterprise.
• AI-powered detection: Anomalo’s ML capabilities automatically detected anomalies in key metrics without requiring teams to manually define every possible rule upfront.

Measurable Results and KPI Improvements

Superior Data Accuracy

Anomalo caught critical data quality issues that their legacy tool had missed entirely. In one case, records that had been passing checks in their old system were found to be genuinely failing when evaluated by Anomalo. The legacy tool had simply not been configured to detect them. Teams noted that what would have been a complex setup in their previous platform was straightforward in Anomalo: selecting a column and letting the system run.

Operational Efficiency Gains

Reduced manual effort: Teams no longer needed to spend extended time manually investigating data quality issues. Anomalo’s root cause analysis helped them quickly identify problem sources and move to resolution.

• Faster time to value: Out-of-the-box checks provided immediate coverage without requiring data engineers to configure anything up front, a significant shift from their previous tool’s implementation requirements.
• Simplified configuration: The contrast with their legacy tool was stark. What previously required specialist involvement could now be set up quickly by the people closest to the data.

Scale Achievements

• Massive table coverage: A single business unit alone was configured to monitor over 1,200 tables, with a roadmap to expand across eight total business units.
• Multiple business units in production: The platform scaled from initial deployment to supporting four business units, with additional markets in progress.
• Hundreds of active checks: Teams rapidly deployed hundreds of checks for customer data in a single business unit alone, with the ability to expand rapidly as new use cases emerged.

Improved Data Trust and Confidence

A persistent challenge before Anomalo was uncertainty about whether data pipelines had completed successfully and whether tables were in a reliable state. After deployment, teams described the confidence that monitored tables had refreshed cleanly as a meaningful operational win in itself.

Team Empowerment and Productivity

• Democratized data quality: The platform enabled data quality rule creation to be federated across business teams through the UI, rather than routing every change through a technical team.
• Business ownership: Data quality responsibility shifted to the business data stewards who understand the data best, rather than remaining centralized with engineers who had to be briefed on business context each time.
• Reduced dependency: Business teams became capable of responding to data quality needs independently, accelerating their ability to monitor what mattered most to their operations.

Key Benefits and Business Impact

Caught Issues Before They Reached the Business

By catching data quality issues before they reached production dashboards and reports, Anomalo helped the team avoid situations where business stakeholders discovered bad data on their own, and the credibility damage that comes with it.

Paradigm Shift in Data Quality

The core value articulated by the team was a fundamental shift in operating model: moving away from reliance on a centralized team of technical specialists for data quality visibility, and toward a model where business teams own and manage the rules that matter to them. That shift from a centralized, engineering-dependent model to a federated, business-owned one, was described as the central transformation the company was driving.

Foundation for AI Initiatives

The team recognized that data quality isn’t a standalone investment. It’s the prerequisite for everything built on top of it, including AI. Their framing: if you can’t trust your data, you can’t trust any AI system that consumes it. Anomalo gave them the foundation to move forward with confidence.

Smooth Operations

As the deployment matured, the absence of escalations from business users became its own signal of success, a sign that monitoring was working and teams were operating without friction.

What Are the Future Plans for Data Quality and AI Initiatives?

The company continues to expand Anomalo across additional business units and use cases, with focus on:

• Scaling to all eight business units across the enterprise
• Expanding from customer data to product, location, and additional data domains
• Building comprehensive data performance management frameworks
• Continuing to empower business data stewards with self-service capabilities
• Integrating Anomalo more deeply into data governance and catalog initiatives
• Piloting agentic AI to support technicians in the field with key production insights

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