List of Anomalo Customers
Palo Alto, 94301, CA,
United States
Since 2010, our global team of researchers has been studying Anomalo customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Anomalo for Database Performance Monitoring from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Anomalo for Database Performance Monitoring include: Nationwide Retirement Plans, a United States based Insurance organisation with 24000 employees and revenues of $68.50 billion, ADP, a United States based Professional Services organisation with 65200 employees and revenues of $20.56 billion, HomeToGo, a Germany based Professional Services organisation with 600 employees and revenues of $170.0 million and many others.
Contact us if you need a completed and verified list of companies using Anomalo, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Anomalo customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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ADP | Professional Services | 65200 | $20.6B | United States | Anomalo | Anomalo | Database Performance Monitoring | 2024 | n/a | In 2024, ADP implemented Anomalo to automate and scale data quality across HR and payroll analytics and governance. Anomalo, categorized as Database Performance Monitoring, was deployed to introduce machine learning-powered validations into ADP’s data governance processes. The implementation configured Anomalo’s ML-driven validation and anomaly detection capabilities to run automated daily checks, expanding coverage from approximately 700 manual checks to more than 16,000 automated checks per day. Configuration work focused on orchestration of validation workflows and scheduling validations to execute across HCM analytics pipelines. Operational scope covered global HCM operations and the centralized Data Governance team, with Anomalo embedded into HR and payroll analytics workflows to proactively surface data quality issues. As part of the rollout ADP reported a reduction in the Data Governance team’s time spent on data quality issues from 70% to 30%, shifting effort toward oversight and exception handling. Governance adjustments aligned with the platform by formalizing ML validation checkpoints in routine governance workflows and establishing monitoring and alerting as part of operational handoffs. The Anomalo implementation standardized automated checks across ADP’s HR and payroll analytics environment and institutionalized ML-driven validations within the company’s data quality governance framework. | |
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HomeToGo | Professional Services | 600 | $170M | Germany | Anomalo | Anomalo | Database Performance Monitoring | 2021 | n/a | In 2021, HomeToGo implemented Anomalo for Data Quality to monitor data quality in its data warehouse and to support analytics and marketplace operations. The deployment was oriented toward operational datasets that feed marketplace listings and analytics pipelines, and the company identified and fixed multiple important data quality issues after initial deployment. Anomalo was configured to provide core data observability capabilities including anomaly detection, automated monitoring, alerting, and centralized dashboards to surface schema and distribution shifts. The implementation emphasized dashboarding and alert workflows so that both engineering and business teams could triage anomalies and validate corrective actions using Anomalo. HomeToGo requested a demo in June 2021, completed a trial, and signed a subscription five months later, then scaled the deployment on Kubernetes to support production monitoring across environments. The scaled Kubernetes deployment was used to broaden access and democratize data quality visibility across engineering and business teams. Operational coverage focused on analytics and marketplace operations with direct impact on data engineering, analytics, and marketplace operations functions. Governance and process adjustments centered on shared visibility and incident triage workflows to accelerate detection and remediation through Anomalo, and the implementation narrative highlights data warehouse monitoring as the primary integration point. | |
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Nationwide Retirement Plans | Insurance | 24000 | $68.5B | United States | Anomalo | Anomalo | Database Performance Monitoring | 2024 | n/a | In 2024, Nationwide Retirement Plans implemented Anomalo as a Data Quality solution to automate data quality for financial reporting, regulatory compliance, and enterprise analytics. The engagement documented in late 2024 delivered a rapid deployment within months and placed Anomalo at the center of proactive quality monitoring for critical reporting pipelines. Anomalo was configured to provide automated anomaly detection, rule-based and behavioral checks, and alerting integrated into existing ETL and analytics workflows. The deployment extended Nationwide Retirement Plans’ rule set and detection capabilities, and the implementation uncovered more issues than the organization’s 3,000 existing rules, prompting expanded monitoring coverage. The implementation integrated Anomalo with Databricks and Alation to surface data issues before they impacted downstream reports, with detection points instrumented at the Databricks processing layer and cataloged assets referenced in Alation. Operational coverage explicitly targeted enterprise analytics, financial reporting, and compliance workflows, aligning detection to the data product and reporting owners responsible for downstream consumption. Governance was formalized through a policy requiring top data assets to be cataloged in Alation and to have their data quality monitored by Anomalo, creating a closed loop between cataloging and observability. Rollout focused on rapid, centralized instrumentation of high-value pipelines, and the program emphasized automated anomaly detection and owner notification to reduce time to detection for reporting and regulatory issues. |
Buyer Intent: Companies Evaluating Anomalo
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