CASE STUDIES

VillageCare: 250% Increase in Data Catalog Usage with Anomalo

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Data Quality and Data Governance at VillageCare

Key Results

250%+

Catalog usage increase in 12 months

4.4K

Average monthly Compose queries

About VillageCare

VillageCare is a community-based, not-for-profit organization serving persons living with HIV/AIDS, senior citizens and individuals in need of continuing care and rehabilitation. They are headquartered in New York, New York.

Tech Stack

Data Warehouse:

Data Catalog:

Size

1,000+ Employees

Industry

Healthcare Non-profit

The Challenge

Providing high-quality data for healthcare delivery

Robert Warburton leads VillageCare’s data strategy, focusing on providing accurate data essential for operational decisions and reporting across the organization. To address data redundancy and improve reporting, VillageCare migrated key datasets to Amazon S3 and Redshift in 2022, increasing the need for high-quality data as the organization grew and required more complex Tableau reports. The data team at VillageCare sought to:

Improve analyst productivity by putting trusted data at users’ fingertips
Strengthen the data culture within the organization
Ensure only trustworthy data is used for reporting

The Solution

Finding trustworthy data with Alation and Anomalo

VillageCare previously used OwlDQ to perform data quality checks. When the tool was acquired by Collibra, VillageCare switched to Anomalo to ensure data validity, accuracy, and timeliness.

Thanks to Anomalo’s seamless integration with Alation’s data catalog, says Diana Melnikov, Data Governance Manager, “We don’t have to create special licenses because our stewards can just check alerts in Alation against any table, view, or object.”

The Results

Trusted data enhances healthcare delivery decision-making

VillageCare’s adoption of Anomalo marks a turning point in its data governance strategy. After moving from Collibra’s OwlDQ, the team saw immediate results through seamless integration with Alation. Thanks to the integration of Alation and Anomalo, stewards not only receive data quality alerts but can see them right in the Alation interface. This eliminates the need for multiple tools or complex workflows, further improving productivity and building trust in data.

“With OwlDQ, the rules were created in and accessed from Tableau dashboards linked to OwlDQ,” says Melnikov. “With Anomalo alerts in Alation, users don’t have to access any other tool. So it’s much more elegant and more efficient.”

Today, Anomalo is helping VillageCare make better data-driven decisions, ensuring data is used to enhance the delivery of timely, reliable, and impactful services and support to VillageCareMAX’s more than 36,000 members.

Unlock the full case study to see how Anomalo helps VillageCare make better data-driven decisions

“It’s very helpful that Alation is linked to Anomalo. We don’t have the create special licenses because our stewards can just check alerts in Alation against any table, view, or object”

Diana Melnikov

Data Governance Manager

Frequently Asked Questions

1 How did VillageCare improve data quality and governance using Anomalo?

VillageCare leveraged Anomalo to significantly enhance its data quality and governance, resulting in an over 250% increase in data catalog usage within 12 months. This case study highlights Anomalo's effectiveness in helping healthcare organizations achieve measurable improvements in their data management practices.

2 What specific data quality challenges in healthcare does Anomalo address?

Anomalo addresses critical data quality challenges in healthcare by providing autonomous data quality monitoring, table observability, and data documentation. These capabilities ensure the accuracy and reliability of complex healthcare datasets, which are essential for robust analytics and informed decision-making.

3 What Anomalo features are most relevant for healthcare analytics and data governance?

For healthcare analytics and data governance, Anomalo offers key features like Data Quality, Data Insights, Table Observability, and Data Documentation. These tools empower organizations to proactively identify data issues, understand data lineage, and maintain high standards for sensitive patient and operational data.