Make data integrity and compliance the default

Anomalo integrates with tools at every level of the data management workflow. First, we integrate with all of the most popular cloud data warehouses, data lakes, and on-premises data storage systems to ensure that we can monitor data at your organization no matter where it is stored. Anomalo is a Databricks Partner and part of the Snowflake Partner Program as well. Second, we integrate with data transformation tools like Airflow and dbt, so you can run Anomalo’s data quality checks directly as part of your data pipelines and workflows. Third, we integrate with data catalogs like Alation so that when a table is failing data quality checks, that will show up directly in the data catalog to warn users that the data may not be trustworthy. And finally, Anomalo integrates with communication platforms like Slack and ticketing platforms like Jira so you can ensure that alerting, triage, and strong resolution practices are in place for any quality incidents.

Anomalo provides a high level of customization. Beyond the comprehensive default checks Anomalo offers, users can enter custom data validation rules in our no-code UI. Or, they can specify a limitless range of SQL-based rules programmatically, using our API. For scalability, Anomalo is designed to run efficiently for the largest enterprises with vast amounts of data, spanning tens of thousands of tables/datasets that can have potentially hundreds of thousands of columns and billions of records.

Anomalo is a cost-effective monitoring solution because it uses table metadata for the broadest range of checks, which can confirm late, incomplete, or missing data without needing to query the data values inside your tables. Then, for any tables where you want to run deeper checks, you can enable AI-based anomaly detection and/or rule-based or metrics-based monitoring. This allows you to scale monitoring to all your tables without having to write extensive rules for each and every table, which is often the default solution without an AI-based monitoring tool like Anomalo. Ultimately, data quality incidents are extremely expensive to fix, so putting comprehensive data quality management in place usually pays for itself in the reduction in personnel resources and reputation risk.

Anomalo implements various security measures to ensure that your data is always protected. We are SOC 2 Type II certified, meaning that we meet the highest levels of security and privacy requirements for our customers. SOC 2 is an extensive auditing procedure that ensures that a company is handling customer data securely and in a manner that protects the organization as well as the privacy of its customers. Anomalo is also HIPPA compliant. We maintain a strict data processing agreement with our customers.

To help organizations mitigate risk, we offer fine-grained role-based access controls. A data governance team can fully administer rules around which users in the organization can see which data inside Anomalo. We also provide (and recommend) a fully in-VPC deployment option. Unlike the SaaS or hybrid deployments that many vendors use, an in-VPC deployment ensures that your data never leaves your system and you maintain complete ownership of your data’s security.

Anomalo works with leaders in data governance at many Fortune 500 companies. We’re adept at adapting to how different organizations and teams approach data governance. For one thing, Anomalo’s tool is flexible enough to incorporate any number of custom rules and metrics tracking. In addition to our native integrations, we provide webhooks for nearly limitless custom integrations, including integrations with internal governance systems you may have developed. To talk to us more about your data governance needs, request a demo.

Anomalo is a comprehensive data quality monitoring platform that integrates with other data governance tools, such as data catalogs. Data governance is a broad area, with data quality being a key component — perhaps the most important component — of a complete governance solution. We believe that organizations are better served by a monitoring system that specializes in data quality in addition to using other tools for data governance, rather than adopting data governance software that tries to be all-inclusive. Not only because all-in-one solutions expose you to the risk of vendor lock-in, but also because data quality monitoring is a deep area. Anomalo’s team is uniquely specialized in AI-based monitoring techniques to find anomalies in your data at scale, without the need for extensive manual setup. Other data governance tools may offer more surface-level monitoring, or may not include data quality monitoring at all.