Data that can monitor, analyze, and explain itself all before anyone has to ask.

Anomalo, the company that built the deepest automated data quality platform in the enterprise, today introduced Self-Driving Data, a new category of agentic capabilities where data can monitor itself, explain what changed, and surface what matters without waiting for humans to ask.

For decades, organizations operated in an environment where data had to be watched, queried, and interpreted by humans. Today, data teams are driving manually by handcrafting queries to answer data questions, constantly refreshing dashboards to look for unexpected changes, and fighting data fires and issues. This constant querying, monitoring, and reactive work consumes the majority of a data team’s time without guaranteeing that the most important insights are found or the most important issues are resolved. Anomalo’s Self-Driving Data System replaces this model entirely, eliminating the tedious and repetitive work consuming most data teams and replacing it with a continuously running, autonomous and self-improving system that proactively monitors, investigates, and acts on data. It is Anomalo's most significant expansion since its founding, and a direct statement about where enterprise AI and enterprise data is headed.

From Automated Data Quality to Self-Driving Data

Anomalo was built to remove manual data work entirely, not just reduce it. It started with data quality because that’s where enterprises felt the most pain. Before Anomalo, data teams were stuck with fragile rules-based systems that required precise rule creation and constant maintenance and upkeep to ensure data quality. Anomalo took a fundamentally different approach, using AI and machine learning to profile an organization's data and learn what normal looks like without anyone having to define it first. That work produced the deepest automated data quality platform in the enterprise, built on their proprietary data profiling and prediction engine that understands data at the content level across billions of rows and finds what's wrong before anyone thinks to look. As agentic AI matured, it became clear that the same foundation could absorb more of the operational work data teams had always done manually. That's what today's expansion is about. Data quality was never Anomalo’s destination. It was the proof that Anomalo’s system could understand your data well enough to act on it reliably. That understanding is what makes Self-Driving Data possible now, as Anomalo’s system monitors, investigates, documents, and acts without waiting to be asked.

“We’ve spent years building the deepest automated data quality platform in the enterprise,” said Elliot Shmukler, cofounder and CEO of Anomalo. “What we’re announcing today is the natural next step. Self-driving cars didn’t happen because one piece of technology improved, they happened when everything came together. With the power of modern Agentic AI, that same convergence has now happened for enterprise data. For the first time, your data can monitor itself, understand itself, and initiate appropriate actions. With Anomalo, you set the direction and priorities and modern AI plus your data do the work.”

The System That Runs Itself

Anomalo’s new autonomous data management platform is a coordinated system of nine intelligent agents that operate continuously across the data lifecycle to monitor pipelines, investigate changes, surface insights, resolve issues, and maintain documentation.

The nine agents include:

• Table Observability Agent: Always-on monitoring of data availability, freshness, and schema consistency – making sure your data pipelines are moving your data as expected.
• Data Quality Agent: Define what good data looks like through natural language and automatically monitor for any deviations that might break dashboards, reports, and other AI models and agents.
• Data Issue First Responder Agent: Automatically investigate and take action on data issues with AI. The First Responder Agent receives and investigates data issue alerts to assess impact and criticality and then follows any established runbooks or policies to take appropriate action, including initiating workflows in tools like ServiceNow and JIRA and appropriately escalating to human team members.
• Data Insights Agent: Never miss out on an important change or issue in your data. The Data Insights Agent proactively and autonomously identifies any noteworthy changes in your data and delivers analyst-grade reports that describe what is going on. No prompts, no waiting, no analyst required to be fully informed of what’s happening in your data.
• Conversational Analytics Agent: Ask questions, perform data analyses, and compose data-driven reports on the data you care about – all through natural language. Anomalo’s Intelligent Data Analyst (AIDA) is an expert in both the nature of your data, how your data is used, and how to query it – allowing you to carry out any data analysis task quickly and easily.
• Dashboarding & Reporting Agent: Build continuously-updated dashboards and reports on the data and metrics that you care about – purely via natural language. Leverage Anomalo’s large library of data visualizations and reporting templates to provide analyst-grade snapshots of your important business metrics and trends – with no coding required. Enhance any report or dashboard with Anomalo’s built in time-series predictive modeling – allowing for quick identification of any anomalies or unusual changes in your data.
• Data Documentation Agent: Finally create up-to-date and comprehensive documentation on any data set. The Documentation Agent pulls in catalog metadata, any existing documentation (from corporate wikis or file shares), and even relevant chat conversations to create the most up-to-date and comprehensive description of what your data means and how it should be used.
• Business KPI Monitoring Agent: Define any important business metrics with natural language and the KPI Monitoring Agent will monitor it continuously for any unexpected changes and anomalies. No need to regularly check dashboards or run queries – the agent will alert you if something unusual is going on.
• Experiment Evaluation Agent: Automatically evaluate any experiments or A/B tests by defining both the test setup and the key evaluation metrics via natural language. The Agent automatically applies the correct statistical techniques to continuously evaluate your experiment, draw appropriate conclusions, and dig deeper to fully understand the results (such as evaluating each experiment across your key customer segments).

Unlike traditional tools that require users to know what to look for or ask the right question, Anomalo inverts the model by starting with the answer, unprompted. The agentic system identifies meaningful changes, determines their cause, translates them into business impact, and delivers them to the right people without prompting, scheduling, or manual effort.

AIDA, Anomalo's Intelligent Data Analyst, acts as the connective tissue across the entire system. AIDA provides the institutional memory of the platform, continuously absorbing what every agent sees and does. Every schema change the Table Observability Agent catches, every table definition the Data Documentation Agent writes, and every human response or meaningful agent interaction gets folded back into AIDA's memory of that organization's data environment. The system doesn't just automate tasks. It gets smarter every time it runs.

Designed for the Agentic Enterprise

As enterprises move from experimenting with AI agents to deploying them in production, the data those agents rely on has become the actual determinant of whether AI works or fails. A pricing agent fed stale data prices wrong. A churn model fed incomplete data predicts wrong. The data layer isn't a plumbing problem anymore. It's where AI reliability gets decided. The Self-Driving Data System is Anomalo's answer to that reality, built for organizations that can no longer afford to have humans in every loop.

Anomalo's current customers analyze more than ten billion rows daily and have saved millions of hours through autonomous monitoring. They are using Anomalo to turn their data from a source of risk into a competitive advantage. The Anomalo platform is designed for all data users operating in environments where data must be trusted and acted on in real time.

To read Elliot Shmukler’s “Introducing Self-Driving Data and the new Anomalo Agentic Platform” blog, simply go to: https://www.anomalo.com/blog/introducing-self…agentic-platform/.

About Anomalo

Anomalo is the autonomous data system for the agentic enterprise. Its self-driving data platform continuously and autonomously monitors, investigates, and surfaces what matters across an organization's data all before anyone thinks to ask. Powered by a proprietary profiling and prediction engine and a suite of nine autonomous agents, Anomalo handles the operational layer of running a data organization so teams can focus on the work that actually requires human judgment. Industry leaders including Atlassian, Block, Casey’s, and Notion rely on Anomalo to ensure every downstream decision, AI model, and business application runs on data they can trust. Anomalo is backed by Databricks Ventures, Snowflake Ventures, SignalFire, Smith Point Capital, Norwest Venture Partners, Foundation Capital, Two Sigma Ventures, Village Global, and First Round Capital. For more information, visit https://www.anomalo.com/.

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Anomalo Introduces Self-Driving Data With New Agentic Platform to Eliminate Manual Data Work for the Agentic Enterprise.