Acceldata, a leading provider of data observability and agentic data management solutions, is announcing a new capability designed to amplify the power of agentic reasoning within the company’s xLake Reasoning Engine. This feature—Adaptive AI Anomaly Detection—automatically detects and identifies hidden, multi-dimensional data anomalies and patterns that a human may miss, improving business operations and AI agent decision making.
Acceldata’s Adaptive AI Anomaly Detection was designed to address the inefficacies of traditional anomaly detection tools, often only capable of identifying one-dimensional errors, such as a misplaced zero in a sales figure.
With Adaptive AI, enterprises are equipped with an autonomous method of not only finding one-dimensional errors, but errors spanning various attributes, such as date of sale, product IDs, region, and more. This helps surface a variety of patterns and interdependencies within an organization’s data that would otherwise be missed, enhanced by Adaptive AI’s continuous improvement without the need for manual tuning.
Additionally, Adaptive AI prioritizes high-risk data segments, improving resource utilization and performance.
“With multi-dimensional capabilities, you can see the system [is] able to automatically accomplish higher level tasks that are more business impactful,” said Rohit Choudhary, co-founder and CEO of Acceldata. “To manually find each of these patterns is humanly impossible or can be done in only some specific scenarios. Imagine an enterprise with hundreds of pipelines and tens of thousands of tables—only an AI-first approach can scale to accommodate the various scenarios that arise in that situation.”
Adaptive AI Anomaly Detection poses significant value for industries such as financial services, life sciences, or retail, improving data quality in a manner that crucially impacts the downstream.
“Adaptive AI Anomaly improves financial controls for the financial services industry by measuring and reconciling discrepancies across multiple sources of data,” noted Choudhary. “For life sciences, clinical trial data anomalies are detected automatically as the data is being fed into their systems from many sources.”
“For the retail industry, detecting inventory data anomalies by product, region, and delivery date ensure you don’t have stock outs. Bad data often causes the wrong product to be delivered or the product to not be delivered on time,” Choudhary continued.
Acceldata’s autonomous data solution unlocks a range of agentic data management use cases, offering a robust foundation for AI agents to take real-time action based on multi-variate anomaly detection. Some use cases include:
- Data quality enhancement by automatically detecting hidden and compound anomalies across multiple data fields
- Root-cause correlation, linking infrastructure failures with pipeline breakdowns and data spikes
- Cost spike diagnosis that traces budget overruns to specific workloads, users, inefficient queries, or processes across systems
- Compliance breach alerts that detect unusual access patterns through the correlation of user identity, location, and data sensitivity
To learn more about Acceldata’s Adaptive AI Anomaly Detection, please visit https://www.acceldata.io/.