Actian, the data division of HCLSoftware, launches Actian Data Observability, which leverages AI and machine learning for comprehensive data quality monitoring and anomaly detection and resolution. By ensuring high data quality and reliability, Actian helps enterprises accelerate AI initiatives, increase the speed of innovation, and reduce risk.
Traditional data quality approaches lack real-time capabilities and struggle to keep pace with the exponential growth in data volume and velocity. Actian Data Observability addresses these limitations by providing comprehensive, continuous monitoring across the entire data ecosystem. Gartner® statistics confirm the growing importance of data observability, noting that “by 2026, 50% of enterprises implementing distributed data architectures will have adopted data observability tools to improve visibility into the health of their data landscape, up from less than 20% in 2024.”¹
“Businesses rely on data to drive decisions, power AI initiatives, and meet regulatory demands, but too often they face unreliable data, hidden quality issues, and ever-increasing cloud costs,” said Emma McGrattan , Chief Technology Officer at Actian. “Actian Data Observability gives teams the visibility and confidence they need to trust their data, reduce risk, and control spend—turning data from a liability into a competitive advantage.”
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Unlike reactive, rule-based approaches, Actian Data Observability defines and executes thousands of data quality rules simultaneously across the entire data landscape. Monitoring includes critical dimensions such as freshness, volume, schema deviation, distribution patterns, and custom business rules. ML-based anomaly detection automatically identifies outliers, deviations, and unexpected patterns, while providing valuable root cause analysis suggestions to facilitate faster resolution.
Actian Data Observability scales to connect any data set in the ecosystem so businesses can maintain data integrity without compromising performance or creating bottlenecks in their data pipelines. With this solution, Actian optimizes cloud resource consumption without data sampling, ensuring predictable cloud costs and avoiding unexpected cost increases.
Designed for enterprises operating with modern, complex, and high-volume data stacks, Actian Data Observability supports the following use cases:
- Data pipeline efficiency : Empowers teams to quickly deliver trusted, AI-ready data products and insights by addressing quality issues closer to the source and as early in the lifecycle as possible using a shift-left approach to data that prevents problems from propagating internally.
- AI Lifecycle Monitoring : Ensure the safety and compliance of AI applications by validating the quality, freshness, and relevance of training data and augmented generation knowledge sources, while enabling rapid intervention.
- Secure self-service analytics : Empower analysts and other consumers to independently assess data reliability before using it with real-time health indicators integrated directly into data catalogs, BI tools, and discovery platforms.
Built on an open architecture, Actian Data Observability seamlessly integrates with cloud data warehouses, data lakes, and streaming platforms. By isolating data quality workloads from production infrastructure, Actian prevents performance degradation and impact to business operations in production environments. For managing large analytical datasets, Actian provides native integration with Apache Iceberg to ensure accurate insights, quality checks, and change tracking across all systems. Additionally, to protect data security and privacy, Actian Data Observability accesses metadata and runs checks directly where the data resides, eliminating the need for insecure or costly data copies.
Source: PRNewswire