Data + AI observability

Observability measures how well a system’s internal states can be inferred from its external outputs. In software systems, observability involves making a system’s internal state visible through metrics, logging, and tracing. This helps developers and operators understand system behavior, diagnose issues, and improve performance.

Data observability is the practice of understanding and monitoring the behavior, quality, and performance of data as it flows through a system. It involves real-time tracking and analysis to ensure data reliability, accuracy, and compliance.

Key aspects:

Data quality: Accuracy, completeness, and consistency.
Data flow: Movement through systems and identification of bottlenecks.
Data dependencies: Relationships and impacts of changes.
Data anomalies: Detection of outliers and errors.
Data compliance: Adherence to regulations and policies.

Organizations achieve data observability using monitoring tools, data pipelines, quality checks, and governance practices. This helps them identify issues, make informed decisions, and maintain data integrity.

Actian Data Observability is an AI-powered enterprise data observability solution that provides complete visibility across your modern data stack. It proactively ensures data quality without sampling or cost surprises so you can confidently build reliable, AI-ready data products.

Our differentiation:

• No data sampling: We provide 100% data coverage for comprehensive and accurate observability, eliminating the blind spots and risks associated with sampled data.
• No cloud cost surge guarantee: Our efficient architecture and processing ensure predictable, lower cloud costs for observability, unlike tools that can drastically increase compute/scan costs.
• Secured zero-copy architecture: We access metadata and run checks directly where data resides without creating insecure or costly data copies, leveraging existing security frameworks.
• Scalable AI workloads for observability: Our Machine Learning (ML) capabilities are designed to scale efficiently on large enterprise datasets without requiring massive resource allocation.
• Native Apache Iceberg support: Deep, optimized integration provides unparalleled observability, specifically for organizations standardizing or migrating to Iceberg.

Data observability refers to the ability to understand and monitor the behavior of data within a system. It involves tracking data flow, detecting errors, and identifying discrepancies in real time, enabling early problem detection and system performance assessment.

With the integration of machine learning, data observability tools can intelligently monitor anomalies and business KPI drifts, offering deeper insights with lower maintenance and total cost of ownership (TCO).

Data quality, on the other hand, focuses on the accuracy, completeness, and consistency of data. It involves identifying and correcting errors, removing duplicates, and ensuring data is entered and stored consistently.

While both concepts provide visibility into data health and can detect quality issues against predefined metrics, data observability goes one step further by offering real-time monitoring and intelligent insights, enhancing overall system understanding and performance.