Artificial Intelligence (AI)-Based Data Observability Software Market Global Report 2026

The artificial intelligence (AI)-based data observability software market size has grown rapidly in recent years. It will grow from $1.08 billion in 2025 to $1.23 billion in 2026 at a compound annual growth rate (CAGR) of 14.2%. The growth in the historic period can be attributed to expanding data volumes and complexity, rising need for dependable business data, growing complexity of data pipelines, increasing occurrence of data quality problems, rising demand for real-time data quality monitoring.

The artificial intelligence (AI)-based data observability software market size is expected to see rapid growth in the next few years. It will grow to $2.06 billion in 2030 at a compound annual growth rate (CAGR) of 13.8%. The growth in the forecast period can be attributed to growing demand for automated resolution of data issues, increasing adoption of predictive data observability solutions, rising need for continuous data quality assurance, expanding demand for intelligent anomaly detection, and increasing focus on proactive data reliability management. Major trends in the forecast period include growing adoption of AI-based data anomaly detection, increasing demand for automated data quality monitoring, rising use of predictive data pipeline monitoring, increasing emphasis on proactive resolution of data issues, and growing demand for continuous data health monitoring.

The increasing adoption of artificial intelligence and machine learning across enterprises is expected to propel the growth of the artificial intelligence-based data observability software market going forward. Artificial intelligence and machine learning across enterprises refer to the use of intelligent algorithms and predictive models by organizations to automate decision-making, optimize business processes, and generate actionable insights from large volumes of data. The adoption of artificial intelligence and machine learning is increasing due to the growing availability of enterprise data and the need for organizations to improve operational efficiency, accelerate innovation, and make faster data-driven decisions. This adoption supports artificial intelligence-based data observability software by creating greater demand for reliable, high-quality, and continuously monitored data pipelines that ensure accurate inputs for AI models and analytics applications. For instance, in July 2025, according to the United States Government Accountability Office, a US-based government department, AI use cases across 11 reviewed federal agencies nearly doubled from 571 in 2023 to 1,110 in 2024, while generative AI applications increased nearly ninefold from 32 to 282. Therefore, the increasing adoption of AI and machine learning across enterprises is driving the growth of the artificial intelligence-based data observability software market.

Leading companies operating in the artificial intelligence (AI)-based data observability software market are focusing on developing advanced solutions such as adaptive artificial intelligence (AI)-powered anomaly detection to improve data quality, automate anomaly detection, and enhance enterprise data reliability. Adaptive artificial intelligence (AI)-powered anomaly detection refers to an intelligent capability that continuously learns from data patterns to automatically identify, prioritize, and help resolve complex anomalies across data pipelines and enterprise systems. For example, in April 2025, Acceldata, a US-based provider of AI-powered data observability and agentic data management solutions, launched Adaptive AI Anomaly Detection, an advanced capability integrated into its xLake Reasoning Engine. The solution autonomously detects hidden multi-dimensional data anomalies, reduces manual investigation time from weeks to minutes, and enables AI agents to proactively identify, prioritize, and resolve data quality issues, improving operational efficiency and ensuring trusted data for artificial intelligence and analytics workloads.

In April 2025, Datadog, Inc., a US-based provider of cloud application observability and security solutions, acquired Metaplane for an undisclosed amount. Through this acquisition, Datadog aimed to accelerate its expansion into AI-based data observability software by enhancing its technological capabilities for monitoring data quality throughout the data lifecycle and integrating application and data observability for enterprises developing AI-driven systems. Metaplane is a US-based end-to-end data observability platform that uses artificial intelligence (AI) and machine learning (ML) to continuously monitor data quality, identify anomalies, track schema modifications, analyze data lineage, and deliver proactive alerts across modern data pipelines.

Major companies operating in the artificial intelligence (AI)-based data observability software market are Amazon Web Services Inc., Anomalo Inc., Arize AI Inc., Atlan Technologies Inc., Bigeye Inc., Collibra NV, Databricks Inc., Datadog Inc., Datafold Inc., DQLabs Inc., Dynatrace LLC, Elastic N.V., Lightup Data Inc., Microsoft Corporation, Monte Carlo Data Inc., Pantomath Inc., Sifflet SAS, Soda Data Inc., Starburst Data Inc., Superconductive Inc., Acceldata Inc., Alation Inc., Cribl Inc., Google LLC, International Business Machines Corporation.

North America was the dominating region in the artificial intelligence (AI)-based data observability software market in 2025. Asia-Pacific is expected to be the rapidly growing region in the forecast period. The regions covered in the artificial intelligence (AI)-based data observability software market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.

The countries covered in the artificial intelligence (AI)-based data observability software market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

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