Segmentation Analysis, Market Trends, and Competitive Landscape in the Artificial Intelligence (AI) in Data Quality Market

The use of artificial intelligence (AI) to improve data quality is rapidly transforming how organizations manage and analyze information. As businesses increasingly rely on accurate and timely data, the market for AI-driven data quality solutions is set for significant expansion. This overview explores the expected market growth, key players, prevailing trends, and major segments shaping the future of AI in data quality.

Projected Market Size and Growth Trajectory for Artificial Intelligence in Data Quality
The artificial intelligence in data quality market is anticipated to experience remarkable growth, reaching a valuation of $4.47 billion by 2030. This expansion corresponds to an impressive compound annual growth rate (CAGR) of 24.7%. Several factors contribute to this surge, including the widespread adoption of AI-powered analytics, rising demand for real-time data accuracy, the development of data fabric architectures, increased automation in data management, and more rigorous data compliance standards. The forecast period is also expected to see advances in automated data cleansing and enrichment, AI-driven anomaly detection, ongoing data quality monitoring, predictive data quality approaches, and self-learning validation models.

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Leading Organizations Driving Innovation in AI for Data Quality
A number of influential companies dominate the artificial intelligence in data quality market, including Alphabet Inc., Microsoft Corporation, Amazon Web Services, accenture* plc, International Business Machines Corporation (IBM), Oracle Corporation, SAP SE, Salesforce.com Inc., Experian plc., Collibra, Dataiku, SAS Institute Inc., Teradata, Informatica Inc., Snowflake Inc., Alteryx Inc., QlikTech International AB, Precisely Corporation, TIBCO Software Inc., Databricks Inc., and Ataccama Corporation.

In a notable development in January 2024, QlikTech International AB, a US-based provider of data integration and analytics solutions, acquired Kyndi for an undisclosed sum. This acquisition aims to enhance Qlik's AI capabilities by incorporating Kyndi's advanced natural language processing and AI-powered search technology. Kyndi specializes in leveraging AI to help businesses extract meaningful insights from unstructured data sources.

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Emerging Trends Positively Influencing AI Adoption in Data Quality
One of the key growth drivers in the AI in data quality market is the integration of generative artificial intelligence (GenAI) to boost data accuracy and operational productivity. GenAI refers to systems designed to produce new content such as text, images, code, or audio by learning patterns from existing datasets. This technology is revolutionizing how data quality processes are managed and automated.

For example, in September 2023, US-based Saama Technologies Inc. launched a Data Quality (DQ) Co-Pilot embedded within its Smart Data Quality (SDQ) platform. This innovative feature uses generative AI to automate data quality checks by allowing users to describe the validation they need in natural language. The tool then automatically generates and executes the corresponding code, significantly reducing manual effort and accelerating workflows, especially in clinical trial data management.

Key Product and Market Segments in AI for Data Quality
The artificial intelligence in data quality market is segmented across multiple dimensions to capture the diversity of solutions and industries served:

1) By Component - Software and Services
2) By Deployment Mode - Cloud-Based and On-premise
3) By Organization Size - Small and Medium-Sized Enterprises and Large Enterprises
4) By Industry Vertical - Banking, Financial Services, and Insurance (BFSI); Information Technology (IT) and Telecommunications; Healthcare; Retail and E-commerce; Manufacturing; Government and Public Sector; and other sectors

Within software, subcategories include data profiling tools, data cleansing tools, data monitoring and validation tools, data integration software, master data management (MDM) solutions, metadata management tools, and predictive data quality analytics. Services encompass professional services, managed services, consulting, implementation and integration, as well as training and support.

This comprehensive segmentation provides a detailed view of the varied applications and delivery models fueling the rapid expansion of AI-driven data quality solutions worldwide.

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This release was published on openPR.