AI in Data Quality Market Report 2026

The artificial intelligence (AI) in data quality market size has grown exponentially in recent years. It will grow from $1.48 billion in 2025 to $1.85 billion in 2026 at a compound annual growth rate (CAGR) of 25%. The growth in the historic period can be attributed to growth of enterprise data volumes, early data governance initiatives, adoption of master data management, increasing regulatory reporting needs, rise of cloud data platforms.

The artificial intelligence (AI) in data quality market size is expected to see exponential growth in the next few years. It will grow to $4.47 billion in 2030 at a compound annual growth rate (CAGR) of 24.7%. The growth in the forecast period can be attributed to expansion of AI driven analytics, demand for real time data accuracy, growth of data fabric architectures, increasing automation in data operations, stricter data compliance requirements. Major trends in the forecast period include automated data cleansing and enrichment, AI driven data anomaly detection, continuous data quality monitoring, predictive data quality management, self learning data validation models.

The growing adoption of cloud computing is expected to accelerate the expansion of artificial intelligence (AI) in the data quality market in the coming years. Cloud computing adoption involves transitioning from traditional on-premises IT infrastructure to cloud-based platforms, which offer scalable, cost-effective, and remotely accessible computing resources. This shift is largely fueled by digital transformation efforts across various industries and the need for more flexible and agile data management solutions. AI contributes to data quality in cloud environments by automating processes such as data profiling, cleansing, and validation, ensuring the accuracy and integrity of data for real-time decision-making. For example, in December 2023, Eurostat, the statistical office of the European Union, reported that 45.2% of EU enterprises used cloud computing, primarily for email, file storage, and office tools. As a result, the rise in cloud computing adoption is driving the growth of AI in the data quality market.

Leading companies in the artificial intelligence (AI) in data quality market are focusing on integrating generative AI to improve data accuracy and operational efficiency. Generative AI refers to AI systems that can create new content, such as text, images, videos, audio, or code, by learning patterns from existing data. For example, in September 2023, Saama Technologies Inc., a US-based software company, launched a Data Quality (DQ) Co Pilot within its Smart Data Quality (SDQ) platform. This innovative feature utilizes generative AI to automate data quality checks. By allowing users to simply describe the desired data validation, the tool automatically generates and tests the corresponding code, significantly reducing manual effort and speeding up clinical trial workflows.

In January 2024, QlikTech International AB, a US-based data integration and analytics company, acquired Kyndi for an undisclosed amount. This acquisition allows Qlik to enhance its artificial intelligence (AI) capabilities by integrating Kyndi’s advanced natural language processing and AI-powered search technology into its platform. Kyndi, a US-based company, specializes in natural language processing and AI software that helps businesses extract actionable insights from unstructured data.

Major companies operating in the artificial intelligence (AI) in data quality market are 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., Ataccama Corporation.

North America was the largest region in the artificial intelligence (AI) in data quality market in 2025. The regions covered in the artificial intelligence (AI) in data quality 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) in data quality market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Tariffs have moderately impacted the AI in data quality market by increasing costs related to imported servers, data infrastructure hardware, and enterprise IT components. These cost pressures have primarily affected on premise deployments across large enterprises and regulated industries. North america and asia pacific have experienced higher infrastructure expenses due to global hardware dependencies. At the same time, tariffs have accelerated migration toward cloud based and software centric data quality solutions. This shift has reduced reliance on physical infrastructure and improved scalability. Overall, market adoption remains strong due to growing data governance priorities.

The artificial intelligence (AI) in data quality market research report is one of a series of new reports that provides artificial intelligence (AI) in data quality market statistics, including artificial intelligence (AI) in data quality industry global market size, regional shares, competitors with a artificial intelligence (AI) in data quality market share, detailed artificial intelligence (AI) in data quality market segments, market trends and opportunities, and any further data you may need to thrive in the artificial intelligence (AI) in data quality industry. This artificial intelligence (AI) in data quality market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

Artificial intelligence (AI) in data quality involves using AI technologies to automate, improve, and sustain the accuracy, consistency, completeness, and dependability of data across various systems. AI-powered tools are capable of identifying and correcting errors, detecting anomalies, filling in missing information, removing duplicate entries, and ensuring that data aligns with established standards.

The key elements of AI in data quality are software and services. Software in this context consists of AI-based tools developed to streamline and enhance data quality operations such as cleansing, validation, and enrichment. These solutions assist organizations in preserving precise and trustworthy data, which is essential for informed decision-making and operational effectiveness. They are available in both cloud-based and on-premise formats, accommodating organizations of different sizes - from small and medium-sized enterprises to large corporations - and are utilized across a broad range of industries, including banking, financial services, and insurance (BFSI), information technology (IT) and telecommunications, healthcare, retail and e-commerce, manufacturing, government, and the public sector, among others.

The artificial intelligence (AI) in data quality market includes revenues earned by entities by providing services such as automated data profiling, anomaly detection, data cleansing, error reduction, and continuous monitoring. The market value includes the value of related goods sold by the service provider or included within the service offering. The artificial intelligence (AI) in data quality market also includes sales of graphics processing units (GPUs), central processing units (CPUs), and high-performance memory. Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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