Data Quality Tools Market - Global Industry Size, Share, Trends, Opportunity, and Forecast Segmented By Component (Software, Services), By Deployment (Cloud, On-premise), By Application (BFSI, Healthcare, Manufacturing, Telecom & IT, Retail & E-commerce, Others), By Region & Competition, 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 2.94 Billion
CAGR (2026-2031)	10.94%
Fastest Growing Segment	Software
Largest Market	North America
Market Size (2031)	USD 5.48 Billion

Market Overview

The Global Data Quality Tools Market will grow from USD 2.94 Billion in 2025 to USD 5.48 Billion by 2031 at a 10.94% CAGR. Data quality tools are software solutions designed to analyze, cleanse, and monitor datasets to ensure accuracy, completeness, and consistency for enterprise operations. The primary drivers fueling market growth include the stringent requirement for regulatory compliance and the critical need to enhance operational efficiency by mitigating financial losses associated with erroneous data. Furthermore, the foundational reliance on trustworthy business intelligence for strategic decision-making serves as a consistent catalyst for adoption, independent of transient technological trends.

However, a significant challenge hindering market expansion is the complexity of ensuring data readiness within evolving technical environments. Organizations frequently struggle to maintain high standards of integrity when integrating new technologies. According to the Association for Intelligent Information Management, in 2024, 52% of organizations encountered significant challenges with data quality and categorization during the implementation of artificial intelligence projects. This persistent gap in data readiness creates a bottleneck that complicates the deployment of effective quality management frameworks.

Key Market Drivers

The increasing adoption of advanced analytics and artificial intelligence is a primary driver propelling the Global Data Quality Tools Market. As enterprises deploy generative AI and machine learning models, the accuracy and reliability of training datasets have become essential to ensure valid outcomes and mitigate algorithmic bias. Organizations are prioritizing automated solutions to establish trust in these high-stakes initiatives, as poor data hygiene can lead to model hallucinations and flawed strategic insights. According to Monte Carlo, June 2024, in the '2024 State of Reliable AI Survey', 68% of data professionals stated they were not completely confident in the quality of the data powering their AI applications. This confidence gap underscores the urgent requirement for dedicated tools capable of validating data integrity before it enters complex analytical pipelines.

Simultaneously, the exponential growth in enterprise data volume and complexity is forcing organizations to modernize their quality management frameworks. The rapid expansion of digital ecosystems has led to fragmented architectures where data resides in disparate on-premise and cloud silos, making manual oversight obsolete. According to Informatica, January 2024, in the 'CDO Insights 2024' report, 79% of data leaders expected the number of data sources within their organizations to increase in the year ahead. This escalating complexity creates significant operational bottlenecks, driving the adoption of scalable software solutions to maintain consistency across vast information estates. Reflecting this widespread struggle, according to dbt Labs, in 2024, 57% of data practitioners identified poor data quality as a predominant challenge hindering their ability to prepare data for analysis.

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Key Market Challenges

The complexity of ensuring data readiness within evolving technical environments creates a significant structural barrier to the growth of the Global Data Quality Tools Market. As enterprises attempt to integrate advanced digital infrastructures, they often discover that their legacy data frameworks lack the necessary integrity to support these modernizations. This issue forces organizations to divert resources toward foundational data repair rather than acquiring advanced quality management solutions. Consequently, the adoption cycle for new tools lengthens, as potential buyers must first address deep-rooted inconsistencies that automated tools cannot instantly resolve.

This friction is further evidenced by recent industry findings regarding organizational confidence in data handling. According to CompTIA, in 2024, only 25% of companies reported being exactly where they aimed to be regarding their corporate data management capabilities. This statistic highlights a widespread maturity gap where the majority of enterprises struggle to establish the baseline reliability required for effective tool deployment. When businesses perceive their data ecosystem as too chaotic to manage, they often delay investment in comprehensive quality platforms, thereby stalling broader market expansion.

Key Market Trends

The Convergence of Data Quality and Data Observability for Full-Pipeline Visibility is transforming how enterprises manage information reliability. Unlike traditional tools that validate static datasets, this integrated approach continuously monitors data health across dynamic pipelines, tracking metrics such as freshness, volume, and schema changes in real-time. This shift allows engineering teams to detect anomalies before they impact downstream analytics, effectively treating data downtime with the same urgency as infrastructure outages. The growing financial commitment to this unified strategy is evident in recent investment patterns. According to dbt Labs, April 2024, in the 'State of Analytics Engineering 2024' report, approximately 25% of data practitioners intended to increase their investment in data quality and observability solutions in the coming year to safeguard their evolving stacks.

Simultaneously, the Democratization of Data Stewardship Through Low-Code Self-Service Tools is decentralizing quality management duties from IT departments to business domain experts. Modern platforms are increasingly incorporating intuitive, non-technical interfaces that enable subject matter experts to define quality rules, remediate errors, and curate assets without writing complex code. This transition ensures that quality standards align closely with actual business context while alleviating the operational burden on technical data teams. The strategic emphasis on formalizing these distributed responsibilities is reshaping organizational priorities. According to Atlan, March 2024, in the 'Insights From 600+ Data Leaders For 2024' report, over 65% of data leaders highlighted data governance as their primary focus area, reinforcing the critical role of structured stewardship in maintaining enterprise-wide data integrity.

Segmental Insights

The Software segment is identified as the fastest-growing category within the Global Data Quality Tools Market due to the increasing necessity for automated data management capabilities. Enterprises are prioritizing scalable applications to address complex compliance requirements mandated by frameworks such as the General Data Protection Regulation. This shift is further accelerated by the widespread adoption of cloud-based infrastructure, which requires continuous monitoring and cleansing mechanisms to ensure information accuracy. Consequently, organizations are investing heavily in integrated software suites to reduce manual errors and streamline operational decision-making processes.

Regional Insights

North America maintains a leading position in the global data quality tools market due to the extensive adoption of data management technologies and the strong presence of key software providers. The dominance of the region is further supported by stringent compliance requirements found in frameworks such as the California Consumer Privacy Act and the Sarbanes-Oxley Act, which necessitate high standards for data accuracy and governance. Additionally, widespread investment in cloud infrastructure by large enterprises supports the continuous demand for effective data validation and cleansing solutions across the United States and Canada.

Recent Developments

• In June 2025, Precisely announced a series of AI-driven enhancements to its Data Integrity Suite, focusing on automating data quality and governance workflows. The update introduced features such as bulk asset loaders and AI-generated descriptions for business assets, designed to streamline collaboration between technical and business users. The company also expanded language support to facilitate broader global adoption. These innovations were aimed at helping enterprises build a resilient data foundation, ensuring that the information used for analytics and advanced AI decision-making retained high accuracy, consistency, and context across hybrid environments.
• In May 2025, Monte Carlo introduced new observability capabilities specifically designed for unstructured data, addressing the growing need to monitor assets such as documents and chat logs for artificial intelligence applications. This development allowed data teams to track the reliability of non-tabular data sources alongside traditional structured datasets within a single platform. The update included native integrations with vector databases and large language model frameworks, ensuring that the data feeding generative AI agents remained accurate and trustworthy. This expansion marked a significant shift in data quality tooling towards supporting the complex requirements of modern AI stacks and unstructured data pipelines.
• In October 2024, Informatica unveiled its Fall 2024 release of the Intelligent Data Management Cloud, featuring significant advancements in its AI-powered CLAIRE GPT engine. The update introduced natural language interfaces for master data management and data quality, enabling users to explore data and generate quality rules through simple conversational prompts. The release also expanded regional availability for its generative AI capabilities and delivered deeper integrations with major cloud ecosystems. These enhancements were aimed at simplifying data governance and ensuring that organizations could leverage high-quality, trusted data to scale their generative AI initiatives effectively across the enterprise.
• In July 2024, Qlik announced the general availability of Qlik Talend Cloud, a comprehensive platform integrating technologies from its acquisition of Talend. This new solution was designed to enhance trust in enterprise data, particularly for artificial intelligence adoption, by offering advanced data integration and quality capabilities. The platform introduced the Talend Trust Score for AI, a novel framework assessing datasets on attributes such as accuracy and consistency to ensure readiness for AI models. This strategic launch provided a unified, no-code/pro-code environment, allowing businesses to transform raw information into reliable, governed data products that support improved operational decision-making.

Key Market Players

• Informatica
• IBM Corporation
• SAP SE
• Oracle Corporation
• SAS Institute Inc.
• Talend Inc.
• Experian PLC
• Precisely
• Microsoft Corporation
• Ataccama Corp

By Component	By Deployment	By Application	By Region
Software Services	Cloud On-premise	BFSI Healthcare Manufacturing Telecom & IT Retail & E-commerce Others	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

In this report, the Global Data Quality Tools Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• Data Quality Tools Market, By Component:

• Software
• Services

• Data Quality Tools Market, By Deployment:

• Cloud
• On-premise

• Data Quality Tools Market, By Application:

• BFSI
• Healthcare
• Manufacturing
• Telecom & IT
• Retail & E-commerce
• Others

• Data Quality Tools Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE