Data Quality Tools Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2021-2031

The Global Data Quality Tools Market is projected to experience substantial growth, rising from a valuation of USD 2.94 Billion in 2025 to USD 5.48 Billion by 2031, achieving a compound annual growth rate of 10.94%. These tools are specialized software solutions engineered to analyze, cleanse, and monitor datasets to verify their accuracy, completeness, and consistency for critical enterprise functions. The market is primarily driven by strict regulatory compliance mandates and the urgent necessity to improve operational efficiency by reducing financial losses caused by data errors. Additionally, the fundamental reliance on dependable business intelligence for strategic decision-making continues to act as a steady catalyst for adoption, irrespective of passing technological trends.

However, the market faces a considerable obstacle regarding the complexity of ensuring data readiness within rapidly changing technical landscapes. Organizations frequently find it difficult to uphold high standards of integrity when incorporating new technologies into their operations. For instance, the Association for Intelligent Information Management reported in 2024 that 52% of organizations faced major difficulties with data quality and categorization while implementing artificial intelligence initiatives. This enduring gap in data readiness creates a bottleneck that hinders the successful deployment of robust quality management frameworks.

Market Drivers

The surge in the adoption of advanced analytics and artificial intelligence acts as a major force propelling the Global Data Quality Tools Market. As enterprises implement generative AI and machine learning models, the reliability and accuracy of training datasets are crucial for ensuring valid results and reducing algorithmic bias. Companies are prioritizing automated solutions to build trust in these high-stakes projects, as poor data hygiene can result in model hallucinations and flawed strategic insights. Highlighted by Monte Carlo’s '2024 State of Reliable AI Survey' in June 2024, 68% of data professionals expressed a lack of complete confidence in the quality of data underlying their AI applications, emphasizing the critical need for tools that validate data integrity before it enters complex analytical pipelines.

Concurrently, the exponential rise in enterprise data volume and complexity is compelling organizations to modernize their quality management frameworks. The rapid growth of digital ecosystems has resulted in fragmented architectures where data is scattered across various on-premise and cloud silos, rendering manual oversight ineffective. According to the 'CDO Insights 2024' report by Informatica in January 2024, 79% of data leaders anticipated an increase in the number of data sources within their organizations in the coming year. This rising complexity creates severe operational bottlenecks, driving the demand for scalable software to maintain consistency across extensive information estates, a challenge echoed by dbt Labs in 2024, where 57% of practitioners identified poor data quality as a primary hurdle to data preparation.

Market Challenges

The difficulty of ensuring data readiness within evolving technical environments presents a significant structural barrier to the expansion of the Global Data Quality Tools Market. As enterprises strive to integrate advanced digital infrastructures, they often discover that their legacy data frameworks lack the necessary integrity to support these modernizations. This issue compels organizations to redirect resources toward fundamental data repair rather than investing in advanced quality management solutions. Consequently, the sales cycle for new tools is prolonged, as prospective buyers must first resolve deep-seated inconsistencies that automated tools cannot immediately fix.

This friction is further illustrated by recent industry findings regarding organizational confidence in data handling. In 2024, CompTIA reported that only 25% of companies felt they were exactly where they intended to be regarding their corporate data management capabilities. This statistic points to a widespread maturity gap where the majority of enterprises struggle to establish the baseline reliability needed for effective tool deployment. When businesses view their data ecosystem as too chaotic to manage, they frequently delay investment in comprehensive quality platforms, thereby stalling broader market growth.

Market Trends

The Integration of Data Quality and Data Observability for Full-Pipeline Visibility is revolutionizing how enterprises manage information reliability. Unlike traditional tools that validate static datasets, this unified 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 identify anomalies before they impact downstream analytics, addressing data downtime with the same urgency as infrastructure failures. The increasing financial commitment to this strategy is clear; according to the 'State of Analytics Engineering 2024' report by dbt Labs in April 2024, approximately 25% of data practitioners planned to increase their investment in data quality and observability solutions to protect their evolving stacks.

Simultaneously, the Democratization of Data Stewardship Through Low-Code Self-Service Tools is shifting 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, correct errors, and curate assets without writing complex code. This transition ensures that quality standards align closely with actual business context while reducing the operational burden on technical teams. The strategic focus on formalizing these distributed responsibilities is reshaping organizational priorities, as evidenced by Atlan’s 'Insights From 600+ Data Leaders For 2024' report in March 2024, where over 65% of data leaders highlighted data governance as a primary focus area, reinforcing the critical role of structured stewardship in maintaining enterprise-wide data integrity.

Key Players Profiled in the Data Quality Tools Market

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

Report Scope

In this report, the Global Data Quality Tools Market has been segmented into the following categories:

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
• Europe
• Asia-Pacific
• South America
• Middle East & Africa

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Data Quality Tools Market.

Available Customization

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• Detailed analysis and profiling of additional market players (up to five).

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