Global Data Quality Tools Market Research Report: Forecast (2026-2032)

Global Data Quality Tools Market Size Forecast: (2026- 2032)

The Global Data Quality Tools Market size is valued at around USD 2.99 billion in 2025 and is projected to reach USD 7.19 billion by 2032. Along with this, the market is estimated to grow at a CAGR of around 13.35% during the forecast period, i.e., 2026-32. The Global Data Quality Tools Market is witnessing a significant growth fueled by the rising reliance of enterprises on accurate, high-quality data to drive analytics, AI, and business intelligence initiatives.

Companies integrating cloud, ERP, and CRM systems need automated data quality solutions to preserve operational effectiveness and minimize expenses from redundant or inconsistent information. For instance, Salesforce's USD8 billion acquisition of Informatica in 2025 highlights the strategic significance of integrated data governance and quality management in corporate ecosystems.

In 2025, 71% of global organizations reported having an active data-governance program, up from 60% in 2023, reflecting a strong institutional shift toward structured data integrity, lineage, and stewardship practices. This rapid governance adoption is directly accelerating demand for advanced data-quality tools, as enterprises require automated validation, metadata management, and compliance-ready controls to support governed, analytics-driven ecosystems.

Governments and large institutions are scaling up AI and data infrastructure worldwide. For instance, in July 2025, the National Science Foundation (NSF) in the U.S. recently announced a USD 100 million investment toward its new National Artificial Intelligence Research Institutes, supporting large-scale data systems and shared scientific datasets. This build-out of national data infrastructure will require robust data-quality frameworks, governance, and traceable metadata to ensure reliability and reproducibility.

At the same time, the European Commission, through its “AI Factories” initiative, is funding AI infrastructure and supercomputing investments across member states, dedicating billions of euros toward upgraded data centers and AI-ready compute facilities. As these facilities come online, organisations feeding data into models and analytics platforms will increasingly depend on data-quality and governance tooling to meet compliance, transparency, and performance demands.

Together, these public investments in AI and data infrastructure will drive a rising need for scalable, cloud-native, ML-enabled data-quality solutions globally, reinforcing the role of data-quality tools as foundational components of modern data infrastructure.          

Global Data Quality Tools Market Recent Developments:

• June 2025: The Stream DaQ architecture, published on arXiv, introduces a real-time data-quality framework for unbounded data streams. It enables continuous monitoring through configurable windows, dynamically adapts quality constraints as data patterns change, and produces a meta-stream of quality metrics. This allows organizations to detect integrity issues instantly and maintain reliable, real-time analytics pipelines.
• May 2025: Salesforce announced that it would acquire Informatica for about USD 8 billion, marking a significant advancement in business data management. In order to support Salesforce's expanding artificial intelligence-driven applications, the agreement aims to improve Salesforce's data governance, cataloguing, privacy, and integration capabilities.

Global Data Quality Tools Market Scope:

Global Data Quality Tools Market Drivers:

Growth of Big Data & AI Analytics Driving Market Demand

The rapid expansion of big data ecosystems and AI-driven analytics is emerging as one of the strongest forces accelerating the size & volume of the Global Data Quality Tools Market. According to the OECD, only 8% of enterprises were using big-data analytics and AI in 2023, revealing a vast opportunity for adoption and an equally significant need for accurate, high-integrity data. With global data generation projected to reach 175 zettabytes by 2025, ensuring data correctness is no longer optional, but it is foundational for any successful AI system.

Government policies and public investments are further amplifying this demand. The OECD’s 2025 digital policy brief urges member states to strengthen data-sharing frameworks and improve data quality governance to support trustworthy AI. In the United States, the White House AI Action Plan emphasizes developing “world-class scientific datasets” and recognizing high-quality data as a strategic national resource, signalling sustained federal investment in data infrastructure.

Looking ahead, rising AI integration will significantly expand the need for data-quality solutions. NITI Aayog projects that AI could add USD 500–600 billion to India’s GDP by 2035, underscoring the economic value of data-driven innovation.

Growing AI adoption across governments and industries will sharply increase the need for clean, validated, and interoperable data, making data-quality tools essential for real-time analytics, cross-border data exchange, and national AI-readiness strategies.

Increasing Regulatory Pressure

Regulatory pressure is one of the core drivers of the Global Data Quality Tools Market because compliance regimes force organisations to collect, validate, reconcile, and disclose accurate data across financial, environmental, and privacy domains. For instance, European GDPR enforcement recorded cumulative fines of USD 6.53 billion as of 1 Mar 2025, illustrating the steep financial consequences of data mismanagement.

Major enforcement actions against individual companies, including LinkedIn’s around USD 358.1 million, Meta’s approximately USD 1.39 billion, and Clearview AI’s nearly USD 35.3 million. Penalties highlight how aggressively regulators are acting against inadequate data governance and non-compliance.

In the U.S., state enforcement under California’s privacy authority (CPPA) has begun issuing monetary penalties and corrective orders, increasing the need for compliance-ready data pipelines. The EU Data Act in 2025 will compel firms to enable access and sharing of IoT and enterprise data, creating new obligations for data quality, interoperability, and traceability.

Future public investments and programmes will sustain demand for data-quality tools. The U.S. National Science Foundation’s NAIRR and related AI research investments (NSF programs invest >USD 700 million annually in AI infrastructure) will create curated, shared datasets that require rigorous quality controls.

As global regulatory mandates expand and penalties escalate, organisations will increasingly rely on advanced data-quality systems to ensure compliance and mitigate risk. The tightening alignment between AI adoption, data-governance rules, and public-sector digital policies guarantees that high-integrity, traceable, and auditable data will become non-negotiable. Consequently, demand for robust data-quality tools is set to intensify across all major industries in the years ahead.

Global Data Quality Tools Market Trends:

Cloud‑Based Solutions Gaining Momentum

Cloud‑based solutions are the main trend driving the Global Data Quality Tools Market because they concentrate data, accelerate analytics, and require cloud-native, scalable quality controls. For instance, Rackspace Technology's 2025 poll of more than 1,400 IT decision-makers revealed that 48% currently consider hybrid cloud deployment to be crucial, and over 90% expect to make substantial changes to their cloud strategy within the next two years. Meanwhile, in the EU, 45.2% of enterprises purchased cloud services in 2023, reflecting rapid enterprise migration to cloud platforms that now host critical business data and applications.

As cloud ecosystems enable faster data ingestion, real-time analytics, and globally distributed access, organizations increasingly rely on automated data-quality mechanisms to maintain accuracy, consistency, and compliance. Major technology providers are adapting accordingly. For instance, IBM is advancing cloud-native data quality through IBM Cloud Pak for Data, offering unified governance, AI-driven observability, and scalable multi-cloud processing for enterprises modernizing legacy infrastructure.

Similarly, Oracle embeds data-quality capabilities directly within Oracle Cloud Infrastructure and Autonomous Database, allowing continuous cleansing, enrichment, and governance as workloads migrate to Oracle’s cloud. This integrated approach supports reliable analytics, large-scale ERP modernization, and seamless multi-cloud interoperability.

As global cloud adoption accelerates, organizations will require stronger, continuous, and automated data-quality controls to support real-time analytics, AI workloads, and regulatory compliance. With enterprises migrating mission-critical systems to cloud platforms, demand for cloud-native data quality tools will expand rapidly, making them indispensable for future digital operations.

Increasing Popularity of ML Dashboards

Interactive, ML-integrated dashboards are becoming a major trend in data-quality tooling because they merge automated, model-driven insights with visual, explainable interfaces that help users detect, diagnose, and remediate data issues in real time. Major vendors introduced significant enhancements in 2024. For instance, Microsoft added Copilot and AI-driven features across Power BI to surface automated insights and augment data exploration.

Meanwhile, AWS launched Amazon Q in QuickSight (GA in 2024), enabling natural-language Q&A, automated insight summaries, and programmatic generation of charts and narratives from governed datasets, reducing manual profiling and speeding remediation.

Google Cloud strengthened the landscape through Dataplex, which integrates automated data profiling, lineage tracking, and its AutoDQ framework for continuous quality checks across lakehouse environments. These tools feed directly into ML-enabled dashboards, giving organizations centralized, real-time monitoring of data accuracy, completeness, and reliability.

Governments are also elevating expectations for trusted data ecosystems. National AI strategies, particularly in the U.S. and Europe, increasingly require curated, high-quality datasets for safe AI deployment, pushing public agencies toward ML-supported dashboards with auditable data-quality controls.

Collectively, these advancements are transforming ML dashboards from optional analytical add-ons into essential operational systems, accelerating enterprise adoption of automated data-quality, governance, and observability platforms.

Global Data Quality Tools Market Challenges:

High Implementation & Integration Costs

The Global Data Quality Tools Industry faces a major challenge due to high implementation and integration costs because modernization, cloud migration, and compliance-driven projects demand substantial capital and sustained operating budgets. For instance, the U.S. Internal Revenue Service reported roughly USD 5 billion in IT spending for 2024 (about USD 1.5 billion of which was earmarked for modernization), underscoring the multi-billion-dollar scope of government IT upgrades that often include data-quality components.

In the UK, auditors estimate that legacy digital systems cost the public sector the equivalent of USD 56.25 billion in lost productivity and unexploited value as an indicator of the large investment required to replace ageing infrastructure and integrate modern data governance tooling.

Enterprise data-quality implementations must interoperate with legacy ERPs, federal systems, and multi-cloud architectures. Integration projects carry hidden expenses like API development, metadata harmonization, contract/license fees, and specialist staffing that commonly exceed initial software license costs.

These escalating costs slow digital transformation, delay enterprise-wide adoption, and limit accessibility for mid-sized organizations. As a result, high integration expenses are expected to restrain overall market expansion in the near term.

Global Data Quality Tools Market (2026-32) Segmentation Analysis:

The Global Data Quality Tools Market Report and Forecast 2026-2032 offers a detailed analysis of the market based on the following segments:

Based on Deployment

• Cloud-based
• On-Premises

The cloud-based segment holds the largest market share in the Global Data Quality Tools Market. Businesses that prioritize scalable, affordable solutions that can easily interface with contemporary data ecosystems like AWS, Microsoft Azure, and Google Cloud are driving this market lead. Cloud solutions, which provide more flexibility, operational agility, and low infrastructure overhead, have been driven by the growth of remote labor, expanding datasets, and the demand for real-time analytics.

Additionally, these technologies make it easier to maintain compliance with new regulations, which is an essential advantage in industries that rely heavily on data. For example, in order to provide more comprehensive public health insights and synchronize healthcare information across agencies, Singapore's Ministry of Health implemented a cloud-native data quality platform.

Based on End-User

• BFSI
• Government
• Retail and E-commerce
• Healthcare
• IT & Telecom
• Others

The BFSI sector is the primary end-user of the Global Data Quality Tools Industry, driven by stringent regulatory, reporting, and risk-management requirements. Banks, insurers, and financial institutions handle massive volumes of customer, transactional, credit-risk, and compliance data, making accuracy and consistency non-negotiable. Regulatory mandates such as anti-money-laundering (AML), Know-Your-Customer (KYC), Basel III, FATCA, and global privacy rules force BFSI organizations to maintain high-integrity, auditable datasets.

Frequent regulatory audits, rising fraud risks, and the expansion of digital banking intensify the need for automated validation, deduplication, and real-time data monitoring. BFSI institutions also have the highest adoption of cloud analytics, AI-driven risk scoring, and customer-experience platforms, each of which requires robust data quality foundations.

As the inaccurate data directly affects financial stability, customer trust, and legal compliance, BFSI consistently accounts for the largest share of enterprise spending on data-governance and data-quality solutions, keeping it the dominant segment in this market.

Leading Players of the Global Data Quality Tools Market:

• IBM Corporation

The headquarters of IBM Corporation, which was founded in 1911, is located in Armonk, New York. Deep development and research, cloud integration, and AI-enabled solutions for extensive data cleansing, profiling, and monitoring are all areas in which IBM excels in the data quality tools market.

• Oracle Corporation

Oracle Corporation was established in 1977. Oracle's end-to-end enterprise data management platforms, which include database technology, integration, and cloud-native solutions, ultimately provide it with its advantages in this industry. Large public enterprises and financial institutions frequently utilize its package, which includes Oracle Enterprise Data Quality, to transfer, clean, and seamlessly enrich mission-critical datasets.

• SAP SE

SAP SE is based in Walldorf, Germany, and was founded in 1972. It is well-known for its history in enterprise resource planning (ERP), has developed its data quality solutions to facilitate cloud adoption, AI-driven analytics, and comprehensive business transformation. Large businesses in the manufacturing, government, and retail sectors depend on the SAP Data Intelligence package for data aggregation, auditing, and regulatory compliance.

SAS Institute Inc., Talend Inc., Information Builders Inc., Experian PLC, Ataccama Corporation, Syncsort Inc., Pitney Bowes Inc., Informatica, International Business Machines Corporation, Tamr, and others are the key players of the Global Data Quality Tools Market.

Global Data Quality Tools Market (2026-32): Regional Projection

The Global Data Quality Tools  Market is dominated by the North America region due to its primarily due to its advanced digital ecosystem, strict regulatory environment, and high enterprise technology adoption. The U.S. and Canada enforce robust data-governance and privacy frameworks such as the California Privacy Rights Act (CPRA), HIPAA, and financial reporting mandates that compel organizations to invest heavily in data-quality, validation, and compliance systems.

The region also hosts the world’s largest concentration of cloud, analytics, and enterprise-software providers, including IBM, Oracle, Microsoft, Amazon, SAS, and Talend, creating a mature demand-and-supply ecosystem. Strong federal digital-transformation programs, large-scale AI adoption, and continuous migration toward multi-cloud infrastructures further drive the need for advanced data-quality solutions.

Additionally, North America’s higher IT spending per enterprise and rapid deployment of AI, machine learning, and automation technologies give it a competitive edge. Together, these factors position North America as the dominant and most innovation-driven market globally.

Gain a Competitive Edge with Our Global Data Quality Tools Market Report 

• Global Data Quality Tools Market Report by MarkNtel Advisors provides a detailed & thorough analysis of market size & share, growth rate, competitive landscape, and key players. This comprehensive analysis helps businesses gain a holistic understanding of the market dynamics & make informed decisions. 
• This report also highlights current market trends & future projections, allowing businesses to identify emerging opportunities & potential challenges. By understanding market forecasts, companies can align their strategies & stay ahead of the competition. 
• Global Data Quality Tools Market Report aids in assessing & mitigating risks associated with entering or operating in the market. By understanding market dynamics, regulatory frameworks, and potential challenges, businesses can develop strategies to minimize risks & optimize their operations.

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