The statistic shows the problems caused by poor quality data for enterprises in North America, according to a survey of North American IT executives conducted by 451 Research in 2015. As of 2015, ** percent of respondents indicated that having poor quality data can result in extra costs for the business.

ABSTRACT The exponential increase of published data and the diversity of systems require the adoption of good practices to achieve quality indexes that enable discovery, access, and reuse. To identify good practices, an integrative review was used, as well as procedures from the ProKnow-C methodology. After applying the ProKnow-C procedures to the documents retrieved from the Web of Science, Scopus and Library, Information Science & Technology Abstracts databases, an analysis of 31 items was performed. This analysis allowed observing that in the last 20 years the guidelines for publishing open government data had a great impact on the Linked Data model implementation in several domains and currently the FAIR principles and the Data on the Web Best Practices are the most highlighted in the literature. These guidelines presents orientations in relation to various aspects for the publication of data in order to contribute to the optimization of quality, independent of the context in which they are applied. The CARE and FACT principles, on the other hand, although they were not formulated with the same objective as FAIR and the Best Practices, represent great challenges for information and technology scientists regarding ethics, responsibility, confidentiality, impartiality, security, and transparency of data.

Data Quality Management Market Outlook

The global data quality management market size was valued at approximately USD 1.7 billion in 2023, and it is projected to reach USD 4.9 billion by 2032, growing at a robust CAGR of 12.4% during the forecast period. This growth is fueled by the increasing demand for high-quality data to drive business intelligence and analytics, enhance customer experience, and ensure regulatory compliance. As organizations continue to recognize data as a critical asset, the importance of maintaining data quality has become paramount, driving the market's expansion significantly.

One of the primary growth factors for the data quality management market is the exponential increase in data generation across various industries. With the advent of digital transformation, the volume of data generated by enterprises has grown multifold, necessitating effective data quality management solutions. Organizations are leveraging big data and analytics to derive actionable insights, but these efforts can only be successful if the underlying data is accurate, consistent, and reliable. As such, the need for robust data quality management solutions has become more urgent, driving market growth.

Another critical driver is the rising awareness of data privacy and compliance regulations globally. Governments and regulatory bodies worldwide have introduced stringent data protection laws, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations necessitate that organizations maintain high standards of data quality and integrity to avoid hefty penalties and reputational damage. As a result, businesses are increasingly adopting data quality management solutions to ensure compliance, thereby propelling market growth.

Additionally, the growing adoption of cloud technologies is also contributing to the market's expansion. Cloud-based data quality management solutions offer scalability, flexibility, and cost-effectiveness, making them attractive to organizations of all sizes. The ease of integration with other cloud-based applications and systems further enhances their appeal. Small and medium enterprises (SMEs), in particular, are adopting cloud-based solutions to improve data quality without the need for significant upfront investments in infrastructure and maintenance, which is further fueling market growth.

Regionally, North America holds the largest share of the data quality management market, driven by the presence of key market players and the early adoption of advanced technologies. The region's strong focus on innovation and data-driven decision-making further supports market growth. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. The rapid digitalization of economies, increasing investments in IT infrastructure, and growing awareness of data quality's importance are significant factors contributing to this growth. Furthermore, the rising number of small and medium enterprises in emerging economies of the region is propelling the demand for data quality management solutions.

Component Analysis

In the data quality management market, the component segment is bifurcated into software and services. The software segment is the most significant contributor to the market, driven by the increasing adoption of data quality tools and platforms that facilitate data cleansing, profiling, matching, and monitoring. These software solutions enable organizations to maintain data accuracy and consistency across various sources and formats, thereby ensuring high-quality data for decision-making processes. The continuous advancements in artificial intelligence and machine learning technologies are further enhancing the capabilities of data quality software, making them indispensable for organizations striving for data excellence.

The services segment, on the other hand, includes consulting, implementation, and support services. These services are crucial for organizations seeking to deploy and optimize data quality solutions effectively. Consulting services help organizations identify their specific data quality needs and devise tailored strategies for implementation. Implementation services ensure the smooth integration of data quality tools within existing IT infrastructures, while support services provide ongoing maintenance and troubleshooting assistance. The demand for services is driven by the growing complexity of data environments and the need for specialized expertise in managing data quality chall

Data Quality Management Service Market Outlook

The global data quality management service market size was valued at approximately USD 1.8 billion in 2023 and is projected to reach USD 5.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.1% during the forecast period. The primary growth factor driving this market is the increasing volume of data being generated across various industries, necessitating robust data quality management solutions to maintain data accuracy, reliability, and relevance.

One of the key growth drivers for the data quality management service market is the exponential increase in data generation due to the proliferation of digital technologies such as IoT, big data analytics, and AI. Organizations are increasingly recognizing the importance of maintaining high data quality to derive actionable insights and make informed business decisions. Poor data quality can lead to significant financial losses, inefficiencies, and missed opportunities, thereby driving the demand for comprehensive data quality management services.

Another significant growth factor is the rising regulatory and compliance requirements across various industry verticals such as BFSI, healthcare, and government. Regulations like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) necessitate organizations to maintain accurate and high-quality data. Non-compliance with these regulations can result in severe penalties and damage to the organization’s reputation, thus propelling the adoption of data quality management solutions.

Additionally, the increasing adoption of cloud-based solutions is further fueling the growth of the data quality management service market. Cloud-based data quality management solutions offer scalability, flexibility, and cost-effectiveness, making them an attractive option for organizations of all sizes. The availability of advanced data quality management tools that integrate seamlessly with existing IT infrastructure and cloud platforms is encouraging enterprises to invest in these services to enhance their data management capabilities.

From a regional perspective, North America is expected to hold the largest share of the data quality management service market, driven by the early adoption of advanced technologies and the presence of key market players. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, owing to the rapid digital transformation, increasing investments in IT infrastructure, and growing awareness about the importance of data quality management in enhancing business operations and decision-making processes.

Component Analysis

The data quality management service market is segmented by component into software and services. The software segment encompasses various data quality tools and platforms that help organizations assess, improve, and maintain the quality of their data. These tools include data profiling, data cleansing, data enrichment, and data monitoring solutions. The increasing complexity of data environments and the need for real-time data quality monitoring are driving the demand for sophisticated data quality software solutions.

Services, on the other hand, include consulting, implementation, and support services provided by data quality management service vendors. Consulting services assist organizations in identifying data quality issues, developing data governance frameworks, and implementing best practices for data quality management. Implementation services involve the deployment and integration of data quality tools with existing IT systems, while support services provide ongoing maintenance and troubleshooting assistance. The growing need for expert guidance and support in managing data quality is contributing to the growth of the services segment.

The software segment is expected to dominate the market due to the continuous advancements in data quality management tools and the increasing adoption of AI and machine learning technologies for automated data quality processes. Organizations are increasingly investing in advanced data quality software to streamline their data management operations, reduce manual intervention, and ensure data accuracy and consistency across various data sources.

Moreover, the services segment is anticipated to witness significant growth during the forecast period, driven by the increasing demand for professional services that can help organizations address complex dat

Data Quality Scorecards Market Outlook

According to our latest research, the global Data Quality Scorecards market size in 2024 stands at USD 1.42 billion, reflecting robust demand across diverse sectors. The market is projected to expand at a CAGR of 14.8% from 2025 to 2033, reaching an estimated USD 4.45 billion by the end of the forecast period. Key growth drivers include the escalating need for reliable data-driven decision-making, stringent regulatory compliance requirements, and the proliferation of digital transformation initiatives across enterprises of all sizes. As per our latest research, organizations are increasingly recognizing the significance of maintaining high data quality standards to fuel analytics, artificial intelligence, and business intelligence capabilities.

One of the primary growth factors for the Data Quality Scorecards market is the exponential rise in data volumes generated by organizations worldwide. The digital economy has led to a surge in data collection from various sources, including customer interactions, IoT devices, and transactional systems. This data explosion has heightened the complexity of managing and ensuring data accuracy, completeness, and consistency. As a result, businesses are investing in comprehensive data quality management solutions, such as scorecards, to monitor, measure, and improve the quality of their data assets. These tools provide actionable insights, enabling organizations to proactively address data quality issues and maintain data integrity across their operations. The growing reliance on advanced analytics and artificial intelligence further amplifies the demand for high-quality data, making data quality scorecards an indispensable component of modern data management strategies.

Another significant growth driver is the increasing regulatory scrutiny and compliance requirements imposed on organizations, particularly in industries such as BFSI, healthcare, and government. Regulatory frameworks such as GDPR, HIPAA, and CCPA mandate stringent controls over data accuracy, privacy, and security. Non-compliance can result in severe financial penalties and reputational damage, compelling organizations to adopt robust data quality management practices. Data quality scorecards help organizations monitor compliance by providing real-time visibility into data quality metrics and highlighting areas that require remediation. This proactive approach to compliance not only mitigates regulatory risks but also enhances stakeholder trust and confidence in organizational data assets. The integration of data quality scorecards into enterprise data governance frameworks is becoming a best practice for organizations aiming to achieve continuous compliance and data excellence.

The rapid adoption of cloud computing and digital transformation initiatives across industries is also fueling the growth of the Data Quality Scorecards market. As organizations migrate their data infrastructure to the cloud and embrace hybrid IT environments, the complexity of managing data quality across disparate systems increases. Cloud-based data quality scorecards offer scalability, flexibility, and ease of deployment, making them an attractive option for organizations seeking to modernize their data management practices. Moreover, the proliferation of self-service analytics and business intelligence tools has democratized data access, necessitating robust data quality monitoring to ensure that decision-makers are working with accurate and reliable information. The convergence of cloud, AI, and data quality management is expected to create new opportunities for innovation and value creation in the market.

From a regional perspective, North America continues to dominate the Data Quality Scorecards market, driven by the presence of leading technology vendors, high adoption rates of advanced analytics, and stringent regulatory frameworks. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by rapid digitalization, increasing investments in IT infrastructure, and growing awareness of data quality management among enterprises. Europe also represents a significant market, characterized by strong regulatory compliance requirements and a mature data management ecosystem. Latin America and the Middle East & Africa are emerging markets, with increasing adoption of data quality solutions in sectors such as BFSI, healthcare, and government. The global market landscape is evolving rapidly, with regional

This data table provides the detailed data quality assessment scores for the Historic Faults dataset. The quality assessment was carried out on the 23rd of September 2025. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy NetworksWe welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.DisclaimerThe data quality assessment may not represent the quality of the current dataset that is published on the Open Data Portal. Please check the date of the latest quality assessment and compare to the 'Modified' date of the corresponding dataset. The data quality assessments will be updated on either a quarterly or annual basis, dependent on the update frequency of the dataset. This information can be found in the dataset metadata, within the Information tab. If you require a more up to date quality assessment, please contact the Open Data Team at opendata@spenergynetworks.co.uk and a member of the team will be in contact.

Records with a completion date and registered with an authority in the USA. The data (58,685 records from Additional file 6: Table S4) were sorted into a “USA_ComplDate” sheet for trials registered with at least one authority in the US, and a “USA_ComplDate_leftovers” sheet with the remaining records. The data are presented in the following six Recruitment Type categories: (1) Active, not recruiting (3350 selected records with 2121 leftovers), (2) Completed (21,030; 17,967), (3) Enrolling by invitation (166; 175), (4) Recruiting (3167; 4666), (5) Suspended (134; 99), and (6) Terminated (3986; 1824). The sheets for these categories are numbered 1–6, respectively. (XLS 6129 kb)

According to Cognitive Market Research, the global Data Quality Tools market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS The Emergence of Big Data & IoT and Increasing Data Proliferation are driving the market growth One of the most significant drivers of the data quality tools market is the emergence of Big Data and the Internet of Things (IoT). As organizations expand their digital operations, they are increasingly reliant on real-time data collected from a vast network of connected devices, including industrial machines, smart home appliances, wearable tech, and autonomous vehicles. This rapid increase in data sources results in immense volumes of complex, high-velocity data that must be processed and analyzed efficiently. However, the quality of this data often varies due to inconsistent formats, transmission errors, or incomplete inputs. Data quality tools are vital in this context, enabling real-time profiling, validation, and cleansing to ensure reliable insights. For Instance, General Electric (GE), uses data quality solutions across its Predix IoT platform to ensure the integrity of sensor data for predictive maintenance and performance optimization. (Source: https://www.ge.com/news/press-releases/ge-predix-software-platform-offers-20-potential-increase-performance-across-customer#:~:text=APM%20Powered%20by%20Predix%20-%20GE%20is%20expanding,total%20cost%20of%20ownership%2C%20and%20reduce%20operational%20risks.) According to a recent Gartner report, over 60% of companies identified poor data quality as the leading challenge in adopting big data technologies. Therefore, the growing dependence on big data and IoT ecosystems is directly driving the need for robust, scalable, and intelligent data quality tools to ensure accurate and actionable analytics. Another major factor fueling the growth of the data quality tools market is the increasing proliferation of enterprise data across sectors. As organizations accelerate their digital transformation journeys, they generate and collect enormous volumes of structured and unstructured data daily—from internal systems like ERPs and CRMs to external sources like social media, IoT devices, and third-party APIs. If not managed properly, this data can become fragmented, outdated, and error-prone, leading to poor analytics and misguided business decisions. Data quality tools are essential for profiling, cleansing, deduplicating, and enriching data to ensure it remains trustworthy and usable. For Instance, Walmart implemented enterprise-wide data quality solutions to clean and harmonize inventory and customer data across global operations. This initiative improved demand forecasting and streamlined its massive supply chain. (Source: https://tech.walmart.com/content/walmart-global-tech/en_us/blog/post/walmarts-ai-powered-inventory-system-brightens-the-holidays.html). According to a Dresner Advisory Services report, data quality ranks among the top priorities for companies focusing on data governance.(Source: https://www.informatica.com/blogs/2024-dresner-advisory-services-data-analytics-and-governance-and-catalog-market-studies.html) In conclusion, as data volumes continue to skyrocket and data environments grow more complex, the demand for data quality tools becomes critical for enabling informed decision-making, enhancing operational efficiency, and ensuring compliance.  Restraints One of the primary challenges restraining the growth of the data quality tools market is the lack of skilled personnel wit...

Data Quality Solution Market Outlook

The global data quality solution market size is projected to grow significantly from USD 1.5 billion in 2023 to approximately USD 4.8 billion by 2032, reflecting a robust CAGR of 13.5%. This growth is driven primarily by the increasing adoption of data-driven decision-making processes across various industries. The surge in Big Data, coupled with the proliferation of IoT devices, has necessitated robust data quality solutions to ensure the accuracy, consistency, and reliability of data that organizations rely on for strategic insights.

One of the notable growth factors in this market is the exponential increase in data volumes, which calls for effective data management strategies. Businesses today are inundated with data from diverse sources such as social media, sensor data, transactional data, and more. Ensuring the quality of this data is paramount for gaining actionable insights and maintaining competitive advantage. Consequently, the demand for sophisticated data quality solutions has surged, propelling market growth. Additionally, stringent regulatory requirements across various sectors, including finance and healthcare, have further emphasized the need for data quality solutions to ensure compliance with data governance standards.

Another significant driver for the data quality solution market is the growing emphasis on digital transformation initiatives. Organizations across the globe are leveraging digital technologies to enhance operational efficiencies and customer experiences. However, the success of these initiatives largely depends on the quality of data being utilized. As a result, there is a burgeoning demand for data quality tools that can automate data cleansing, profiling, and enrichment processes, ensuring that the data is fit for purpose. This trend is particularly evident in sectors such as BFSI and retail, where accurate data is crucial for risk management, customer personalization, and strategic decision-making.

The rise of artificial intelligence and machine learning technologies also contributes significantly to the market's growth. These technologies rely heavily on high-quality data to train models and generate accurate predictions. Poor data quality can lead to erroneous insights and suboptimal decisions, thus undermining the potential benefits of AI and ML initiatives. Therefore, organizations are increasingly investing in advanced data quality solutions to enhance their AI capabilities and drive innovation. This trend is expected to further accelerate market growth over the forecast period.

Component Analysis

The data quality solution market can be segmented based on components, primarily into software and services. The software segment encompasses various tools and platforms designed to enhance data quality through cleansing, profiling, enrichment, and monitoring. These software solutions are equipped with advanced features like data matching, de-duplication, and standardization, which are crucial for maintaining high data quality standards. The increasing complexity of data environments and the need for real-time data quality management are driving the adoption of these sophisticated software solutions, making this segment a significant contributor to the market's growth.

In addition to the software, the services segment plays a crucial role in the data quality solution market. This segment includes professional services such as consulting, implementation, training, and support. Organizations often require expert guidance to deploy data quality solutions effectively and ensure they are tailored to specific business needs. Consulting services help in assessing current data quality issues, defining data governance frameworks, and developing customized solutions. Implementation services ensure seamless integration of data quality tools with existing systems, while training and support services empower users with the necessary skills to manage and maintain data quality effectively. The growth of the services segment is bolstered by the increasing complexity of data ecosystems and the need for specialized expertise.

Report Scope

Data Quality Coverage Analytics Market Outlook

According to our latest research, the global Data Quality Coverage Analytics market size in 2024 stands at USD 2.13 billion, reflecting a robust and expanding interest in data-driven decision-making across industries. The market is projected to exhibit a CAGR of 17.4% from 2025 to 2033, reaching an estimated market size of USD 9.12 billion by 2033. The primary growth factor driving this surge is the escalating demand for high-quality, reliable data to fuel advanced analytics, regulatory compliance, and digital transformation initiatives across enterprises of all scales.

The growth of the Data Quality Coverage Analytics market is fundamentally underpinned by the increasing volume and complexity of data generated by businesses worldwide. As organizations accelerate their digital transformation journeys, the need for robust data quality frameworks has become paramount. Enterprises are realizing that poor data quality can significantly hamper business intelligence, operational efficiency, and customer satisfaction. Consequently, investments in data quality coverage analytics solutions are rising, as these tools help ensure data accuracy, completeness, consistency, and reliability across diverse data sources and environments. The proliferation of big data, IoT devices, and cloud computing has only heightened the urgency for comprehensive data quality management, further propelling market growth.

Another critical factor contributing to the market's expansion is the tightening regulatory landscape surrounding data privacy and protection. Industries such as BFSI, healthcare, and government face mounting pressure to comply with regulations like GDPR, HIPAA, and CCPA. These mandates necessitate rigorous data governance, transparency, and auditability, all of which hinge on maintaining high data quality standards. Data quality coverage analytics solutions empower organizations to proactively identify and remediate data issues, thereby minimizing compliance risks and avoiding costly penalties. This regulatory impetus is prompting both large enterprises and SMEs to adopt advanced analytics platforms that prioritize data quality as a strategic asset.

Moreover, the integration of artificial intelligence and machine learning technologies into data quality coverage analytics platforms is revolutionizing the market. These advanced capabilities enable automated data profiling, anomaly detection, and predictive analytics, reducing manual intervention and accelerating time-to-insight. Businesses are leveraging these innovations to gain real-time visibility into their data ecosystems, enhance decision-making, and unlock new growth opportunities. The convergence of data quality analytics with business intelligence and risk management applications is creating a holistic approach to data stewardship, further fueling market adoption. As organizations increasingly recognize the value of data as a competitive differentiator, the demand for comprehensive data quality coverage analytics solutions is expected to sustain its upward trajectory.

From a regional perspective, North America currently leads the Data Quality Coverage Analytics market, accounting for the largest share due to the presence of technologically advanced industries and early adoption of digital solutions. Europe follows closely, driven by stringent data protection regulations and a mature enterprise landscape. The Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, increasing investments in IT infrastructure, and the rising importance of data-driven strategies among emerging economies. Latin America and the Middle East & Africa are also showing promising growth potential, albeit from a smaller base, as organizations in these regions gradually prioritize data quality to drive business excellence and regulatory compliance.

Component Analysis

The Data Quality Coverage Analytics market is segmented by component into Software and Services. The software seg

Records selected for their start dates. Records having a “Start date” from 1 January 2005 to 31 Deccember 2014 (both inclusive) are listed in a “StartDate” sheet, with the remaining records in a “StartDate_leftovers” sheet. The data (112,013 records from Additional file 3: Table S1) are presented in the following six Recruitment Type categories: (1) Active, not recruiting (8582 selected records, with 2512 leftovers), (2) Completed (50,012; 17,282), (3) Enrolling by invitation (606; 416), (4) Recruiting (12,991; 10,232), (5) Suspended (432; 165), and (6) Terminated (7215, 1568). The sheets are numbered 1–6, respectively. The file is available at https://osf.io/jcb92 . (ODS 3850 kb)

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The data quality tools market has the potential to grow by USD 1.09 billion during 2021-2025, and the market’s growth momentum will accelerate at a CAGR of 14.30%.

This data quality tools market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers market segmentation by deployment (on-premise and cloud-based) and geography (North America, Europe, APAC, South America, and Middle East and Africa). The data quality tools market report also offers information on several market vendors, including Accenture Plc, Ataccama Corp., DQ Global, Experian Plc, International Business Machines Corp., Oracle Corp., Precisely, SAP SE, SAS Institute Inc., and TIBCO Software Inc. among others.

What will the Data Quality Tools Market Size be in 2021?

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Data Quality Tools Market: Key Drivers and Trends

The increasing use of data quality tools for marketing is notably driving the data quality tools market growth, although factors such as high implementation and production cost may impede market growth. To unlock information on the key market drivers and the COVID-19 pandemic impact on the data quality tools industry get your FREE report sample now.

Enterprises are increasingly using data quality tools, to clean and profile the data to target customers with appropriate products, for digital marketing. Data quality tools help in digital marketing by collecting accurate customer data that is stored in databases and translate that data into rich cross-channel customer profiles. This data helps enterprises in making better decisions on how to maximize the funds coming in. Thus, the rising use of data quality tools to change company processes of marketing is driving the data quality tools market growth.

This data quality tools market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. Get detailed insights on the trends and challenges, which will help companies evaluate and develop growth strategies.

Who are the Major Data Quality Tools Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Accenture Plc
Ataccama Corp.
DQ Global
Experian Plc
International Business Machines Corp.
Oracle Corp.
Precisely
SAP SE
SAS Institute Inc.
TIBCO Software Inc.

The data quality tools market is fragmented and the vendors are deploying organic and inorganic growth strategies to compete in the market. Click here to uncover other successful business strategies deployed by the vendors.

To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

Download a free sample of the data quality tools market forecast report for insights on complete key vendor profiles. The profiles include information on the production, sustainability, and prospects of the leading companies.

Which are the Key Regions for Data Quality Tools Market?

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39% of the market’s growth will originate from North America during the forecast period. The US is the key market for data quality tools market in North America. Market growth in this region will be slower than the growth of the market in APAC, South America, and MEA.

The expansion of data in the region, fueled by the increasing adherence to mobile and Internet of Things (IoT), the presence of major data quality tools vendors, stringent data-related regulatory compliances, and ongoing projects will facilitate the data quality tools market growth in North America over the forecast period. To garner further competitive intelligence and regional opportunities in store for vendors, view our sample report.

What are the Revenue-generating Deployment Segments in the Data Quality Tools Market?

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Although the on-premises segment is expected to grow at a slower rate than the cloud-based segment, primarily due to the high cost of on-premises deployment, its prime advantage of total ownership by the end-user will retain its market share. Also, in an on-premise solution, customization is high, which makes it more adaptable among large enterprises, thus driving the revenue growth of the market.

Fetch actionable market insights on post COVID-19 impact on each segment. This report provides an accurate prediction of the contribution of all the segments to the growth of the data qualit

The Alternative Data Services market is experiencing robust growth, driven by the increasing demand for non-traditional data sources among financial institutions and investment firms. The market's expansion is fueled by several key factors. Firstly, the need for enhanced investment strategies and improved risk management is pushing firms to explore alternative data sources beyond traditional financial statements. This includes incorporating web scraping, social media sentiment analysis, satellite imagery, and transactional data to gain a competitive edge in market prediction and portfolio management. Secondly, advancements in data analytics and machine learning capabilities have made it easier to process and interpret this complex, unstructured alternative data, leading to more actionable insights. Finally, the rising availability of alternative data providers, many specializing in niche data segments, has fostered a dynamic and competitive market. While the exact market size in 2025 is unavailable, a reasonable estimation based on a plausible CAGR of 25% (a common growth rate for rapidly expanding technology sectors) from a hypothetical base year 2019 figure of $5 Billion, would place the 2025 market size at approximately $15 billion. This estimate acknowledges the market's dynamic nature and potential for faster or slower growth based on economic conditions and technological advancements. However, the upward trend remains undeniable. The market's segmentation includes various data types and service models. Companies are categorized into providers specializing in specific data sources (e.g., transactional data, satellite imagery) and those offering integrated platforms that combine multiple data types. Geopolitically, North America currently dominates the market, given the concentration of financial institutions and technology firms in the region. However, significant growth is expected from Asia-Pacific and Europe, driven by increasing adoption of alternative data in developing financial markets. Restraints include challenges related to data quality, regulation, and data privacy concerns. The increasing regulatory scrutiny around the use of alternative data necessitates robust compliance strategies for both data providers and users. Despite these challenges, the long-term outlook for the Alternative Data Services market remains extremely positive, with a projected substantial increase in market size over the next decade. This growth will be driven by continuous technological innovation, expanding data availability, and the increasing demand for data-driven investment decision-making.

Network Model Data Quality Market Outlook

According to our latest research, the Global Network Model Data Quality market size was valued at $1.8 billion in 2024 and is projected to reach $6.4 billion by 2033, expanding at a CAGR of 14.7% during 2024–2033. The rapid proliferation of digital transformation initiatives across industries is a primary driver fueling the adoption of network model data quality solutions worldwide. As organizations increasingly rely on interconnected systems, the need for accurate, consistent, and reliable data models has become critical to ensure seamless operations, robust analytics, and informed decision-making. This market’s expansion is further catalyzed by the growing complexity of network infrastructures and the rising emphasis on data governance, compliance, and real-time analytics, making network model data quality a central pillar in the digital era.

Regional Outlook

North America currently dominates the Network Model Data Quality market, accounting for the largest share of global revenue in 2024. The region’s leadership is underpinned by its mature IT ecosystem, early adoption of advanced data management technologies, and the presence of major industry players. The United States, in particular, has established robust data governance frameworks and regulatory policies that encourage enterprises to invest heavily in data quality solutions. Furthermore, the high concentration of Fortune 500 companies, coupled with a strong focus on digital innovation and cybersecurity, has led to widespread deployment of both on-premises and cloud-based network model data quality platforms. These factors collectively contribute to North America’s market share, which is estimated to exceed 38% of the global total in 2024.

In contrast, the Asia Pacific region is emerging as the fastest-growing market for network model data quality, projected to register a remarkable CAGR of 18.2% from 2025 to 2033. The exponential growth in this region is driven by rapid digitization across sectors such as banking, healthcare, and manufacturing, particularly in China, India, Japan, and Southeast Asia. Governments in these countries are actively promoting data-driven initiatives, smart city projects, and digital infrastructure upgrades, all of which necessitate robust data quality management. Additionally, the influx of foreign direct investment, the rise of tech-savvy SMEs, and the expansion of cloud service providers are accelerating adoption rates. The Asia Pacific market is expected to capture a significant portion of global revenue by 2033, narrowing the gap with mature markets.

Emerging economies in Latin America and Middle East & Africa are witnessing gradual but steady adoption of network model data quality solutions. These regions face unique challenges such as fragmented IT infrastructure, limited technical expertise, and varying regulatory landscapes. However, increasing awareness about the importance of high-quality data, coupled with government-led digital transformation programs, is gradually boosting market penetration. Localized demand is often driven by multinational corporations operating in these regions, seeking to standardize data quality across their global operations. Despite the slower pace, these markets offer untapped potential, especially as cloud adoption and mobile connectivity continue to rise, creating new opportunities for network model data quality vendors.

Report Scope

Records that list matching numbers of names and roles of the investigators. The data (31,392 records from Additional file 10: Table S8) were sorted into the “MatchedPipes” sheet, where the number of pipes (each one of which delineates one name or role) was the same in the name and corresponding role cells, and an “UnmatchedPipes” sheet with the remaining records. The data are presented in the following six Recruitment Type categories: (1) Active, not recruiting (4051 selected records with 1 leftover), (2) Completed (19,392; 12), (3) Enrolling by invitation (162; 0), (4) Recruiting (3782; 2), (5) Suspended (181; 1), and (6) Terminated (3807; 1). The sheets for these categories are numbered 1–6, respectively. (XLS 6429 kb)

Data Quality Rules Engine for AMI Market Outlook

According to our latest research, the global Data Quality Rules Engine for AMI market size reached USD 1.21 billion in 2024, with a robust growth trajectory supported by a CAGR of 13.8% from 2025 to 2033. The market is forecasted to attain a value of USD 3.77 billion by 2033, driven by the rapid proliferation of smart metering infrastructure and the escalating demand for actionable, high-integrity data in utility operations. This growth is underpinned by the increasing deployment of Advanced Metering Infrastructure (AMI) across regions, as utilities and energy providers seek to optimize meter data management, regulatory compliance, and grid analytics. As per the most recent industry analysis, the integration of data quality rules engines has become pivotal in ensuring the reliability and accuracy of AMI-generated data, fueling market expansion.

One of the primary growth factors for the Data Quality Rules Engine for AMI market is the exponential rise in smart grid initiatives worldwide. As governments and utilities invest heavily in modernizing grid infrastructure, AMI systems have become the backbone of real-time data collection, billing, and operational analytics. However, the accuracy of AMI data is often challenged by transmission errors, device malfunctions, and integration complexities. The implementation of advanced data quality rules engines addresses these challenges by providing automated validation, cleansing, and standardization of meter data. This, in turn, enhances operational efficiency, reduces revenue leakage, and supports predictive maintenance strategies. The growing need for reliable data to support demand response, outage management, and distributed energy resources integration is further accelerating the adoption of these solutions across the utility sector.

Another significant driver is the tightening regulatory landscape and the increasing emphasis on data governance in the utilities sector. Regulatory bodies worldwide are mandating stringent data accuracy and reporting standards for energy providers, especially in regions with liberalized energy markets. Data quality rules engines play a crucial role in ensuring compliance with these regulations by automating data validation processes and providing audit trails for all data transformations. This not only minimizes the risk of penalties and non-compliance but also enhances customer trust and satisfaction by ensuring accurate billing and transparent energy usage reporting. The convergence of data privacy laws and energy market regulations is expected to further propel the demand for robust data quality management solutions within AMI environments.

Technological advancements, particularly the integration of artificial intelligence (AI) and machine learning (ML) algorithms into data quality rules engines, are opening new avenues for market growth. These technologies enable dynamic rule creation, anomaly detection, and predictive analytics, allowing utilities to proactively identify and rectify data issues before they impact downstream processes. The shift towards cloud-based deployment models is also contributing to market expansion, offering utilities scalable, flexible, and cost-effective solutions to manage the growing volume and complexity of AMI data. As the energy sector continues its digital transformation journey, the role of data quality rules engines will become increasingly central in enabling data-driven decision-making and supporting the transition to more resilient, sustainable energy systems.

From a regional perspective, North America currently dominates the Data Quality Rules Engine for AMI market, accounting for the largest share in 2024, primarily due to the extensive rollout of AMI systems and supportive regulatory frameworks. Europe follows closely, driven by aggressive smart grid investments and the EU’s ambitious energy transition goals. The Asia Pacific region is poised for the fastest growth, propelled by rapid urbanization, government-led smart city projects, and increasing investments in grid modernization. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, as utilities in these regions begin to recognize the value of high-quality AMI data in optimizing resource management and enhancing grid reliability.

Records of drugs or biologicals. Records with “drug:”, “biological: ”, or both of these words in the “intervention” field are listed in a “Drugs+Bio” sheet, with the remaining in a “Drugs+Bio_leftovers” sheet. The data (79,838 records from Additional file 4: Table S2) are presented in the following six Recruitment Type categories: (1) Active, not recruiting (6742 selected records with 1840 leftovers), (2) Completed (41,451; 8561), (3) Enrolling by invitation (386; 220), (4) Recruiting (9379; 3612), (5) Suspended (339; 93), and (6) Terminated (6199; 1016). The sheets for these categories are numbered 1–6, respectively. The file is available at https://osf.io/jcb92 . (ODS 2830 kb)

This data table provides the detailed data quality assessment scores for the Technical Limits dataset. The quality assessment was carried out on the 16th of September 2025. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks.We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.DisclaimerThe data quality assessment may not represent the quality of the current dataset that is published on the Open Data Portal. Please check the date of the latest quality assessment and compare to the 'Modified' date of the corresponding dataset. The data quality assessments will be updated on either a quarterly or annual basis, dependent on the update frequency of the dataset. This information can be found in the dataset metadata, within the Information tab. If you require a more up to date quality assessment, please contact the Open Data Team at opendata@spenergynetworks.co.uk and a member of the team will be in contact.

Data Quality Coverage Analytics Market Outlook

According to our latest research, the Global Data Quality Coverage Analytics market size was valued at $1.8 billion in 2024 and is projected to reach $6.5 billion by 2033, expanding at a robust CAGR of 15.2% during the forecast period of 2025–2033. A primary factor driving the growth of the Data Quality Coverage Analytics market globally is the increasing reliance on advanced analytics and artificial intelligence by organizations to ensure data integrity, regulatory compliance, and to unlock actionable business insights. As businesses across sectors accelerate digital transformation, the demand for sophisticated data quality solutions that provide comprehensive coverage analytics has surged, enabling organizations to maintain high standards of data reliability and drive strategic decision-making.

Regional Outlook

North America currently dominates the Data Quality Coverage Analytics market, accounting for the largest share, with an estimated market value exceeding $700 million in 2024. This regional leadership is attributed to the presence of a mature technology ecosystem, widespread cloud adoption, and stringent regulatory frameworks such as GDPR and CCPA, which mandate superior data management practices. Furthermore, North America’s concentration of leading analytics vendors, coupled with high enterprise IT spending, has fostered a fertile environment for rapid innovation and deployment of data quality solutions. The region’s advanced infrastructure and early adoption of machine learning and AI-powered analytics tools have further cemented its position at the forefront of the global market.

Asia Pacific is emerging as the fastest-growing region in the Data Quality Coverage Analytics market, projected to register an impressive CAGR of 18.7% from 2025 to 2033. The region’s growth trajectory is underpinned by soaring investments in digital transformation, expanding internet penetration, and the proliferation of cloud-based business models across industries such as BFSI, healthcare, and retail. Governments in countries like China, India, and Singapore are rolling out supportive policies and incentives to encourage data-driven innovation, further accelerating the adoption of data quality analytics solutions. The rise of large-scale enterprises and SMEs seeking to harness big data for competitive advantage has also contributed to the region’s rapid market expansion.

In emerging economies, particularly in Latin America and the Middle East & Africa, adoption of Data Quality Coverage Analytics is gaining momentum, albeit at a more measured pace. These regions face unique challenges such as limited technical expertise, budget constraints, and fragmented regulatory landscapes, which can impede widespread implementation. However, localized demand for robust data governance, fraud detection, and compliance management is steadily growing, especially in sectors like banking, telecommunications, and government. Regional governments are increasingly recognizing the importance of data quality for economic development and are introducing policies aimed at fostering digital literacy and infrastructure development, setting the stage for future growth.

Report Scope

The association between facility characteristics and the risk that records contained missing values during baseline RDQAs.