NSF information quality guidelines designed to fulfill the OMB guidelines.

Data Quality Tools Market size was valued at USD 2.71 Billion in 2024 and is projected to reach USD 4.15 Billion by 2031, growing at a CAGR of 5.46% from 2024 to 2031.

Global Data Quality Tools Market Drivers

Growing Data Volume and Complexity: Sturdy data quality technologies are necessary to guarantee accurate, consistent, and trustworthy information because of the exponential increase in the volume and complexity of data supplied by companies. Growing Knowledge of Data Governance: Businesses are realizing how critical it is to uphold strict standards for data integrity and data governance. Tools for improving data quality are essential for advancing data governance programs. Needs for Regulatory Compliance: Adoption of data quality technologies is prompted by strict regulatory requirements, like GDPR, HIPAA, and other data protection rules, which aim to ensure compliance and reduce the risk of negative legal and financial outcomes. Growing Emphasis on Analytics and Business Intelligence (BI): The requirement for accurate and trustworthy data is highlighted by the increasing reliance on corporate intelligence and analytics for well-informed decision-making. Tools for improving data quality contribute to increased data accuracy for analytics and reporting. Initiatives for Data Integration and Migration: Companies engaged in data integration or migration initiatives understand how critical it is to preserve data quality throughout these procedures. The use of data quality technologies is essential for guaranteeing seamless transitions and avoiding inconsistent data. Real-time data quality management is in demand: Organizations looking to make prompt decisions based on precise and current information are driving an increased need for real-time data quality management systems. The emergence of cloud computing and big data: Strong data quality tools are required to manage many data sources, formats, and environments while upholding high data quality standards as big data and cloud computing solutions become more widely used. Pay attention to customer satisfaction and experience: Businesses are aware of how data quality affects customer happiness and experience. Establishing and maintaining consistent and accurate customer data is essential to fostering trust and providing individualized services. Preventing Fraud and Data-Related Errors: By detecting and fixing mistakes in real time, data quality technologies assist firms in preventing errors, discrepancies, and fraudulent activities while lowering the risk of monetary losses and reputational harm. Linking Master Data Management (MDM) Programs: Integrating with MDM solutions improves master data management overall and guarantees high-quality, accurate, and consistent maintenance of vital corporate information. Offerings for Data Quality as a Service (DQaaS): Data quality tools are now more widely available and scalable for companies of all sizes thanks to the development of Data Quality as a Service (DQaaS), which offers cloud-based solutions to firms.

This dataset includes information on quality control and data management of researchers and data curators from a social science organization. Four data curators and 24 researchers provided responses for the study. Data collection techniques, data processing strategies, data storage and preservation, metadata standards, data sharing procedures, and the perceived significance of quality control and data quality assurance are the main areas of focus. The dataset attempts to provide insight on the RDM procedures that are being used by a social science organization as well as the difficulties that researchers and data curators encounter in upholding high standards of data quality. The goal of the study is to encourage more investigations aimed at enhancing scientific community data management practices and guidelines.

Data Quality Tools Market Outlook

The global data quality tools market size was valued at $1.8 billion in 2023 and is projected to reach $4.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.9% during the forecast period. The growth of this market is driven by the increasing importance of data accuracy and consistency in business operations and decision-making processes.

One of the key growth factors is the exponential increase in data generation across industries, fueled by digital transformation and the proliferation of connected devices. Organizations are increasingly recognizing the value of high-quality data in driving business insights, improving customer experiences, and maintaining regulatory compliance. As a result, the demand for robust data quality tools that can cleanse, profile, and enrich data is on the rise. Additionally, the integration of advanced technologies such as AI and machine learning in data quality tools is enhancing their capabilities, making them more effective in identifying and rectifying data anomalies.

Another significant driver is the stringent regulatory landscape that requires organizations to maintain accurate and reliable data records. Regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States necessitate high standards of data quality to avoid legal repercussions and financial penalties. This has led organizations to invest heavily in data quality tools to ensure compliance. Furthermore, the competitive business environment is pushing companies to leverage high-quality data for improved decision-making, operational efficiency, and competitive advantage, thus further propelling the market growth.

The increasing adoption of cloud-based solutions is also contributing significantly to the market expansion. Cloud platforms offer scalable, flexible, and cost-effective solutions for data management, making them an attractive option for organizations of all sizes. The ease of integration with various data sources and the ability to handle large volumes of data in real-time are some of the advantages driving the preference for cloud-based data quality tools. Moreover, the COVID-19 pandemic has accelerated the digital transformation journey for many organizations, further boosting the demand for data quality tools as companies seek to harness the power of data for strategic decision-making in a rapidly changing environment.

Data Wrangling is becoming an increasingly vital process in the realm of data quality tools. As organizations continue to generate vast amounts of data, the need to transform and prepare this data for analysis is paramount. Data wrangling involves cleaning, structuring, and enriching raw data into a desired format, making it ready for decision-making processes. This process is essential for ensuring that data is accurate, consistent, and reliable, which are critical components of data quality. With the integration of AI and machine learning, data wrangling tools are becoming more sophisticated, allowing for automated data preparation and reducing the time and effort required by data analysts. As businesses strive to leverage data for competitive advantage, the role of data wrangling in enhancing data quality cannot be overstated.

On a regional level, North America currently holds the largest market share due to the presence of major technology companies and a high adoption rate of advanced data management solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The increasing digitization across industries, coupled with government initiatives to promote digital economies in countries like China and India, is driving the demand for data quality tools in this region. Additionally, Europe remains a significant market, driven by stringent data protection regulations and a strong emphasis on data governance.

Component Analysis

The data quality tools market is segmented into software and services. The software segment includes various tools and applications designed to improve the accuracy, consistency, and reliability of data. These tools encompass data profiling, data cleansing, data enrichment, data matching, and data monitoring, among others. The software segment dominates the market, accounting for a substantial share due to the increasing need for automated data management solutions. The integration of AI and machine learning into these too

The Data Quality Solutions market, currently valued at $3785.8 million (2025), is projected to experience steady growth, exhibiting a Compound Annual Growth Rate (CAGR) of 2.3% from 2025 to 2033. This growth is fueled by several key factors. The increasing reliance on data-driven decision-making across various industries necessitates high-quality, reliable data. This demand is driving investments in advanced data quality solutions capable of handling large volumes of diverse data sources, including structured and unstructured data from cloud platforms, on-premises systems, and third-party providers. Furthermore, stringent data privacy regulations like GDPR and CCPA are forcing organizations to prioritize data accuracy and compliance, further boosting the market. The rising adoption of cloud-based data management solutions also contributes to market expansion as these platforms often include integrated data quality features. Competitive landscape includes established players like IBM, Informatica, and Oracle, alongside emerging innovative companies focusing on specific data quality niches, fostering innovation and competition. The market segmentation, although not explicitly detailed, can be reasonably inferred to include solutions categorized by deployment (cloud, on-premise, hybrid), data type (structured, unstructured), and industry vertical (finance, healthcare, retail, etc.). Growth will likely be uneven across these segments, with cloud-based solutions and those addressing the needs of data-intensive sectors (like finance and healthcare) experiencing faster adoption rates. While technological advancements are driving growth, challenges remain, including the complexity of implementing and maintaining data quality solutions, the need for specialized skills, and the potential for high initial investment costs. However, the long-term benefits of improved data quality, including enhanced decision-making, reduced operational costs, and improved regulatory compliance, outweigh these challenges, ensuring continued market expansion in the coming years.

Spatial association rule mining (SARM) is an important data mining task for understanding implicit and sophisticated interactions in spatial data. The usefulness of SARM results, represented as sets of rules, depends on their reliability: the abundance of rules, control over the risk of spurious rules, and accuracy of rule interestingness measure (RIM) values. This study presents crisp-fuzzy SARM, a novel SARM method that can enhance the reliability of resultant rules. The method firstly prunes dubious rules using statistically sound tests and crisp supports for the patterns involved, and then evaluates RIMs of accepted rules using fuzzy supports. For the RIM evaluation stage, the study also proposes a Gaussian-curve-based fuzzy data discretization model for SARM with improved design for spatial semantics. The proposed techniques were evaluated by both synthetic and real-world data. The synthetic data was generated with predesigned rules and RIM values, thus the reliability of SARM results could be confidently and quantitatively evaluated. The proposed techniques showed high efficacy in enhancing the reliability of SARM results in all three aspects. The abundance of resultant rules was improved by 50% or more compared with using conventional fuzzy SARM. Minimal risk of spurious rules was guaranteed by statistically sound tests. The probability that the entire result contained any spurious rules was below 1%. The RIM values also avoided large positive errors committed by crisp SARM, which typically exceeded 50% for representative RIMs. The real-world case study on New York City points of interest reconfirms the improved reliability of crisp-fuzzy SARM results, and demonstrates that such improvement is critical for practical spatial data analytics and decision support.

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

Comment on proposed rulemaking. Comment period runs from 11/15/16-12/23/16. Comments submitted outside this comment period will not be considered.

EPA has compiled state, territorial, and authorized tribal water quality standards that EPA has approved or are otherwise in effect for Clean Water Act purposes. This compilation is continuously updated as EPA approves new or revised WQS.Please note the water quality standards may contain additional provisions outside the scope of the Clean Water Act, its implementing federal regulations, or EPA's authority. In some cases, these additional provisions have been included as supplementary information. EPA is posting the water quality standards as a convenience to users and has made a reasonable effort to assure their accuracy. Additionally, EPA has made a reasonable effort to identify parts of the standards that are approved, disapproved, or are otherwise not in effect for Clean Water Act purposes.

Cloud Data Quality Monitoring and Testing Market Outlook

The global cloud data quality monitoring and testing market size was valued at USD 1.5 billion in 2023 and is expected to reach USD 4.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.8% during the forecast period. This robust growth is driven by increasing cloud adoption across various industries, coupled with the rising need for ensuring data quality and compliance.

One of the primary growth factors of the cloud data quality monitoring and testing market is the exponential increase in data generation and consumption. As organizations continue to integrate cloud solutions, the volume of data being processed and stored on the cloud has surged dramatically. This data influx necessitates stringent quality monitoring to ensure data integrity, accuracy, and consistency, thus driving the demand for advanced data quality solutions. Moreover, as businesses enhance their data-driven decision-making processes, the need for high-quality data becomes ever more critical, further propelling market growth.

Another significant driver is the growing complexity of data architectures due to diverse data sources and types. The modern data environment is characterized by a mix of structured, semi-structured, and unstructured data originating from various sources like IoT devices, social media platforms, and enterprise applications. Ensuring the quality of such heterogeneous data sets requires sophisticated monitoring and testing tools that can seamlessly operate within cloud ecosystems. Consequently, organizations are increasingly investing in cloud data quality solutions to manage this complexity, thereby fueling market expansion.

Compliance and regulatory requirements also play a pivotal role in the growth of the cloud data quality monitoring and testing market. Industries such as BFSI, healthcare, and government are subject to stringent data governance and privacy regulations that mandate regular auditing and validation of data quality. Failure to comply with these regulations can result in severe penalties and reputational damage. Hence, companies are compelled to adopt cloud data quality monitoring and testing solutions to ensure compliance and mitigate risks associated with data breaches and inaccuracies.

From a regional perspective, North America dominates the market due to its advanced IT infrastructure and early adoption of cloud technologies. However, significant growth is also expected in the Asia Pacific region, driven by rapid digital transformation initiatives and increasing investments in cloud infrastructure by emerging economies like China and India. Europe also presents substantial growth opportunities, with industries embracing cloud solutions to enhance operational efficiency and innovation. The regional dynamics indicate a wide-ranging impact of cloud data quality monitoring and testing solutions across the globe.

Component Analysis

The cloud data quality monitoring and testing market is broadly segmented into software and services. The software segment encompasses various tools and platforms designed to automate and streamline data quality monitoring processes. These solutions include data profiling, data cleansing, data integration, and master data management software. The demand for such software is on the rise due to its ability to provide real-time insights into data quality issues, thereby enabling organizations to take proactive measures in addressing discrepancies. Advanced software solutions often leverage AI and machine learning algorithms to enhance data accuracy and predictive capabilities.

The services segment is equally crucial, offering a gamut of professional and managed services to support the implementation and maintenance of data quality monitoring systems. Professional services include consulting, system integration, and training services, which help organizations in the seamless adoption of data quality tools and best practices. Managed services, on the other hand, provide ongoing support and maintenance, ensuring that data quality standards are consistently met. As organizations seek to optimize their cloud data environments, the demand for comprehensive service offerings is expected to rise, driving market growth.

One of the key trends within the component segment is the increasing integration of software and services to offer holistic data quality solutions. Vendors are increasingly bundling their software products with complementary services, providing a one-stop solution that covers all aspects of data quality managem

As part of the “From Data Quality for AI to AI for Data Quality: A Systematic Review of Tools for AI-Augmented Data Quality Management in Data Warehouses” (Tamm & Nikifovora, 2025), a systematic review of DQ tools was conducted to evaluate their automation capabilities, particularly in detecting and recommending DQ rules in data warehouse - a key component of data ecosystems.

To attain this objective, five key research questions were established.

Q1. What is the current landscape of DQ tools?

Q2. What functionalities do DQ tools offer?

Q3. Which data storage systems DQ tools support? and where does the processing of the organization’s data occur?

Q4. What methods do DQ tools use for rule detection?

Q5. What are the advantages and disadvantages of existing solutions?

Candidate DQ tools were identified through a combination of rankings from technology reviewers and academic sources. A Google search was conducted using keyword (“the best data quality tools” OR “the best data quality software” OR “top data quality tools” OR “top data quality software”) AND "2023" (search conducted in December 2023). Additionally, this list was complemented by DQ tools found in academic articles, identified with two queries in Scopus, namely "data quality tool" OR "data quality software" and ("information quality" OR "data quality") AND ("software" OR "tool" OR "application") AND "data quality rule". For selecting DQ tools for further systematic analysis, several exclusion criteria were applied. Tools from sponsored, outdated (pre-2023), non-English, or non-technical sources were excluded. Academic papers were restricted to those published within the last ten years, focusing on the computer science field.

This resulted in 151 DQ tools, which are provided in the file "DQ Tools Selection".

To structure the review process and facilitate answering the established questions (Q1-Q3), a review protocol was developed, consisting of three sections. The initial tool assessment was based on availability, functionality, and trialability (e.g., open-source, demo version, or free trial). Tools that were discontinued or lacked sufficient information were excluded. The second phase (and protocol section) focused on evaluating the functionalities of the identified tools. Initially, the core DQM functionalities were assessed, such as data profiling, custom DQ rule creation, anomaly detection, data cleansing, report generation, rule detection, data enrichment. Subsequently, additional data management functionalities such as master data management, data lineage, data cataloging, semantic discovery, and integration were considered. The final stage of the review examined the tools' compatibility with data warehouses and General Data Protection Regulation (GDPR) compliance. Tools that did not meet these criteria were excluded. As such, the 3^rd section of the protocol evaluated the tool's environment and connectivity features, such as whether it operates in the cloud, hybrid, or on-premises, its API support, input data types (.txt, .csv, .xlsx, .json), and its ability to connect to data sources including relational and non-relational databases, data warehouses, cloud data storages, data lakes. Additionally, it assessed whether the tool processes data on-premises or in the vendor’s cloud environment. Tools were excluded based on criteria such as not supporting data warehouses or processing data externally.

These protocols (filled) are available in file "DQ Tools Analysis"

Data Quality Management Software Market size was valued at USD 4.32 Billion in 2023 and is projected to reach USD 10.73 Billion by 2030, growing at a CAGR of 17.75% during the forecast period 2024-2030.Global Data Quality Management Software Market DriversThe growth and development of the Data Quality Management Software Market can be credited with a few key market drivers. Several of the major market drivers are listed below:Growing Data Volumes: Organizations are facing difficulties in managing and guaranteeing the quality of massive volumes of data due to the exponential growth of data generated by consumers and businesses. Organizations can identify, clean up, and preserve high-quality data from a variety of data sources and formats with the use of data quality management software.Increasing Complexity of Data Ecosystems: Organizations function within ever-more-complex data ecosystems, which are made up of a variety of systems, formats, and data sources. Software for data quality management enables the integration, standardization, and validation of data from various sources, guaranteeing accuracy and consistency throughout the data landscape.Regulatory Compliance Requirements: Organizations must maintain accurate, complete, and secure data in order to comply with regulations like the GDPR, CCPA, HIPAA, and others. Data quality management software ensures data accuracy, integrity, and privacy, which assists organizations in meeting regulatory requirements.Growing Adoption of Business Intelligence and Analytics: As BI and analytics tools are used more frequently for data-driven decision-making, there is a greater need for high-quality data. With the help of data quality management software, businesses can extract actionable insights and generate significant business value by cleaning, enriching, and preparing data for analytics.Focus on Customer Experience: Put the Customer Experience First: Businesses understand that providing excellent customer experiences requires high-quality data. By ensuring data accuracy, consistency, and completeness across customer touchpoints, data quality management software assists businesses in fostering more individualized interactions and higher customer satisfaction.Initiatives for Data Migration and Integration: Organizations must clean up, transform, and move data across heterogeneous environments as part of data migration and integration projects like cloud migration, system upgrades, and mergers and acquisitions. Software for managing data quality offers procedures and instruments to guarantee the accuracy and consistency of transferred data.Need for Data Governance and Stewardship: The implementation of efficient data governance and stewardship practises is imperative to guarantee data quality, consistency, and compliance. Data governance initiatives are supported by data quality management software, which offers features like rule-based validation, data profiling, and lineage tracking.Operational Efficiency and Cost Reduction: Inadequate data quality can lead to errors, higher operating costs, and inefficiencies for organizations. By guaranteeing high-quality data across business processes, data quality management software helps organizations increase operational efficiency, decrease errors, and minimize rework.

Data Quality Management Software Market Outlook

The global data quality management software market size was valued at approximately USD 1.5 billion in 2023 and is anticipated to reach around USD 3.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.8% during the forecast period. This growth is largely driven by the increasing complexity and exponential growth of data generated across various industries, necessitating robust data management solutions to ensure the accuracy, consistency, and reliability of data. As organizations strive to leverage data-driven decision-making and optimize their operations, the demand for efficient data quality management software solutions continues to rise, underscoring their significance in the current digital landscape.

One of the primary growth factors for the data quality management software market is the rapid digital transformation across industries. With businesses increasingly relying on digital tools and platforms, the volume of data generated and collected has surged exponentially. This data, if managed effectively, can unlock valuable insights and drive strategic business decisions. However, poor data quality can lead to erroneous conclusions and suboptimal performance. As a result, enterprises are investing heavily in data quality management solutions to ensure data integrity and enhance decision-making processes. The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in data quality management software is further propelling the market, offering automated data cleansing, enrichment, and validation capabilities that significantly improve data accuracy and utility.

Another significant driver of market growth is the increasing regulatory requirements surrounding data governance and compliance. As data privacy laws become more stringent worldwide, organizations are compelled to adopt comprehensive data quality management practices to ensure adherence to these regulations. The implementation of data protection acts such as GDPR in Europe has heightened the need for data quality management solutions to ensure data accuracy and privacy. Organizations are thus keen to integrate robust data quality measures to safeguard their data assets, maintain customer trust, and avoid hefty regulatory fines. This regulatory-driven push has resulted in heightened awareness and adoption of data quality management solutions across various industry verticals, further contributing to market growth.

The growing emphasis on customer experience and personalization is also fueling the demand for data quality management software. As enterprises strive to deliver personalized and seamless customer experiences, the accuracy and reliability of customer data become paramount. High-quality data enables organizations to gain a 360-degree view of their customers, tailor their offerings, and engage customers more effectively. Companies in sectors such as retail, BFSI, and healthcare are prioritizing data quality initiatives to enhance customer satisfaction, retention, and loyalty. This consumer-centric approach is prompting organizations to invest in data quality management solutions that facilitate comprehensive and accurate customer insights, thereby driving the market's growth trajectory.

Regionally, North America is expected to dominate the data quality management software market, driven by the region's technological advancements and high adoption rate of data management solutions. The presence of leading market players and the increasing demand for data-driven insights to enhance business operations further bolster market growth in this region. Meanwhile, the Asia Pacific region is witnessing substantial growth opportunities, attributed to the rapid digitalization across emerging economies and the growing awareness of data quality's role in business success. The rising adoption of cloud-based solutions and the expanding IT sector are also contributing to the market's regional expansion, with a projected CAGR that surpasses other regions during the forecast period.

Component Analysis

The data quality management software market is segmented by component into software and services, each playing a pivotal role in delivering comprehensive data quality solutions to enterprises. The software component, constituting the core of data quality management, encompasses a wide array of tools designed to facilitate data cleansing, validation, enrichment, and integration. These software solutions are increasingly equipped with advanced features such as AI and ML algorithms, enabling automated data quality processes that si

The research is to find out if the rules of continuity and symmetry are consistent with street network data

Corrections and Clarifications to Toxics Water Quality Standards Comment Form

Dataset on domestic fiscal rules in force in the time period since 1990 across EU countries, covering all types of numerical fiscal rules (budget balance, debt, expenditure, and revenue rules) at all levels of government (central, regional, and local, general government, and social security), as well as indices on the strength and quality of those budgetary rules. The maintained fiscal rules database is updated annually.

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This operations dashboard shows historic and current data related to this performance measure.The performance measure dashboard is available at 1.19 Housing Quality Standards. Data Dictionary

This document provides available health-based reference values (i.e., reference concentrations and reference doses [RfDs]) and a summary of the analytical results to date for the contaminants being monitored under the fifth Unregulated Contaminant Monitoring Rule (UCMR 5). The health information and occurrence data will be updated on a quarterly basis until completion of data reporting in 2026. Data are added and possibly removed or updated over the course of this reporting cycle following further review by analytical laboratories, PWSs, states, and EPA.

The Data Quality Software and Solutions market is experiencing robust growth, driven by the increasing volume and complexity of data generated by businesses across all sectors. The market's expansion is fueled by a rising demand for accurate, consistent, and reliable data for informed decision-making, improved operational efficiency, and regulatory compliance. Key drivers include the surge in big data adoption, the growing need for data integration and governance, and the increasing prevalence of cloud-based solutions offering scalable and cost-effective data quality management capabilities. Furthermore, the rising adoption of advanced analytics and artificial intelligence (AI) is enhancing data quality capabilities, leading to more sophisticated solutions that can automate data cleansing, validation, and profiling processes. We estimate the 2025 market size to be around $12 billion, growing at a compound annual growth rate (CAGR) of 10% over the forecast period (2025-2033). This growth trajectory is being influenced by the rapid digital transformation across industries, necessitating higher data quality standards. Segmentation reveals a strong preference for cloud-based solutions due to their flexibility and scalability, with large enterprises driving a significant portion of the market demand. However, market growth faces some restraints. High implementation costs associated with data quality software and solutions, particularly for large-scale deployments, can be a barrier to entry for some businesses, especially SMEs. Also, the complexity of integrating these solutions with existing IT infrastructure can present challenges. The lack of skilled professionals proficient in data quality management is another factor impacting market growth. Despite these challenges, the market is expected to maintain a healthy growth trajectory, driven by increasing awareness of the value of high-quality data, coupled with the availability of innovative and user-friendly solutions. The competitive landscape is characterized by established players such as Informatica, IBM, and SAP, along with emerging players offering specialized solutions, resulting in a diverse range of options for businesses. Regional analysis indicates that North America and Europe currently hold significant market shares, but the Asia-Pacific region is projected to witness substantial growth in the coming years due to rapid digitalization and increasing data volumes.