NSF information quality guidelines designed to fulfill the OMB guidelines.

Ontologies play an important role in the representation, standardization, and integration of biomedical data, but are known to have data quality (DQ) issues. We aimed to understand if the Harmonized Data Quality Framework (HDQF), developed to standardize electronic health record DQ assessment strategies, could be used to improve ontology quality assessment. A novel set of 14 ontology checks was developed. These DQ checks were aligned to the HDQF and examined by HDQF developers. The ontology checks were evaluated using 11 Open Biomedical Ontology Foundry ontologies. 85.7% of the ontology checks were successfully aligned to at least 1 HDQF category. Accommodating the unmapped DQ checks (n=2), required modifying an original HDQF category and adding a new Data Dependency category. While all of the ontology checks were mapped to an HDQF category, not all HDQF categories were represented by an ontology check presenting opportunities to strategically develop new ontology checks. The HDQF is a valuable resource and this work demonstrates its ability to categorize ontology quality assessment strategies.

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.

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

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.

Policy Framework

This publication includes National level-specific data for QOF 2006-2007. For links to all QOF data for 2006-2007, see The Quality and Outcomes Framework, 2006-07. You can also browse the QOF online database to find results for your local surgery.

This Quality and Outcomes Framework (QOF) publication provides data for the reporting year April 2014 to March 2015. 18/01/2017: Previously age-specific register sizes were given with unadjusted numbers of registered patients in the prevalence CSV files. Prevalence rates in the CSVs based on these figures were incorrect. This has been corrected in: PREVALENCE_BY_AT.csv; PREVALENCE_BY_CCG.csv; PREVALENCE_BY_PRAC.csv; PREVALENCE_BY_REGION.csv; PREVALENCE_BY_SUBREGION.csv; PREVALENCE_ENGLAND.csv 17/11/2015: Updates to the main report and excel spreadsheet entitled 'QOF 2014-15: Exceptions at practice level, all domains' have been published. These correct the overall exception percentages. The list size percentages for those aged 75+ have also been updated in the region-nation and sub-region-AT spreadsheets. All updated figures are highlighted in yellow in the documents. The QOF was introduced as part of the new General Medical Services (GMS) contract on 1 April 2004. The objective of the QOF is to improve the quality of care patients are given by rewarding practices for the quality of care they provide to their patients. The Calculating Quality Reporting Service1 (CQRS), together with the General Practice Extraction Service2 (GPES) was used for the extraction of QOF data. There have been many changes to QOF coding and indicators. These are referred to throughout this publication. Consideration must be given to changes to indicators and their definitions each year when interpreting differences and comparing data from one year to the next. 27th November 2015 New data has been released today showing the practice register sizes for Heart Failure due to LVD. This is available in 'Resources' below.

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 Software and Solutions Market Outlook

The global data quality software and solutions market size was valued at $2.5 billion in 2023, and it is projected to reach $7.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.5% over the forecast period. This significant growth is driven by factors such as the increasing amount of data generated across various industries, the rising need for data accuracy and consistency, and advancements in artificial intelligence and machine learning technologies.

One of the primary growth drivers for the data quality software and solutions market is the exponential increase in data generation across different industry verticals. With the advent of digital transformation, businesses are experiencing unprecedented volumes of data. This surge necessitates robust data quality solutions to ensure that data is accurate, consistent, and reliable. As organizations increasingly rely on data-driven decision-making, the demand for data quality software is expected to escalate, thereby propelling market growth.

Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) into data quality solutions has significantly enhanced their capabilities. AI and ML algorithms can automate data cleansing processes, identify patterns, and predict anomalies, which improves data accuracy and reduces manual intervention. The continuous advancements in these technologies are expected to further bolster the adoption of data quality software, as businesses seek to leverage AI and ML for optimized data management.

The growing regulatory landscape concerning data privacy and security is another crucial factor contributing to market growth. Governments and regulatory bodies across the world are implementing stringent data protection laws, compelling organizations to maintain high standards of data quality. Compliance with these regulations not only helps in avoiding hefty penalties but also enhances the trust and credibility of businesses. Consequently, companies are increasingly investing in data quality solutions to ensure adherence to regulatory requirements, thereby driving market expansion.

Regionally, North America is expected to dominate the data quality software and solutions market, followed by Europe and Asia Pacific. North America's leadership position can be attributed to the early adoption of advanced technologies, a high concentration of data-driven enterprises, and robust infrastructure. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest CAGR over the forecast period, spurred by the rapid digitization of economies, increasing internet penetration, and the growing focus on data analytics and management.

Component Analysis

In the data quality software and solutions market, the component segment is bifurcated into software and services. The software segment encompasses various solutions designed to improve data accuracy, consistency, and reliability. These software solutions include data profiling, data cleansing, data matching, and data enrichment tools. The increasing complexity of data management and the need for real-time data quality monitoring are driving the demand for comprehensive software solutions. Businesses are investing in advanced data quality software that integrates seamlessly with their existing data infrastructure, providing actionable insights and enhancing operational efficiency.

The services segment includes professional and managed services aimed at helping organizations implement, maintain, and optimize their data quality initiatives. Professional services comprise consulting, implementation, and training services, wherein experts assist businesses in deploying data quality solutions tailored to their specific needs. Managed services, on the other hand, involve outsourcing data quality management to third-party providers, allowing organizations to focus on their core competencies while ensuring high data quality standards. The growing reliance on data quality services is attributed to the increasing complexity of data ecosystems and the need for specialized expertise.

Companies are increasingly seeking professional services to navigate the complexities associated with data quality management. These services provide valuable insights into best practices, enabling organizations to establish effective data governance frameworks. Moreover, the demand for managed services is rising as businesses look to offload the burden of continuous data quality monitoring and maintenance. By outsourcing these functions, organ

The triple redundant air aspirated temperature data used in the manuscript are provided in the zip file “Data S1”. The zip files includes two directories: Directory “Final Modified Data” contains the calibrated error-injected data and directory “Raw Data” provides the uncalibrated raw sensor measurements from the three PRTs together with their calibration equations and sensor specific calibration coefficients. (ZIP)

This data package contains quality measures such as Air Quality, Austin Airport, LBB Performance Report, School Survey, Child Poverty, System International Units, Weight Measures, etc.

The Quality and Outcomes Framework (QOF) allows practices to exception-report (exclude) specific patients from data collected to calculate achievement scores. Patients can be exception-reported from individual indicators for various reasons, for example if they are newly diagnosed or newly registered with a practice, if they do not attend appointments or where the treatment is judged to be inappropriate by the GP (such as medication cannot be prescribed due to side-effects). The General Medical Services contract sets out the criteria which allow practices to participate in QOF but not to be penalised where exception reporting occurs. Patient exception reporting applies to QOF indicators where the level of achievement is determined by the percentage of patients receiving the designated level of care. The information presented here refers to exception reporting for indicators with the ‘clinical domain' of the QOF, and two indicator sets in the ‘additional services domain' (cervical screening and contraceptive services). For background information on QOF exception reporting, and for notes on the way exception reporting rates are calculated, see the detailed notes in the statistical bulletin in QOF exception reporting.

Data related to the paper Everything you should know about process model quality- The Comprehensive Process Model Quality Framework

The market size of the Data Quality Software Market is categorized based on Data Cleansing (Data Profiling, Data Standardization, Data Enrichment, Data Deduplication, Data Validation) and Data Integration (Real-time Integration, Batch Integration, Cloud Integration, On-premises Integration, Data Migration) and Data Governance (Metadata Management, Data Stewardship, Data Quality Frameworks, Policy Management, Compliance Management) and Data Quality Monitoring (Automated Monitoring, Manual Monitoring, Reporting & Analytics, Alert Management, Dashboarding) and Professional Services (Consulting Services, Implementation Services, Training Services, Support & Maintenance, Managed Services) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Use cases were collected using a number of methods to maximise responses. Lead authors of papers published using data accessed via the Atlas of Living Australia (ALA) were contacted and asked to contribute their research data use cases, and a number of papers describing fitness for use determination were sent to the ALA Data Quality group. Fitness for use and quality check information from these papers were extracted and transferred to the use case library. These are the results of those surveys.

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.

A comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework consists of three major phases: Phase 1—Preliminary raw data sets exploration, including time formatting and combining datasets of different lengths and different time intervals; Phase 2—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme values; and Phase 3—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado) were analyzed. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.The files that are in this data package include one excel file, converted to CSV format (Billy_Barr_raw_qaqc.csv) that contains the raw meteorological data, i.e., input data used for the QA/QC analysis. The second CSV file (Billy_Barr_1hr.csv) is the QA/QC and flagged meteorological data, i.e., output data from the QA/QC analysis. The last file (QAQC_Billy_Barr_2021-03-22.R) is a script written in R that implements the QA/QC and flagging process. The purpose of the CSV data files included in this package is to provide input and output files implemented in the R script.

Administrative data are increasingly important in statistics, but, like other types of data, may contain measurement errors. To prevent such errors from invalidating analyses of scientific interest, it is therefore essential to estimate the extent of measurement errors in administrative data. Currently, however, most approaches to evaluate such errors involve either prohibitively expensive audits or comparison with a survey that is assumed perfect. We introduce the “generalized multitrait-multimethod” (GMTMM) model, which can be seen as a general framework for evaluating the quality of administrative and survey data simultaneously. This framework allows both survey and administrative data to contain random and systematic measurement errors. Moreover, it accommodates common features of administrative data such as discreteness, nonlinearity, and nonnormality, improving similar existing models. The use of the GMTMM model is demonstrated by application to linked survey-administrative data from the German Federal Employment Agency on income from of employment, and a simulation study evaluates the estimates obtained and their robustness to model misspecification. Supplementary materials for this article are available online.

This publication includes Practice-specific data for QOF 2009-2010. For links to all QOF data for 2009-2010, see The Quality and Outcomes Framework, 2009-10. You can also browse the QOF online database to find results for your local surgery.