The global data validation services market size was valued at USD XXX million in 2025 and is projected to grow at a CAGR of XX% during the forecast period. Growing concerns over data inaccuracy and the increasing volume of data being generated by organizations are the key factors driving the market growth. Additionally, the adoption of cloud-based data validation solutions is expected to further fuel the market expansion. North America and Europe are the largest markets for data validation services, with a significant presence of large enterprises and stringent data regulations. The market is fragmented with several established players and a number of emerging vendors offering specialized solutions. Key market participants include TELUS Digital, Experian Data Quality, Flatworld Solutions Inc., Precisely, LDC, InfoCleanse, Level Data, Damco Solutions, Environmental Data Validation Inc., DataCaptive, Process Fusion, Ann Arbor Technical Services, Inc., and others. These companies are focusing on expanding their geographical reach, developing new products and features, and offering value-added services to gain a competitive edge in the market. The growing demand for data privacy and security solutions is also expected to drive the adoption of data validation services in the coming years.

The global data validation services market is anticipated to grow exponentially over the coming years. The market is projected to reach a value of USD 25.47 billion by 2033, expanding at a CAGR of 14.2% from 2025 to 2033. The increasing volume of data, growing need for data accuracy, and stringent regulatory compliance are major drivers fueling the market growth. Moreover, the adoption of cloud-based data validation solutions, growing adoption of AI and ML technologies, and increasing investments in data governance initiatives are anticipated to create lucrative opportunities for market players. The market is segmented based on type, application, enterprise size, and region. The cloud-based segment is expected to hold the largest market share due to its scalability, cost-effectiveness, and accessibility. The SMEs segment is projected to grow at a higher CAGR, driven by the increasing adoption of data validation solutions among small and medium-sized businesses. The North American region is anticipated to dominate the market, followed by Europe and Asia Pacific. Key market players include TELUS Digital, Experian Data Quality, Flatworld Solutions Inc., Precisely, LDC, InfoCleanse, Level Data, Damco Solutions, Environmental Data Validation Inc., DataCaptive, Process Fusion, Ann Arbor Technical Services, Inc., among others.

The Validation extension for CKAN enhances data quality within the CKAN ecosystem by leveraging the Frictionless Framework to validate tabular data. This extension allows for automated data validation, generating comprehensive reports directly accessible within the CKAN interface. The validation process helps identify structural and schema-level issues, ensuring data consistency and reliability. Key Features: Automated Data Validation: Performs data validation automatically in the background or during dataset creation, streamlining the quality assurance process. Comprehensive Validation Reports: Generates detailed reports on data quality, highlighting issues such as missing headers, blank rows, incorrect data types, or values outside of defined ranges. Frictionless Framework Integration: Utilizes the Frictionless Framework library for robust and standardized data validation. Exposed Actions: Provides accessible action functions that allows data validation to be integrated into custom workflows from other CKAN extensions. Command Line Interface: Offers a command-line interface (CLI) to manually trigger validation jobs for specific datasets, resources, or based on search criteria. Reporting Utilities: Enables the generation of global reports summarizing validation statuses across all resources. Use Cases: Improve Data Quality: Ensures data integrity and adherence to defined schemas, leading to better data-driven decision-making. Streamline Data Workflows: Integrates validation as part of data creation or update processes, automating quality checks and saving time. Customize Data Validation Rules: Allows developers to extend the validation process with their own custom workflows and integrations using the exposed actions. Technical Integration: The Validation extension integrates deeply within CKAN by providing new action functions (resourcevalidationrun, resourcevalidationshow, resourcevalidationdelete, resourcevalidationrunbatch) that can be called via the CKAN API. It also includes a plugin interface (IPipeValidation) for more advanced customization, which allows other extensions to receive and process validation reports. Users can utilize the command-line interface to trigger validation jobs and generate overview reports. Benefits & Impact: By implementing the Validation extension, CKAN installations can significantly improve the quality and reliability of their data. This leads to increased trust in the data, better data governance, and reduced errors in downstream applications that rely on the data. Automated validation helps to proactively identify and resolve data issues, contributing to a more efficient data management process.

The Data Validation Services market is experiencing robust growth, driven by the increasing reliance on data-driven decision-making across various industries. The market's expansion is fueled by several key factors, including the rising volume and complexity of data, stringent regulatory compliance requirements (like GDPR and CCPA), and the growing need for data quality assurance to mitigate risks associated with inaccurate or incomplete data. Businesses are increasingly investing in data validation services to ensure data accuracy, consistency, and reliability, ultimately leading to improved operational efficiency, better business outcomes, and enhanced customer experience. The market is segmented by service type (data cleansing, data matching, data profiling, etc.), deployment model (cloud, on-premise), and industry vertical (healthcare, finance, retail, etc.). While the exact market size in 2025 is unavailable, a reasonable estimation, considering typical growth rates in the technology sector and the increasing demand for data validation solutions, could be placed in the range of $15-20 billion USD. This estimate assumes a conservative CAGR of 12-15% based on the overall IT services market growth and the specific needs for data quality assurance. The forecast period of 2025-2033 suggests continued strong expansion, primarily driven by the adoption of advanced technologies like AI and machine learning in data validation processes. Competitive dynamics within the Data Validation Services market are characterized by the presence of both established players and emerging niche providers. Established firms like TELUS Digital and Experian Data Quality leverage their extensive experience and existing customer bases to maintain a significant market share. However, specialized companies like InfoCleanse and Level Data are also gaining traction by offering innovative solutions tailored to specific industry needs. The market is witnessing increased mergers and acquisitions, reflecting the strategic importance of data validation capabilities for businesses aiming to enhance their data management strategies. Furthermore, the market is expected to see further consolidation as larger players acquire smaller firms with specialized expertise. Geographic expansion remains a key growth strategy, with companies targeting emerging markets with high growth potential in data-driven industries. This makes data validation a lucrative market for both established and emerging players.

The Validator extension for CKAN enables data validation within the CKAN ecosystem, leveraging the 'goodtables' library. This allows users to ensure the quality and integrity of tabular data resources published and managed within their CKAN instances. By integrating data validation capabilities, the extension aims to improve data reliability and usability. Key Features: Data Validation using Goodtables: Utilizes the 'goodtables' library for validating tabular data resources, providing a standardized and robust validation process. Automated Validation: Automatically validate packages, resources or datasets upon each upload or update. Technical Integration: Given the limited information in the README, it can be assumed that the extension integrates with the CKAN resource creation and editing workflow. The extension likely adds validation steps to the data upload and modification process, possibly providing feedback to users on any data quality issues detected. Benefits & Impact: By implementing the Validator extension, data publishers increase the reliability and reusability of data resources. This directly improves data quality control, enhances collaboration, lowers the risk of data-driven problems in data applications, and creates opportunities for data-driven organizations to scale up.

This Data contains the PEN-Predictor-Keras-Model as well as the 100 validation data sets.

The construction of a robust healthcare information system is fundamental to enhancing countries’ capabilities in the surveillance and control of hepatitis B virus (HBV). Making use of China’s rapidly expanding primary healthcare system, this innovative approach using big data and machine learning (ML) could help towards the World Health Organization’s (WHO) HBV infection elimination goals of reaching 90% diagnosis and treatment rates by 2030. We aimed to develop and validate HBV detection models using routine clinical data to improve the detection of HBV and support the development of effective interventions to mitigate the impact of this disease in China. Relevant data records extracted from the Family Medicine Clinic of the University of Hong Kong-Shenzhen Hospital’s Hospital Information System were structuralized using state-of-the-art Natural Language Processing techniques. Several ML models have been used to develop HBV risk assessment models. The performance of the ML model was then interpreted using the Shapley value (SHAP) and validated using cohort data randomly divided at a ratio of 2:1 using a five-fold cross-validation framework. The patterns of physical complaints of patients with and without HBV infection were identified by processing 158,988 clinic attendance records. After removing cases without any clinical parameters from the derivation sample (n = 105,992), 27,392 cases were analysed using six modelling methods. A simplified model for HBV using patients’ physical complaints and parameters was developed with good discrimination (AUC = 0.78) and calibration (goodness of fit test p-value >0.05). Suspected case detection models of HBV, showing potential for clinical deployment, have been developed to improve HBV surveillance in primary care setting in China. (Word count: 264) This study has developed a suspected case detection model for HBV, which can facilitate early identification and treatment of HBV in the primary care setting in China, contributing towards the achievement of WHO’s elimination goals of HBV infections.We utilized the state-of-art natural language processing techniques to structure the data records, leading to the development of a robust healthcare information system which enhances the surveillance and control of HBV in China. This study has developed a suspected case detection model for HBV, which can facilitate early identification and treatment of HBV in the primary care setting in China, contributing towards the achievement of WHO’s elimination goals of HBV infections. We utilized the state-of-art natural language processing techniques to structure the data records, leading to the development of a robust healthcare information system which enhances the surveillance and control of HBV in China.

Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training and validation data from the map georeferencing challenge are provided here, as well as competition details and a baseline solution. The data were derived from published sources and are provided to the public to support continued development of automated georeferencing and feature extraction tools. References for all maps are included with the data.

The objective of the fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to Artificial Intelligence for automatization and improving reliability of control processes.

This data package contains information on Structured Product Labeling (SPL) Terminology for SPL validation procedures and information on performing SPL validations.

Data for method validation on detecting pmp-glucose by HPLC

The data describe the validation process of the HiReSPECT II scanner. Experimental and simulated sensitivities and spatial resolution are presented. Other data will be presented into the manuscript.

This paper drives the process of creating VMLA, a language test meant to be used during awake craniotomies. It focuses on step by step process and aims to help other developers to build their own assessment. This project was designed as a prospective study and registered in the Ethic Committee of Educational and Research Institute of Sirio Libanês Hospital. Ethics committee approval number: HSL 2018-37 / CAEE 90603318.9.0000.5461. Images were bought by Shutterstock.com and generated the following receipts: SSTK-0CA8F-1358 and SSTK-0235F-6FC2 VMLA is a neuropsychological assessment of language function, comprising object naming (ON) and semantic. Originally composed by 420 slides, validation among Brazilian native speakers left 368 figures plus fifteen other elements, like numbers, sentences and count. Validation was focused on educational level (EL), gender and age. Volunteers were tested in fourteen different states of Brazil. Cultural differences resulted in improvements to final Answer Template. EL and age were identified as factors that influenced VLMA assessment results. Highly educated volunteers performed better for both ON and semantic. People over 50 and 35 years old had better performance for ON and semantic, respectively. Further validation in unevaluated regions of Brazil, including more balanced number of males and females and more even distribution of age and EL, could confirm our statistical analysis. After validation, ON-VMLA was framed in batteries of 100 slides each, mixing images of six different complexity categories. Semantic-VMLA kept all the original seventy verbal and non-verbal combinations. The validation process resulted in increased confidence during intraoperative test application. We are now able to score and evaluate patient´s language deficits. Currently, VLMA fits its purpose of dynamical application and accuracy during language areas mapping. It is the first test targeted to Brazilians, representing much of our culture and collective imagery. Our experience may be of value to clinicians and researchers working with awake craniotomy who seek to develop their own language test.

The test is available for free use at www.vemotests.com (beginning in February, 2021)

As fault diagnosis and prognosis systems in aerospace applications become more capable, the ability to utilize information supplied by them becomes increasingly important. While certain types of vehicle health data can be effectively processed and acted upon by crew or support personnel, others, due to their complexity or time constraints, require either automated or semi-automated reasoning. Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. The newly developed PDM algorithms require suitable software and hardware platforms for testing under realistic fault scenarios. The paper describes the development of such a platform, based on the K11 planetary rover prototype. A variety of injectable fault modes are being investigated for electrical, mechanical, and power subsystems of the testbed, along with methods for data collection and processing. In addition to the hardware platform, a software simulator with matching capabilities has been developed. The simulator allows for prototyping and initial validation of the algorithms prior to their deployment on the K11. The simulator is also available to the PDM algorithms to assist with the reasoning process. A reference set of diagnostic, prognostic, and decision making algorithms is also described, followed by an overview of the current test scenarios and the results of their execution on the simulator.

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.

Have you ever assessed the quality of your data? Just as you would run spell check before publishing an important document, it is also beneficial to perform a quality control (QC) review before delivering data or map products. This course gives you the opportunity to learn how you can use ArcGIS Data Reviewer to manage and automate the quality control review process. While exploring the fundamental concepts of QC, you will gain hands-on experience configuring and running automated data checks. You will also practice organizing data review and building a comprehensive quality control model. You can easily modify and reuse this QC model over time as your organizational requirements change.After completing this course, you will be able to:Explain the importance of data quality.Select data checks to find specific errors.Apply a workflow to run individual data checks.Build a batch job to run cumulative data checks.

This section presents a discussion of the research data. The data was received as secondary data however, it was originally collected using the time study techniques. Data validation is a crucial step in the data analysis process to ensure that the data is accurate, complete, and reliable. Descriptive statistics was used to validate the data. The mean, mode, standard deviation, variance and range determined provides a summary of the data distribution and assists in identifying outliers or unusual patterns. The data presented in the dataset show the measures of central tendency which includes the mean, median and the mode. The mean signifies the average value of each of the factors presented in the tables. This is the balance point of the dataset, the typical value and behaviour of the dataset. The median is the middle value of the dataset for each of the factors presented. This is the point where the dataset is divided into two parts, half of the values lie below this value and the other half lie above this value. This is important for skewed distributions. The mode shows the most common value in the dataset. It was used to describe the most typical observation. These values are important as they describe the central value around which the data is distributed. The mean, mode and median give an indication of a skewed distribution as they are not similar nor are they close to one another. In the dataset, the results and discussion of the results is also presented. This section focuses on the customisation of the DMAIC (Define, Measure, Analyse, Improve, Control) framework to address the specific concerns outlined in the problem statement. To gain a comprehensive understanding of the current process, value stream mapping was employed, which is further enhanced by measuring the factors that contribute to inefficiencies. These factors are then analysed and ranked based on their impact, utilising factor analysis. To mitigate the impact of the most influential factor on project inefficiencies, a solution is proposed using the EOQ (Economic Order Quantity) model. The implementation of the 'CiteOps' software facilitates improved scheduling, monitoring, and task delegation in the construction project through digitalisation. Furthermore, project progress and efficiency are monitored remotely and in real time. In summary, the DMAIC framework was tailored to suit the requirements of the specific project, incorporating techniques from inventory management, project management, and statistics to effectively minimise inefficiencies within the construction project.

Email Validation Tools Market Outlook

The global email validation tools market size was valued at approximately USD 1.1 billion in 2023 and is expected to reach around USD 2.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.2% during the forecast period. The robust growth in this market is driven by increasing demand for accurate and reliable email communication, as well as the rising awareness of the necessity to maintain clean email lists to enhance marketing effectiveness and ensure compliance with data protection regulations.

One of the key growth factors propelling the email validation tools market is the increasing adoption of digital marketing strategies by businesses across various sectors. As companies strive to reach their target audience efficiently, the need for accurate email lists has become paramount. Invalid email addresses can lead to wasted resources, and lower email deliverability rates, and even harm the sender's reputation. Therefore, businesses are investing in email validation tools to ensure that their email marketing campaigns reach the intended recipients, thereby maximizing their return on investment.

Furthermore, the growing emphasis on data security and privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, has significantly contributed to the market growth. These regulations mandate businesses to maintain clean and accurate email lists to avoid penalties and ensure compliance. Email validation tools help organizations adhere to these regulations by identifying and removing invalid or risky email addresses, thus mitigating the risk of data breaches and improving email deliverability.

Another factor driving the market is the increasing use of artificial intelligence (AI) and machine learning (ML) technologies in email validation tools. These advanced technologies enhance the accuracy and efficiency of email validation processes by analyzing large volumes of data and identifying patterns that indicate invalid or fraudulent email addresses. The integration of AI and ML in email validation tools not only improves the quality of email lists but also reduces the time and effort required for manual validation, thereby enhancing overall operational efficiency for businesses.

Regionally, North America holds the largest share in the email validation tools market due to the early adoption of advanced technologies and the presence of a large number of email marketing companies in the region. The United States, in particular, is a major contributor to market growth, driven by the high penetration of digital marketing and stringent data protection regulations. Europe follows closely, with significant growth opportunities arising from the strict enforcement of GDPR. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid digital transformation of businesses and the increasing adoption of email marketing strategies in emerging economies such as India and China.

Component Analysis

The email validation tools market is segmented into software and services. The software segment dominates the market and is anticipated to maintain its dominance throughout the forecast period. Email validation software solutions offer comprehensive features such as syntax verification, domain validation, and email address checking, which are essential for maintaining a clean and accurate email list. The growing adoption of cloud-based software solutions is further driving the growth of this segment, as businesses seek scalable and cost-effective solutions to manage their email marketing campaigns.

Services, on the other hand, represent a smaller but steadily growing segment within the email validation tools market. These services include consulting, implementation, and support services that help businesses optimize their email validation processes. As the competition intensifies, service providers are offering customized solutions to meet the specific needs of different industries, thereby enhancing the overall customer experience. Additionally, the increasing complexity of email validation processes, driven by the evolving nature of email threats and spam, is leading to a higher demand for expert services to ensure the effectiveness of email validation tools.

Within the software segment, the integration of artificial intelligence and machine learning technologies is a notable trend. These technologies enhance the accuracy and efficiency of email v

These are tables used to process the loads of gulf shrimp data. It contains pre-validation tables, error tables and information about statistics on data loads.

It contains no data tables and no code tables.

This information need not be published

data set contains catch (landed catch) and effort for fishing trips made by the larger vessels that fish near and offshore for the various species...

The datasetvalidation extension for CKAN enforces a mandatory data validation step before datasets can be published. This plugin ensures that only validated datasets are made publicly available, thus promoting data quality and reliability within the CKAN data portal. By integrating a validation process, the extension helps maintain the integrity of the data catalog and reduces the risk of publishing flawed or incorrect information.