The statistic shows the problems caused by poor quality data for enterprises in North America, according to a survey of North American IT executives conducted by 451 Research in 2015. As of 2015, 44 percent of respondents indicated that having poor quality data can result in extra costs for the business.

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

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

Data Quality Management Tool Market Outlook

The global data quality management tool market size was valued at $2.3 billion in 2023 and is projected to reach $6.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.3% during the forecast period. The increasing demand for high-quality data across various industry verticals and the growing importance of data governance are key factors driving the market growth.

One of the primary growth factors for the data quality management tool market is the exponential increase in the volume of data generated by organizations. With the rise of big data and the Internet of Things (IoT), businesses are accumulating vast amounts of data from various sources. This surge in data generation necessitates the use of advanced data quality management tools to ensure the accuracy, consistency, and reliability of data. Companies are increasingly recognizing that high-quality data is crucial for making informed business decisions, enhancing operational efficiency, and gaining a competitive edge in the market.

Another significant growth driver is the growing emphasis on regulatory compliance and data privacy. Governments and regulatory bodies across the globe are imposing stringent data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations require organizations to maintain high standards of data quality and integrity, thereby driving the adoption of data quality management tools. Furthermore, the increasing instances of data breaches and cyber-attacks have heightened the need for robust data quality management solutions to safeguard sensitive information and mitigate risks.

The rising adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) is also fueling the growth of the data quality management tool market. AI and ML algorithms can automate various data quality processes, including data profiling, cleansing, and enrichment, thereby reducing manual efforts and improving efficiency. These technologies can identify patterns and anomalies in data, enabling organizations to detect and rectify data quality issues in real-time. The integration of AI and ML with data quality management tools is expected to further enhance their capabilities and drive market growth.

Regionally, North America holds the largest share of the data quality management tool market, driven by the presence of major technology companies and a high level of digitalization across various industries. The region's strong focus on data governance and regulatory compliance also contributes to market growth. Europe is another significant market, with countries such as Germany, the UK, and France leading the adoption of data quality management tools. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the rapid digital transformation of businesses in countries like China, India, and Japan.

Component Analysis

The data quality management tool market is segmented by component into software and services. Software tools are essential for automating and streamlining data quality processes, including data profiling, cleansing, enrichment, and monitoring. The software segment holds a significant share of the market due to the increasing demand for comprehensive data quality solutions that can handle large volumes of data and integrate with existing IT infrastructure. Organizations are investing in advanced data quality software to ensure the accuracy, consistency, and reliability of their data, which is crucial for informed decision-making and operational efficiency.

Within the software segment, there is a growing preference for cloud-based solutions due to their scalability, flexibility, and cost-effectiveness. Cloud-based data quality management tools offer several advantages, such as ease of deployment, reduced infrastructure costs, and the ability to access data from anywhere, anytime. These solutions also enable organizations to leverage advanced technologies such as AI and ML for real-time data quality monitoring and anomaly detection. With the increasing adoption of cloud computing, the demand for cloud-based data quality management software is expected to rise significantly during the forecast period.

The services segment encompasses various professional and managed services that support the implementation, maintenance, and optimization of data quality management tools. Professional services include c

This data table provides the detailed data quality assessment scores for the Historic Faults dataset. The quality assessment was carried out on the 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

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

Evaluation of data quality in large healthcare datasets.

abstract: Data quality and fitness for analysis are crucial if outputs of big data analyses should be trusted by the public and the research community. Here we analyze the output from a data quality tool called Achilles Heel as it was applied to 24 datasets across seven different organizations. We highlight 12 data quality rules that identified issues in at least 10 of the 24 datasets and provide a full set of 71 rules identified in at least one dataset. Achilles Heel is developed by Observational Health Data Sciences and Informatics (OHDSI) community and is a freely available software that provides a useful starter set of data quality rules. Our analysis represents the first data quality comparison of multiple datasets across several countries in America, Europe and Asia.

GIS quality control checks are intended to identify issues in the source data that may impact a variety of9-1-1 end use systems.The primary goal of the initial CalOES NG9-1-1 implementation is to facilitate 9-1-1 call routing. Thesecondary goal is to use the data for telephone record validation through the LVF and the GIS-derivedMSAG.With these goals in mind, the GIS QC checks, and the impact of errors found by them are categorized asfollows in this document:Provisioning Failure Errors: GIS data issues resulting in ingest failures (results in no provisioning of one or more layers)Tier 1 Critical errors: Impact on initial 9-1-1 call routing and discrepancy reportingTier 2 Critical errors: Transition to GIS derived MSAGTier 3 Warning-level errors: Impact on routing of call transfersTier 4 Other errors: Impact on PSAP mapping and CAD systemsGeoComm's GIS Data Hub is configurable to stop GIS data that exceeds certain quality control check error thresholdsfrom provisioning to the SI (Spatial Interface) and ultimately to the ECRFs, LVFs and the GIS derivedMSAG.

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

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

This data table provides the detailed data quality assessment scores for the Technical Limits dataset. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

Snapshot img { margin: 10px !important; } Below are some of the key findings from this data quality tools market in APAC analysis report

See the complete table of contents and list of exhibits, as well as selected illustrations and example pages from this report.

Get a FREE sample now!

Data quality tools market in APAC overview

The need to improve customer engagement is the primary factor driving the growth of data quality tools market in APAC. The reputation of a company gets hampered if there is a delay in product delivery or response to payment-related queries. To avoid such issues organizations are integrating their data with software such as CRM for effective communication with customers. To capitalize on market opportunities, organizations are adopting data quality strategies to perform accurate customer profiling and improve customer satisfaction.

Also, by using data quality tools, companies can ensure that targeted communications reach the right customers which will enable companies to take real-time action as per the requirements of the customer. Organizations use data quality tool to validate e-mails at the point of capture and clean their database of junk e-mail addresses. Thus, the need to improve customer engagement is driving the data quality tools market growth in APAC at a CAGR of close to 23% during the forecast period.

Top data quality tools companies in APAC covered in this report

The data quality tools market in APAC is highly concentrated. To help clients improve their revenue shares in the market, this research report provides an analysis of the market’s competitive landscape and offers information on the products offered by various leading companies. Additionally, this data quality tools market in APAC analysis report suggests strategies companies can follow and recommends key areas they should focus on, to make the most of upcoming growth opportunities.

The report offers a detailed analysis of several leading companies, including:

IBM
Informatica
Oracle
SAS Institute
Talend

Data quality tools market in APAC segmentation based on end-user

Banking, financial services, and insurance (BFSI)
Telecommunication
Retail
Healthcare
Others

BFSI was the largest end-user segment of the data quality tools market in APAC in 2018. The market share of this segment will continue to dominate the market throughout the next five years.

Data quality tools market in APAC segmentation based on region

China
Japan
Australia
Rest of Asia

China accounted for the largest data quality tools market share in APAC in 2018. This region will witness an increase in its market share and remain the market leader for the next five years.

Key highlights of the data quality tools market in APAC for the forecast years 2019-2023:

CAGR of the market during the forecast period 2019-2023
Detailed information on factors that will accelerate the growth of the data quality tools market in APAC during the next five years
Precise estimation of the data quality tools market size in APAC and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
The growth of the data quality tools market in APAC across China, Japan, Australia, and Rest of Asia
A thorough analysis of the market’s competitive landscape and detailed information on several vendors
Comprehensive details on factors that will challenge the growth of data quality tools companies in APAC

We can help! Our analysts can customize this market research report to meet your requirements. Get in touch

This data table provides the detailed data quality assessment scores for the Curtailment dataset. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

Regulatory Quality: Standard Error in Japan was reported at 0.23762 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Regulatory Quality: Standard Error - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

This data table provides the detailed data quality assessment scores for the SPD DG Connections Network Info dataset. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refresehed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

Replication data for "Knowing What We Don't": downloadable source verification framework and online appendices

This dataset provides the detailed data quality assessment scores for the Voltage dataset. The quality assessment was carried out on the 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please not that the quality assessment may be based on an earlier version of the dataset. To access our full suite of aggregated quality assessments and learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding our approach to data quality. Our Open Data team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the dataset schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the datasets with the results when available.

This data table provides the detailed data quality assessment scores for the Operational Forecasting dataset. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

Characteristics of the 27 facilities that participated in both baseline and follow-up RDQAs.

This RDF document contains a library of data quality constraints represented as SPARQL query templates based on the SPARQL Inferencing Framework (SPIN). The data quality constraint templates are especially useful for the identification of data quality problems during data entry and for periodic quality checks during data usage. @en

This data table provides the detailed data quality assessment scores for the Transmission Generation Heat Map. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the dataset schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.