Data - Quality assessment table

This data table provides the detailed data quality assessment scores for the Single Digital View 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.

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

Data Quality 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

Blockchain-based applications are becoming more and more widespread in business operations. In view of the shortcomings of existing enterprise blockchain evaluation methods, this paper proposes a multi-source heterogeneous blockchain data quality evaluation model for enterprise business activities, so as to achieve efficient evaluation of business activity information consistency, credibility and value. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method firstly proposes an entity information representation method based on the Representation learning for fusing entity category information (CEKGRL) model, which introduces the triad structure of related entities in blockchain, then associates them with enterprise business activity categories, and carries out similarity calculation through contextual information to achieve blockchain information consistency assessment. After that, a trustworthiness characterization method is proposed based on information sources, information comments, and information contents, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain.

An examination of the intention behind the Data Quality and Reporting self-assessment tools, how they may be used, and what constitutes “strong evidence.” This webinar provided information on how to use data to understand service needs, assess effectiveness, and improve practice. The self-assessment tools may be found as appendices G and J through Technical Bulletin #7 on the Children’s Bureau website: CCWIS Technical Bulletin #7.

Metadata-only record linking to the original dataset. Open original dataset below.

This data table provides the detailed data quality assessment scores for the Long Term Development Statement dataset. The quality assessment was carried out on 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 annual assessments of our data quality in line with the dataset refresh rate. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks.We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.DisclaimerThe data quality assessment may not represent the quality of the current dataset that is published on the Open Data Portal. Please check the date of the latest quality assessment and compare to the 'Modified' date of the corresponding dataset. The data quality assessments will be updated on either a quarterly or annual basis, dependent on the update frequency of the dataset. This information can be found in the dataset metadata, within the Information tab. If you require a more up to date quality assessment, please contact the Open Data Team at opendata@spenergynetworks.co.uk and a member of the team will be in contact.

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.

Replication package supporting the article

Maria Angela Pellegrino, Pasquale Esposito, and Gabriele Tuozzo. 2025. FAIRness of the Linguistic Linked Open Data Cloud: an Empirical Investigation. Submitted to the Special Issue on Data quality dimensions in Data FAIRification design and processes (JDIQ ’25)

The project contains:

• LLODsurvey-0_initial.csv - csv containing all the 1,788 articles returned by Scopus running the query TITLE-ABS-KEY ( ("knowledge graph*" OR "linked data" OR "linked open data" ) AND ( linguistic* ) and posing January 2014 - March 2024 as timeframe, only considering articles fully written in English. Articles are attached to codes by two reviewers and their agreement.
• LLODsurvey-1_eligible.csv - csv containing the 457 articles considered eligible according to the following inclusion criteria: • Published between January 2014 and March 2024. • Fully written in English, beyond just the abstract. • Published in a peer-reviewed venue. • Freely accessible. • Focus on the definition or utilization of linguistic data published in accordance with Semantic Web technologies.
• LLODsurvey-2_included-USE.csv - csv containing the 92 articles talking about the REUSE of Linguisitc Linked Open Data (LLOD).
• LLODsurvey-2_included-DEF.csv - csv containing the 89 articles talking about the DEFINITION of LLOD.
• LLODsurvey-3_qualityAssessment-LLODResources.csv - csv containing the quality assessment of the 69 included linguistic resources
• LLODsurvey-3_usedVocabularies.csv - csv documenting used vocabularies of resources indexed in the LLOD Cloud.
• LLODsurvey-3_qualityAssessment-workingSPARQLendpoint.csv - csv documenting the quality assessment of linguistic resources computed via KGHeartBeat.
• LLODsurvey-3_mediatypes.csv - csv documenting mediatypes as reported in the LLOD Cloud.
• LLODsurvey-3_void.csv - csv documenting void files as reported in the LLOD Cloud.

Data Quality Tools Market is Segmented by Deployment Type (Cloud-Based, On-Premise), Size of the Organization (SMEs, Large Enterprises), Component (Software, Services), Data Domain (Customer Data, Product Data, and More), Tool Type (Data Profiling, Data Cleansing/Standardisation, and More), End-User Vertical (BFSI, Government and Public Sector, and More), Geography. The Market Forecasts are Provided in Terms of Value (USD).

Under the Open Government Action Plan, and related National Action Plan, the FGP is required to report on its commitments related to: supporting a user-friendly open government platform; improving the quality of open data available on open.canada.ca; and reviewing additional geospatial datasets to assess their quality. This report summarizes the FGP’s action on meeting these commitments.

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.

Groundwater samples were collected and analyzed from 1,015 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and the water-quality data and quality-control data are included in this data release. The samples were collected from three types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; and major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including nutrients, major ions, trace elements, volatile organic compounds (VOCs), pesticides, radionuclides, and microbial indicators. Data from samples collected between 2012 and 2019 are associated with networks described in a collection of data series reports and associated data releases (Arnold and others, 2016a,b, 2017a,b, 2018a,b, 2020a,b; Kingsbury and others, 2020 and 2021). This data release includes data from networks sampled in 2019 through 2023. For some networks, certain constituent group data were not completely reviewed and released by the analyzing laboratory for all network sites in time for publication of this data release. For networks with incomplete data, no data were published for the incomplete constituent group(s). Datasets excluded from this data release because of incomplete results will be included in the earliest data release published after the dataset is complete. NOTE: While previous versions are available from the author, all the records in previous versions can be found in version 4.0. First posted - December 12, 2021 (available from author) Revised - January 27, 2023 (version 2.0: available from author) Revised - November 2, 2023 (version 3.0: available from author) Revised - April 18, 2025 (version 4.0) The compressed file (NWQP_GW_QW_DataRelease_v4.zip) contains 24 files: 23 files of groundwater-quality, quality-control data, and general information in ASCII text tab-delimited format, and one corresponding metadata file in xml format that includes descriptions of all the tables and attributes. A shapefile containing study areas for each of the sampled groundwater networks also is provided in folder NWQP_GW_QW_Network_Boundaries_v4 of this data release and is described in the metadata (Network_Boundaries_v4.zip). The 23 data files are as follows: Description_of_Data_Fields_v4.txt: Information for all constituents and ancillary information found in Tables 3 through 21. Network_Reference_List_v4.txt: References used for the description of the networks sampled by the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project. Table_1_site_list_v4.txt: Information about wells that have environmental data. Table_2_parameters_v4.txt: Constituent primary uses and sources; laboratory analytical schedules and sampling period; USGS parameter codes (pcodes); comparison thresholds; and reporting levels. Table_3_qw_indicators_v4.txt: Water-quality indicators in groundwater samples collected by the USGS NAWQA Project. Table_4_nutrients_v4.txt: Nutrients and dissolved organic carbon in groundwater samples collected by the USGS NAWQA Project. Table_5_major_ions_v4.txt: Major and minor ions in groundwater samples collected by the USGS NAWQA Project. Table_6_trace_elements_v4.txt: Trace elements in groundwater samples collected by the USGS NAWQA Project. Table_7_vocs_v4.txt: Volatile organic compounds (VOCs) in groundwater samples collected by the USGS NAWQA Project. Table_8_pesticides_v4.txt: Pesticides in groundwater samples collected by the USGS NAWQA Project. Table_9_radchem_v4.txt: Radionuclides in groundwater samples collected by the USGS NAWQA Project. Table_10_micro_v4.txt: Microbiological indicators in groundwater samples collected by the USGS NAWQA Project. Table_11_qw_ind_QC_v4.txt: Water-quality indicators in groundwater replicate samples collected by the USGS NAWQA Project. Table_12_nuts_QC_v4.txt: Nutrients and dissolved organic carbon in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_13_majors_QC_v4.txt: Major and minor ions in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_14_trace_element_QC_v4.txt: Trace elements in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_15_vocs_QC_v4.txt: Volatile organic compounds (VOCs) in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_16_pesticides_QC_v4.txt: Pesticide compounds in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_17_radchem_QC_v4.txt: Radionuclides in groundwater replicate samples collected by the USGS NAWQA Project. Table_18_micro_QC_v4.txt: Microbiological indicators in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_19_TE_SpikeStats_v4.txt: Statistics for trace elements in groundwater spike samples collected by the USGS NAWQA Project. Table_20_VOCLabSpikeStats_v4.txt: Statistics for volatile organic compounds (VOCs) in groundwater spike samples collected by the USGS NAWQA Project. Table_21_PestFieldSpikeStats_v4.txt: Statistics for pesticide compounds in groundwater spike samples collected by the USGS NAWQA Project. References Arnold, T.L., Bexfield, L.M., Musgrove, MaryLynn, Lindsey, B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, B.J., and Belitz, Kenneth, 2017a, Groundwater-quality data from the National Water-Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014: U.S. Geological Survey Data Series 1063, 83 p., https://doi.org/10.3133/ds1063. Arnold, T.L., Bexfield, L.M., Musgrove, MaryLynn, Lindsey, B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, B.J., and Belitz, Kenneth, 2017b, Datasets from Groundwater quality data from the National Water Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014: U.S. Geological Survey data release, https://doi.org/10.5066/F7W0942N. Arnold, T.L., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020a, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey Data Series 1124, 135 p., https://doi.org/10.3133/ds1124. Arnold, T.L., Bexfield, L.M., Musgrove, M., Lindsey, B.D., Stackelberg, P.E., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., and Belitz, K., 2018b, Datasets from Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January through December 2015 and Previously Unpublished Data from 2013-2014, U.S. Geological Survey data release, https://doi.org/10.5066/F7XK8DHK. Arnold, T.L., Bexfield, L.M., Musgrove, M., Stackelberg, P.E., Lindsey, B.D., Kingsbury, J.A., Kulongoski, J.T., and Belitz, K., 2018a, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2015, and previously unpublished data from 2013 to 2014: U.S. Geological Survey Data Series 1087, 68 p., https://doi.org/10.3133/ds1087. Arnold, T.L., DeSimone, L.A., Bexfield, L.M., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., Musgrove, MaryLynn, Kingsbury, J.A., and Belitz, Kenneth, 2016a, Groundwater quality data from the National Water-Quality Assessment Project, May 2012 through December 2013 (ver. 1.1, November 2016): U.S. Geological Survey Data Series 997, 56 p., https://doi.org/10.3133/ds997. Arnold, T.L., DeSimone, L.A., Bexfield, L.M., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., Musgrove, MaryLynn, Kingsbury, J.A., and Belitz, Kenneth, 2016b, Groundwater quality data from the National Water Quality Assessment Project, May 2012 through December 2014 and select quality-control data from May 2012 through December 2013: U.S. Geological Survey data release, https://doi.org/10.5066/F7HQ3X18. Arnold, T.L., Sharpe, J.B., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020b, Datasets from groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey data release, https://doi.org/10.5066/P9W4RR74. Kingsbury, J.A., Sharpe, J.B., Bexfield, L.M., Arnold, T.L., Musgrove, M., Erickson, M.L., Degnan, J.R., Kulongoski, J.T., Lindsey, B.D., and Belitz, K., 2020, Datasets from Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January 2017 through December 2019 (ver. 1.1, January 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P9XATXV1. Kingsbury, J.A., Bexfield, L.M., Arnold, T.L., Musgrove, M., Erickson, M.L., Degnan, J.R., Tesoriero, A.J., Lindsey B.D., and Belitz, K., 2021, Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January 2017 through December 2019: U.S. Geological Survey Data Series 1136, 97 p., https://doi.org/10.3133/ds1136.

Organizations are increasingly accepting data quality (DQ) as a major key to their success. In order to assess and improve DQ, methods have been devised. Many of these methods attempt to raise DQ by directly manipulating low quality data. Such methods operate reactively and are suitable for organizations with highly developed integrated systems. However, there is a lack of a proactive DQ method for businesses with weak IT infrastructure where data quality is largely affected by tasks that are performed by human agents. This study aims to develop and evaluate a new method for structured data, which is simple and practical so that it can easily be applied to real world situations. The new method detects the potentially risky tasks within a process, and adds new improving tasks to counter them. To achieve continuous improvement, an award system is also developed to help with the better selection of the proposed improving tasks. The task-based DQ method (TBDQ) is most appropriate for small and medium organizations, and simplicity in implementation is one of its most prominent features. TBDQ is case studied in an international trade company. The case study shows that TBDQ is effective in selecting optimal activities for DQ improvement in terms of cost and improvement.

For more up to date quality metadata, please visit https://w3id.org/lodquator

This dataset is a collection of TRiG files with quality metadata for different datasets on the LOD cloud. Each dataset was assessed for

1. The length of URIs
2. Usage of RDF primitives
3. Re-use of existing terms
4. Usage of undefined terms
5. Usage of blank nodes
6. Indication for different serialisation formats
7. Usage of multiple languages

This data dump is part of the empirical study conducted for the paper "Are LOD Cloud Datasets Well Represented? A Data Representation Quality Survey."

For more information visit http://jerdeb.github.io/lodqa

Political scientists routinely face the challenge of assessing the quality (validity and reliability) of measures in order to use them in substantive research. While stand-alone assessment tools exist, researchers rarely combine them comprehensively. Further, while a large literature informs data producers, data consumers lack guidance on how to assess existing measures for use in substantive research. We delineate a three-component practical approach to data quality assessment that integrates complementary multi-method tools to assess: 1) content validity; 2) the validity and reliability of the data generation process; and 3) convergent validity. We apply our quality assessment approach to the corruption measures from the Varieties of Democracy (V-Dem) project, both illustrating our rubric and unearthing several quality advantages and disadvantages of the V-Dem measures, compared to other existing measures of corruption.

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

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

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

Browse TOC and LoE with selected illustrations and example pages of Data Quality Tools Market

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Data Quality Tools Market: Key Drivers and Trends

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

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

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

Who are the Major Data Quality Tools Market Vendors?

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

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

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

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

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

Which are the Key Regions for Data Quality Tools Market?

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

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

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

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

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

The EOSC-A FAIR Metrics and Data Quality Task Force (TF) supported the European Open Science Cloud Association (EOSC-A) by providing strategic directions on FAIRness (Findable, Accessible, Interoperable, and Reusable) and data quality. The Task Force conducted a survey using the EUsurvey tool between 15.11.2022 and 18.01.2023, targeting both developers and users of FAIR assessment tools. The survey aimed at supporting the harmonisation of FAIR assessments, in terms of what it evaluated and how, across existing (and future) tools and services, as well as explore if and how a community-driven governance on these FAIR assessments would look like. The survey received 78 responses, mainly from academia, representing various domains and organisational roles. This is the anonymised survey dataset in csv format; most open-ended answers have been dropped. The codebook contains variable names, labels, and frequencies.

In this seminar, you will learn about ArcGIS Data Reviewer tools that allow you to automate, centrally manage, and improve your GIS data quality control processes.This seminar was developed to support the following:ArcGIS 10.0 For Desktop (ArcView, ArcEditor, Or ArcInfo)ArcGIS Data Reviewer for Desktop

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.