Data Quality Management Software Market size was valued at USD 4.32 Billion in 2023 and is projected to reach USD 10.73 Billion by 2030, growing at a CAGR of 17.75% during the forecast period 2024-2030.

Global Data Quality Management Software Market Drivers

The growth and development of the Data Quality Management Software Market can be credited with a few key market drivers. Several of the major market drivers are listed below:

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

The Report Covers Global Data Quality Tool Market Analysis & Growth and it is Segmented by Deployment Type (On-Premise, Cloud-based), Organization Size (Small and Medium Enterprises, Large Enterprises), Component (Software, Services), End-user Vertical (BFSI, Government, IT and Telecom, Retail and E-commerce, Healthcare), and Geography (North America, Europe, Asia-Pacific, Latin America and Middle East and Africa). The market sizes and forecasts are provided in terms of value (USD million) for all the above segments.

Metrics used to give an indication of data quality between our test’s groups. This includes whether documentation was used and what proportion of respondents rounded their answers. Unit and item non-response are also reported.

The Global Data Quality Tools Market size was valued at USD 2.1 billion in 2022 and is projected to grow at a CAGR of around 18.5% during the forecast period 2024-30. Experian PLC, IBM Corporation, Informatica, Information Builders Inc are top data quality tools companies.

The Open Data Maturity (ODM) assessment is carried out yearly and provides a benchmark of European countries development in the field of open data. It is based on the following dimensions:

• Policy: focusing on countries’ open data policies and strategies;
• Impact: looking into the activities to monitor and measure open data reuse and its impact;
• Portal: assessing portal functions and features that enable users to access open data via the national portal and support interaction within the open data community;
• Quality: focusing on mechanisms that ensure the quality of the (meta)data..

This assessment helps the countries to better understand their level of maturity, to capture their progress over time and to find areas for improvement. Additionally, the study provides an overview of best practices implemented across Europe that could be transferred to other national and local contexts.

The 35 participant countries in the 2022 edition are the 27 EU Member States, 3 European Trade Association (EFTA) countries (Norway, Switzerland, Iceland), 4 candidate countries (Albania, Montenegro, Serbia, Ukraine) and Bosnia and Herzegovina.

The scores of the ODM assessment for each participating country and the questionnaire used in the survey are provided as a re-usable dataset. The complete report and the methodology can be found under documentation.

In 2013, the first of several Regional Stream Quality Assessments (RSQA) was done in the Midwest United States. The Midwest Stream Quality Assessment (MSQA) was a collaborative study by the U.S. Geological Survey National Water Quality Assessment and the U.S. Environmental Protection Agency National Rivers and Streams Assessment. One of the objectives of the RSQA, and thus the MSQA, is to characterize relations between stream ecology and water-quality stressors to determine the relative effects of these stressors on aquatic biota in streams. Data required to meet this objective included fish species and abundance data and physical and chemical water-quality characteristics of the ecological reaches of the sites that were sampled. This dataset comprises 135 fish species, 39,920 fish, 10 selected water-quality stressor metrics, and six selected fish community stressor response variables for 98 sites sampled for the MSQA.

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.

Analyze the market segmentation of the Data Quality Tools industry. Gain insights into market share distribution with a detailed breakdown of key segments and their growth.

This statistic shows the size of the data quality assurance industry in South Korea from 2010 to 2016 with an estimate for 2017. It was estimated that the data quality assurance market n South Korea would value around 112.7 billion South Korean won in 2017.

The global data quality tools market is anticipated to grow at a CAGR of 12.3% during the forecast period of 2025-2033, reaching a value of $20,340 million by 2033. The rising need to improve data quality for accurate decision-making, increasing data volumes and complexity, and growing adoption of cloud-based data management solutions are some of the key factors driving the market growth. The increasing demand for data governance and compliance, as well as the need to mitigate risks associated with poor data quality, are also contributing to the market's expansion. The data quality tools market is segmented by type (on-premises, cloud), application (enterprise, government), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). The cloud segment is expected to witness the highest growth rate during the forecast period due to the increasing adoption of cloud-based data storage and management solutions. The enterprise application segment is anticipated to dominate the market, as businesses of all sizes are increasingly focusing on improving data quality to drive better decision-making and optimize operations. The North American region is expected to remain the largest market for data quality tools, while the Asia Pacific region is projected to exhibit the highest growth rate during the forecast period.

ADBNet is an online database tracking Iowa's water quality assessments. These assessments are prepared under guidance provided by the US EPA under Section 305b of the Clean Water Act. The assessments are intended to estimate the extent to which Iowa's waterbodies meet the goals of the Clean Water Act and attain state water quality standards, and share this information with planners, citizens and other partners in basin planning and watershed management activities. Water quality in Iowa is measured by comparisons of recent monitoring data to the Iowa Water Quality Standards. Results of recent water quality monitoring, special water quality studies, and other assessments of the quality of Iowa's waters are used to determine the degree to which Iowa's rivers, streams, lakes, and wetlands support the beneficial uses for which they are designated in the Iowa Water Quality Standards (for example, aquatic life (fishing), swimming, and/or use as a source of a public water supply). Other information from water quality monitoring and studies that are up to five years old are also used to expand the coverage of assessments in the report. Waters assessed as impaired (that is, either partially supporting or not supporting their designated uses) form the basis for the state's list of impaired waters as required by Section 303(d) of the Clean Water Act.

Groundwater-quality data were collected from 559 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program from January through December 2014. The data were collected from four types of well networks: principal aquifer study networks, which assess the quality of groundwater used for public water supply; land-use study networks, which assess land-use effects on shallow groundwater quality; major aquifer study networks, which assess the quality of groundwater used for domestic supply; and enhanced trends networks, which evaluate the time scales during which groundwater quality changes. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, and some special interest constituents (arsenic speciation, chromium (VI) and perchlorate). These groundwater quality data are tabulated in a U.S. Geological Survey Data Series Report DS-1063 which is available at https://dx.doi.org/10.3133/ds1063 and in this data release. Quality-control samples also were collected and select data from 2012-2014 are included in the report DS1063 and this data release. Data from the environmental and QC blank and replicate samples from the 2012-2013 sampling period were presented in Arnold and others (2016). Data from spike QC samples have not previously been published Data from VOC spike QC data from May 2012-December 2014 are published in this report (see larger work citation) and data release along with an analysis of QC data where detections in field blank samples, variability in replicate samples, and recoveries in VOC spike samples are described for the entire sampling period through the date covered in this report (May 2012-December 2014). Pesticide spike samples and analysis of recoveries will be presented in a later report. There are 42 tables that are part of the larger work citation. There are 9 tables in the text of the larger work citation and 33 external tables included in this data release. The 9 tables not included in the data release are summary tables derived from some of the other 33 tables. Tables in the text include table 1, Appendix 1 tables 1-1 and 1-2; Appendix 2 table 2-1, and Appendix 4 tables 4-1 through 4-6. Tables that are external files are tables 1 through 12; Appendix 4 tables 4-7 through 4-27. This compressed file contains 33 files of groundwater-quality data in ASCII text tab-delimited format and 33 corresponding metadata in xml format for wells sampled for the U.S. Geological Survey National Water-Quality Assessment Project.

Quality characteristics for 21586 river flow time series from 13 datasets worldwide. The 13 datasets are: the Global Runoff Database from the Global Runoff Data Center (GRDC), the Global River Discharge Data (RIVDIS; Vörösmarty et al., 1998), Surface-Water Data from the United States Geological Survey (USGS), HYDAT from the Water Survey of Canada (WSC), WISKI from the Swedish Meteorological and Hydrological Institute (SMHI), Hidroweb from the Brazilian National Water Agency (ANA), National data from the Australian Bureau of Meteorology (BOM), Spanish river flow data from the Ecological Transition Ministry (Spain), R-ArcticNet v. 4.0 from the Pan-Arctic Project Consortium (R-ArcticNet), Russian River data (NCAR-UCAR; Bodo, 2000), Chinese river flow data from the China Hydrology Data Project (CHDP; Henck et al., 2010, 2011), the European Water Archive from GRDC - EURO-FRIEND-Water (EWA), and the GEWEX Asian Monsoon Experiment (GAME) – Tropics dataset provided by the Royal Irrigation Department of Thailand. Quality characteristics are based on availability, outliers, homogeneity and trends: overall availability (%), longest availability (%), continuity (%), monthly availability (%), outliers ratio (%), homogeneity of annual flows (number of statistical tests agreeing), trend in annual flows, trend in one month of the year.

Bodo, B. (2000) Russian River Flow Data by Bodo. Boulder CO: Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. Retrieved from http://rda.ucar.edu/datasets/ds553.1/

Henck, A. C., Huntington, K. W., Stone, J. O., Montgomery, D. R. & Hallet, B. (2011) Spatial controls on erosion in the Three Rivers Region, southeastern Tibet and southwestern China. Earth and Planetary Science Letters 303(1–2), 71–83. doi:10.1016/j.epsl.2010.12.038

Henck, A. C., Montgomery, David R., Huntington, K. W. & Liang, C. (2010) Monsoon control of effective discharge, Yunnan and Tibet. Geology 38(11), 975–978. doi:10.1130/G31444.1

Vörösmarty, C. J., Fekete, B. M. & Tucker, B. A. (1998) Global River Discharge, 1807-1991, V[ersion]. 1.1 (RivDIS). doi:10.3334/ornldaac/199

Groundwater-quality data were collected from 648 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and are included in this report. Most of the wells (514) were sampled from January through December 2016 and 60 of them were sampled in 2013 and 74 in 2014. The data were collected from seven 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; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; enhanced trends networks, which are used to evaluate the time scales during which groundwater quality changes; vertical flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths; flow path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths over a horizontal distance; and modeling support studies, which are used to provide data to support groundwater modeling. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, and some special interest constituents (arsenic speciation, chromium [VI] and perchlorate). These groundwater quality data are tabulated in a U.S. Geological Survey Data Series Report DS-1124 which is available at https://dx.doi.org/XXXXXX and in this data release. Some data from environmental samples collected in 2013-14 and quality-control samples collected in 2012-15 also are included in the associated data release. Data from samples collected in 2016 are associated with networks described in this report and have not been published previously; data from samples collected between 2012 and 2015 are associated with networks described in previous reports in this data series (Arnold and others, 2016a,b, 2017a,b, 2018a,b). There are 23 data tables included in this data release and they are referenced as tables 1-13 and appendix tables 4.10-4.19 in the larger work citation. There are 36 tables that are part of the larger work citation; the 13 tables not included in the data release are summary tables derived from some of the other tables (tables 1.1, 2.2-2.3, 3.1, 4.1-4.9). A version of table 1 is included in both the text and data release. This compressed file contains 23 files of groundwater-quality data in ASCII text tab-delimited format and one corresponding metadata in xml format that describes all the tables and attributes.

Data stream D includes the meta-information on the area-related assessment methods that result from the assessment regime (data stream C). For the stationary and orienting measurements, this is the substance-specific meta information about the measuring stations, such as name, code, measurement configuration, station classification, data quality goals, etc. The link to the assessment areas (data stream B) is done via the coordinates of the measuring stations.

In 2013, the first of several Regional Stream Quality Assessments (RSQA) was done in the Midwest United States. The Midwest Stream Quality Assessment (MSQA) was a collaborative study by the U.S. Geological Survey (USGS) National Water Quality Assessment (NAWQA), the USGS Columbia Environmental Research Center, and the U.S. Environmental Protection Agency (USEPA) National Rivers and Streams Assessment (NRSA). One of the objectives of the RSQA, and thus the MSQA, is to characterize the relationships between water-quality stressors and stream ecology and to determine the relative effects of these stressors on aquatic biota within the streams (U.S. Geological Survey, 2012a). To meet this objective, a framework of fundamental geospatial data was required to develop physical and anthropogenic characteristics of the study region, sampled sites and corresponding watersheds, and riparian zones. This dataset represents the boundaries for the 100 watersheds studied in the MSQA, and is one of the four fundamental geospatial data layers that were developed for the Midwest study.

Data collected to assess water quality conditions in the natural creeks, aquifers and lakes in the Austin area. This is raw data, provided directly from our Water Resources Monitoring database (WRM) and should be considered provisional. Data may or may not have been reviewed by project staff. A map of site locations can be found by searching for LOCATION.WRM_SAMPLE_SITES; you may then use those WRM_SITE_IDs to filter in this dataset using the field SAMPLE_SITE_NO.

Several quality control measures were taken during the project. These included: - Central provision of sampling equipment and sample bags to all field teams - Randomised sample identification scheme so that samples were presented to the laboratories in a sequence unrelated to the order in which they were collected (as much as practically feasible) - Prevention of contamination in the field and in the lab - Prevention of sample mix-up in the field and in the lab - Field duplicates: every 10th site, a field duplicate sample was collected to help quantify total (sampling + analytical) precision (not identified as such to the lab) - Certified Reference Materials (CRMs) TILL-1, TILL-2 (Natural Resources Canada) were run with every batch on GA's XRF & ICP-MS to help quantify analytical precision and bias - Laboratory duplicates (splits), internal project standards (MRIS, WRIS, ORIS, MRIS2, WRIS2), exchanged project standards (GEMAS-Ap, GEMAS-Gr from EuroGeoSurveys; SoNE-1 from United States Geological Survey), and international CRMs (TILL-1, TILL-3, LKSD-1, STSD-3 from Natural Resources Canada) were covertly inserted in the analytical suites for in-house and external analyses to help quantify analytical precision and bias (not identified as such to the lab) - Internal project standard (GRIS) for pH 1:5, EC 1:5 and grain size measurements (not identified as such to the lab) In addition to the above measures, the analytical labs applied their own QA/QC procedures, including running CRMs and/or internal standards, replicating digests and/or analysis, and analysis of blanks. The present report uses some of the above data to quantitatively assess the quality of the NGSA data, which allows a quality statement to be made about the NGSA data.

Statewide River Water Quality Assessment (SRWQA) 2004 & 2008 uses water quality data collected as far back as 1998 to determine the status and trends of nine water quality parameters for all waterways in the state, where consistent data is available. The project was undertaken by the Water Science Branch of the Department of Water and Environmental Regulation in 1999 and 2004 has now been updated to include water quality information up to the end of 2007. This dataset only shows the classifications and trends from the 2004 and 2008 assessment. The Assessment focused on colour, dissolved organic carbon, dissolved oxygen, pH, total nitrogen, total phosphorus, total dissolved salts, total suspended solids and turbidity. A total of 255 sites from 23 basins in Western Australia (out of a total of 44) were included in the 2008 update with 126 of these being assessed for the first time in 2008. In 2004 232 sites were assessed. Due to a lack of data numerous sites that were assessed in 2004 are not included in the 2008 update. Many basins had no data, whilst the others lacked recent monitoring data. The status and trend results were compiled into an excel spreadsheet. Dataset was formerly known as Statewide River Water Quality Assessment (DOW-056)

Data stream D includes the meta information on the area-related assessment methods that result from the assessment regime (data stream C). For the stationary and indicative measurements, this is substance-specific meta information about the measuring stations, such as name, code, measurement configuration, station classification, data quality goals, etc. The link to the assessment areas (data stream B) is made via the coordinates of the measuring stations.