NSF information quality guidelines designed to fulfill the OMB guidelines.

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.

Data Quality Management Market Outlook

The global data quality management market size was valued at approximately USD 1.7 billion in 2023, and it is projected to reach USD 4.9 billion by 2032, growing at a robust CAGR of 12.4% during the forecast period. This growth is fueled by the increasing demand for high-quality data to drive business intelligence and analytics, enhance customer experience, and ensure regulatory compliance. As organizations continue to recognize data as a critical asset, the importance of maintaining data quality has become paramount, driving the market's expansion significantly.

One of the primary growth factors for the data quality management market is the exponential increase in data generation across various industries. With the advent of digital transformation, the volume of data generated by enterprises has grown multifold, necessitating effective data quality management solutions. Organizations are leveraging big data and analytics to derive actionable insights, but these efforts can only be successful if the underlying data is accurate, consistent, and reliable. As such, the need for robust data quality management solutions has become more urgent, driving market growth.

Another critical driver is the rising awareness of data privacy and compliance regulations globally. Governments and regulatory bodies worldwide have introduced stringent data protection laws, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations necessitate that organizations maintain high standards of data quality and integrity to avoid hefty penalties and reputational damage. As a result, businesses are increasingly adopting data quality management solutions to ensure compliance, thereby propelling market growth.

Additionally, the growing adoption of cloud technologies is also contributing to the market's expansion. Cloud-based data quality management solutions offer scalability, flexibility, and cost-effectiveness, making them attractive to organizations of all sizes. The ease of integration with other cloud-based applications and systems further enhances their appeal. Small and medium enterprises (SMEs), in particular, are adopting cloud-based solutions to improve data quality without the need for significant upfront investments in infrastructure and maintenance, which is further fueling market growth.

Regionally, North America holds the largest share of the data quality management market, driven by the presence of key market players and the early adoption of advanced technologies. The region's strong focus on innovation and data-driven decision-making further supports market growth. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. The rapid digitalization of economies, increasing investments in IT infrastructure, and growing awareness of data quality's importance are significant factors contributing to this growth. Furthermore, the rising number of small and medium enterprises in emerging economies of the region is propelling the demand for data quality management solutions.

Component Analysis

In the data quality management market, the component segment is bifurcated into software and services. The software segment is the most significant contributor to the market, driven by the increasing adoption of data quality tools and platforms that facilitate data cleansing, profiling, matching, and monitoring. These software solutions enable organizations to maintain data accuracy and consistency across various sources and formats, thereby ensuring high-quality data for decision-making processes. The continuous advancements in artificial intelligence and machine learning technologies are further enhancing the capabilities of data quality software, making them indispensable for organizations striving for data excellence.

The services segment, on the other hand, includes consulting, implementation, and support services. These services are crucial for organizations seeking to deploy and optimize data quality solutions effectively. Consulting services help organizations identify their specific data quality needs and devise tailored strategies for implementation. Implementation services ensure the smooth integration of data quality tools within existing IT infrastructures, while support services provide ongoing maintenance and troubleshooting assistance. The demand for services is driven by the growing complexity of data environments and the need for specialized expertise in managing data quality chall

Data Quality Tools Market size was valued at USD 2.71 Billion in 2024 and is projected to reach USD 4.15 Billion by 2031, growing at a CAGR of 5.46% from 2024 to 2031.

Global Data Quality Tools Market Drivers

Growing Data Volume and Complexity: Sturdy data quality technologies are necessary to guarantee accurate, consistent, and trustworthy information because of the exponential increase in the volume and complexity of data supplied by companies. Growing Knowledge of Data Governance: Businesses are realizing how critical it is to uphold strict standards for data integrity and data governance. Tools for improving data quality are essential for advancing data governance programs. Needs for Regulatory Compliance: Adoption of data quality technologies is prompted by strict regulatory requirements, like GDPR, HIPAA, and other data protection rules, which aim to ensure compliance and reduce the risk of negative legal and financial outcomes. Growing Emphasis on Analytics and Business Intelligence (BI): The requirement for accurate and trustworthy data is highlighted by the increasing reliance on corporate intelligence and analytics for well-informed decision-making. Tools for improving data quality contribute to increased data accuracy for analytics and reporting. Initiatives for Data Integration and Migration: Companies engaged in data integration or migration initiatives understand how critical it is to preserve data quality throughout these procedures. The use of data quality technologies is essential for guaranteeing seamless transitions and avoiding inconsistent data. Real-time data quality management is in demand: Organizations looking to make prompt decisions based on precise and current information are driving an increased need for real-time data quality management systems. The emergence of cloud computing and big data: Strong data quality tools are required to manage many data sources, formats, and environments while upholding high data quality standards as big data and cloud computing solutions become more widely used. Pay attention to customer satisfaction and experience: Businesses are aware of how data quality affects customer happiness and experience. Establishing and maintaining consistent and accurate customer data is essential to fostering trust and providing individualized services. Preventing Fraud and Data-Related Errors: By detecting and fixing mistakes in real time, data quality technologies assist firms in preventing errors, discrepancies, and fraudulent activities while lowering the risk of monetary losses and reputational harm. Linking Master Data Management (MDM) Programs: Integrating with MDM solutions improves master data management overall and guarantees high-quality, accurate, and consistent maintenance of vital corporate information. Offerings for Data Quality as a Service (DQaaS): Data quality tools are now more widely available and scalable for companies of all sizes thanks to the development of Data Quality as a Service (DQaaS), which offers cloud-based solutions to firms.

Data on long-form data quality indicators for 2021 Census commuting content, Canada, provinces and territories, census metropolitan areas, census agglomerations and census subdivisions.

The excel file contains time series data of flow rates, concentrations of alachlor , atrazine, ammonia, total phosphorus, and total suspended solids observed in two watersheds in Indiana from 2002 to 2007. The aggregate time series data corresponding or representative to all these parameters was obtained using a specialized, data-driven technique. The aggregate data is hypothesized in the published paper to represent the overall health of both watersheds with respect to various potential water quality impairments. The time series data for each of the individual water quality parameters were used to compute corresponding risk measures (Rel, Res, and Vul) that are reported in Table 4 and 5. The aggregation of the risk measures, which is computed from the aggregate time series and water quality standards in Table 1, is also reported in Table 4 and 5 of the published paper. Values under column heading "uncertainty" reports uncertainties associated with reconstruction of missing records of the water quality parameters. Long-term records of the water quality parameters were reconstructed in order to estimate the (R-R-V) and corresponding aggregate risk measures. This dataset is associated with the following publication: Hoque, Y., S. Tripathi, M. Hantush , and R. Govindaraju. Aggregate Measures of Watershed Health from Reconstructed Water Quality Data with Uncertainty. Ed Gregorich JOURNAL OF ENVIRONMENTAL QUALITY. American Society of Agronomy, MADISON, WI, USA, 45(2): 709-719, (2016).

Data DescriptionWater Quality Parameters: Ammonia, BOD, DO, Orthophosphate, pH, Temperature, Nitrogen, Nitrate.Countries/Regions: United States, Canada, Ireland, England, China.Years Covered: 1940-2023.Data Records: 2.82 million.Definition of ColumnsCountry: Name of the water-body region.Area: Name of the area in the region.Waterbody Type: Type of the water-body source.Date: Date of the sample collection (dd-mm-yyyy).Ammonia (mg/l): Ammonia concentration.Biochemical Oxygen Demand (BOD) (mg/l): Oxygen demand measurement.Dissolved Oxygen (DO) (mg/l): Concentration of dissolved oxygen.Orthophosphate (mg/l): Orthophosphate concentration.pH (pH units): pH level of water.Temperature (°C): Temperature in Celsius.Nitrogen (mg/l): Total nitrogen concentration.Nitrate (mg/l): Nitrate concentration.CCME_Values: Calculated water quality index values using the CCME WQI model.CCME_WQI: Water Quality Index classification based on CCME_Values.Data Directory Description:Category 1: DatasetCombined Data: This folder contains two CSV files: Combined_dataset.csv and Summary.xlsx. The Combined_dataset.csv file includes all eight water quality parameter readings across five countries, with additional data for initial preprocessing steps like missing value handling, outlier detection, and other operations. It also contains the CCME Water Quality Index calculation for empirical analysis and ML-based research. The Summary.xlsx provides a brief description of the datasets, including data distributions (e.g., maximum, minimum, mean, standard deviation).Combined_dataset.csvSummary.xlsxCountry-wise Data: This folder contains separate country-based datasets in CSV files. Each file includes the eight water quality parameters for regional analysis. The Summary_country.xlsx file presents country-wise dataset descriptions with data distributions (e.g., maximum, minimum, mean, standard deviation).England_dataset.csvCanada_dataset.csvUSA_dataset.csvIreland_dataset.csvChina_dataset.csvSummary_country.xlsxCategory 2: CodeData processing and harmonization code (e.g., Language Conversion, Date Conversion, Parameter Naming and Unit Conversion, Missing Value Handling, WQI Measurement and Classification).Data_Processing_Harmonnization.ipynbThe code used for Technical Validation (e.g., assessing the Data Distribution, Outlier Detection, Water Quality Trend Analysis, and Vrifying the Application of the Dataset for the ML Models).Technical_Validation.ipynbCategory 3: Data Collection SourcesThis category includes links to the selected dataset sources, which were used to create the dataset and are provided for further reconstruction or data formation. It contains links to various data collection sources.DataCollectionSources.xlsxOriginal Paper Title: A Comprehensive Dataset of Surface Water Quality Spanning 1940-2023 for Empirical and ML Adopted ResearchAbstractAssessment and monitoring of surface water quality are essential for food security, public health, and ecosystem protection. Although water quality monitoring is a known phenomenon, little effort has been made to offer a comprehensive and harmonized dataset for surface water at the global scale. This study presents a comprehensive surface water quality dataset that preserves spatio-temporal variability, integrity, consistency, and depth of the data to facilitate empirical and data-driven evaluation, prediction, and forecasting. The dataset is assembled from a range of sources, including regional and global water quality databases, water management organizations, and individual research projects from five prominent countries in the world, e.g., the USA, Canada, Ireland, England, and China. The resulting dataset consists of 2.82 million measurements of eight water quality parameters that span 1940 - 2023. This dataset can support meta-analysis of water quality models and can facilitate Machine Learning (ML) based data and model-driven investigation of the spatial and temporal drivers and patterns of surface water quality at a cross-regional to global scale.Note: Cite this repository and the original paper when using this dataset.

Cloud Data Quality Monitoring Market Outlook

The global cloud data quality monitoring market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 4.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.6% during the forecast period. The robust market growth can be attributed primarily to the increasing adoption of cloud-based solutions across various industry verticals, coupled with the growing need to maintain high data quality standards in an era of big data and analytics.

One of the significant growth factors driving the cloud data quality monitoring market is the exponential rise in data generation. As more businesses move their operations online and leverage digital tools, the volume of data generated has skyrocketed. This massive influx of data has necessitated the deployment of sophisticated data quality monitoring tools to ensure data accuracy, consistency, and reliability. Furthermore, the increasing reliance on data-driven decision-making processes has further underscored the importance of maintaining high data quality standards, thereby fueling market growth.

Another key driver is the rapid digital transformation witnessed across various industry verticals. Companies in sectors such as healthcare, BFSI, and retail are increasingly investing in cloud-based data quality monitoring solutions to enhance their operational efficiency and customer experience. For instance, in the healthcare sector, maintaining high data quality is crucial for accurate patient diagnosis and treatment planning. Similarly, in the BFSI sector, data quality monitoring helps in reducing risks associated with financial transactions and compliance reporting.

Additionally, the increasing adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in data quality monitoring is significantly contributing to market growth. These technologies enable more efficient and accurate identification of data anomalies and inconsistencies, thus enhancing the overall data quality. Furthermore, the integration of AI and ML with cloud data quality monitoring solutions helps in automating various data management processes, thereby reducing manual intervention and operational costs.

From a regional perspective, North America holds a significant share of the global cloud data quality monitoring market, primarily due to the early adoption of advanced technologies and the presence of major market players in the region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This can be attributed to the rapid digitalization and increasing investments in cloud infrastructure across countries such as China, India, and Japan. Additionally, the growing awareness about the importance of data quality in driving business success is further propelling market growth in this region.

Component Analysis

The cloud data quality monitoring market is segmented by component into software and services. The software segment holds the largest market share and is expected to continue dominating the market throughout the forecast period. The increasing demand for advanced data quality monitoring tools and platforms that offer real-time analytics and reporting capabilities is driving the growth of this segment. Additionally, the integration of AI and ML technologies with data quality monitoring software is further enhancing its effectiveness and efficiency, thus boosting its adoption across various industry verticals.

The services segment, on the other hand, is projected to witness significant growth during the forecast period. This can be attributed to the increasing demand for professional and managed services to support the implementation and maintenance of cloud data quality monitoring solutions. Professional services include consulting, training, and support services, which help organizations in effectively deploying and utilizing these solutions. Managed services, on the other hand, offer ongoing monitoring and maintenance of data quality, thus ensuring continuous data accuracy and consistency.

Furthermore, the growing trend of outsourcing data quality monitoring services to specialized service providers is also contributing to the growth of the services segment. Organizations are increasingly leveraging the expertise of these service providers to achieve high data quality standards without investing heavily in in-house capabilities. This trend is particularly prominent among small and medium enterprises (SMEs) that often lack the resou

Water Quality Metrics and Filter Performance Dataset

Description

This dataset provides simulated data on various water quality parameters and their impact on the performance of water filtration systems. The dataset includes 19K+ samples, with attributes such as Total Dissolved Solids (TDS), turbidity, pH, water depth, and flow discharge. These parameters are used to estimate the filter life span (in hours) and filter efficiency (in percentage) under different conditions.

All the conditions for each feature is based on the data found on the Internet.

The dataset is ideal for exploring relationships between water quality metrics and filter performance, building predictive models, or conducting data analysis for environmental and engineering studies.

Features

• TDS (mg/l): Total Dissolved Solids in milligrams per liter (values < 500 mg/l).
• Turbidity (NTU): Measurement of water clarity in Nephelometric Turbidity Units (values < 10 NTU).
• pH: Measure of acidity/alkalinity (range: 6.0 to 8.5).
• Depth (m): Water depth in meters (range: 0.5 to 5 m).
• Flow Discharge (L/min): Water flow rate in liters per minute (range: 1 to 100 L/min).
• Filter Life Span (hours): Estimated lifespan of the filter based on input parameters (minimum value capped at 500 hours).
• Filter Efficiency (%): Estimated filtration efficiency (minimum value capped at 75%).

Applications

• Predicting filter performance based on water quality parameters.
• Analyzing the impact of water quality on filter lifespan and efficiency.
• Training machine learning models for environmental monitoring.

Note: This dataset is entirely synthetic and created for educational and research purposes. It does not represent real-world measurements but can be used to simulate scenarios for water filtration system analysis.

This dataset comprises a collection of records detailing the water quality across different cities, regions, and countries. Each entry contains information regarding the city, region, and country where the water sample was taken. Additionally, the dataset records various water quality parameters, including air quality (AirQuality), water pollution (WaterPollution), pH level (ph), water hardness (Hardness), soluble solids content (Solids), chloramines concentration (Chloramines), sulfate levels (Sulfate), conductivity (Conductivity), organic carbon content (Organic_carbon), trihalomethanes concentration (Trihalomethanes), turbidity (Turbidity), and potability status (Potability) of the water. By analyzing this dataset, we can explore the relationships between various factors and draw valuable conclusions regarding water quality and potability across different locations worldwide.

Water is the most precious and essential resource among all-natural resources. Some organism survives without oxygen and food such as Tardigrades. But no one can survive without water. The increase in the development of industries and human activities over the previous century is having an overwhelming impact on our environment. Most cities in the world have started to implement the aqua management system. The development of cloud computing, artificial intelligence, remote sensing, big data and the Internet of Things provide new opening and move toward the improvement and application of aqua resource monitoring system. For predicting water quality of rivers, dams and lakes in India, water quality parameter dataset is created. The name of the data set is Aquaattributes. Completely 1360 samples are presented in the Aquaattributes. The data set size is 190 KB. Attributes of the dataset location name along with its longitude and latitude values and water quality parameters.

The data quality monitoring system (DQMS) developed by the Satellite Oceanography Program at the NOAA National Centers for Environmental Information (NCEI) is based on the concept of a Rich Inventory developed by the previous NCEI Enterprise Data Systems Group. The principal concept of a Rich Inventory is to calculate the data Quality Assurance (QA) descriptive statistics for selected parameters in each Level-2 data file and publish the pre-generated images and NetCDF-format data to the public. The QA descriptive statistics include valid observation number, observation number over 3-sigma edited, minimum, maximum, mean, and standard deviation. The parameters include sea surface height anomaly, significant wave height, altimeter, and radiometer wind speed, radiometer water vapor content, and radiometer wet tropospheric correction from Jason-3 Level-2 Final Geophysical Data Record (GDR) and Interim Geophysical Data Record (IGDR) products.

This dataset includes water quality- related parameters that were used for calculating the different indexes used in the study such as:

Potential Hydrogen pH

Conductivity

Temperature

Dissolved oxygen

Turbidity

Total dissolved solids

Phosphates (PO4)

Nitrates -N (NO3)

Total hardness

Alkalinity

DBO5

DQO

Total coliforms

Fecal Coliforms

Provide Statistics on Drinking Water Quality (Part C. Radiological parameters)

This dataset contains water quality samples collected from Puget Sound, lakes, and streams in the region. Sample ID: Unique identifier for each sample taken. Grab ID: Identifier for the specific grab instance associated with the sample. Profile ID: Identifier for the profile associated with the sample. Sample Number: Sequential number assigned to each sample. Collect DateTime: Date and time when the sample was collected. Depth (m): Depth at which the sample was collected, measured in meters. Site Type: Type of site where the sample was collected (e.g., river, lake, well). Area: Geographic area or region where the sample was collected. Locator: Locator information indicating the precise location of the sample. Site: Specific site or location where the sample was collected. Parameter: The parameter measured or analyzed in the sample (e.g., pH, dissolved oxygen). Value: Value of the parameter measured in the sample. Units: Units of measurement for the parameter value. QualityId: Identifier indicating the quality of the data. Lab Qualifier: Qualifier assigned by the laboratory indicating any special conditions or characteristics of the sample. MDL (Method Detection Limit): Method detection limit for the parameter. RDL (Reporting Detection Limit): Reporting detection limit for the parameter. Text Value: Textual representation of the parameter value. Sample Info: Additional information related to the sample. Steward Note: Notes or comments provided by the data steward. Replicates: Number of replicates taken for the sample. Replicate Of: Identifier indicating the sample of which this is a replicate. Method: Method used for analysis or measurement. Date Analyzed: Date when the sample was analyzed. Data Source: Source of the data.

The Gridded Population of the World, Version 4 (GPWv4): Data Quality Indicators, Revision 11 consists of three data layers created to provide context for the population count and density rasters, and explicit information on the spatial precision of the input boundary data. The Data Context raster explains pixels with a "0" population estimate in the population count and density rasters based on information included in the census documents, such as areas that are part of a national park, areas that have no households, etc. The Water Mask raster distinguishes between pixels that are completely water and/or ice (Total Water Pixels), pixels that contain water and land (Partial Water Pixels), pixels that are completely land (Total Land Pixels), and pixels that are completely ocean water (Ocean Pixels). The Mean Administrative Unit Area raster represents the mean input Unit size in square kilometers and provides a quantitative surface that indicates the size of the input Unit(s) from which population count and density rasters are created. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.

Open the Data Resource: https://www.chesapeakeprogress.com/clean-water/water-quality This Chesapeake Bay Program indicator of progress toward the Water Quality Standards Attainment and Monitoring Outcome shows the estimated percentage of the tidal Chesapeake Bay that is considered to be "in attainment" of water quality standards. Water quality is evaluated using three parameters: dissolved oxygen, water clarity or underwater grass abundance, and chlorophyll a (a measure of algae growth). For a more detailed look at water quality standards attainment, open the Chesapeake Bay Water Quality Standards Attainment Indicator Visualization Tool or the Chesapeake Bay Water Quality Standards Attainment Deficit Visualization Tool.

The water quality index provides a single number (like a grade) that expresses overall water quality.

Each month Water Quality Specialists measure 8 water quality parameters at 53 streams. The parameters are temperature, dissolved oxygen, bacteria (fecal coliform), total nitrogen, total phosphorus, pH, total suspended sediment, and turbidity.

Long-term freshwater quality data from federal and federal-provincial sampling sites throughout Canada's aquatic ecosystems are included in this dataset. Measurements regularly include physical-chemical parameters such as temperature, pH, alkalinity, major ions, nutrients and metals. Collection includes data from active sites, as well as historical sites that have a period of record suitable for trend analysis. Sampling frequencies vary according to monitoring objectives. The number of sites in the network varies slightly from year-to-year, as sites are adjusted according to a risk-based adaptive management framework. The Great Lakes are sampled on a rotation basis and not all sites are sampled every year. Data are collected to meet federal commitments related to transboundary watersheds (rivers and lakes crossing international, inter-provincial and territorial borders) or under authorities such as the Department of the Environment Act, the Canada Water Act, the Canadian Environmental Protection Act, 1999, the Federal Sustainable Development Strategy, or to meet Canada's commitments under the 1969 Master Agreement on Apportionment.

Description

This dataset contains measurements of water physicochemical parameters and in-situ readings of water flow direction and speed at thirty-minute intervals. The data can be useful for building time series models and exploring correlations between measurements, as well as investigating how the river changes throughout the year.

Dataset structure

brisbane_water_quality.csv: water quality measurements

Acknowledgements

The data is provided by the Queensland Government open data portal. Link: https://www.data.qld.gov.au/dataset/brisbane-river-colmslie-site-water-quality-monitoring-buoy/resource/0ec4dacc-8e78-4c2a-aa70-d7865ec098e2