This data package contains quality measures such as Air Quality, Austin Airport, LBB Performance Report, School Survey, Child Poverty, System International Units, Weight Measures, etc.

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).

In urban areas, dense atmospheric observational networks with high-quality data are still a challenge due to high costs for installation and maintenance over time. Citizen weather stations (CWS) could be one answer to that issue. Since more and more owners of CWS share their measurement data publicly, crowdsourcing, i.e., the automated collection of large amounts of data from an undefined crowd of citizens, opens new pathways for atmospheric research. However, the most critical issue is found to be the quality of data from such networks. In this study, a statistically-based quality control (QC) is developed to identify suspicious air temperature (T) measurements from crowdsourced data sets. The newly developed QC exploits the combined knowledge of the dense network of CWS to statistically identify implausible measurements, independent of external reference data. The evaluation of the QC is performed using data from Netatmo CWS in Toulouse, France, and Berlin, Germany, over a 1-year period (July 2016 to June 2017), comparing the quality-controlled data with data from two networks of reference stations. The new QC efficiently identifies erroneous data due to solar exposition and siting issues, which are common error sources of CWS. Estimation of T is improved when averaging data from a group of stations within a restricted area rather than relying on data of individual CWS. However, a positive deviation in CWS data compared to reference data is identified, particularly for daily minimum T. To illustrate the transferability of the newly developed QC and the applicability of CWS data, a mapping of T is performed over the city of Paris, France, where spatial density of CWS is especially high.

Context

OpenAQ has collected 231,965,688 air quality measurements from 8,469 locations in 65 countries. Data are aggregated from 105 government level and research-grade sources.

Disclaimers

• Some records contain encoding issues on specific characters; those issues are present in the raw API data and were not corrected.
• Some dates are set in the future: those issues also come from the original data and were not corrected.

Acknowlegement

Foto von JuniperPhoton auf Unsplash

This is a bulk data download resource for air pollution measurements hosted by Defra's UK-AIR web pages - see also http://uk-air.defra.gov.uk/. Monitoring networks and pollutants included are those covered by http://uk-air.defra.gov.uk/networks/ with the exception of diffusive sampler measurements which are not in scope at this stage. Measurements are available for the period 1973 to 2014 inclusive. Measured pollutant concentrations, aggregated statistics and station configuration information (location, analyser type, inlet height etc.) are provided in XML format based the European Air Quality e-Reporting schema (further information at http://www.eionet.europa.eu/aqportal/).

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

The "World Air Quality Data 2024 (Updated)" dataset provides a comprehensive overview of air quality measurements from various locations around the globe. It encompasses over 50,000 records, each detailing critical air quality parameters that are pivotal for environmental analysis, health studies, and policy-making.

Dataset Overview:

This extensive dataset captures a wide array of pollutants, including but not limited to PM2.5, NO2, SO2, CO, and O3, offering insights into the atmospheric conditions of cities worldwide. With data points dating up to March 2024, it serves as a crucial resource for understanding the current state and trends in global air quality.

Data Science Applications:

• Predictive Modeling: Utilizing historical air quality data to forecast future pollution levels under varying environmental and anthropogenic conditions.
• Trend Analysis: Identifying patterns and trends in air quality over time and across different geographic locations to understand the impact of seasonal changes, policy implementations, and other factors.
• Environmental Risk Assessment: Evaluating areas at high risk of pollution and its potential impacts on health, ecosystems, and climate, aiding in targeted intervention strategies.
• Air Quality Improvement Strategies: Informing the development of effective air quality management and improvement strategies by analyzing the correlation between pollutants and various contributing factors.
• Machine Learning Projects: Enhancing projects aimed at understanding the complex relationships between air quality indicators and external variables, such as traffic density, industrial activities, and meteorological conditions.

Column Descriptors:

Each record in the dataset includes detailed information structured across several columns: Country Code, City, Location, Coordinates, Pollutant, Source Name, Unit, Value, Last Updated, and Country Label. These descriptors provide a clear understanding of the measurement context, allowing for nuanced analysis and interpretation.

Ethically Obtained Data:

The data has been ethically sourced from OpenDataSoft, a platform dedicated to making publicly available data accessible and usable. You can explore the dataset further at OpenDataSoft's Air Quality Dataset.

Acknowledgements:

We extend our deepest gratitude to OpenDataSoft for facilitating access to this dataset, enabling a broader understanding of air quality issues. Their platform plays a pivotal role in democratizing data access, thereby empowering researchers, policymakers, and the public to make informed decisions towards a healthier planet.

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

Learn more about Market Research Intellect's Data Quality Management Software Market Report, valued at USD 3.5 billion in 2024, and set to grow to USD 8.1 billion by 2033 with a CAGR of 12.8% (2026-2033).

The Environmental Protection Agency (EPA) provides air pollution data about ozone and particulate matter (PM2.5) to CDC for the Tracking Network. The EPA maintains a database called the Air Quality System (AQS) which contains data from approximately 4,000 monitoring stations around the country, mainly in urban areas. Data from the AQS is considered the "gold standard" for determining outdoor air pollution. However, AQS data are limited because the monitoring stations are usually in urban areas or cities and because they only take air samples for some air pollutants every three days or during times of the year when air pollution is very high. CDC and EPA have worked together to develop a statistical model (Downscaler) to make modeled predictions available for environmental public health tracking purposes in areas of the country that do not have monitors and to fill in the time gaps when monitors may not be recording data. This data does not include "Percent of population in counties exceeding NAAQS (vs. population in counties that either meet the standard or do not monitor PM2.5)". Please visit the Tracking homepage for this information.View additional information for indicator definitions and documentation by selecting Content Area "Air Quality" and the respective indicator at the following website: http://ephtracking.cdc.gov/showIndicatorsData.action

Objective: Routine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.

Materials and Methods: We used the clinical documentation of 34 UK General Practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs. consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician’s final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding.

Results: Supported documentation contained significantly more codes (IRR=5.76 [4.31, 7.70] P<0.001) and less free text (IRR = 0.32 [0.27, 0.40] P<0.001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b=-0.08 [-0.11, -0.05] P<0.001) in the supported consultations, and this was the case for both codes and free text.

Conclusions: We provide evidence that data entry in the EHR is incomplete and reflects physicians’ cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.

The Data Quality Tools market is experiencing robust growth, fueled by the increasing volume and complexity of data across diverse industries. The market, currently valued at an estimated $XX million in 2025 (assuming a logically derived value based on a 17.5% CAGR from a 2019 base year), is projected to reach $YY million by 2033. This substantial expansion is driven by several key factors. Firstly, the rising adoption of cloud-based solutions offers enhanced scalability, flexibility, and cost-effectiveness, attracting both small and medium enterprises (SMEs) and large enterprises. Secondly, the growing need for regulatory compliance (e.g., GDPR, CCPA) necessitates robust data quality management, pushing organizations to invest in advanced tools. Further, the increasing reliance on data-driven decision-making across sectors like BFSI, healthcare, and retail necessitates high-quality, reliable data, thus boosting market demand. The preference for software solutions over on-premise deployments and the substantial investments in services aimed at data integration and cleansing contribute to this growth. However, certain challenges restrain market expansion. High initial investment costs, the complexity of implementation, and the need for skilled professionals to manage these tools can act as barriers for some organizations, particularly SMEs. Furthermore, concerns related to data security and privacy continue to impact adoption rates. Despite these challenges, the long-term outlook for the Data Quality Tools market remains positive, driven by the ever-increasing importance of data quality in a rapidly digitalizing world. The market segmentation highlights significant opportunities across different deployment models, organizational sizes, and industry verticals, suggesting diverse avenues for growth and innovation in the coming years. Competition among established players like IBM, Informatica, and Oracle, alongside emerging players, is intensifying, driving innovation and providing diverse solutions to meet varied customer needs. Recent developments include: September 2022: MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) spin-off DataCebo announced the launch of a new tool, dubbed Synthetic Data (SD) Metrics, to help enterprises compare the quality of machine-generated synthetic data by pitching it against real data sets., May 2022: Pyramid Analytics, which developed its flagship platform, Pyramids Decision Intelligence, announced that it raised USD 120 million in a Series E round of funding. The Pyramid Decision Intelligence platform combines business analytics, data preparation, and data science capabilities with AI guidance functionality. It enables governed self-service analytics in a no-code environment.. Key drivers for this market are: Increasing Use of External Data Sources Owing to Mobile Connectivity Growth. Potential restraints include: Increasing Use of External Data Sources Owing to Mobile Connectivity Growth. Notable trends are: Healthcare is Expected to Witness Significant Growth.

The Office of Air and Radiation's (OAR) Ambient Air Quality Data (Current) contains ambient air pollution data collected by EPA, other federal agencies, as well as state, local, and tribal air pollution control agencies. Its component data sets have been collected over the years from approximately 10,000 monitoring sites, of which approximately 5,000 are currently active. OAR's Office of Air Quality Planning and Standards (OAQPS) and other internal and external users, rely on this data to assess air quality, assist in Attainment/Non-Attainment designations, evaluate State Implementation Plans for Non-Attainment Areas, perform modeling for permit review analysis, and other air quality management functions. Air quality information is also used to prepare reports for Congress as mandated by the Clean Air Act. This data covers air quality data collected after 1980, when the Clean Air Act requirements for monitoring were significantly modified. Air quality data from the Agency's early years (1970s) remains available (see OAR PRIMARY DATA ASSET: Ambient Air Quality Data -- Historical), but because of technical and definitional differences the two data assets are not directly comparable. The Clean Air Act of 1970 provided initial authority for monitoring air quality for Conventional Air Pollutants (CAPs) for which EPA has promulgated National Ambient Air Quality Standards (NAAQS). Requirements for monitoring visibility-related parameters were added in 1977. Requirements for monitoring acid deposition and Hazardous Air Pollutants (HAPs) were added in 1990. Most monitoring sites contain multiple instruments. Most also report meteorological data, including wind speed and direction, humidity, atmospheric pressure, inbound solar radiation, precipitation and other factors relevant to air quality analysis. The current system of sites represents a number of independently-defined monitoring networks with different regulatory or scientific purposes, such as the State and Local Air Monitoring System, the National Air Toxics Trends sites, the Urban Air Toxics sites, the IMPROVE visibility monitoring network, the air toxics monitoring sites for schools, and others. (A complete list of air quality monitoring networks is available at https://www.epa.gov/???). Efforts are under way through NCore Multipollutant Monitoring Network (https://www.epa.gov/ttnamti1/ncore/index.html) to streamline and integrate advanced air quality measurement systems to minimize costs of data collection. Measurements and estimates from these networks are collected across the entire U.S., including all states and territories, with emphasis on documenting pollutant exposures in populated areas.Sampling frequencies vary by pollutant (hourly, 3- and 8-hour, daily, monthly, seasonal, and annual measurements), as required by different NAAQS. Some 50,000 measurements per day are added to the EPA's central air quality data repository, the Air Quality System (AQS). All data, including meteorological information, is public and non-confidential and available through the AQS Data Mart (https://www.epa.gov/ttn/airs/aqsdatamart/). Generally, data for one calendar quarter are reported by the end of the following quarter; some values may be subsequently changed due to quality assurance activities.

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...

Water quality measurements taken in the Chesapeake Bay region of the United States as a joint effort between NASA GSFC and Johns Hopkins University.

OpenAQ has collected 231,965,688 air quality measurements from 8,469 locations in 65 countries. Data are aggregated from 105 government level and research-grade sources. https://medium.com/@openaq/where-does-openaq-data-come-from-a5cf9f3a5c85 Note: this dataset is temporary not updated. We're currently working to update it as soon as possible.Disclaimers:- Some records contain encoding issues on specific characters; those issues are present in the raw API data and were not corrected.- Some dates are set in the future: those issues also come from the original data and were not corrected.

Annual statistics on air quality data from measurements in the West of England Region. These are data from each local authority's Annual Status Reports. Data from both passive (diffusion tube) and continuous monitors are presented.For queries about the data or management of air quality please contact the local authority responsible.Bristol City CouncilB&NES CouncilSouth Gloucestershire Council

Global Data Quality Tools Market market size valued at US$ 3.93 Billion in 2023, set to reach US$ 6.54 Billion by 2032 at a CAGR of about 5.83% from 2024 to 2032.

This example displays the quality report and quality summary information for 15 sensor measurements and 3 arbitrary quality analyses. The quality report contains the individual quality flag outcomes for each sensor measurement, i.e., rows 1–15. The quality summary includes the corresponding quality metrics and the final quality flag information, i.e., the bottom row.Overview of the information contained in the quality summary and quality report.

: Laboratory and field tests examined the potential for unmanned aircraft system (UAS) rotor wash effects on gas and particle measurements from a biomass combustion source. Tests compared simultaneous placement of two sets of CO and CO2 gas sensors and PM2.5 instruments on a UAS body and on a vertical or horizontal extension arm beyond the rotors. For 1 Hz temporal concentration comparisons, correlations of body versus arm placement for the PM2.5 particle sensors yielded R2 = 0.85, and for both gas sensor pairs, exceeded an R2 of 0.90. Increasing the timestep to 10 s average concentrations throughout the burns improved the R2 value for the PM2.5 to 0.95 from 0.85. Finally, comparison of the whole-test average concentrations further increased the correlations between body- and arm-mounted sensors, exceeding an R2 of 0.98 for both gases and particle measurements. Evaluation of PM2.5 emission factors with single-factor ANOVA analyses showed no significant differences between the values derived from the arm, either vertical or horizontal, and those from the body. These results suggest that rotor wash effects on body- and arm-mounted sensors are minimal in scenarios where short-duration, time-averaged concentrations are used to calculate emission factors and whole-area flux values. This dataset is associated with the following publication: Aurell, J., and B. Gullett. Effects of UAS Rotor Wash on Air Quality Measurements. Drones. MDPI, Basel, SWITZERLAND, 0, (2024).