Data Quality Rule Generation AI Market Outlook

According to our latest research, the global Data Quality Rule Generation AI market size reached USD 1.42 billion in 2024, reflecting the growing adoption of artificial intelligence in data management across industries. The market is projected to expand at a compound annual growth rate (CAGR) of 26.8% from 2025 to 2033, reaching an estimated USD 13.29 billion by 2033. This robust growth trajectory is primarily driven by the increasing need for high-quality, reliable data to fuel digital transformation initiatives, regulatory compliance, and advanced analytics across sectors.

One of the primary growth factors for the Data Quality Rule Generation AI market is the exponential rise in data volumes and complexity across organizations worldwide. As enterprises accelerate their digital transformation journeys, they generate and accumulate vast amounts of structured and unstructured data from diverse sources, including IoT devices, cloud applications, and customer interactions. This data deluge creates significant challenges in maintaining data quality, consistency, and integrity. AI-powered data quality rule generation solutions offer a scalable and automated approach to defining, monitoring, and enforcing data quality standards, reducing manual intervention and improving overall data trustworthiness. Moreover, the integration of machine learning and natural language processing enables these solutions to adapt to evolving data landscapes, further enhancing their value proposition for enterprises seeking to unlock actionable insights from their data assets.

Another key driver for the market is the increasing regulatory scrutiny and compliance requirements across various industries, such as BFSI, healthcare, and government sectors. Regulatory bodies are imposing stricter mandates around data governance, privacy, and reporting accuracy, compelling organizations to implement robust data quality frameworks. Data Quality Rule Generation AI tools help organizations automate the creation and enforcement of complex data validation rules, ensuring compliance with industry standards like GDPR, HIPAA, and Basel III. This automation not only reduces the risk of non-compliance and associated penalties but also streamlines audit processes and enhances stakeholder confidence in data-driven decision-making. The growing emphasis on data transparency and accountability is expected to further drive the adoption of AI-driven data quality solutions in the coming years.

The proliferation of cloud-based analytics platforms and data lakes is also contributing significantly to the growth of the Data Quality Rule Generation AI market. As organizations migrate their data infrastructure to the cloud to leverage scalability and cost efficiencies, they face new challenges in managing data quality across distributed environments. Cloud-native AI solutions for data quality rule generation provide seamless integration with leading cloud platforms, enabling real-time data validation and cleansing at scale. These solutions offer advanced features such as predictive data quality assessment, anomaly detection, and automated remediation, empowering organizations to maintain high data quality standards in dynamic cloud environments. The shift towards cloud-first strategies is expected to accelerate the demand for AI-powered data quality tools, particularly among enterprises with complex, multi-cloud, or hybrid data architectures.

From a regional perspective, North America continues to dominate the Data Quality Rule Generation AI market, accounting for the largest share in 2024 due to early adoption, a strong technology ecosystem, and stringent regulatory frameworks. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in AI and analytics by enterprises and governments. Europe is also a significant market, driven by robust data privacy regulations and a mature enterprise landscape. Latin America and the Middle East & Africa are emerging as promising markets, supported by growing awareness of data quality benefits and the proliferation of cloud and AI technologies. The global outlook remains highly positive as organizations across regions recognize the strategic importance of data quality in achieving business objectives and competitive advantage.

ESG Data Quality Management for Banks Market Outlook

According to our latest research, the global ESG Data Quality Management for Banks market size reached USD 1.37 billion in 2024, reflecting a robust and accelerating demand for high-integrity ESG data in the banking sector. The market is expected to grow at a CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 5.12 billion by 2033. This growth is primarily driven by stringent regulatory requirements, increasing stakeholder pressure for transparency, and the need for reliable ESG metrics to inform risk management and investment decisions.

One of the core growth drivers for the ESG Data Quality Management for Banks market is the intensifying regulatory landscape. Governments and regulatory bodies across the globe are mandating stricter ESG disclosure norms, compelling banks to invest in sophisticated data management solutions to ensure compliance. The European Union’s Sustainable Finance Disclosure Regulation (SFDR) and the US Securities and Exchange Commission’s (SEC) proposed climate-related disclosure rules are prime examples of such regulatory frameworks. These regulations not only require banks to collect, verify, and report ESG data but also emphasize the quality and reliability of this information. As a result, banks are increasingly adopting advanced ESG data quality management platforms to streamline data collection, validation, and reporting processes, thereby mitigating compliance risks and enhancing their reputation among stakeholders.

Another significant growth factor is the rising importance of ESG factors in risk management and investment analysis. Banks are recognizing that ESG risks, such as climate change, social unrest, and governance failures, can have profound financial implications. To effectively identify, assess, and mitigate these risks, banks require high-quality ESG data that is accurate, timely, and auditable. The integration of ESG data quality management solutions enables banks to develop more robust risk models, improve credit assessments, and make informed lending and investment decisions. Furthermore, investors and clients are increasingly demanding transparency regarding banks’ ESG performance, further driving the adoption of data quality management tools that can provide granular, verifiable, and actionable ESG insights.

Technological advancements also play a pivotal role in the growth trajectory of the ESG Data Quality Management for Banks market. With the advent of artificial intelligence, machine learning, and big data analytics, banks can now automate the collection, cleansing, and analysis of large volumes of ESG data from diverse sources. These technologies enhance data accuracy, reduce manual intervention, and provide real-time insights, enabling banks to respond swiftly to evolving ESG risks and opportunities. Additionally, the proliferation of cloud-based ESG data management platforms offers scalability, flexibility, and cost-effectiveness, making it easier for banks of all sizes to implement and scale their ESG data quality initiatives.

From a regional perspective, Europe currently leads the ESG Data Quality Management for Banks market, driven by its progressive regulatory environment and strong emphasis on sustainable finance. North America follows closely, with increasing regulatory scrutiny and growing investor demand for ESG transparency propelling market growth. The Asia Pacific region is poised for the fastest growth, fueled by rapid digitalization in the banking sector and emerging ESG regulations in key markets such as China, Japan, and Australia. Latin America and the Middle East & Africa, while still nascent, are witnessing rising awareness of ESG issues and gradually strengthening regulatory frameworks, which are expected to contribute to market expansion over the forecast period.

Component Analysis

The Component segment of the ESG Data Quality Management for Banks market is primarily bifurcated into Software and

This resource contains Jupyter Notebooks with examples for conducting quality control post processing for in situ aquatic sensor data. The code uses the Python pyhydroqc package. The resource is part of set of materials for hydroinformatics and water data science instruction. Complete learning module materials are found in HydroLearn: Jones, A.S., Horsburgh, J.S., Bastidas Pacheco, C.J. (2022). Hydroinformatics and Water Data Science. HydroLearn. https://edx.hydrolearn.org/courses/course-v1:USU+CEE6110+2022/about.

This resources consists of 3 example notebooks and associated data files.

Notebooks: 1. Example 1: Import and plot data 2. Example 2: Perform rules-based quality control 3. Example 3: Perform model-based quality control (ARIMA)

Data files: Data files are available for 6 aquatic sites in the Logan River Observatory. Each file contains data for one site for a single year. Each file corresponds to a single year of data. The files are named according to monitoring site (FranklinBasin, TonyGrove, WaterLab, MainStreet, Mendon, BlackSmithFork) and year. The files were sourced by querying the Logan River Observatory relational database, and equivalent data could be obtained from the LRO website or on HydroShare. Additional information on sites, variables, and methods can be found on the LRO website (http://lrodata.usu.edu/tsa/) or HydroShare (https://www.hydroshare.org/search/?q=logan%20river%20observatory). Each file has the same structure indexed with a datetime column (mountain standard time) with three columns corresponding to each variable. Variable abbreviations and units are: - temp: water temperature, degrees C - cond: specific conductance, μS/cm - ph: pH, standard units - do: dissolved oxygen, mg/L - turb: turbidity, NTU - stage: stage height, cm

For each variable, there are 3 columns: - Raw data value measured by the sensor (column header is the variable abbreviation). - Technician quality controlled (corrected) value (column header is the variable abbreviation appended with '_cor'). - Technician labels/qualifiers (column header is the variable abbreviation appended with '_qual').

Data Quality Rules Engines for Health Data Market Outlook

According to our latest research, the global Data Quality Rules Engines for Health Data market size reached USD 1.42 billion in 2024, reflecting the rapid adoption of advanced data management solutions across the healthcare sector. The market is expected to grow at a robust CAGR of 16.1% from 2025 to 2033, reaching a forecasted value of USD 5.12 billion by 2033. This growth is primarily driven by the increasing demand for accurate, reliable, and regulatory-compliant health data to support decision-making and operational efficiency across various healthcare stakeholders.

The surge in the Data Quality Rules Engines for Health Data market is fundamentally propelled by the exponential growth in healthcare data volume and complexity. With the proliferation of electronic health records (EHRs), digital claims, and patient management systems, healthcare providers and payers face mounting challenges in ensuring the integrity, accuracy, and consistency of their data assets. Data quality rules engines are increasingly being deployed to automate validation, standardization, and error detection processes, thereby reducing manual intervention, minimizing costly errors, and supporting seamless interoperability across disparate health IT systems. Furthermore, the growing trend of value-based care models and data-driven clinical research underscores the strategic importance of high-quality health data, further fueling market demand.

Another significant growth factor is the tightening regulatory landscape surrounding health data privacy, security, and reporting requirements. Regulatory frameworks such as HIPAA in the United States, GDPR in Europe, and various local data protection laws globally, mandate stringent data governance and auditability. Data quality rules engines help healthcare organizations proactively comply with these regulations by embedding automated rules that enforce data accuracy, completeness, and traceability. This not only mitigates compliance risks but also enhances organizational reputation and patient trust. Additionally, the increasing adoption of cloud-based health IT solutions is making advanced data quality management tools more accessible to organizations of all sizes, further expanding the addressable market.

Technological advancements in artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) are also transforming the capabilities of data quality rules engines. Modern solutions are leveraging these technologies to intelligently identify data anomalies, suggest rule optimizations, and adapt to evolving data standards. This level of automation and adaptability is particularly critical in the healthcare domain, where data sources are highly heterogeneous and prone to frequent updates. The integration of AI-driven data quality engines with clinical decision support systems, population health analytics, and regulatory reporting platforms is creating new avenues for innovation and efficiency. Such advancements are expected to further accelerate market growth over the forecast period.

Regionally, North America continues to dominate the Data Quality Rules Engines for Health Data market, owing to its mature healthcare IT infrastructure, high regulatory compliance standards, and significant investments in digital health transformation. However, the Asia Pacific region is emerging as the fastest-growing market, driven by large-scale healthcare digitization initiatives, increasing healthcare expenditure, and a rising focus on data-driven healthcare delivery. Europe also holds a substantial market share, supported by strong regulatory frameworks and widespread adoption of electronic health records. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth as healthcare providers in these regions increasingly recognize the value of data quality management in improving patient outcomes and operational efficiency.

Component Analysis

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Data Quality Rules Engine for AMI Market Outlook

According to our latest research, the global Data Quality Rules Engine for AMI market size reached USD 1.21 billion in 2024, with a robust growth trajectory supported by a CAGR of 13.8% from 2025 to 2033. The market is forecasted to attain a value of USD 3.77 billion by 2033, driven by the rapid proliferation of smart metering infrastructure and the escalating demand for actionable, high-integrity data in utility operations. This growth is underpinned by the increasing deployment of Advanced Metering Infrastructure (AMI) across regions, as utilities and energy providers seek to optimize meter data management, regulatory compliance, and grid analytics. As per the most recent industry analysis, the integration of data quality rules engines has become pivotal in ensuring the reliability and accuracy of AMI-generated data, fueling market expansion.

One of the primary growth factors for the Data Quality Rules Engine for AMI market is the exponential rise in smart grid initiatives worldwide. As governments and utilities invest heavily in modernizing grid infrastructure, AMI systems have become the backbone of real-time data collection, billing, and operational analytics. However, the accuracy of AMI data is often challenged by transmission errors, device malfunctions, and integration complexities. The implementation of advanced data quality rules engines addresses these challenges by providing automated validation, cleansing, and standardization of meter data. This, in turn, enhances operational efficiency, reduces revenue leakage, and supports predictive maintenance strategies. The growing need for reliable data to support demand response, outage management, and distributed energy resources integration is further accelerating the adoption of these solutions across the utility sector.

Another significant driver is the tightening regulatory landscape and the increasing emphasis on data governance in the utilities sector. Regulatory bodies worldwide are mandating stringent data accuracy and reporting standards for energy providers, especially in regions with liberalized energy markets. Data quality rules engines play a crucial role in ensuring compliance with these regulations by automating data validation processes and providing audit trails for all data transformations. This not only minimizes the risk of penalties and non-compliance but also enhances customer trust and satisfaction by ensuring accurate billing and transparent energy usage reporting. The convergence of data privacy laws and energy market regulations is expected to further propel the demand for robust data quality management solutions within AMI environments.

Technological advancements, particularly the integration of artificial intelligence (AI) and machine learning (ML) algorithms into data quality rules engines, are opening new avenues for market growth. These technologies enable dynamic rule creation, anomaly detection, and predictive analytics, allowing utilities to proactively identify and rectify data issues before they impact downstream processes. The shift towards cloud-based deployment models is also contributing to market expansion, offering utilities scalable, flexible, and cost-effective solutions to manage the growing volume and complexity of AMI data. As the energy sector continues its digital transformation journey, the role of data quality rules engines will become increasingly central in enabling data-driven decision-making and supporting the transition to more resilient, sustainable energy systems.

From a regional perspective, North America currently dominates the Data Quality Rules Engine for AMI market, accounting for the largest share in 2024, primarily due to the extensive rollout of AMI systems and supportive regulatory frameworks. Europe follows closely, driven by aggressive smart grid investments and the EU’s ambitious energy transition goals. The Asia Pacific region is poised for the fastest growth, propelled by rapid urbanization, government-led smart city projects, and increasing investments in grid modernization. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, as utilities in these regions begin to recognize the value of high-quality AMI data in optimizing resource management and enhancing grid reliability.

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Data Integration Market Size 2024-2028

The data integration market size is forecast to increase by USD 10.94 billion, at a CAGR of 12.88% between 2023 and 2028.

The market is experiencing significant growth due to the increasing need for seamless data flow between various systems and applications. This requirement is driven by the digital transformation initiatives undertaken by businesses to enhance operational efficiency and gain competitive advantage. A notable trend in the market is the increasing adoption of cloud-based integration solutions, which offer flexibility, scalability, and cost savings. However, despite these benefits, many organizations face challenges in implementing effective data integration strategies. One of the primary obstacles is the complexity involved in integrating diverse data sources and ensuring data accuracy and security.
Additionally, the lack of a comprehensive integration strategy can hinder the successful implementation of data integration projects. To capitalize on the market opportunities and navigate these challenges effectively, companies need to invest in robust integration platforms and adopt best practices for data management and security. By doing so, they can streamline their business processes, improve data quality, and gain valuable insights from their data to drive growth and innovation.

What will be the Size of the Data Integration Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, driven by the ever-increasing volume, velocity, and variety of data. Seamless integration of entities such as data profiling, synchronization, quality rules, monitoring, and storytelling are essential for effective business intelligence and data warehousing. Embedded analytics and cloud data integration have gained significant traction, enabling real-time insights. Data governance, artificial intelligence, security, observability, and fabric are integral components of the data integration landscape.

How is this Data Integration Industry segmented?

The data integration industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  IT and telecom
  Healthcare
  BFSI
  Government and defense
  Others


Component

  Tools
  Services


Application Type

  Data Warehousing
  Business Intelligence
  Cloud Migration
  Real-Time Analytics


Solution Type

  ETL (Extract, Transform, Load)
  ELT
  Data Replication
  Data Virtualization


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The it and telecom segment is estimated to witness significant growth during the forecast period.

In today's data-driven business landscape, organizations are increasingly relying on integrated data management solutions to optimize operations and gain competitive advantages. The data mesh architecture facilitates the decentralization of data ownership and management, enabling real-time, interconnected data access. Data profiling and monitoring ensure data quality and accuracy, while data synchronization and transformation processes maintain consistency across various systems. Business intelligence, data warehousing, and embedded analytics provide valuable insights for informed decision-making. Cloud data integration and data virtualization enable seamless data access and sharing, while data governance ensures data security and compliance. Artificial intelligence and machine learning algorithms enhance data analytics capabilities, enabling predictive and prescriptive insights.

Data security, observability, and anonymization are crucial components of data management, ensuring data privacy and protection. Schema mapping and metadata management facilitate data interoperability and standardization. Data enrichment, deduplication, and data mart creation optimize data utilization. Real-time data integration, ETL processes, and batch data integration cater to various data processing requirements. Data migration and data cleansing ensure data accuracy and consistency. Data cataloging, data lineage, and data discovery enable efficient data management and access. Hybrid data integration, data federation, and on-premise data integration cater to diverse data infrastructure needs. Data alerting and data validation ensure data accuracy and reliability.

Change data capture and data masking maintain data security and privacy. API integration and self-service a

This United States Environmental Protection Agency (US EPA) feature layer represents monitoring site data, updated hourly concentrations and Air Quality Index (AQI) values for the latest hour received from monitoring sites that report to AirNow.Map and forecast data are collected using federal reference or equivalent monitoring techniques or techniques approved by the state, local or tribal monitoring agencies. To maintain "real-time" maps, the data are displayed after the end of each hour. Although preliminary data quality assessments are performed, the data in AirNow are not fully verified and validated through the quality assurance procedures monitoring organizations used to officially submit and certify data on the EPA Air Quality System (AQS).This data sharing, and centralization creates a one-stop source for real-time and forecast air quality data. The benefits include quality control, national reporting consistency, access to automated mapping methods, and data distribution to the public and other data systems. The U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service, tribal, state, and local agencies developed the AirNow system to provide the public with easy access to national air quality information. State and local agencies report the Air Quality Index (AQI) for cities across the US and parts of Canada and Mexico. AirNow data are used only to report the AQI, not to formulate or support regulation, guidance or any other EPA decision or position.About the AQIThe Air Quality Index (AQI) is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The AQI focuses on health effects you may experience within a few hours or days after breathing polluted air. EPA calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide. For each of these pollutants, EPA has established national air quality standards to protect public health. Ground-level ozone and airborne particles (often referred to as "particulate matter") are the two pollutants that pose the greatest threat to human health in this country.A number of factors influence ozone formation, including emissions from cars, trucks, buses, power plants, and industries, along with weather conditions. Weather is especially favorable for ozone formation when it’s hot, dry and sunny, and winds are calm and light. Federal and state regulations, including regulations for power plants, vehicles and fuels, are helping reduce ozone pollution nationwide.Fine particle pollution (or "particulate matter") can be emitted directly from cars, trucks, buses, power plants and industries, along with wildfires and woodstoves. But it also forms from chemical reactions of other pollutants in the air. Particle pollution can be high at different times of year, depending on where you live. In some areas, for example, colder winters can lead to increased particle pollution emissions from woodstove use, and stagnant weather conditions with calm and light winds can trap PM2.5 pollution near emission sources. Federal and state rules are helping reduce fine particle pollution, including clean diesel rules for vehicles and fuels, and rules to reduce pollution from power plants, industries, locomotives, and marine vessels, among others.How Does the AQI Work?Think of the AQI as a yardstick that runs from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health concern. For example, an AQI value of 50 represents good air quality with little potential to affect public health, while an AQI value over 300 represents hazardous air quality.An AQI value of 100 generally corresponds to the national air quality standard for the pollutant, which is the level EPA has set to protect public health. AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.Understanding the AQIThe purpose of the AQI is to help you understand what local air quality means to your health. To make it easier to understand, the AQI is divided into six categories:Air Quality Index(AQI) ValuesLevels of Health ConcernColorsWhen the AQI is in this range:..air quality conditions are:...as symbolized by this color:0 to 50GoodGreen51 to 100ModerateYellow101 to 150Unhealthy for Sensitive GroupsOrange151 to 200UnhealthyRed201 to 300Very UnhealthyPurple301 to 500HazardousMaroonNote: Values above 500 are considered Beyond the AQI. Follow recommendations for the Hazardous category. Additional information on reducing exposure to extremely high levels of particle pollution is available here.Each category corresponds to a different level of health concern. The six levels of health concern and what they mean are:"Good" AQI is 0 to 50. Air quality is considered satisfactory, and air pollution poses little or no risk."Moderate" AQI is 51 to 100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people. For example, people who are unusually sensitive to ozone may experience respiratory symptoms."Unhealthy for Sensitive Groups" AQI is 101 to 150. Although general public is not likely to be affected at this AQI range, people with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air."Unhealthy" AQI is 151 to 200. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects."Very Unhealthy" AQI is 201 to 300. This would trigger a health alert signifying that everyone may experience more serious health effects."Hazardous" AQI greater than 300. This would trigger a health warnings of emergency conditions. The entire population is more likely to be affected.AQI colorsEPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.Air Quality Index Levels of Health ConcernNumericalValueMeaningGood0 to 50Air quality is considered satisfactory, and air pollution poses little or no risk.Moderate51 to 100Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups101 to 150Members of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy151 to 200Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy201 to 300Health alert: everyone may experience more serious health effects.Hazardous301 to 500Health warnings of emergency conditions. The entire population is more likely to be affected.Note: Values above 500 are considered Beyond the AQI. Follow recommendations for the "Hazardous category." Additional information on reducing exposure to extremely high levels of particle pollution is available here.

†Proportion obtained by removing parallel pairs with one empty and one non-empty values. ‡Proportion obtained by treating pairs with one empty and one non-empty values as symmetrical examples.

This resource was created for the 2024 New Zealand Hydrological Society Data Workshop in Queenstown, NZ. This resource contains Jupyter Notebooks with examples for conducting quality control post processing for in situ aquatic sensor data. The code uses the Python pyhydroqc package to detect anomalies. This resource consists of 3 example notebooks and associated data files. For more information, see the original resource from which this was derived: http://www.hydroshare.org/resource/451c4f9697654b1682d87ee619cd7924.

Notebooks: 1. Example 1: Import and plot data 2. Example 2: Perform rules-based quality control 3. Example 3: Perform model-based quality control (ARIMA) 4. Example 4: Model-based quality control (ARIMA) with user data

Data files: Data files are available for 6 aquatic sites in the Logan River Observatory. Each file contains data for one site for a single year. Each file corresponds to a single year of data. The files are named according to monitoring site (FranklinBasin, TonyGrove, WaterLab, MainStreet, Mendon, BlackSmithFork) and year. The files were sourced by querying the Logan River Observatory relational database, and equivalent data could be obtained from the LRO website or on HydroShare. Additional information on sites, variables, and methods can be found on the LRO website (http://lrodata.usu.edu/tsa/) or HydroShare (https://www.hydroshare.org/search/?q=logan%20river%20observatory). Each file has the same structure indexed with a datetime column (mountain standard time) with three columns corresponding to each variable. Variable abbreviations and units are: - temp: water temperature, degrees C - cond: specific conductance, μS/cm - ph: pH, standard units - do: dissolved oxygen, mg/L - turb: turbidity, NTU - stage: stage height, cm

For each variable, there are 3 columns: - Raw data value measured by the sensor (column header is the variable abbreviation). - Technician quality controlled (corrected) value (column header is the variable abbreviation appended with '_cor'). - Technician labels/qualifiers (column header is the variable abbreviation appended with '_qual').

There is also a file "data.csv" for use with Example 4. If any user wants to bring their own data file, they should structure it similarly to this file with a single column of datetime values and a single column of numeric observations labeled "raw".

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AI In Data Quality Market Size 2025-2029

The ai in data quality market size is valued to increase by USD 1.9 billion, at a CAGR of 22.9% from 2024 to 2029. Proliferation of big data and escalating data complexity will drive the ai in data quality market.

Major Market Trends & Insights

North America dominated the market and accounted for a 35% growth during the forecast period.
By Deployment - Cloud-based segment accounted for the largest market revenue share in 2023
CAGR from 2024 to 2029 : 22.9%

Market Summary

In the realm of data management, the integration of Artificial Intelligence (AI) in data quality has emerged as a game-changer. According to recent estimates, The market is projected to reach a value of USD12.2 billion by 2025, underscoring its growing significance. This growth is driven by the proliferation of big data and escalating data complexity. AI's ability to analyze vast amounts of data and extract valuable insights has become indispensable for businesses seeking to enhance their data quality and gain a competitive edge. The fusion of generative AI and natural language interfaces is another key trend.
This development enables more intuitive and user-friendly interactions with data, making it easier for businesses to identify and address data quality issues. However, the complexity of integrating AI with heterogeneous and legacy IT environments poses a significant challenge. Despite these hurdles, the future direction of AI in data quality is undeniably forward. As businesses continue to grapple with the intricacies of managing and leveraging their data, the role of AI in ensuring data quality and accuracy will only become more essential.

What will be the Size of the AI In Data Quality Market during the forecast period?

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How is the AI In Data Quality Market Segmented and what are the key trends of market segmentation?

The ai in data quality industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services


Deployment

  Cloud-based
  On premises


Industry Application

  BFSI
  IT and telecommunications
  Healthcare
  Retail and e commerce
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The market continues to evolve, with the software segment driving innovation. This segment encompasses platforms, tools, and applications that automate data integrity processes. Traditional rule-based systems have given way to AI-driven solutions, which autonomously monitor data quality. The software segment can be divided into standalone platforms, integrated modules, and embedded features. Standalone platforms offer end-to-end capabilities, while integrated modules function within larger data management or governance suites. Embedded features, found in cloud data warehouses and lakehouse platforms, provide AI-powered checks as native functionalities. In 2021, the market size for AI-driven data quality solutions was estimated at USD3.5 billion, reflecting the growing importance of maintaining data accuracy and consistency.

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Regional Analysis

North America is estimated to contribute 35% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market is witnessing significant growth and evolution, with North America leading the charge. Comprising the United States and Canada, this region is home to the world's most advanced technology companies and a thriving venture capital ecosystem. This unique combination of technological expertise and investment has led to the early adoption of foundational technologies such as cloud computing, big data analytics, and machine learning. As a result, the North American market is characterized by a sophisticated customer base that recognizes the strategic value of data and the importance of its integrity.

This growth is driven by the increasing demand for data accuracy, security, and compliance in various industries, including finance, healthcare IT, and retail. AI technologies, such as machine learning algorithms and natural language processing, are increasingly being used to improve data quality, enhance customer experiences, and drive business growth.

Market Dynamics

Our researchers analyzed

Dataset quality ***: High quality dataset that was quality-checked by the EIDC team

The United States Environmental Protection Agency (EPA) collects occurrence data for contaminants that may be present in drinking water, but are not currently subject to the agency's drinking water regulations.

Dataset for the maps accompanying the Housing in Aotearoa New Zealand: 2025 report. This dataset contains counts and measures for:

• average number of private dwellings per square kilometre
• home ownership rates
• mould and damp.

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Data is available by statistical area 2.

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Average number of private dwellings per square kilometre has data for occupied, unoccupied, and total private dwellings from the 2013, 2018, and 2023 Censuses, including:

• dwelling counts
• percentage change in the count of dwellings
• average number of dwellings per square kilometre.

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Home ownership rates has data for households in occupied private dwellings from the 2013, 2018, and 2023 Censuses, including:

• counts and percentages for households that owned their home or held it in a family trust, or did not own their home
• percentage change in the count of households that owned their home or held it in a family trust, or did not own their home.

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Mould and damp has data for occupied private dwellings from the 2018 and 2023 Censuses, including:

• counts and percentages for dwellings with or without mould or damp
• percentage change in the count of dwellings with or without mould or damp.

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Map shows the average number of private dwellings per square kilometre for the 2023 Census.

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Map shows the percentage of households in occupied private dwellings that owned their home or held it in a family trust for the 2023 Census.

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Map shows the percentage of occupied private dwellings that were damp or mouldy for the 2023 Census.

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Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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Dwelling density

This data shows the average number of private dwellings (occupied and unoccupied) per square kilometre of land for an area. This is a measure of dwelling density.

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About the 2023 Census dataset

For information on the 2023 Census dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Dwelling occupancy status quality rating

Dwelling occupancy status is rated as high quality.

Dwelling occupancy status – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Dwelling type quality rating

Dwelling type is rated as moderate quality.

Dwelling type – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Tenure of household quality rating

Tenure of household is rated as moderate quality.

Tenure of household – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Dwelling dampness indicator quality rating

Dwelling dampness indicator is rated as moderate quality.

Housing quality – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Dwelling mould indicator quality rating

Dwelling mould indicator is rated as moderate quality.

Housing quality – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Symbol

-998 Not applicable

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

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📘 Overview

This dataset provides hourly air-quality measurements for 50 major global cities over a continuous 15-day period, including pollutant concentrations, meteorological conditions, geographical metadata, and an engineered AQI index.

All values are synthetically generated using historically consistent pollutant patterns and statistical ranges, allowing researchers and ML practitioners to work with realistic air-quality trends without licensing restrictions or data-collection barriers.

This dataset is ideal for time-series modeling, forecasting, environmental analytics, and machine-learning experimentation.

🧭 Cities Included

Covers all major regions:

North America — New York, Los Angeles, Toronto

Europe — London, Paris, Berlin, Zurich

Asia — Delhi, Tokyo, Seoul, Beijing, Singapore

Middle East — Dubai, Riyadh, Doha

Africa — Lagos, Cairo, Nairobi

Oceania — Sydney, Melbourne, Auckland

South America — São Paulo, Buenos Aires

🧱 Dataset Structure

Each hourly record includes:

Air Pollutants

PM2.5 (µg/m³)

PM10 (µg/m³)

NO₂ (ppb)

SO₂ (ppb)

O₃ (ppb)

CO (ppm)

Weather Features

Temperature (°C)

Humidity (%)

Wind Speed (m/s)

Location Metadata

City

Country

Latitude

Longitude

Other

Timestamp (ISO-8601)

AQI (Computed index)

🧹 Data Quality & Formatting

No missing values — 100% complete

Numeric values rounded to 3 decimals

Clean column names (snake_case)

Consistent hourly frequency

Fully ML-ready

📊 Example Use Cases

✔ AQI forecasting (LSTM, GRU, Transformers) ✔ Multivariate time-series modeling ✔ Clustering cities by pollution patterns ✔ Environmental trend visualization ✔ Weather–pollution correlation studies ✔ Anomaly detection (peak pollution events)

🧪 Data Generation Method (Provenance)

This dataset is synthetically generated using realistic pollutant behavior patterns based on historical studies and open-source environmental datasets.

Modeling steps included:

City-specific pollutant baseline ranges

Randomized variation using Gaussian noise

Temporal patterns using sinusoidal diurnal cycles (morning & evening peaks)

Weather-pollution correlation rules (e.g., low wind → higher PM)

AQI computed using standard US-EPA breakpoints

All numeric values standardized to 3-decimal precision

This ensures that although synthetic, the dataset follows realistic environmental dynamics.

📁 File Information

global_air_quality_50_cities.csv

Rows: 18,000+

Columns: 16

Format: UTF-8 CSV

†Proportion obtained by removing parallel pairs with one empty and one non-empty values. ‡Proportion obtained by treating pairs with one empty and one non-empty values as symmetrical examples.

The Transmute extension for CKAN provides a data pipeline for validating and converting data using schemas. It allows users to define schemas that specify validation rules and data transformations, thus ensuring data quality and consistency. The extension enables transformations using an action API with the ability to transform data using defined schemas. Key Features: * Schema-Driven Validation: Uses schemas to define data types, required fields, and validation rules providing the opportunity to validate data against these rules. * Data Transformation: Supports data transformation based on schemas. This includes modifying fields, adding new fields, and removing unnecessary data to fit the desired output format. * Inline Schema Definition: Allows defining schemas directly within the CKAN API calls. This provides a convenient way to apply transformations on-the-fly. * Custom Validators: Supports creation of custom validators, enabling tailored data validation logic. The readme specifically identifies "tsm_concat" as an example of a custom validator. * Field Weighting: Enables control over the order in which fields are processed during validation and transformation, by specifying weight values. * Post-Processing: Provides the option to define steps to execute after processing fields, such as removing fields that are no longer needed after transformation. Technical Integration: The Transmute extension integrates with CKAN by adding a new action API called tsm_transmute. This API allows users to submit data and a schema, and the extension applies the schema to validate and transform the data. The extension is enabled by adding transmute to the list of enabled plugins in the CKAN configuration file. Benefits & Impact: Implementing the Transmute extension enhances CKAN's data quality control and transformation capabilities. It provides a flexible and configurable way to ensure data consistency and conformity to defined standards, thus improving the overall reliability and usability of datasets managed within CKAN. Furthermore, it automates the data transformation process using defined schemas, which can reduce the manual workload of data administrators.

Snapshot img

Big Data As A Service Market Size 2025-2029

The big data as a service market size is forecast to increase by USD 75.71 billion, at a CAGR of 20.5% between 2024 and 2029.

The Big Data as a Service (BDaaS) market is experiencing significant growth, driven by the increasing volume of data being generated daily. This trend is further fueled by the rising popularity of big data in emerging technologies, such as blockchain, which requires massive amounts of data for optimal functionality. However, this market is not without challenges. Data privacy and security risks pose a significant obstacle, as the handling of large volumes of data increases the potential for breaches and cyberattacks. Edge computing solutions and on-premise data centers facilitate real-time data processing and analysis, while alerting systems and data validation rules maintain data quality.
Companies must navigate these challenges to effectively capitalize on the opportunities presented by the BDaaS market. By implementing robust data security measures and adhering to data privacy regulations, organizations can mitigate risks and build trust with their customers, ensuring long-term success in this dynamic market.

What will be the Size of the Big Data As A Service Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market continues to evolve, offering a range of solutions that address various data management needs across industries. Hadoop ecosystem services play a crucial role in handling large volumes of data, while ETL process optimization ensures data quality metrics are met. Data transformation services and data pipeline automation streamline data workflows, enabling businesses to derive valuable insights from their data. Nosql database solutions and custom data solutions cater to unique data requirements, with Spark cluster management optimizing performance. Data security protocols, metadata management tools, and data encryption methods protect sensitive information. Cloud data storage, predictive modeling APIs, and real-time data ingestion facilitate agile data processing.
Data anonymization techniques and data governance frameworks ensure compliance with regulations. Machine learning algorithms, access control mechanisms, and data processing pipelines drive automation and efficiency. API integration services, scalable data infrastructure, and distributed computing platforms enable seamless data integration and processing. Data lineage tracking, high-velocity data streams, data visualization dashboards, and data lake formation provide actionable insights for informed decision-making.
For instance, a leading retailer leveraged data warehousing services and predictive modeling APIs to analyze customer buying patterns, resulting in a 15% increase in sales. This success story highlights the potential of big data solutions to drive business growth and innovation.

How is this Big Data As A Service Industry segmented?

The big data as a service industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Data Analytics-as-a-service (DAaaS)
  Hadoop-as-a-service (HaaS)
  Data-as-a-service (DaaS)


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


End-user

  Large enterprises
  SMEs


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Type Insights

The Data analytics-as-a-service (DAaas) segment is estimated to witness significant growth during the forecast period. The data analytics-as-a-service (DAaaS) segment experiences significant growth within the market. Currently, over 30% of businesses adopt cloud-based data analytics solutions, reflecting the increasing demand for flexible, cost-effective alternatives to traditional on-premises infrastructure. Furthermore, industry experts anticipate that the DAaaS market will expand by approximately 25% in the upcoming years. This market segment offers organizations of all sizes the opportunity to access advanced analytical tools without the need for substantial capital investment and operational overhead. DAaaS solutions encompass the entire data analytics process, from data ingestion and preparation to advanced modeling and visualization, on a subscription or pay-per-use basis. Data integration tools, data cataloging systems, self-service data discovery, and data version control enhance data accessibility and usability.

The continuous evolution of this market is driven by the increasing volume, variety, and velocity of data, as well as the growing recognition of the business value that can be derived from data insights. Organizations across var

Dataset shows an individual’s statistical area 3 (SA3) of usual residence and the SA3 of their workplace address, for the employed census usually resident population count aged 15 years and over, by main means of travel to work from the 2018 and 2023 Censuses.

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The main means of travel to work categories are:

• Work at home
• Drive a private car, truck, or van
• Drive a company car, truck, or van
• Passenger in a car, truck, van, or company bus
• Public bus
• Train
• Bicycle
• Walk or jog
• Ferry
• Other.

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Main means of travel to work is the usual method which an employed person aged 15 years and over used to travel the longest distance to their place of work.

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Workplace address refers to where someone usually works in their main job, that is the job in which they worked the most hours. For people who work at home, this is the same address as their usual residence address. For people who do not work at home, this could be the address of the business they work for or another address, such as a building site.

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Workplace address is coded to the most detailed geography possible from the available information. This dataset only includes travel to work information for individuals whose workplace address is available at SA3 level. The sum of the counts for each region in this dataset may not equal the total employed census usually resident population count aged 15 years and over for that region. Workplace address – 2023 Census: Information by concept has more information.

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This dataset can be used in conjunction with the following spatial files by joining on the SA3 code values:

• Statistical area 3 2023 (generalised)

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Download data table using the instructions in the Koordinates help guide.

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Footnotes

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Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data).

​

Workplace address time series

Workplace address time series data should be interpreted with care at lower geographic levels, such as statistical area 2 (SA2). Methodological improvements in 2023 Census resulted in greater data accuracy, including a greater proportion of people being counted at lower geographic areas compared to the 2018 Census. Workplace address – 2023 Census: Information by concept has more information.

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Working at home

In the census, working at home captures both remote work, and people whose business is at their home address (e.g. farmers or small business owners operating from their home). The census asks respondents whether they ‘mostly’ work at home or away from home. It does not capture whether someone does both, or how frequently they do one or the other.

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Rows excluded from the dataset

Rows show SA3 of usual residence by SA3 of workplace address. Rows with a total population count of less than six have been removed to reduce the size of the dataset, given only a small proportion of SA3-SA3 combinations have commuter flows.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

​

Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

​

Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

​

Main means of travel to work quality rating

Main means of travel to work is rated as moderate quality.

Main means of travel to work – 2023 Census: Information by concept has more information, for example, definitions and data quality.

​

Workplace address quality rating

Workplace address is rated as moderate quality.

Workplace address – 2023 Census: Information by concept has more information, for example, definitions and data quality.

​

Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

​

Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

​

Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

​

Symbol

-999 Confidential

​

Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

This data release includes all pesticide results from selected batches of water samples analyzed by the U.S Geological Survey National Water Quality Laboratory (NWQL). Samples were analyzed using gas chromatography/mass spectrometry (GCMS) or liquid chromatography/mass spectrometry (LCMS) methods. Eight datasets are included in this data release; 1) all environmental and field quality control (QC) results for 11 pesticide compounds from 70 selected batches of GCMS data from schedules 2001, 2003, 2032, and 2033 (Sandstrom and others, 2001; Zaugg and others, 1995) from May 2001-June 2015, 2) all environmental and field QC results for 10 pesticide compounds from 43 selected batches of LCMS data from schedule 2060 (Furlong and others, 2001) from October 2001-July 2015, 3) All available GCMS set blank results from January 2001-May 2016, 4) All available LCMS set blank results from May 2001-August 2015, 5 and 6) All available blind-blank GCMS and LCMS results from the NWQL from 2004 and from the USGS Branch of Quality systems from 2007 through 2012, and 7 and 8) Blind-spike results from the USGS Organic Blind Sample Project from 2001 through 2016 for the 11 GCMS and 10 LCMS compounds that were investigated in the larger work cited in this metadata record. In addition to the pesticide data originally analyzed and published by the NWQL, a reevaluation of the data in the first two datasets listed in this abstract was performed using current 2017 identification practices. NWQL standard operating procedures have evolved over the 15 years encompassed by this study to provide more specific guidance on the application of identification rules for determining detections. In addition, technology advances were implemented at the NWQL that resulted in improvements in method performance and sample analysis over time. In the data reevaluation process, NWQL reevaluated every result from the 70 GCMS batches and 43 LCMS batches of samples using current 2017 operating procedures and consistently applied criteria for the qualitative identification of pesticides as described in the methods documents (Sandstrom and others, 2001; Zaugg and others, 1995; Furlong and others, 2001). This data release supports the following publication: Medalie, L., Sandstrom, M.W., Toccalino, P.L., Foreman, W.T., ReVello, R.C., Bexfield, L.M., and Riskin, M.L., 2019, Use of set blanks in reporting pesticide results at the U.S. Geological Survey National Water Quality Laboratory, 2001–15: U.S. Geological Survey Scientific Investigations Report 2019–5055, 147 p., https://doi.org/10.3133/sir20195055. References: Furlong, E.T., Anderson, B.D., Werner, S.L., Soliven, P.P., Coffey, L.J., and Burkhardt, M.R., 2001, Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory—Determination of pesticides in water by graphitized carbon-based solid-phase extraction and high-performance liquid chromatography/mass spectrometry: U.S. Geological Survey Water-Resources Investigations Report 01–4134, 73 p. [Also available at https://doi.org/10.3133/wri014134.] Sandstrom, M.W., Stroppel, M.E., Foreman, W.T., and Schroeder, M.P., 2001, Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory—Determination of moderate-use pesticides and selected degradates in water by C-18 solid-phase extraction and gas chromatography/mass spectrometry: U.S. Geological Survey Water-Resources Investigations Report 01–4098, 70 p. [Also available at https://nwql.usgs.gov/Public/pubs/WRIR/WRIR-01-4098.pdf.] Zaugg, S.D., Sandstrom, M.W., Smith, S.G., and Fehlberg, K.M., 1995, Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory—Determination of pesticides in water by C–18 solid-phase extraction and capillary-column gas chromatography/mass spectrometry with selected-ion monitoring: U.S. Geological Survey Open-File Report 95–181, 49 p. [Also available at https://doi.org/10.3133/ofr95181.]

Exposure to sewage contaminated recreational waters may cause gastrointestinal illnesses in swimmers. The State of Hawaii Department of Health (HIDOH) Clean Water Branch (CWB) monitors the waters of Hawaii's beaches for concentrations of Enterococcus, which acts as an indicator of pathogens. The CWB also uses Clostridium perfringens as a secondary tracer of sewage contamination. Results of this monitoring are evaluated using a decision rule to determine whether a beach is safe ("Compliant") or not safe (on "Alert") for swimming and other water contact activities. If a beach is found to be on "Alert" due to elevated indicator bacteria levels, the CWB issues public warnings and alerts and determines whether resampling of the area is necessary. Under the U.S. BEACH Act, the State of Hawaii receives an annual grant to implement its beach monitoring program. This requires the State to conduct a monitoring and notification program that is consistent with performance criteria published by the U.S. Environmental Protection Agency (EPA) in 2002. In March 2010, the EPA approved amendments to the Hawaii Administrative Rules (HAR), Chapter 11-54, Water Quality Standards (CWB QAPrgP, HIDOH 2011, Appendix D), which revised the previous State Enterococcus criteria of a geometric mean (GM) of 7 colony-forming units (CFU) per 100 mL and a single sample maximum (SSM) of 100 CFU/100 mL to meet current EPA guidelines. The State of Hawaii now uses the EPA recommended Enterococcus GM and SSM for recreational waters consistent in the 1986 Ambient Water Quality Criteria for Bacteria. The criterion lists the GM and SSM for marine waters as 35 CFU/100 mL and 104 CFU/100 mL, respectively. The CWB utilizes Clostridium perfringens as a secondary tracer in addition to the Enterococcus indicator to help distinguish between sewage and non-sewage sources of elevated Enterococcus levels in marine coastal waters. The reliability of Enterococcus as an indicator organism in tropical environments has been questioned. This issue was formally documented in the report, Tropical Water Quality Indicator Workshop (Fujioka and Byappanahalli, 2003). One of the limitations of all available and EPA-approved test methods is that the sample must be incubated for about 24 hours. As a result, the public finds out today when they shouldn't have gone in the water yesterday. As a result, warning signs on the beach may or may not be reflective of actual water quality because they are based on tests performed one or more days ago.

Quality control and system suitability testing are vital protocols implemented to ensure the repeatability and reproducibility of data in mass spectrometry investigations. However, mass spectrometry imaging (MSI) analyses present added complexity since both chemical and spatial information are measured. Herein, we employ various machine learning algorithms and a novel quality control mixture to classify the working conditions of an MSI platform. Each algorithm was evaluated in terms of its performance on unseen data, validated with negative control data sets to rule out confounding variables or chance agreement, and utilized to determine the necessary sample size to achieve a high level of accurate classifications. In this work, a robust machine learning workflow was established where models could accurately classify the instrument condition as clean or compromised based on data metrics extracted from the analyzed quality control sample. This work highlights the power of machine learning to recognize complex patterns in MSI data and use those relationships to perform a system suitability test for MSI platforms.