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Data quality tools market in APAC overview

The need to improve customer engagement is the primary factor driving the growth of data quality tools market in APAC. The reputation of a company gets hampered if there is a delay in product delivery or response to payment-related queries. To avoid such issues organizations are integrating their data with software such as CRM for effective communication with customers. To capitalize on market opportunities, organizations are adopting data quality strategies to perform accurate customer profiling and improve customer satisfaction.

Also, by using data quality tools, companies can ensure that targeted communications reach the right customers which will enable companies to take real-time action as per the requirements of the customer. Organizations use data quality tool to validate e-mails at the point of capture and clean their database of junk e-mail addresses. Thus, the need to improve customer engagement is driving the data quality tools market growth in APAC at a CAGR of close to 23% during the forecast period.

Top data quality tools companies in APAC covered in this report

The data quality tools market in APAC is highly concentrated. To help clients improve their revenue shares in the market, this research report provides an analysis of the market’s competitive landscape and offers information on the products offered by various leading companies. Additionally, this data quality tools market in APAC analysis report suggests strategies companies can follow and recommends key areas they should focus on, to make the most of upcoming growth opportunities.

The report offers a detailed analysis of several leading companies, including:

IBM
Informatica
Oracle
SAS Institute
Talend

Data quality tools market in APAC segmentation based on end-user

Banking, financial services, and insurance (BFSI)
Telecommunication
Retail
Healthcare
Others

BFSI was the largest end-user segment of the data quality tools market in APAC in 2018. The market share of this segment will continue to dominate the market throughout the next five years.

Data quality tools market in APAC segmentation based on region

China
Japan
Australia
Rest of Asia

China accounted for the largest data quality tools market share in APAC in 2018. This region will witness an increase in its market share and remain the market leader for the next five years.

Key highlights of the data quality tools market in APAC for the forecast years 2019-2023:

CAGR of the market during the forecast period 2019-2023
Detailed information on factors that will accelerate the growth of the data quality tools market in APAC during the next five years
Precise estimation of the data quality tools market size in APAC and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
The growth of the data quality tools market in APAC across China, Japan, Australia, and Rest of Asia
A thorough analysis of the market’s competitive landscape and detailed information on several vendors
Comprehensive details on factors that will challenge the growth of data quality tools companies in APAC

We can help! Our analysts can customize this market research report to meet your requirements. Get in touch

Data Quality Tools Market Outlook

According to our latest research, the global Data Quality Tools market size reached USD 2.65 billion in 2024, reflecting robust demand across industries for solutions that ensure data accuracy, consistency, and reliability. The market is poised to expand at a CAGR of 17.6% from 2025 to 2033, driven by increasing digital transformation initiatives, regulatory compliance requirements, and the exponential growth of enterprise data. By 2033, the Data Quality Tools market is forecasted to attain a value of USD 12.06 billion, as organizations worldwide continue to prioritize data-driven decision-making and invest in advanced data management solutions.

A key growth factor propelling the Data Quality Tools market is the proliferation of data across diverse business ecosystems. Enterprises are increasingly leveraging big data analytics, artificial intelligence, and cloud computing, all of which demand high-quality data as a foundational element. The surge in unstructured and structured data from various sources such as customer interactions, IoT devices, and business operations has made data quality management a strategic imperative. Organizations recognize that poor data quality can lead to erroneous insights, operational inefficiencies, and compliance risks. As a result, the adoption of comprehensive Data Quality Tools for data profiling, cleansing, and enrichment is accelerating, particularly among industries with high data sensitivity like BFSI, healthcare, and retail.

Another significant driver for the Data Quality Tools market is the intensifying regulatory landscape. Data privacy laws such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and other country-specific mandates require organizations to maintain high standards of data integrity and traceability. Non-compliance can result in substantial financial penalties and reputational damage. Consequently, businesses are investing in sophisticated Data Quality Tools that provide automated monitoring, data lineage, and audit trails to ensure regulatory adherence. This regulatory push is particularly prominent in sectors like finance, healthcare, and government, where the stakes for data accuracy and security are exceptionally high.

Advancements in cloud technology and the growing trend of digital transformation across enterprises are also fueling market growth. Cloud-based Data Quality Tools offer scalability, flexibility, and cost-efficiency, enabling organizations to manage data quality processes remotely and in real-time. The shift towards Software-as-a-Service (SaaS) models has lowered the entry barrier for small and medium enterprises (SMEs), allowing them to implement enterprise-grade data quality solutions without substantial upfront investments. Furthermore, the integration of machine learning and artificial intelligence capabilities into data quality platforms is enhancing automation, reducing manual intervention, and improving the overall accuracy and efficiency of data management processes.

From a regional perspective, North America continues to dominate the Data Quality Tools market due to its early adoption of advanced technologies, a mature IT infrastructure, and the presence of leading market players. However, the Asia Pacific region is emerging as a high-growth market, driven by rapid digitalization, increasing investments in IT, and a burgeoning SME sector. Europe maintains a strong position owing to stringent data privacy regulations and widespread enterprise adoption of data management solutions. Latin America and the Middle East & Africa, while relatively nascent, are witnessing growing awareness and adoption, particularly in the banking, government, and telecommunications sectors.

Component Analysis

The Component segment of the Data Quality Tools market is bifurcated into software and services. Software dominates the segment, accounting for a significant share of the global market revenue in 2024. This dominance is

Superstore Sales - The Data Quality Challenge Edition (25K Records)

This dataset is an expanded version of the popular "Sample - Superstore Sales" dataset, commonly used for introductory data analysis and visualization. It contains detailed transactional data for a US-based retail company, covering orders, products, and customer information.

This version is specifically designed for practicing Data Quality (DQ) and Data Wrangling skills, featuring a unique set of real-world "dirty data" problems (like those encountered in tools like SPSS Modeler, Tableau Prep, or Alteryx) that must be cleaned before any analysis or machine learning can begin.

This dataset combines the original Superstore data with 15,000 plausibly generated synthetic records, totaling 25,000 rows of transactional data. It includes 21 columns detailing: - Order Information: Order ID, Order Date, Ship Date, Ship Mode. - Customer Information: Customer ID, Customer Name, Segment. - Geographic Information: Country, City, State, Postal Code, Region. - Product Information: Product ID, Category, Sub-Category, Product Name. - Financial Metrics: Sales, Quantity, Discount, and Profit.

🚨 Introduced Data Quality Challenges (The Dirty Data)

This dataset is intentionally corrupted to provide a robust practice environment for data cleaning. Challenges include: Missing/Inconsistent Values: Deliberate gaps in Profit and Discount, and multiple inconsistent entries (-- or blank) in the Region column.

• Data Type Mismatches: Order Date and Ship Date are stored as text strings, and the Profit column is polluted with comma-formatted strings (e.g., "1,234.56"), forcing the entire column to be read as an object (string) type.

• Categorical Inconsistencies: The Category field contains variations and typos like "Tech", "technologies", "Furni", and "OfficeSupply" that require standardization.

• Outliers and Invalid Data: Extreme outliers have been added to the Sales and Profit fields, alongside a subset of transactions with an invalid Sales value of 0.

• Duplicate Records: Over 200 rows are duplicated (with slight financial variations) to test your deduplication logic.

❓ Suggested Analysis and Modeling Tasks

This dataset is ideal for:

Data Wrangling/Cleaning (Primary Focus): Fix all the intentional data quality issues before proceeding.

Exploratory Data Analysis (EDA): Analyze sales distribution by region, segment, and category.

Regression: Predict the Profit based on Sales, Discount, and product features.

Classification: Build an RFM Model (Recency, Frequency, Monetary) and create a target variable (HighValueCustomer = 1 if total sales are* $>$ $1000$*) to be predicted by logistical regression or decision trees.

Time Series Analysis: Aggregate sales by month/year to perform forecasting.

Acknowledgements

This dataset is an expanded and corrupted derivative of the original Sample Superstore dataset, credited to Tableau and widely shared for educational purposes. All synthetic records were generated to follow the plausible distribution of the original data.

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.

Retail Data Quality Platform Market Outlook

As per our latest research, the global retail data quality platform market size in 2024 stands at USD 1.62 billion, with a robust compound annual growth rate (CAGR) of 17.8% projected from 2025 to 2033. By the end of 2033, the market is expected to reach USD 6.01 billion. The primary growth driver for this market is the accelerating digital transformation across the retail sector, which has amplified the need for reliable, actionable data to optimize operations, enhance customer experiences, and ensure regulatory compliance.

The increasing complexity of retail operations, driven by omnichannel strategies and the proliferation of digital touchpoints, is compelling retailers to invest in advanced data quality platforms. These platforms facilitate the integration, cleansing, and enrichment of data from disparate sources, ensuring that business decisions are based on accurate and up-to-date information. Retailers are recognizing that poor data quality can lead to significant revenue losses, customer dissatisfaction, and compliance risks. As a result, the demand for robust retail data quality solutions is surging, particularly among enterprises seeking to leverage advanced analytics, artificial intelligence, and machine learning for personalized customer engagement and operational efficiency.

Another significant growth factor is the evolving regulatory landscape, with stringent data governance and privacy requirements such as GDPR, CCPA, and other region-specific mandates. Retailers are under mounting pressure to maintain high data quality standards to avoid hefty penalties and reputational damage. This has spurred investments in platforms that offer automated data validation, auditing, and monitoring capabilities. Furthermore, the rise of cloud-based solutions is democratizing access to sophisticated data quality tools, enabling small and medium enterprises (SMEs) to compete effectively with larger players by harnessing high-quality data for strategic decision-making and customer-centric innovation.

The rapid expansion of e-commerce and the increasing adoption of artificial intelligence and big data analytics in retail are further propelling the market. Retailers are leveraging data quality platforms to gain deeper insights into customer behavior, optimize inventory management, and streamline supply chain operations. The integration of these platforms with existing retail management systems ensures seamless data flow and consistency across all business functions. Additionally, the growing emphasis on personalized marketing and customer relationship management is making data quality an indispensable asset for retailers aiming to differentiate themselves in a highly competitive landscape.

Regionally, North America leads the retail data quality platform market, followed closely by Europe and Asia Pacific. North America's dominance is attributed to the early adoption of advanced technologies, a mature retail ecosystem, and the presence of leading market players. However, Asia Pacific is poised for the highest growth rate over the forecast period, fueled by rapid digitalization, expanding e-commerce, and increasing investments in data-driven retail strategies. Latin America and the Middle East & Africa are also witnessing steady growth, driven by the modernization of retail infrastructure and the adoption of cloud-based solutions. These regional trends underscore the global momentum towards data-driven retail transformation.

Component Analysis

The component segment of the retail data quality platform market is bifurcated into software and services, each playing a pivotal role in shaping the market dynamics. Software solutions form the backbone of data quality platforms by providing the necessary tools for data profiling, cleansing, matching, enrichment, and monitoring. These solutions are increasingly leveraging artificial intelligence and

Map Data Quality Assurance Market Outlook

As per our latest research, the global map data quality assurance market size reached USD 1.85 billion in 2024, driven by the surging demand for high-precision geospatial information across industries. The market is experiencing robust momentum, growing at a CAGR of 10.2% during the forecast period. By 2033, the global map data quality assurance market is forecasted to attain USD 4.85 billion, fueled by the integration of advanced spatial analytics, regulatory compliance needs, and the proliferation of location-based services. The expansion is primarily underpinned by the criticality of data accuracy for navigation, urban planning, asset management, and other geospatial applications.

One of the primary growth factors for the map data quality assurance market is the exponential rise in the adoption of location-based services and navigation solutions across various sectors. As businesses and governments increasingly rely on real-time geospatial insights for operational efficiency and strategic decision-making, the need for high-quality, reliable map data has become paramount. Furthermore, the evolution of smart cities and connected infrastructure has intensified the demand for accurate mapping data to enable seamless urban mobility, effective resource allocation, and disaster management. The proliferation of Internet of Things (IoT) devices and autonomous systems further accentuates the significance of data integrity and completeness, thereby propelling the adoption of advanced map data quality assurance solutions.

Another significant driver contributing to the market’s expansion is the growing regulatory emphasis on geospatial data accuracy and privacy. Governments and regulatory bodies worldwide are instituting stringent standards for spatial data collection, validation, and sharing to ensure public safety, environmental conservation, and efficient governance. These regulations mandate comprehensive quality assurance protocols, fostering the integration of sophisticated software and services for data validation, error detection, and correction. Additionally, the increasing complexity of spatial datasets—spanning satellite imagery, aerial surveys, and ground-based sensors—necessitates robust quality assurance frameworks to maintain data consistency and reliability across platforms and applications.

Technological advancements are also playing a pivotal role in shaping the trajectory of the map data quality assurance market. The advent of artificial intelligence (AI), machine learning, and cloud computing has revolutionized the way spatial data is processed, analyzed, and validated. AI-powered algorithms can now automate anomaly detection, spatial alignment, and feature extraction, significantly enhancing the speed and accuracy of quality assurance processes. Moreover, the emergence of cloud-based platforms has democratized access to advanced geospatial tools, enabling organizations of all sizes to implement scalable and cost-effective data quality solutions. These technological innovations are expected to further accelerate market growth, opening new avenues for product development and service delivery.

From a regional perspective, North America currently dominates the map data quality assurance market, accounting for the largest revenue share in 2024. This leadership position is attributed to the region’s early adoption of advanced geospatial technologies, strong regulatory frameworks, and the presence of leading industry players. However, the Asia Pacific region is poised to witness the fastest growth over the forecast period, propelled by rapid urbanization, infrastructure development, and increased investments in smart city projects. Europe also maintains a significant market presence, driven by robust government initiatives for environmental monitoring and urban planning. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by growing digitalization and expanding geospatial applications in transportation, utilities, and resource management.

Healthcare Data Quality Tools Market Outlook

According to our latest research, the global healthcare data quality tools market size reached USD 1.52 billion in 2024, reflecting robust demand for advanced data management solutions across the healthcare sector. The market is poised for sustained expansion, projected to achieve a value of USD 4.07 billion by 2033, growing at a strong CAGR of 11.7% from 2025 to 2033. This impressive growth is primarily driven by the increasing digitization of healthcare records, the proliferation of big data analytics, and the urgent need for accurate, reliable data to support clinical, operational, and regulatory decision-making.

One of the most significant growth factors for the healthcare data quality tools market is the rapid digital transformation witnessed across the healthcare industry. The adoption of electronic health records (EHRs), the integration of IoT-enabled medical devices, and the expansion of telehealth solutions have led to an exponential surge in data volumes. However, the utility of this data is contingent upon its quality, consistency, and integrity. Healthcare providers and payers are increasingly investing in data quality tools to eliminate duplicate records, correct data entry errors, and standardize disparate data sources. These initiatives are not only enhancing clinical outcomes and patient safety but also streamlining administrative processes and reducing operational costs.

Regulatory compliance remains another pivotal driver propelling the healthcare data quality tools market forward. Stringent regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, the General Data Protection Regulation (GDPR) in Europe, and various country-specific mandates necessitate the maintenance of high-quality, secure patient data. Healthcare organizations must ensure that their data management practices align with these evolving regulatory frameworks to avoid penalties and reputational damage. Consequently, there is a growing demand for sophisticated data quality tools that offer real-time monitoring, automated data cleansing, and comprehensive audit trails, enabling organizations to meet compliance requirements efficiently.

Furthermore, the rising focus on value-based care models and data-driven decision-making is accelerating the adoption of healthcare data quality tools. As healthcare systems transition from volume-based to outcome-based reimbursement structures, the need for accurate, timely, and actionable data becomes paramount. Quality data underpins advanced analytics, artificial intelligence (AI), and machine learning (ML) applications—empowering providers to identify care gaps, predict patient risks, and personalize treatment pathways. This paradigm shift is fostering greater collaboration between IT vendors, healthcare organizations, and regulatory bodies to develop and implement innovative data quality solutions that drive better patient and business outcomes.

From a regional perspective, North America continues to dominate the healthcare data quality tools market, accounting for the largest revenue share in 2024. The region's leadership can be attributed to its advanced healthcare infrastructure, high adoption rates of EHRs, and a strong emphasis on regulatory compliance. Europe follows closely, driven by growing digital health initiatives and stringent data protection laws. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by significant investments in healthcare IT, expanding healthcare access, and increasing awareness of the importance of data quality. Latin America and the Middle East & Africa are also showing promising growth trajectories, supported by ongoing healthcare reforms and digitalization efforts.

Component Analysis

The component segment of the healthcare data quality tools market is bifurcated into software and services, each playing a critical role in the overall ecosystem. The software segment currently holds th

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Data standardization is an important part of effective management. However, sometimes people have data that doesn't match. This dataset includes different ways that counties might get written by different people. It can be used as a lookup table when you need County to be your unique identifier. For example, it allows you to match St. Mary's, St Marys, and Saint Mary's so that you can use it with disparate data from other data sets.

A dashboard used by government agencies to monitor key performance indicators (KPIs) and communicate progress made on strategic outcomes with the general public and other interested stakeholders.

Snapshot img

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

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

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

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

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Data Quality Tools Market: Key Drivers and Trends

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

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

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

Who are the Major Data Quality Tools Market Vendors?

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

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

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

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

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

Which are the Key Regions for Data Quality Tools Market?

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

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

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

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

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

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').

The increasing popularity of online surveys in the social sciences led to an ongoing discussion about mode effects in survey research. The following article tests if commonly discussed mode-effects (e.g. sample differences, data quality; item-non response, social desirability and open-ended question) can indeed be reproduced in a non-experimental mixed-mode study. Using data from two non-full-probabilityrandom samples, collected via an online and face-to-face survey concerning itself with opinions on migration and refugees, most assumptions found in experimental literature can indeed be replicated via research data. Thus, the mode effects need to be accounted for if the usage of mixed-mode designs is necessary, especially if online surveys are involved.

This dataset contains replicate samples collected in the field by community technicians. No field replicates were collected in 2012. Replicate constituents with differences less than 10 percent are considered acceptable.

Our poster is essential for understanding the process of creating a community of practice in the context of Open Science. Building such a community and at the same time being part of the culture change that offers openness in science is challenging. No single researcher or librarian would be able to achieve those results alone. Gdańsk Tech Library’s strategy to popularise and practice Open Science requires many actions supported by a team of people with different competencies

This dataset includes laboratory instrument detection limit data associated with laboratory instruments used in the analysis of surface water samples collected as part of the USGS - Yukon River Inter-Tribal Watershed Council collaborative water quality monitoring project.

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.

Real-Time Data Quality Monitoring AI Market Outlook

According to our latest research, the global Real-Time Data Quality Monitoring AI market size reached USD 1.82 billion in 2024, reflecting robust demand across multiple industries. The market is expected to grow at a CAGR of 19.4% during the forecast period, reaching a projected value of USD 8.78 billion by 2033. This impressive growth trajectory is primarily driven by the increasing need for accurate, actionable data in real time to support digital transformation, compliance, and competitive advantage across sectors. The proliferation of data-intensive applications and the growing complexity of data ecosystems are further fueling the adoption of AI-powered data quality monitoring solutions worldwide.

One of the primary growth factors for the Real-Time Data Quality Monitoring AI market is the exponential increase in data volume and velocity generated by digital business processes, IoT devices, and cloud-based applications. Organizations are increasingly recognizing that poor data quality can have significant negative impacts on business outcomes, ranging from flawed analytics to regulatory penalties. As a result, there is a heightened focus on leveraging AI-driven tools that can continuously monitor, cleanse, and validate data streams in real time. This shift is particularly evident in industries such as BFSI, healthcare, and retail, where real-time decision-making is critical and the cost of errors can be substantial. The integration of machine learning algorithms and natural language processing in data quality monitoring solutions is enabling more sophisticated anomaly detection, pattern recognition, and predictive analytics, thereby enhancing overall data governance frameworks.

Another significant driver is the increasing regulatory scrutiny and compliance requirements surrounding data integrity and privacy. Regulations such as GDPR, HIPAA, and CCPA are compelling organizations to implement robust data quality management systems that can provide audit trails, ensure data lineage, and support automated compliance reporting. Real-Time Data Quality Monitoring AI tools are uniquely positioned to address these challenges by providing continuous oversight and immediate alerts on data quality issues, thereby reducing the risk of non-compliance and associated penalties. Furthermore, the rise of cloud computing and hybrid IT environments is making it imperative for enterprises to maintain consistent data quality across disparate systems and geographies, further boosting the demand for scalable and intelligent monitoring solutions.

The growing adoption of advanced analytics, artificial intelligence, and machine learning across industries is also contributing to market expansion. As organizations seek to leverage predictive insights and automate business processes, the need for high-quality, real-time data becomes paramount. AI-powered data quality monitoring solutions not only enhance the accuracy of analytics but also enable proactive data management by identifying potential issues before they impact downstream applications. This is particularly relevant in sectors such as manufacturing and telecommunications, where operational efficiency and customer experience are closely tied to data reliability. The increasing investment in digital transformation initiatives and the emergence of Industry 4.0 are expected to further accelerate the adoption of real-time data quality monitoring AI solutions in the coming years.

From a regional perspective, North America continues to dominate the Real-Time Data Quality Monitoring AI market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of leading technology providers, early adoption of AI and analytics, and stringent regulatory frameworks are key factors driving market growth in these regions. Asia Pacific is anticipated to witness the highest CAGR during the forecast period, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in AI technologies across countries such as China, India, and Japan. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, supported by growing awareness of data quality issues and the gradual adoption of advanced data management solutions.

This DSS presentation describes the Comprehensive Child Welfare Information System (CCWIS) Data Quality Requirements as defined by Federal Regulation 45 CFR 1355.52(d) and provides examples.

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

Abstract Data quality control programs used in the mineral industry normally define tolerance limits based on values considered as good practice or those that have previously been applied to similar deposits, although the precision and accuracy of estimates depend on a combination of geological characteristics, estimation parameters, sample spacing and data quality. This study investigates how the sample quality limits affect the estimates results. The proposed methodology is based on a series of metrics used to compare the impact on the estimates using a synthetic database with an increasing amount of error added to the original sample grades or positions, emulating different levels of precision. The proposed approach results lead to tolerance limits for the grades similar to those recommended in literature. The influence of the positional uncertainty on model estimates is at a minimum, because of the accuracy of current surveying methods that have a deviation in the order of millimeters, so its impact can be considered negligible.