The statistic shows the problems caused by poor quality data for enterprises in North America, according to a survey of North American IT executives conducted by 451 Research in 2015. As of 2015, ** percent of respondents indicated that having poor quality data can result in extra costs for the business.

The statistic depicts the causes of poor data quality for enterprises in North America, according to a survey of North American IT executives conducted by 451 Research in 2015. As of 2015, 47 percent of respondents indicated that poor data quality at their company was attributable to data migration or conversion projects.

Data Quality AI Market Outlook

According to our latest research, the global Data Quality AI market size reached USD 1.92 billion in 2024, driven by a robust surge in data-driven business operations across industries. The sector has demonstrated a remarkable compound annual growth rate (CAGR) of 18.6% from 2024, with projections indicating that the market will expand to USD 9.38 billion by 2033. This impressive growth trajectory is underpinned by the increasing necessity for automated data quality management solutions, as organizations recognize the strategic value of high-quality data for analytics, compliance, and digital transformation initiatives.

One of the primary growth factors for the Data Quality AI market is the exponential increase in data volume and complexity generated by modern enterprises. With the proliferation of IoT devices, cloud platforms, and digital business models, organizations are inundated with vast and diverse datasets. This data deluge, while offering immense potential, also introduces significant challenges related to data consistency, accuracy, and reliability. As a result, businesses are increasingly turning to AI-powered data quality solutions that can automate data cleansing, profiling, matching, and enrichment processes. These solutions not only enhance data integrity but also reduce manual intervention, enabling organizations to extract actionable insights more efficiently and cost-effectively.

Another significant driver fueling the growth of the Data Quality AI market is the mounting regulatory pressure and compliance requirements across various sectors, particularly in BFSI, healthcare, and government. Stringent regulations such as GDPR, HIPAA, and CCPA mandate organizations to maintain high standards of data accuracy, security, and privacy. AI-driven data quality tools are instrumental in ensuring compliance by continuously monitoring data flows, identifying anomalies, and providing real-time remediation. This proactive approach to data governance mitigates risks associated with data breaches, financial penalties, and reputational damage, thereby making AI-based data quality management a strategic investment for organizations operating in highly regulated environments.

The rapid adoption of advanced analytics, machine learning, and artificial intelligence across industries has also amplified the demand for high-quality data. As organizations increasingly leverage AI and advanced analytics for decision-making, the importance of data quality becomes paramount. Poor data quality can lead to inaccurate predictions, flawed business strategies, and suboptimal outcomes. Consequently, enterprises are prioritizing investments in AI-powered data quality solutions to ensure that their analytics initiatives are built on a foundation of reliable and consistent data. This trend is particularly pronounced among large enterprises and digitally mature organizations that view data as a critical asset for competitive differentiation and innovation.

Data Quality Tools have become indispensable in the modern business landscape, particularly as organizations grapple with the complexities of managing vast amounts of data. These tools are designed to ensure that data is accurate, consistent, and reliable, which is crucial for making informed business decisions. By leveraging advanced algorithms and machine learning, Data Quality Tools can automate the processes of data cleansing, profiling, and enrichment, thereby reducing the time and effort required for manual data management. This automation not only enhances data integrity but also empowers businesses to derive actionable insights more efficiently. As a result, companies are increasingly investing in these tools to maintain a competitive edge in their respective industries.

From a regional perspective, North America continues to dominate the Data Quality AI market, accounting for the largest share in 2024. The region's leadership is attributed to the presence of major technology vendors, early adoption of AI-driven solutions, and a robust ecosystem of data-centric enterprises. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digital transformation, increasing investments in cloud infrastructure, and a burgeoning startup ecosystem. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, driven by regulatory mandat

Dataset

This dataset was created by badgenius

Contents

The Good/Bad data set is used for the image-quality research, containing unsuccessfully and successfully synthetic samples.

Hype surrounds the promotions, aspirations, and notions of “artificial intelligence (AI) for social good” and its related permutations. These terms, as used in data science and particularly in public discourse, are vague. Far from being irrelevant to data scientists or practitioners of AI, the terms create the public notion of the systems built. Through a critical reflection, I explore how notions of AI for social good are vague, offer insufficient criteria for judgement, and elide the externalities and structural interdependence of AI systems. Instead, the field known as “AI for social good” is best understood and referred to as “AI for not bad.”

The overarching goal of the project is to create a highly efficient framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse advanced metering infrastructure (AMI) devices and phasor measurement units (PMUs) in order to accommodate extreme levels of PV. For this goal, we aim at creating a highly efficient AI framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse AMI devices and PMUs. The files contain the integrated bad data detection with a pre-trained Deep Neural Network-based State Estimation (DNN-SE) model with a voltage regulation control algorithm to manage over-voltage issues in J-1 Feeder with high PV penetration.

Wrong-Way Driving Data Hubs Market Outlook

As per our latest research, the global Wrong-Way Driving Data Hubs market size stood at USD 1.37 billion in 2024, reflecting rising investments in intelligent transportation systems and heightened awareness of road safety worldwide. The market is experiencing robust momentum, registering a compound annual growth rate (CAGR) of 13.8% from 2025 to 2033. At this pace, the market is forecasted to reach USD 4.09 billion by 2033. This impressive growth is fueled by the urgent need to reduce traffic fatalities caused by wrong-way driving incidents, coupled with technological advancements in real-time data analytics and automated detection systems.

The primary growth factor propelling the Wrong-Way Driving Data Hubs market is the escalating global focus on road safety and the reduction of traffic-related fatalities. Governments and transportation authorities are increasingly adopting advanced data-driven solutions to detect, monitor, and mitigate wrong-way driving incidents, which are among the most dangerous and often fatal types of road accidents. The integration of artificial intelligence, machine learning, and sensor technologies into wrong-way detection systems has significantly improved the accuracy and speed of incident identification, enabling timely interventions. Moreover, legislative mandates in several countries require the deployment of intelligent transportation systems, further driving the adoption of wrong-way driving data hubs across urban and interurban road networks.

Another significant driver is the rapid urbanization and the consequent rise in vehicular traffic, which has exacerbated the challenges associated with wrong-way driving, particularly on highways and expressways. Urban areas are witnessing an unprecedented increase in traffic density, making the need for efficient traffic management and incident detection systems more critical than ever. Wrong-way driving data hubs, equipped with real-time data aggregation and analytics capabilities, enable authorities to proactively monitor high-risk locations and deploy countermeasures effectively. Additionally, the proliferation of connected vehicles and advancements in vehicle-to-infrastructure (V2I) communication have created new opportunities for seamless integration of data hubs with existing traffic management infrastructure, enhancing their effectiveness and operational reach.

The expansion of smart city initiatives globally is also playing a pivotal role in shaping the Wrong-Way Driving Data Hubs market landscape. Smart cities rely heavily on interconnected systems and real-time data to optimize urban mobility, reduce congestion, and improve public safety. Wrong-way driving data hubs are increasingly being integrated with broader urban mobility platforms, allowing for comprehensive incident management and data sharing across multiple agencies. This holistic approach not only streamlines response efforts but also facilitates data-driven policymaking and resource allocation. As more cities embrace digital transformation, the demand for sophisticated wrong-way driving detection and data management solutions is expected to surge, further bolstering market growth.

From a regional perspective, North America continues to dominate the Wrong-Way Driving Data Hubs market, accounting for the largest revenue share in 2024. This leadership is attributed to the region’s early adoption of intelligent transportation systems, significant government investments in road safety, and a high incidence of wrong-way driving accidents, particularly in the United States. Europe follows closely, driven by stringent regulatory frameworks and a strong focus on technological innovation in traffic management. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by rapid urbanization, expanding transportation networks, and increasing government initiatives to modernize road infrastructure. Latin America and the Middle East & Africa, while currently representing smaller market shares, are expected to witness accelerating adoption rates due to growing awareness and investments in transportation safety technologies.

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

We study processing and acquisition of objective information regarding qualities that people care about, intelligence and beauty. Subjects receiving negative feedback did not respect the strength of these signals, were far less predictable in their updating behavior and exhibited an aversion to new information. In response to good news, inference conformed more closely to Bayes' Rule, both in accuracy and precision. Signal direction did not affect updating or acquisition in our neutral control. Unlike past work, our design varied direction and agreement with priors independently. The results indicate that confirmation bias is driven by direction; confirmation alone had no effect. (JEL D82, D83)

Dataset

This dataset was created by monicaooi

Released under CC0: Public Domain

Contents

Find data on fair or poor health among adults in Massachusetts. These data come from the Behavioral Risk Factor Surveillance System.

Overview

This dataset provides fast response wind and virtual sonic temperature data.

Data Details

Each meteorological (met) station has one sonic anemometer (Gill R3-50, omnidirectional) mounted on top of a 10-m tower. Sensor verticality (within a degree) has been verified by the analog inclinometer mounted on the base plate alongside the sonic anemometer. The sonic anemometer has been oriented to magnetic North.

The serial data stream is transmitted via radio link (9XTend RF modem by MaxStream) to the data acquisition computer housed in a temperature-controlled enclosure at the base of the 80-m tower.

The original data were stored in flat ASCII files in 30-min pieces (".00." level). The current version of the data is ".a0." level. All evidently erroneous and/or broken lines were marked as bad and/or replaced with a "baddata" place holder, the housekeeping data were stripped off, and the data were split into 5-min portions with no internal time stamp. The data have been prepared for processing with EddyPro and stored in ASCII comma delimited files formatted as follows:

u,v,w,T,qc

where:

• "u, v, w" are the three wind components (m/s)
• "T" is the sonic virtual temperature (C)
• "qc" is basic quality control code: 0 - OK, 1 - sonic bad data code, 2 - broken data line, and 3 - missed line.

Baddata place holder is 99.99

NOTE: No attempt has been made to fill gaps in the data.

Data Quality

• Includes raw data with basic quality control (QC) applied.
• All housekeeping fields removed.

Jordan whatsapp number list helps businesses reach more people easily. You can use the numbers right away since they are ready and organized. Thus, you can quickly find the right ones. You sort the numbers by location, age, gender, and more. This helps you find the best audience for your business. You check the numbers often to make sure they are correct. You won’t waste time on bad data. These whatsapp data help you grow your business. You can contact people who have an interest in your services. On our site, List to Data, you can easily locate important phone numbers. Jordan whatsapp phone number data provides valuable information. Trusted sources collect the data, so you know it’s reliable. You can check where the data comes from, which builds trust. The data updates regularly, so you always get the newest information when you need it. With List to Data, you can effortlessly search for important phone numbers. Jordan whatsapp phone number data stays open 24/7, so you access the numbers whenever you want. If you need help, support is available at all times. This makes it easier for businesses to find the right data. Overall, this whatsapp data helps businesses expand and connect with more customers.

United States CCI: Present Situation: sa: Business Conditions: Bad data was reported at 16.100 % in Apr 2025. This records a decrease from the previous number of 16.500 % for Mar 2025. United States CCI: Present Situation: sa: Business Conditions: Bad data is updated monthly, averaging 19.600 % from Feb 1967 (Median) to Apr 2025, with 637 observations. The data reached an all-time high of 57.000 % in Dec 1982 and a record low of 6.000 % in Dec 1968. United States CCI: Present Situation: sa: Business Conditions: Bad data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H049: Consumer Confidence Index. [COVID-19-IMPACT]

When there is no treatment available for a life-threatening disease, providing personal health information could lead to despair or fatalistic behaviors resulting in negative health outcomes. We document this possibility utilizing an experiment in Malawi that randomized incentives to learn HIV testing results in a context where anti-retroviral treatment was not yet available. Six years after the experiment, among HIV+s, those who learned their status were 23 percentage points less likely to survive than those who did not, with effects persisting after 15 years. Receiving an HIV+ diagnosis resulted in riskier health behaviors, greater anxiety, and higher discount rates.

Context

Series created by: Vince Gilligan Number of seasons: 5 Number of episodes: 62 Original air dates: January 20, 2008 – September 29, 2013

Content

Data was acquired through downloading IMDb TV episodes datasets and scraping information from Wikipedia.

Acknowledgements

Thanks to IMDb, Wikipedia, and community curators.

Use

It should be easy to join these data files together on Title and Air Date fields to compare (for example) US viewers and IMDb ratings.

Motivation

I wanted to share a dataset about Breaking Bad, one of my favorite TV shows to binge watch.

The global Data Quality Tools market is poised for substantial expansion, projected to reach approximately USD 4216.1 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of 12.6% anticipated over the forecast period of 2025-2033. This significant growth is primarily fueled by the escalating volume and complexity of data generated across all sectors, coupled with an increasing awareness of the critical need for accurate, consistent, and reliable data for informed decision-making. Businesses are increasingly recognizing that poor data quality can lead to flawed analytics, inefficient operations, compliance risks, and ultimately, lost revenue. The demand for sophisticated data quality solutions is further propelled by the growing adoption of advanced analytics, artificial intelligence, and machine learning, all of which are heavily dependent on high-quality foundational data. The market is witnessing a strong inclination towards cloud-based solutions due to their scalability, flexibility, and cost-effectiveness, while on-premises deployments continue to cater to organizations with stringent data security and regulatory requirements. The data quality tools market is characterized by its diverse applications across both enterprise and government sectors, highlighting the universal need for data integrity. Key market drivers include the burgeoning big data landscape, the increasing emphasis on data governance and regulatory compliance such as GDPR and CCPA, and the drive for enhanced customer experience through personalized insights derived from accurate data. However, certain restraints, such as the high cost of implementing and maintaining comprehensive data quality programs and the scarcity of skilled data professionals, could temper growth. Despite these challenges, the persistent digital transformation initiatives and the continuous evolution of data management technologies are expected to create significant opportunities for market players. Leading companies like Informatica, IBM, SAS, and Oracle are at the forefront, offering comprehensive suites of data quality tools, fostering innovation, and driving market consolidation. The market's trajectory indicates a strong future, where data quality will be paramount for organizational success. This report offers a deep dive into the global Data Quality Tools market, providing a granular analysis of its trajectory from the historical period of 2019-2024, through the base year of 2025, and extending into the forecast period of 2025-2033. With an estimated market size of $2,500 million in 2025, this dynamic sector is poised for significant expansion driven by an increasing reliance on accurate and reliable data across diverse industries. The study encompasses a detailed examination of key players, market trends, growth drivers, challenges, and future opportunities, offering invaluable intelligence for stakeholders seeking to navigate this evolving landscape.

According to Cognitive Market Research, the global Data Quality Software market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS of

Data Quality Software

The Emergence of Big Data and IoT drives the Market

The rise of big data analytics and Internet of Things (IoT) applications has significantly increased the volume and complexity of data that businesses need to manage. As more connected devices generate real-time data, the amount of information businesses handle grows exponentially. This surge in data requires organizations to ensure its accuracy, consistency, and relevance to prevent decision-making errors. For instance, in industries like healthcare, where real-time data from medical devices and patient monitoring systems is used for diagnostics and treatment decisions, inaccurate data can lead to critical errors. To address these challenges, organizations are increasingly investing in data quality software to manage large volumes of data from various sources. Companies like GE Healthcare use data quality software to ensure the integrity of data from connected medical devices, allowing for more accurate patient care and operational efficiency. The demand for these tools continues to rise as businesses realize the importance of maintaining clean, consistent, and reliable data for effective big data analytics and IoT applications. With the growing adoption of digital transformation strategies and the integration of advanced technologies, organizations are generating vast amounts of structured and unstructured data across various sectors. For instance, in the retail sector, companies are collecting data from customer interactions, online transactions, and social media channels. If not properly managed, this data can lead to inaccuracies, inconsistencies, and unreliable insights that can adversely affect decision-making. The proliferation of data highlights the need for robust data quality solutions to profile, cleanse, and validate data, ensuring its integrity and usability. Companies like Walmart and Amazon rely heavily on data quality software to manage vast datasets for personalized marketing, inventory management, and customer satisfaction. Without proper data management, these businesses risk making decisions based on faulty data, potentially leading to lost revenue or customer dissatisfaction. The increasing volumes of data and the need to ensure high-quality, reliable data across organizations are significant drivers behind the rising demand for data quality software, as it enables companies to stay competitive and make informed decisions.

Key Restraints to

Data Quality Software

Lack of Skilled Personnel and High Implementation Costs Hinders the market growth

The effective use of data quality software requires expertise in areas like data profiling, cleansing, standardization, and validation, as well as a deep understanding of the specific business needs and regulatory requirements. Unfortunately, many organizations struggle to find personnel with the right skill set, which limits their ability to implement and maximize the potential of these tools. For instance, in industries like finance or healthcare, where data quality is crucial for compliance and decision-making, the lack of skilled personnel can lead to inefficiencies in managing data and missed opportunities for improvement. In turn, organizations may fail to extract the full value from their data quality investments, resulting in poor data outcomes and suboptimal decision-ma...

Dirty Cafe Sales Dataset

Overview

The Dirty Cafe Sales dataset contains 10,000 rows of synthetic data representing sales transactions in a cafe. This dataset is intentionally "dirty," with missing values, inconsistent data, and errors introduced to provide a realistic scenario for data cleaning and exploratory data analysis (EDA). It can be used to practice cleaning techniques, data wrangling, and feature engineering.

File Information

• File Name: dirty_cafe_sales.csv
• Number of Rows: 10,000
• Number of Columns: 8

Columns Description

Data Characteristics

1. Missing Values:

   • Some columns (e.g., Item, Payment Method, Location) may contain missing values represented as None or empty cells.

2. Invalid Values:

   • Some rows contain invalid entries like "ERROR" or "UNKNOWN" to simulate real-world data issues.

3. Price Consistency:

   • Prices for menu items are consistent but may have missing or incorrect values introduced.

Menu Items

The dataset includes the following menu items with their respective price ranges:

Use Cases

This dataset is suitable for: - Practicing data cleaning techniques such as handling missing values, removing duplicates, and correcting invalid entries. - Exploring EDA techniques like visualizations and summary statistics. - Performing feature engineering for machine learning workflows.

Cleaning Steps Suggestions

To clean this dataset, consider the following steps: 1. Handle Missing Values: - Fill missing numeric values with the median or mean. - Replace missing categorical values with the mode or "Unknown."

1. Handle Invalid Values:

   • Replace invalid entries like "ERROR" and "UNKNOWN" with NaN or appropriate values.

2. Date Consistency:

   • Ensure all dates are in a consistent format.
   • Fill missing dates with plausible values based on nearby records.

3. Feature Engineering:

   • Create new columns, such as Day of the Week or Transaction Month, for further analysis.

License

This dataset is released under the CC BY-SA 4.0 License. You are free to use, share, and adapt it, provided you give appropriate credit.

Feedback

If you have any questions or feedback, feel free to reach out through the dataset's discussion board on Kaggle.