The global Data Cleansing Software market is poised for substantial growth, estimated to reach approximately USD 3,500 million by 2025, with a projected Compound Annual Growth Rate (CAGR) of around 18% through 2033. This robust expansion is primarily driven by the escalating volume of data generated across all sectors, coupled with an increasing awareness of the critical importance of data accuracy for informed decision-making. Organizations are recognizing that flawed data can lead to significant financial losses, reputational damage, and missed opportunities. Consequently, the demand for sophisticated data cleansing solutions that can effectively identify, rectify, and prevent data errors is surging. Key drivers include the growing adoption of AI and machine learning for automated data profiling and cleansing, the increasing complexity of data sources, and the stringent regulatory requirements around data quality and privacy, especially within industries like finance and healthcare. The market landscape for data cleansing software is characterized by a dynamic interplay of trends and restraints. Cloud-based solutions are gaining significant traction due to their scalability, flexibility, and cost-effectiveness, particularly for Small and Medium-sized Enterprises (SMEs). Conversely, large enterprises and government agencies often opt for on-premise solutions, prioritizing enhanced security and control over sensitive data. While the market presents immense opportunities, challenges such as the high cost of implementation and the need for specialized skill sets to manage and operate these tools can act as restraints. However, advancements in user-friendly interfaces and the integration of data cleansing capabilities within broader data management platforms are mitigating these concerns, paving the way for wider adoption. Major players like IBM, SAP SE, and SAS Institute Inc. are continuously innovating, offering comprehensive suites that address the evolving needs of businesses navigating the complexities of big data.

The data cleansing tools market is experiencing robust growth, driven by the escalating volume and complexity of data across various sectors. The increasing need for accurate and reliable data for decision-making, coupled with stringent data privacy regulations (like GDPR and CCPA), fuels demand for sophisticated data cleansing solutions. Businesses, regardless of size, are recognizing the critical role of data quality in enhancing operational efficiency, improving customer experiences, and gaining a competitive edge. The market is segmented by application (agencies, large enterprises, SMEs, personal use), deployment type (cloud, SaaS, web, installed, API integration), and geography, reflecting the diverse needs and technological preferences of users. While the cloud and SaaS models are witnessing rapid adoption due to scalability and cost-effectiveness, on-premise solutions remain relevant for organizations with stringent security requirements. The historical period (2019-2024) showed substantial growth, and this trajectory is projected to continue throughout the forecast period (2025-2033). Specific growth rates will depend on technological advancements, economic conditions, and regulatory changes. Competition is fierce, with established players like IBM, SAS, and SAP alongside innovative startups continuously improving their offerings. The market's future depends on factors such as the evolution of AI and machine learning capabilities within data cleansing tools, the increasing demand for automated solutions, and the ongoing need to address emerging data privacy challenges. The projected Compound Annual Growth Rate (CAGR) suggests a healthy expansion of the market. While precise figures are not provided, a realistic estimate based on industry trends places the market size at approximately $15 billion in 2025. This is based on a combination of existing market reports and understanding of the growth of related fields (such as data analytics and business intelligence). This substantial market value is further segmented across the specified geographic regions. North America and Europe currently dominate, but the Asia-Pacific region is expected to exhibit significant growth potential driven by increasing digitalization and adoption of data-driven strategies. The restraints on market growth largely involve challenges related to data integration complexity, cost of implementation for smaller businesses, and the skills gap in data management expertise. However, these are being countered by the emergence of user-friendly tools and increased investment in data literacy training.

Data Cleaning or Data cleansing is to clean the data by imputing missing values, smoothing noisy data, and identifying or removing outliers. In general, the missing values are found due to collection error or data is corrupted.

Here some info in details :Feature Engineering - Handling Missing Value

Wine_Quality.csv dataset have the numerical missing data, and students_Performance.mv.csv dataset have Numerical and categorical missing data's.

The dissertation_demo.zip contains the base code and demonstration purpose for the dissertation: A Conceptual Model for Transparent, Reusable, and Collaborative Data Cleaning. Each chapter has a demo folder for demonstrating provenance queries or tools. The Airbnb dataset for demonstration and simulation is not included in this demo but is available to access directly from the reference website. Any updates on demonstration and examples can be found online at: https://github.com/nikolausn/dissertation_demo

Dataset

This dataset was created by Mohanad Hazem Qabil

Contents

Discover the booming market for data cleaning tools! Our comprehensive analysis reveals a $10 billion+ market in 2025, driven by AI, cloud adoption, and the critical need for high-quality data. Explore key trends, leading companies (Dundas BI, IBM, Sisense), and future growth projections to 2033.

All data are prone to error and require data cleaning prior to analysis. An important example is longitudinal growth data, for which there are no universally agreed standard methods for identifying and removing implausible values and many existing methods have limitations that restrict their usage across different domains. A decision-making algorithm that modified or deleted growth measurements based on a combination of pre-defined cut-offs and logic rules was designed. Five data cleaning methods for growth were tested with and without the addition of the algorithm and applied to five different longitudinal growth datasets: four uncleaned canine weight or height datasets and one pre-cleaned human weight dataset with randomly simulated errors. Prior to the addition of the algorithm, data cleaning based on non-linear mixed effects models was the most effective in all datasets and had on average a minimum of 26.00% higher sensitivity and 0.12% higher specificity than other methods. Data cleaning methods using the algorithm had improved data preservation and were capable of correcting simulated errors according to the gold standard; returning a value to its original state prior to error simulation. The algorithm improved the performance of all data cleaning methods and increased the average sensitivity and specificity of the non-linear mixed effects model method by 7.68% and 0.42% respectively. Using non-linear mixed effects models combined with the algorithm to clean data allows individual growth trajectories to vary from the population by using repeated longitudinal measurements, identifies consecutive errors or those within the first data entry, avoids the requirement for a minimum number of data entries, preserves data where possible by correcting errors rather than deleting them and removes duplications intelligently. This algorithm is broadly applicable to data cleaning anthropometric data in different mammalian species and could be adapted for use in a range of other domains.

Materials from workshop conducted for Monroe Library faculty as part of TLT/Faculty Development/Digital Scholarship on 2018-04-05. Objectives:Clean dataAnalyze data using pivot tablesVisualize dataDesign accessible instruction for working with dataAssociated Research Guide at http://researchguides.loyno.edu/data_workshopData sets are from the following:

BaroqueArt Dataset by CulturePlex Lab is licensed under CC0 What's on the Menu? Menus by New York Public Library is licensed under CC0 Dog movie stars and dog breed popularity by Ghirlanda S, Acerbi A, Herzog H is licensed under CC BY 4.0 NOPD Misconduct Complaints, 2016-2018 by City of New Orleans Open Data is licensed under CC0 U.S. Consumer Product Safety Commission Recall Violations by CU.S. Consumer Product Safety Commission, Violations is licensed under CC0 NCHS - Leading Causes of Death: United States by Data.gov is licensed under CC0 Bob Ross Elements by Episode by Walt Hickey, FiveThirtyEight, is licensed under CC BY 4.0 Pacific Walrus Coastal Haulout 1852-2016 by U.S. Geological Survey, Alaska Science Center is licensed under CC0 Australia Registered Animals by Sunshine Coast Council is licensed under CC0

Ahoy, data enthusiasts! Join us for a hands-on workshop where you will hoist your sails and navigate through the Statistics Canada website, uncovering hidden treasures in the form of data tables. With the wind at your back, you’ll master the art of downloading these invaluable Stats Can datasets while braving the occasional squall of data cleaning challenges using Excel with your trusty captains Vivek and Lucia at the helm.

Restaurant Menu DatasetWith approximately 45,000 menus dating from the 1840s to the present, The New York Public Library’s restaurant menu collection is one of the largest in the world. The menu data has been transcribed, dish by dish, into this dataset. For more information, please see http://menus.nypl.org/about.This dataset is not clean and contains many missing values, making it perfect to practice data cleaning tools and techniques.Dataset Variables:id: identifier for menuname: sponsor: who sponsored the meal (organizations, people, name of restaurant)event: categoryvenue: type of place (commercial, social, professional)place: where the meal took place (often a geographic location)physical_description: dimension and material description of the menuoccasion: occasion of the meal (holidays, anniversaries, daily)notes: notes by librarians about the original materialcall_number: call number of the menukeywords: language: date: date of the menulocation: organization or business who produced the menulocation_typecurrency: system of money the menu uses (dollars, etc)currency_symbol: symbol for the currency ($, etc)status: completeness of the menu transcription (transcribed, under review, etc)page_count: how many pages the menu hasdish_count: how many dishes the menu has

Dataset

This dataset was created by AbdElRahman16

Contents

In this project, we work on repairing three datasets:

• Trials design: This dataset was obtained from the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT) register and the ground truth was created from external registries. In the dataset, multiple countries, identified by the attribute country_protocol_code, conduct the same clinical trials which is identified by eudract_number. Each clinical trial has a title that can help find informative details about the design of the trial.
• Trials population: This dataset delineates the demographic origins of participants in clinical trials primarily conducted across European countries. This dataset include structured attributes indicating whether the trial pertains to a specific gender, age group or healthy volunteers. Each of these categories is labeled as (`1') or (`0') respectively denoting whether it is included in the trials or not. It is important to note that the population category should remain consistent across all countries conducting the same clinical trial identified by an eudract_number. The ground truth samples in the dataset were established by aligning information about the trial populations provided by external registries, specifically the CT.gov database and the German Trials database. Additionally, the dataset comprises other unstructured attributes that categorize the inclusion criteria for trial participants such as inclusion.
• Allergens: This dataset contains information about products and their allergens. The data was collected from the German version of the `Alnatura' (Access date: 24 November, 2020), a free database of food products from around the world `Open Food Facts', and the websites: `Migipedia', 'Piccantino', and `Das Ist Drin'. There may be overlapping products across these websites. Each product in the dataset is identified by a unique code. Samples with the same code represent the same product but are extracted from a differentb source. The allergens are indicated by (‘2’) if present, or (‘1’) if there are traces of it, and (‘0’) if it is absent in a product. The dataset also includes information on ingredients in the products. Overall, the dataset comprises categorical structured data describing the presence, trace, or absence of specific allergens, and unstructured text describing ingredients.

N.B: Each '.zip' file contains a set of 5 '.csv' files which are part of the afro-mentioned datasets:

• "{dataset_name}_train.csv": samples used for the ML-model training. (e.g "allergens_train.csv")
• "{dataset_name}_test.csv": samples used to test the the ML-model performance. (e.g "allergens_test.csv")
• "{dataset_name}_golden_standard.csv": samples represent the ground truth of the test samples. (e.g "allergens_golden_standard.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used for the ML-model training. (e.g "allergens_parker_train.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used to test the the ML-model performance. (e.g "allergens_parker_test.csv")

Sample data for exercises in Further Adventures in Data Cleaning.

Dataset

This dataset was created by Hassane Skikri

Released under CC0: Public Domain

Contents

Discover the booming Data Preparation Tools market! Learn about its 18.5% CAGR, key players (Microsoft, Tableau, IBM), and regional growth trends from our comprehensive analysis. Explore market segments, drivers, and restraints shaping this crucial sector for businesses of all sizes.

AI in Data Cleaning Market Outlook

According to our latest research, the global AI in Data Cleaning market size reached USD 1.82 billion in 2024, demonstrating remarkable momentum driven by the exponential growth of data-driven enterprises. The market is projected to grow at a CAGR of 28.1% from 2025 to 2033, reaching an estimated USD 17.73 billion by 2033. This exceptional growth trajectory is primarily fueled by increasing data volumes, the urgent need for high-quality datasets, and the adoption of artificial intelligence technologies across diverse industries.

The surging demand for automated data management solutions remains a key growth driver for the AI in Data Cleaning market. As organizations generate and collect massive volumes of structured and unstructured data, manual data cleaning processes have become insufficient, error-prone, and costly. AI-powered data cleaning tools address these challenges by leveraging machine learning algorithms, natural language processing, and pattern recognition to efficiently identify, correct, and eliminate inconsistencies, duplicates, and inaccuracies. This automation not only enhances data quality but also significantly reduces operational costs and improves decision-making capabilities, making AI-based solutions indispensable for enterprises aiming to achieve digital transformation and maintain a competitive edge.

Another crucial factor propelling market expansion is the growing emphasis on regulatory compliance and data governance. Sectors such as BFSI, healthcare, and government are subject to stringent data privacy and accuracy regulations, including GDPR, HIPAA, and CCPA. AI in data cleaning enables these industries to ensure data integrity, minimize compliance risks, and maintain audit trails, thereby safeguarding sensitive information and building stakeholder trust. Furthermore, the proliferation of cloud computing and advanced analytics platforms has made AI-powered data cleaning solutions more accessible, scalable, and cost-effective, further accelerating adoption across small, medium, and large enterprises.

The increasing integration of AI in data cleaning with other emerging technologies such as big data analytics, IoT, and robotic process automation (RPA) is unlocking new avenues for market growth. By embedding AI-driven data cleaning processes into end-to-end data pipelines, organizations can streamline data preparation, enable real-time analytics, and support advanced use cases like predictive modeling and personalized customer experiences. Strategic partnerships, investments in R&D, and the rise of specialized AI startups are also catalyzing innovation in this space, making AI in data cleaning a cornerstone of the broader data management ecosystem.

From a regional perspective, North America continues to lead the global AI in Data Cleaning market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The region’s dominance is attributed to the presence of major technology vendors, robust digital infrastructure, and high adoption rates of AI and cloud technologies. Meanwhile, Asia Pacific is witnessing the fastest growth, propelled by rapid digitalization, expanding IT sectors, and increasing investments in AI-driven solutions by enterprises in China, India, and Southeast Asia. Europe remains a significant market, supported by strict data protection regulations and a mature enterprise landscape. Latin America and the Middle East & Africa are emerging as promising markets, albeit at a relatively nascent stage, with growing awareness and gradual adoption of AI-powered data cleaning solutions.

Component Analysis

The AI in Data Cleaning market is broadly segmented by component into software and services, with each segment playing a pivotal role in shaping the industry’s evolution. The software segment dominates the market, driven by the rapid adoption of advanced AI-based data cleaning platforms that automate complex data preparation tasks. These platforms leverage sophisticated algorithms to detect anomalies, standardize formats, and enrich datasets, thereby enabling organizations to maintain high-quality data repositories. The increasing demand for self-service data cleaning software, which empowers business users to cleanse data without extensive IT intervention, is further fueling growth in this segment. Vendors are continuously enhancing their offerings with intuitive interfaces, integration capabilities, and support for diverse data sources to cater to a wide r

Autonomous Data Cleaning with AI Market Outlook

According to our latest research, the Global Autonomous Data Cleaning with AI market size was valued at $1.4 billion in 2024 and is projected to reach $8.2 billion by 2033, expanding at a robust CAGR of 21.8% during 2024–2033. This remarkable growth is primarily fueled by the exponential increase in enterprise data volumes and the urgent need for high-quality, reliable data to drive advanced analytics, machine learning, and business intelligence initiatives. The autonomous data cleaning with AI market is being propelled by the integration of artificial intelligence and machine learning algorithms that automate the tedious and error-prone processes of data cleansing, normalization, and validation, enabling organizations to unlock actionable insights with greater speed and accuracy. As businesses across diverse sectors increasingly recognize the strategic value of data-driven decision-making, the demand for autonomous data cleaning solutions is expected to surge, transforming how organizations manage and leverage their data assets globally.

Regional Outlook

North America currently holds the largest share of the autonomous data cleaning with AI market, accounting for over 38% of the global market value in 2024. This dominance is underpinned by the region’s mature technological infrastructure, high adoption rates of AI-driven analytics, and the presence of leading technology vendors and innovative startups. The United States, in particular, leads in enterprise digital transformation, with sectors such as BFSI, healthcare, and IT & telecommunications aggressively investing in automated data quality solutions. Stringent regulatory requirements around data governance, such as HIPAA and GDPR, have further incentivized organizations to deploy advanced data cleaning platforms to ensure compliance and mitigate risks. The region’s robust ecosystem of cloud service providers and AI research hubs also accelerates the deployment and integration of autonomous data cleaning tools, positioning North America at the forefront of market innovation and growth.

Asia Pacific is emerging as the fastest-growing region in the autonomous data cleaning with AI market, projected to register a remarkable CAGR of 25.6% through 2033. The region’s rapid digitalization, expanding e-commerce sector, and government-led initiatives to promote smart manufacturing and digital health are driving significant investments in AI-powered data management solutions. Countries such as China, India, Japan, and South Korea are witnessing a surge in data generation from mobile applications, IoT devices, and cloud platforms, necessitating robust autonomous data cleaning capabilities to ensure data integrity and business agility. Local enterprises are increasingly partnering with global technology providers and investing in in-house AI talent to accelerate adoption. Furthermore, favorable policy reforms and incentives for AI research and development are catalyzing the advancement and deployment of autonomous data cleaning technologies across diverse industry verticals.

In contrast, emerging economies in Latin America, the Middle East, and Africa are experiencing a gradual uptake of autonomous data cleaning with AI, shaped by unique challenges such as limited digital infrastructure, skills gaps, and budget constraints. While the potential for market expansion is substantial, particularly in sectors like banking, government, and telecommunications, adoption is often hindered by concerns over data privacy, lack of standardized frameworks, and the high upfront costs of AI integration. However, localized demand for real-time analytics, coupled with international investments in digital transformation and capacity building, is gradually fostering an environment conducive to the adoption of autonomous data cleaning solutions. Policy initiatives aimed at enhancing digital literacy and supporting startup ecosystems are also expected to play a pivotal role in bridging the adoption gap and unleashing new growth opportunities in these regions.

Report Scope

Autonomous Data Cleaning with AI Market Outlook

According to our latest research, the global Autonomous Data Cleaning with AI market size reached USD 1.68 billion in 2024, with a robust year-on-year growth driven by the surge in enterprise data volumes and the mounting demand for high-quality, actionable insights. The market is projected to expand at a CAGR of 24.2% from 2025 to 2033, which will take the overall market value to approximately USD 13.1 billion by 2033. This rapid growth is fueled by the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies across industries, aiming to automate and optimize the data cleaning process for improved operational efficiency and decision-making.

The primary growth driver for the Autonomous Data Cleaning with AI market is the exponential increase in data generation across various industries such as BFSI, healthcare, retail, and manufacturing. Organizations are grappling with massive amounts of structured and unstructured data, much of which is riddled with inconsistencies, duplicates, and inaccuracies. Manual data cleaning is both time-consuming and error-prone, leading businesses to seek automated AI-driven solutions that can intelligently detect, correct, and prevent data quality issues. The integration of AI not only accelerates the data cleaning process but also ensures higher accuracy, enabling organizations to leverage clean, reliable data for analytics, compliance, and digital transformation initiatives. This, in turn, translates into enhanced business agility and competitive advantage.

Another significant factor propelling the market is the increasing regulatory scrutiny and compliance requirements in sectors such as banking, healthcare, and government. Regulations such as GDPR, HIPAA, and others mandate strict data governance and quality standards. Autonomous Data Cleaning with AI solutions help organizations maintain compliance by ensuring data integrity, traceability, and auditability. Additionally, the evolution of cloud computing and the proliferation of big data analytics platforms have made it easier for organizations of all sizes to deploy and scale AI-powered data cleaning tools. These advancements are making autonomous data cleaning more accessible, cost-effective, and scalable, further driving market adoption.

The growing emphasis on digital transformation and real-time decision-making is also a crucial growth factor for the Autonomous Data Cleaning with AI market. As enterprises increasingly rely on analytics, machine learning, and artificial intelligence for business insights, the quality of input data becomes paramount. Automated, AI-driven data cleaning solutions enable organizations to process, cleanse, and prepare data in real-time, ensuring that downstream analytics and AI models are fed with high-quality inputs. This not only improves the accuracy of business predictions but also reduces the time-to-insight, helping organizations stay ahead in highly competitive markets.

From a regional perspective, North America currently dominates the Autonomous Data Cleaning with AI market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of leading technology companies, early adopters of AI, and a mature regulatory environment are key factors contributing to North America’s leadership. However, Asia Pacific is expected to witness the highest CAGR over the forecast period, driven by rapid digitalization, expanding IT infrastructure, and increasing investments in AI and data analytics, particularly in countries such as China, India, and Japan. Latin America and the Middle East & Africa are also gradually emerging as promising markets, supported by growing awareness and adoption of AI-driven data management solutions.

Component Analysis

The Autonomous Data Cleaning with AI market is segmented by component into Software and Services. The software segment currently holds the largest market share, driven

Access and clean an open source herbarium dataset using Excel or RStudio.

Petroleum production data are usually stored in a format that makes it easy to determine the year and month production started, if there are any breaks, and when production ends. However, in some cases, you may want to compare production runs where the start of production for all wells starts at month one regardless of the year the wells started producing. This report describes the JAVA program the U.S. Geological Survey developed to examine water-to-oil and water-to-gas ratios in the form of month one, month two, and so on with the objective of estimating quantities of water and proppant used in low-permeability petroleum production. The text covers the data used by the program, the challenges with production data, the program logic for checking the quality of the production data, and the program logic for checking the completeness of the data.