The Data Cleaning Tools Market is projected to grow at 16.9% CAGR, reaching $6.78 Billion by 2029. Where is the industry heading next? Get the sample report now!

The global data cleansing tools market is projected to reach USD 4.7 billion by 2033, expanding at a CAGR of 9.6% during the forecast period (2025-2033). The market growth is attributed to factors such as the increasing volume and complexity of data, the need for accurate and reliable data for decision-making, and the growing adoption of cloud-based data cleansing solutions. The market is also witnessing the emergence of new technologies such as artificial intelligence (AI) and machine learning (ML), which are expected to further drive market growth in the coming years. Among the different application segments, large enterprises are expected to hold the largest market share during the forecast period. This is due to the fact that large enterprises have large volumes of data that need to be cleaned and processed, and they have the resources to invest in data cleansing tools. The SaaS segment is expected to grow at the highest CAGR during the forecast period. This is due to the increasing popularity of cloud-based solutions, which offer benefits such as scalability, cost-effectiveness, and ease of deployment. The North America region is expected to hold the largest market share during the forecast period. This is due to the presence of a large number of technology companies and the early adoption of data cleansing tools in the region.

The Data Preparation Tools market is experiencing robust growth, projected to reach a market size of $3 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 17.7% from 2025 to 2033. This significant expansion is driven by several key factors. The increasing volume and velocity of data generated across industries necessitates efficient and effective data preparation processes to ensure data quality and usability for analytics and machine learning initiatives. The rising adoption of cloud-based solutions, coupled with the growing demand for self-service data preparation tools, is further fueling market growth. Businesses across various sectors, including IT and Telecom, Retail and E-commerce, BFSI (Banking, Financial Services, and Insurance), and Manufacturing, are actively seeking solutions to streamline their data pipelines and improve data governance. The diverse range of applications, from simple data cleansing to complex data transformation tasks, underscores the versatility and broad appeal of these tools. Leading vendors like Microsoft, Tableau, and Alteryx are continuously innovating and expanding their product offerings to meet the evolving needs of the market, fostering competition and driving further advancements in data preparation technology. This rapid growth is expected to continue, driven by ongoing digital transformation initiatives and the increasing reliance on data-driven decision-making. The segmentation of the market into self-service and data integration tools, alongside the varied applications across different industries, indicates a multifaceted and dynamic landscape. While challenges such as data security concerns and the need for skilled professionals exist, the overall market outlook remains positive, projecting substantial expansion throughout the forecast period. The adoption of advanced technologies like artificial intelligence (AI) and machine learning (ML) within data preparation tools promises to further automate and enhance the process, contributing to increased efficiency and reduced costs for businesses. The competitive landscape is dynamic, with established players alongside emerging innovators vying for market share, leading to continuous improvement and innovation within the industry.

If you work with large spreadsheets and have been known to spend hours going cell by cell to find errors, we can show you how to save time and aggravation. OpenRefine is a powerful free tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data. OpenRefine always keeps your data private on your own computer until YOU want to share or collaborate. Your private data never leaves your computer unless you want it to. (It works by running a small server on your computer and you use your web browser to interact with it). Participants will download OpenRefine to their own devices and learn by doing.

Global Data Cleaning Tools market size 2025 was XX Million. Data Cleaning Tools Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Cleaned_Dataset.csv – The combined CSV files of all scraped documents from DABI, e-LiS, o-bib and Springer.

Data_Cleaning.ipynb – The Jupyter Notebook with python code for the analysis and cleaning of the original dataset.

ger_train.csv – The German training set as CSV file.

ger_validation.csv – The German validation set as CSV file.

en_test.csv – The English test set as CSV file.

en_train.csv – The English training set as CSV file.

en_validation.csv – The English validation set as CSV file.

splitting.py – The python code for splitting a dataset into train, test and validation set.

DataSetTrans_de.csv – The final German dataset as a CSV file.

DataSetTrans_en.csv – The final English dataset as a CSV file.

translation.py – The python code for translating the cleaned dataset.

The size and share of the market is categorized based on Application (Data cleansing tools, Data integration software, Data transformation tools, Data enrichment solutions, Data validation tools) and Product (Data preparation, Data integration, Data cleansing, Data transformation, Data enrichment) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

244 Global import shipment records of Cleaning Tool with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

China Mane Processing, Brush & Cleaning Tool: YoY: Cost of Sales: Year to Date data was reported at -1.570 % in Oct 2015. This records an increase from the previous number of -3.979 % for Sep 2015. China Mane Processing, Brush & Cleaning Tool: YoY: Cost of Sales: Year to Date data is updated monthly, averaging 20.088 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 36.930 % in Feb 2008 and a record low of -14.090 % in Mar 2015. China Mane Processing, Brush & Cleaning Tool: YoY: Cost of Sales: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIM: Daily Sundry Article: Mane Processing, Brush and Cleaning Tool.

Snapshot img

Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the integration of artificial intelligence (AI) and machine learning (ML). This enhancement enables more advanced data analysis and prediction capabilities, making data science platforms an essential tool for businesses seeking to gain insights from their data. Another trend shaping the market is the emergence of containerization and microservices in platforms. This development offers increased flexibility and scalability, allowing organizations to efficiently manage their projects. 
However, the use of platforms also presents challenges, particularly In the area of data privacy and security. Ensuring the protection of sensitive data is crucial for businesses, and platforms must provide strong security measures to mitigate risks. In summary, the market is witnessing substantial growth due to the integration of AI and ML technologies, containerization, and microservices, while data privacy and security remain key challenges.

What will be the Size of the Data Science Platform Market During the Forecast Period?

Request Free Sample

The market is experiencing significant growth due to the increasing demand for advanced data analysis capabilities in various industries. Cloud-based solutions are gaining popularity as they offer scalability, flexibility, and cost savings. The market encompasses the entire project life cycle, from data acquisition and preparation to model development, training, and distribution. Big data, IoT, multimedia, machine data, consumer data, and business data are prime sources fueling this market's expansion. Unstructured data, previously challenging to process, is now being effectively managed through tools and software. Relational databases and machine learning models are integral components of platforms, enabling data exploration, preprocessing, and visualization.
Moreover, Artificial intelligence (AI) and machine learning (ML) technologies are essential for handling complex workflows, including data cleaning, model development, and model distribution. Data scientists benefit from these platforms by streamlining their tasks, improving productivity, and ensuring accurate and efficient model training. The market is expected to continue its growth trajectory as businesses increasingly recognize the value of data-driven insights.

How is this Data Science Platform Industry segmented and which is the largest segment?

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

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    India
    Japan


  South America

    Brazil


  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

On-premises deployment is a traditional method for implementing technology solutions within an organization. This approach involves purchasing software with a one-time license fee and a service contract. On-premises solutions offer enhanced security, as they keep user credentials and data within the company's premises. They can be customized to meet specific business requirements, allowing for quick adaptation. On-premises deployment eliminates the need for third-party providers to manage and secure data, ensuring data privacy and confidentiality. Additionally, it enables rapid and easy data access, and keeps IP addresses and data confidential. This deployment model is particularly beneficial for businesses dealing with sensitive data, such as those in manufacturing and large enterprises. While cloud-based solutions offer flexibility and cost savings, on-premises deployment remains a popular choice for organizations prioritizing data security and control.

Get a glance at the Data Science Platform Industry report of share of various segments. Request Free Sample

The on-premises segment was valued at USD 38.70 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 48% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request F

China Mane Processing, Brush & Cleaning Tool: Sales Tax and Surcharge: Year to Date data was reported at 0.233 RMB bn in Oct 2015. This records an increase from the previous number of 0.205 RMB bn for Sep 2015. China Mane Processing, Brush & Cleaning Tool: Sales Tax and Surcharge: Year to Date data is updated monthly, averaging 0.066 RMB bn from Dec 2003 (Median) to Oct 2015, with 97 observations. The data reached an all-time high of 0.289 RMB bn in Dec 2014 and a record low of 0.007 RMB bn in Feb 2007. China Mane Processing, Brush & Cleaning Tool: Sales Tax and Surcharge: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIM: Daily Sundry Article: Mane Processing, Brush and Cleaning Tool.

25037 Global export shipment records of Cleaning Tool with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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

China Mane Processing, Brush & Cleaning Tool: Selling and Distribution Cost: Year to Date data was reported at 0.816 RMB bn in Oct 2015. This records an increase from the previous number of 0.732 RMB bn for Sep 2015. China Mane Processing, Brush & Cleaning Tool: Selling and Distribution Cost: Year to Date data is updated monthly, averaging 0.303 RMB bn from Dec 2004 (Median) to Oct 2015, with 96 observations. The data reached an all-time high of 1.056 RMB bn in Dec 2014 and a record low of 0.039 RMB bn in Feb 2006. China Mane Processing, Brush & Cleaning Tool: Selling and Distribution Cost: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIM: Daily Sundry Article: Mane Processing, Brush and Cleaning Tool.

This dataset is about book subjects and is filtered where the books is Data cleaning and exploration with machine learning : clean data with machine learning algorithms and techniques, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

Recent developments include: January 2022: IBM and Francisco Partners disclosed the execution of a definitive contract under which Francisco Partners will purchase medical care information and analytics resources from IBM, which are currently part of the IBM Watson Health business., October 2021: Informatica LLC announced an important cloud storage agreement with Google Cloud in October 2021. This collaboration allows Informatica clients to transition to Google Cloud as much as twelve times quicker. Informatica's Google Cloud Marketplace transactable solutions now incorporate Master Data Administration and Data Governance capabilities., Completing a unit of labor with incorrect data costs ten times more estimates than the Harvard Business Review, and finding the correct data for effective tools has never been difficult. A reliable system may be implemented by selecting and deploying intelligent workflow-driven, self-service options tools for data quality with inbuilt quality controls.. Key drivers for this market are: Increasing demand for data quality: Businesses are increasingly recognizing the importance of data quality for decision-making and operational efficiency. This is driving demand for data quality tools that can automate and streamline the data cleansing and validation process.

Growing adoption of cloud-based data quality tools: Cloud-based data quality tools offer several advantages over on-premises solutions, including scalability, flexibility, and cost-effectiveness. This is driving the adoption of cloud-based data quality tools across all industries.

Emergence of AI-powered data quality tools: AI-powered data quality tools can automate many of the tasks involved in data cleansing and validation, making it easier and faster to achieve high-quality data. This is driving the adoption of AI-powered data quality tools across all industries.. Potential restraints include: Data privacy and security concerns: Data privacy and security regulations are becoming increasingly stringent, which can make it difficult for businesses to implement data quality initiatives.

Lack of skilled professionals: There is a shortage of skilled data quality professionals who can implement and manage data quality tools. This can make it difficult for businesses to achieve high-quality data.

Cost of data quality tools: Data quality tools can be expensive, especially for large businesses with complex data environments. This can make it difficult for businesses to justify the investment in data quality tools.. Notable trends are: Adoption of AI-powered data quality tools: AI-powered data quality tools are becoming increasingly popular, as they can automate many of the tasks involved in data cleansing and validation. This makes it easier and faster to achieve high-quality data.

Growth of cloud-based data quality tools: Cloud-based data quality tools are becoming increasingly popular, as they offer several advantages over on-premises solutions, including scalability, flexibility, and cost-effectiveness.

Focus on data privacy and security: Data quality tools are increasingly being used to help businesses comply with data privacy and security regulations. This is driving the development of new data quality tools that can help businesses protect their data..

The global data wrangling market, valued at $1.41 billion in 2025, is projected to experience robust growth, driven by the increasing volume and velocity of data generated across various sectors. A Compound Annual Growth Rate (CAGR) of 14.8% from 2025 to 2033 indicates a significant expansion of this market, reaching an estimated $5.2 billion by 2033. This growth is fueled primarily by the rising adoption of cloud-based data warehousing solutions, the expanding use of big data analytics, and the growing need for data quality and consistency across industries. Key sectors driving demand include BFSI (Banking, Financial Services, and Insurance), government and public sector, and healthcare, all facing challenges in managing and utilizing the vast amount of data they collect. The increasing complexity of data formats and sources is necessitating sophisticated data wrangling tools and expertise. Competition in the data wrangling market is intense, with major players like Altair, Alteryx, Dataiku, and others vying for market share through innovative solutions and strategic partnerships. The market is witnessing a shift towards automated and self-service data wrangling tools, lowering the barrier to entry for businesses of all sizes. While the market enjoys significant growth potential, challenges remain, including the need for skilled data professionals, data security concerns, and the high cost of implementation for certain advanced solutions. Despite these restraints, the continued digital transformation across industries and the growing demand for data-driven decision-making are expected to propel the market towards sustained and significant expansion in the coming years.

Hong Kong Composite Consumer Price Index (CPI): Weights: Misc Goods: Household Cleansing Tools & Supp data was reported at 0.170 % in 2010. This stayed constant from the previous number of 0.170 % for 2009. Hong Kong Composite Consumer Price Index (CPI): Weights: Misc Goods: Household Cleansing Tools & Supp data is updated yearly, averaging 0.170 % from Dec 2006 (Median) to 2010, with 5 observations. The data reached an all-time high of 0.170 % in 2010 and a record low of 0.170 % in 2010. Hong Kong Composite Consumer Price Index (CPI): Weights: Misc Goods: Household Cleansing Tools & Supp data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong – Table HK.I010: Composite Consumer Price Index: 10/04-9/05=100: Weights: Annual.

Data Wrangling Market size was valued at USD 1.63 Billion in 2024 and is projected to reach USD 3.2 Billion by 2031, growing at a CAGR of 8.80 % during the forecast period 2024-2031.

Global Data Wrangling Market Drivers

Growing Volume and Variety of Data: As digitalization has progressed, organizations have produced an exponential increase in both volume and variety of data. Data from a variety of sources, including social media, IoT devices, sensors, and workplace apps, is included in this, both structured and unstructured. Data wrangling tools are an essential part of contemporary data management methods because they allow firms to manage this heterogeneous data landscape effectively.

Growing Adoption of Advanced Analytics: To extract useful insights from data, companies in a variety of sectors are utilizing advanced analytics tools like artificial intelligence and machine learning. Nevertheless, access to clean, well-researched data is essential to the accomplishment of many analytics projects. The need for data wrangling solutions is fueled by the necessity of ensuring that data is accurate, consistent, and clean for usage in advanced analytics models.

Self-service data preparation solutions are becoming more and more necessary as data volumes rise. These technologies enable business users to prepare and analyze data on their own without requiring significant IT assistance. Platforms for data wrangling provide non-technical users with easy-to-use interfaces and functionalities that make it simple for them to clean, manipulate, and combine data. Data wrangling solutions are being used more quickly because of this self-service approach’s ability to increase agility and facilitate quicker decision-making within enterprises.

Emphasis on Data Governance and Compliance: With the rise of regulated sectors including healthcare, finance, and government, data governance and compliance have emerged as critical organizational concerns. Data wrangling technologies offer features for auditability, metadata management, and data quality control, which help with adhering to data governance regulations. The adoption of data wrangling solutions is fueled by these features, which assist enterprises in ensuring data integrity, privacy, and regulatory compliance.

Big Data Technologies’ Emergence: Companies can now store and handle enormous amounts of data more affordably because to the emergence of big data technologies like Hadoop, Spark, and NoSQL databases. However, efficient data preparation methods are needed to extract value from massive data. Organizations may accelerate their big data analytics initiatives by preprocessing and cleansing large amounts of data at scale with the help of data wrangling solutions that seamlessly interact with big data platforms.

Put an emphasis on cost-cutting and operational efficiency: Organizations are under pressure to maximize operational efficiency and cut expenses in the cutthroat business environment of today. Organizations can increase productivity and reduce resource requirements by implementing data wrangling solutions, which automate manual data preparation processes and streamline workflows. Furthermore, the danger of errors and expensive aftereffects is reduced when data quality problems are found and fixed early in the data pipeline.