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

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.

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.

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 LSC (Leicester Scientific Corpus)

April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk) Supervised by Prof Alexander Gorban and Dr Evgeny MirkesThe data are extracted from the Web of Science [1]. You may not copy or distribute these data in whole or in part without the written consent of Clarivate Analytics.[Version 2] A further cleaning is applied in Data Processing for LSC Abstracts in Version 1*. Details of cleaning procedure are explained in Step 6.* Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v1.Getting StartedThis text provides the information on the LSC (Leicester Scientific Corpus) and pre-processing steps on abstracts, and describes the structure of files to organise the corpus. This corpus is created to be used in future work on the quantification of the meaning of research texts and make it available for use in Natural Language Processing projects.LSC is a collection of abstracts of articles and proceeding papers published in 2014, and indexed by the Web of Science (WoS) database [1]. The corpus contains only documents in English. Each document in the corpus contains the following parts:1. Authors: The list of authors of the paper2. Title: The title of the paper 3. Abstract: The abstract of the paper 4. Categories: One or more category from the list of categories [2]. Full list of categories is presented in file ‘List_of _Categories.txt’. 5. Research Areas: One or more research area from the list of research areas [3]. Full list of research areas is presented in file ‘List_of_Research_Areas.txt’. 6. Total Times cited: The number of times the paper was cited by other items from all databases within Web of Science platform [4] 7. Times cited in Core Collection: The total number of times the paper was cited by other papers within the WoS Core Collection [4]The corpus was collected in July 2018 online and contains the number of citations from publication date to July 2018. We describe a document as the collection of information (about a paper) listed above. The total number of documents in LSC is 1,673,350.Data ProcessingStep 1: Downloading of the Data Online

The dataset is collected manually by exporting documents as Tab-delimitated files online. All documents are available online.Step 2: Importing the Dataset to R

The LSC was collected as TXT files. All documents are extracted to R.Step 3: Cleaning the Data from Documents with Empty Abstract or without CategoryAs our research is based on the analysis of abstracts and categories, all documents with empty abstracts and documents without categories are removed.Step 4: Identification and Correction of Concatenate Words in AbstractsEspecially medicine-related publications use ‘structured abstracts’. Such type of abstracts are divided into sections with distinct headings such as introduction, aim, objective, method, result, conclusion etc. Used tool for extracting abstracts leads concatenate words of section headings with the first word of the section. For instance, we observe words such as ConclusionHigher and ConclusionsRT etc. The detection and identification of such words is done by sampling of medicine-related publications with human intervention. Detected concatenate words are split into two words. For instance, the word ‘ConclusionHigher’ is split into ‘Conclusion’ and ‘Higher’.The section headings in such abstracts are listed below:

Background Method(s) Design Theoretical Measurement(s) Location Aim(s) Methodology Process Abstract Population Approach Objective(s) Purpose(s) Subject(s) Introduction Implication(s) Patient(s) Procedure(s) Hypothesis Measure(s) Setting(s) Limitation(s) Discussion Conclusion(s) Result(s) Finding(s) Material (s) Rationale(s) Implications for health and nursing policyStep 5: Extracting (Sub-setting) the Data Based on Lengths of AbstractsAfter correction, the lengths of abstracts are calculated. ‘Length’ indicates the total number of words in the text, calculated by the same rule as for Microsoft Word ‘word count’ [5].According to APA style manual [6], an abstract should contain between 150 to 250 words. In LSC, we decided to limit length of abstracts from 30 to 500 words in order to study documents with abstracts of typical length ranges and to avoid the effect of the length to the analysis.

Step 6: [Version 2] Cleaning Copyright Notices, Permission polices, Journal Names and Conference Names from LSC Abstracts in Version 1Publications can include a footer of copyright notice, permission policy, journal name, licence, author’s right or conference name below the text of abstract by conferences and journals. Used tool for extracting and processing abstracts in WoS database leads to attached such footers to the text. For example, our casual observation yields that copyright notices such as ‘Published by Elsevier ltd.’ is placed in many texts. To avoid abnormal appearances of words in further analysis of words such as bias in frequency calculation, we performed a cleaning procedure on such sentences and phrases in abstracts of LSC version 1. We removed copyright notices, names of conferences, names of journals, authors’ rights, licenses and permission policies identified by sampling of abstracts.Step 7: [Version 2] Re-extracting (Sub-setting) the Data Based on Lengths of AbstractsThe cleaning procedure described in previous step leaded to some abstracts having less than our minimum length criteria (30 words). 474 texts were removed.Step 8: Saving the Dataset into CSV FormatDocuments are saved into 34 CSV files. In CSV files, the information is organised with one record on each line and parts of abstract, title, list of authors, list of categories, list of research areas, and times cited is recorded in fields.To access the LSC for research purposes, please email to ns433@le.ac.uk.References[1]Web of Science. (15 July). Available: https://apps.webofknowledge.com/ [2]WoS Subject Categories. Available: https://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html [3]Research Areas in WoS. Available: https://images.webofknowledge.com/images/help/WOS/hp_research_areas_easca.html [4]Times Cited in WoS Core Collection. (15 July). Available: https://support.clarivate.com/ScientificandAcademicResearch/s/article/Web-of-Science-Times-Cited-accessibility-and-variation?language=en_US [5]Word Count. Available: https://support.office.com/en-us/article/show-word-count-3c9e6a11-a04d-43b4-977c-563a0e0d5da3 [6]A. P. Association, Publication manual. American Psychological Association Washington, DC, 1983.

What Makes Our Data Unique?

Autoscraping’s Mexico Real Estate Listings Data is an invaluable resource for anyone seeking in-depth, reliable, and up-to-date information on the Mexican property market. What sets this dataset apart is its breadth and depth, covering over 150,000 property listings from four of the most reputable real estate platforms in Mexico: Propiedades.com, Lamudi, ValoresAMPI, REMAX, and Century21. These platforms are trusted sources of real estate data, ensuring that our dataset is both comprehensive and of the highest quality.

Our data is distinguished by its extensive detail and accuracy. Each listing includes a wide range of attributes, such as property type, location (including geolocation data with latitude and longitude), pricing, surface area (built and terrain), number of bedrooms and bathrooms, amenities (such as balconies, swimming pools, parking spaces), and much more. The data is continually updated to reflect the latest market conditions, including price changes and property status updates.

Additionally, our dataset captures rich metadata from each listing, including the seller’s information (contact details like phone numbers and emails), publication dates, and URLs linking back to the original listings. This level of detail makes our dataset a powerful tool for conducting granular analysis and making informed decisions.

How is the Data Generally Sourced?

The data is sourced from four of Mexico’s leading real estate platforms: Propiedades.com, Lamudi, ValoresAMPI, REMAX, and Century21. Our robust web scraping technology is designed to extract every relevant detail from these platforms efficiently and accurately. We employ advanced scraping techniques that allow us to capture comprehensive data across all major property types, including residential, commercial, and land listings.

The scraping process is automated and conducted at regular intervals to ensure that the data remains current and reflects real-time changes in the market. Each listing undergoes rigorous data cleaning and validation processes to remove duplicates, correct inconsistencies, and ensure the highest possible data quality. The result is a dataset that users can trust to be accurate, up-to-date, and reflective of the actual market conditions.

Primary Use-Cases and Verticals

This Mexico Real Estate Listings Data Product serves a wide range of use cases across various verticals, making it a versatile resource for professionals in different fields:

Real Estate Investment and Analysis: Investors and analysts can use this dataset to identify profitable investment opportunities by analyzing property prices, market trends, and location-based attributes. The detailed metadata, combined with historical pricing information and geolocation data, provides a solid foundation for making informed investment decisions.

Market Research and Trends Analysis: Researchers and market analysts can leverage this data to track and analyze real estate trends across Mexico. The dataset’s comprehensive coverage allows for detailed segmentation by property type, location, price range, and more, enabling users to gain deep insights into market dynamics and consumer behavior.

Urban Planning and Development: Government bodies, urban planners, and developers can utilize this dataset to assess the current state of the real estate market in various regions of Mexico. The geolocation data is particularly valuable for spatial analysis, helping planners understand urban sprawl, housing density, and infrastructure needs.

Real Estate Marketing and Lead Generation: Real estate agencies, marketers, and brokers can use this data to generate leads and tailor their marketing strategies. The inclusion of contact details, such as phone numbers and emails, makes it easier for these professionals to connect with potential buyers and sellers directly, enhancing their ability to close deals.

Location-Based Services and Applications: Companies that offer location-based services or applications can integrate this data to provide users with precise and relevant property information. The high-precision geolocation data allows for accurate mapping and location analysis, adding significant value to location-based tools and platforms.

How Does This Data Product Fit into Our Broader Data Offering?

AUTOScraping’s Mexico Real Estate Listings Data is a key component of our extensive data offering, which spans multiple industries and geographies. This dataset complements our broader portfolio of real estate data products, including those covering the U.S., Europe, and other Latin American countries. By integrating this dataset with our other offerings, users can gain a comprehensive understanding of the global real estate market, allowing for cross-regional comparisons and insights.

In addition to real estate, our broader data offering includes datasets for financial services, consumer behavior, geospatial analysis, an...

The global hull cleaning tool market is experiencing robust growth, driven by increasing maritime activities, stringent environmental regulations aimed at reducing biofouling, and the rising adoption of automated cleaning systems. The market's expansion is fueled by several factors, including the growing awareness of fuel efficiency improvements achievable through clean hulls, the need to prevent the spread of invasive species through ballast water, and the increasing demand for effective and efficient hull cleaning solutions across both civilian and military applications. While the manual cleaning segment currently holds a larger market share due to its established presence and lower initial investment costs, the automatic hull cleaning segment is projected to witness significant growth over the forecast period (2025-2033) due to technological advancements, improved efficiency, and reduced labor costs. Key players in this market are continuously innovating to offer more efficient, environmentally friendly, and remotely operated cleaning tools. Regional analysis suggests a strong market presence in North America and Europe, owing to the large shipping fleets and well-established maritime infrastructure in these regions. However, the Asia-Pacific region is anticipated to show substantial growth due to increasing investments in port infrastructure and expanding maritime activities in developing economies. Restraints on market growth include the high initial investment cost associated with automated systems, the need for specialized trained personnel, and potential risks related to hull damage during the cleaning process. Despite these restraints, the market is poised for continued expansion. The adoption of advanced technologies like robotic systems, AI-powered cleaning solutions, and remotely operated vehicles (ROVs) is enhancing cleaning efficiency, safety, and environmental compliance. Furthermore, the rising adoption of hull coating technologies that minimize biofouling offers a synergistic effect, reducing the frequency of cleaning operations. The market's segmentation by application (civilian and military) and type (automatic and manual) allows for tailored solutions catering to specific needs and operational contexts. The continued focus on sustainable maritime practices will further propel market growth in the coming years, driving demand for innovative and environmentally responsible hull cleaning tools.

According to Cognitive Market Research, the global Floor Cleaning Robot market size will be USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of 24.80% from 2024 to 2031.

The global Floor Cleaning Robot market will expand significantly by 24.80% CAGR from 2024 to 2031.
North America held the major market of more than 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 23.0% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD XX million.
Asia Pacific held a market of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 26.8% from 2024 to 2031.
Latin America's market will have more than 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 24.2% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 24.5% from 2024 to 2031.

Growing Penetration of AI and IoT in Household Appliances to Increase the Demand Globally

AI and IoT technologies are one of the main drivers revolutionizing the functionality and capabilities of household appliances, making them smarter, more efficient, and easier to use. With AI, appliances can analyze data and learn user preferences over time, optimizing performance. IoT connectivity allows controlled and monitored systems via smartphones or other devices, offering convenience and flexibility to users. In the context of household appliances, integrating AI and IoT is particularly impactful, enabling features such as predictive maintenance, energy optimization, and personalized settings.

Consumers increasingly seek connected and intelligent solutions to simplify their daily routines and improve overall efficiency in managing their homes. As a result, manufacturers are investing in AI and IoT technologies to stay competitive and meet the growing demand for smart household appliances worldwide.

Environmental Concerns and Sustainability to Propel Market Growth

The market for floor-cleaning robots is set to experience growth driven by various industrial factors. Consumers seek products and solutions that minimize their environmental impact due to a sense of awareness of environmental issues. In response to this demand, manufacturers of household appliances are integrating sustainability, which includes using eco-friendly materials, optimizing energy efficiency, reducing water consumption, and designing products for longevity and recyclability.

Many businesses recognize sustainability's economic benefits, such as reduced energy consumption and waste cost savings. As a result, environmental concerns and sustainability considerations are propelling market growth, reshaping industry dynamics, and driving innovation toward a more sustainable future.

Market Restraints of the Floor Cleaning Robot Market

High Initial Cost and Limited Cleaning Capabilities to Limit the Growth

The initial investments required to purchase a cleaning robot are often higher compared to traditional cleaning tools, deterring price-sensitive consumers from adoption. Consumers may hesitate to invest in cleaning robots if they perceive the initial cost as prohibitive or doubt their ability to deliver satisfactory cleaning results. Additionally, despite technological advancements, cleaning robots may need to be improved in addressing specific cleaning needs or tackling heavy-duty cleaning tasks. Manufacturers and developers in the cleaning robot industry need to address these challenges by enhancing affordability and continuously improving the cleaning capabilities of their products to drive broader adoption and sustain market growth.

Impact of Covid-19 on the Floor Cleaning Robot Market

The COVID-19 pandemic has significantly impacted the floor-cleaning robot market, both positively and negatively. Firstly, the economic uncertainties and disruptions caused by the pandemic have impacted consumer spending patterns, leading some households and businesses to postpone non-essential purchases, including cleaning robots. Further, manufacturing delays resulting from lockdown measures and restrictions on intern...

Website visitation is nice, but sales and revenue are better. Grips tracks e-commerce-based sales across 5,000+ product categories, 30k retailers, and brands, enabling you to understand market size, share, opportunities, and threats.

Use Cases

Domain e-commerce performance Harness the power of data-driven analysis to evaluate critical metrics such as revenue, average order value (AOV), conversion rate, channels, and product assortment for an extensive selection of 30,000 leading e-commerce retailers, enabling you to make strategic decisions and stay ahead in the dynamic online marketplace.

Product Category e-commerce performance Unlock the potential of your business with our game-changing Share of Wallet analysis. Gain valuable insights into the market size and growth of over 5000+ product categories, as well as your retailer or brand's market share within each category.

Brand e-commerce performance Gain deep insights into the market size, share, and revenue growth of 30,000 top e-commerce brands in the digital ecosystem, exploring key metrics such as units sold, average price, and more. Empower your business with comprehensive data to make informed decisions and capitalize on lucrative opportunities in the ever-evolving online marketplace.

Data Methodology

We have a unique mix of sources from where we gather digital signals.

• Raw data collection - we have developed several productivity tools, including Retailer Benchmarking, which collectively create the world’s largest transactional dataset - public data captured from millions of sites and partnerships with top data providers.

• Data processing - cleaning and formatting, classification of products, sites and more preparation for the modelling phase.

• Data modeling: from the billions of digital signals we extrapolate in detail how global e-commerce sites and products are performing.

7-day free trial available Sign up for free at: https://gripsintelligence.com/

The global Cleaning Service Scheduling Software market is experiencing robust growth, driven by the increasing adoption of technology within the cleaning industry and the rising demand for efficient scheduling and management solutions. The market size in 2025 is estimated at $500 million, exhibiting a Compound Annual Growth Rate (CAGR) of 15% during the forecast period (2025-2033). This growth is fueled by several key trends, including the rising popularity of cloud-based solutions offering scalability and accessibility, the increasing need for real-time data and analytics for better operational efficiency, and a growing preference for integrated platforms offering features like customer relationship management (CRM), invoicing, and payment processing. Large enterprises are leading the adoption, but the market is witnessing significant penetration amongst Small and Medium-sized Enterprises (SMEs) due to the cost-effectiveness and ease of use offered by these software solutions. Market restraints include the initial investment costs associated with software implementation, the need for employee training, and concerns regarding data security and privacy. The competitive landscape is highly fragmented, with several established players and emerging startups vying for market share. This competitive environment fosters innovation, pushing developers to continuously enhance their offerings with features such as AI-powered route optimization and customer communication tools. The market segmentation reveals a strong preference for cloud-based solutions due to their inherent flexibility and accessibility, surpassing on-premise deployments. Large enterprises represent a significant portion of the market, primarily due to their greater need for sophisticated scheduling and management capabilities. However, the SME segment is experiencing rapid growth, presenting a significant opportunity for software providers. Regionally, North America and Europe currently dominate the market, but significant growth is projected in Asia-Pacific and other emerging economies driven by increasing urbanization and the rise of professional cleaning services. The forecast period of 2025-2033 anticipates continued market expansion, fueled by technological advancements and the increasing demand for improved operational efficiency across the cleaning service sector. The projected market size in 2033 is estimated to be around $1.8 Billion, reflecting the significant potential for growth in this dynamic market segment.

The Greek-English parallel corpus MaCoCu-el-en 1.0 was built by crawling the “.gr", ".ελ", ".cy" and ".eu" internet top-level domain in 2023, extending the crawl dynamically to other domains as well.

All the crawling process was carried out by the MaCoCu crawler (https://github.com/macocu/MaCoCu-crawler). Websites containing documents in both target languages were identified and processed using the tool Bitextor (https://github.com/bitextor/bitextor). Considerable effort was devoted into cleaning the extracted text to provide a high-quality parallel corpus. This was achieved by removing boilerplate and near-duplicated paragraphs and documents that are not in one of the targeted languages. Document and segment alignment as implemented in Bitextor were carried out, and Bifixer (https://github.com/bitextor/bifixer) and BicleanerAI (https://github.com/bitextor/bicleaner-ai) were used for fixing, cleaning, and deduplicating the final version of the corpus.

The corpus is available in three formats: two sentence-level formats, TXT and TMX, and a document-level TXT format. TMX is an XML-based format and TXT is a tab-separated format. They both consist of pairs of source and target segments (one or several sentences) and additional metadata. The following metadata is included in both sentence-level formats: - source and target document URL; - paragraph ID which includes information on the position of the sentence in the paragraph and in the document (e.g., “p35:77s1/3” which means “paragraph 35 out of 77, sentence 1 out of 3”); - quality score as provided by the tool Bicleaner AI (a likelihood of a pair of sentences being mutual translations, provided with a score between 0 and 1); - similarity score as provided by the sentence alignment tool Bleualign (value between 0 and 1); - personal information identification (“biroamer-entities-detected”): segments containing personal information are flagged, so final users of the corpus can decide whether to use these segments; - translation direction and machine translation identification (“translation-direction”): the source segment in each segment pair was identified by using a probabilistic model (https://github.com/RikVN/TranslationDirection), which also determines if the translation has been produced by a machine-translation system; - a DSI class (“dsi”): information whether the segment is connected to any of Digital Service Infrastructure (DSI) classes (e.g., cybersecurity, e-health, e-justice, open-data-portal), defined by the Connecting Europe Facility (https://github.com/RikVN/DSI); - English language variant: the language variant of English (British or American, using a lexicon-based English variety classifier - https://pypi.org/project/abclf/) was identified on document and domain level.

Furthermore, the sentence-level TXT format provides additional metadata: - web domain of the text; - source and target document title; - the date when the original file was retrieved; - the original type of the file (e.g., “html”), from which the sentence was extracted; - paragraph quality (labels, such as “short” or “good”, assigned based on paragraph length, URL and stopword density via the jusText tool - https://corpus.tools/wiki/Justext); - information whether the sentence is a heading or not in the original document.

The document-level TXT format provides pairs of documents identified to contain parallel data. In addition to the parallel documents (in base64 format), the corpus includes the following metadata: source and target document URL, a DSI category and the English language variant (British or American).

Notice and take down: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: (1) Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. (2) Clearly identify the copyrighted work claimed to be infringed. (3) Clearly identify the material that is claimed to be infringing and information reasonably sufficient in order to allow us to locate the material. (4) Please write to the contact person for this resource whose email is available in the full item record. We will comply with legitimate requests by removing the affected sources from the next release of the corpus.

This action has received funding from the European Union's Connecting Europe Facility 2014-2020 - CEF Telecom, under Grant Agreement No. INEA/CEF/ICT/A2020/2278341. This communication reflects only the author’s view. The Agency is not responsible for any use that may be made of the information it contains.