Data Prep Market Outlook

The global data preparation market size was estimated at USD 3.5 billion in 2023 and is projected to reach USD 10.8 billion by 2032, growing at a CAGR of 13.2% from 2024 to 2032. This robust growth can be attributed to the increasing need for businesses to manage and process large volumes of data effectively to gain actionable insights and maintain a competitive edge.

One of the primary growth factors driving the data preparation market is the rapid digital transformation across various industries. The digital shift has led to an exponential increase in data generation, necessitating advanced data preparation tools and solutions to handle the influx of information efficiently. Moreover, the proliferation of Internet of Things (IoT) devices and the subsequent rise in data from these devices is further fuelling the demand for robust data prep solutions. Companies are keen on leveraging this data to gain real-time insights, optimize operations, and drive innovation.

Another significant growth driver is the increasing adoption of advanced analytics and artificial intelligence (AI) in business processes. Organizations are investing heavily in AI and machine learning to enhance decision-making, predictive analytics, and automation. However, the effectiveness of these technologies is heavily reliant on the quality of data being fed into the systems. This has made data prep solutions indispensable, as they ensure data consistency, accuracy, and quality, which are critical for the success of AI initiatives. Additionally, regulatory requirements and data privacy laws are compelling companies to adopt stringent data governance practices, further boosting the data prep market.

Cloud computing is also playing a pivotal role in the expansion of the data prep market. The shift towards cloud-based solutions offers scalability, flexibility, and cost-efficiency, making it an attractive option for businesses of all sizes. Cloud-based data prep tools facilitate seamless integration with various data sources, enhance collaboration, and provide real-time data processing capabilities. As a result, the adoption of cloud-based data prep solutions is on the rise, contributing significantly to market growth.

Regionally, North America holds the largest market share in the data prep market, driven by the presence of leading technology companies and early adoption of advanced data analytics solutions. The region's robust IT infrastructure and high investment in research and development are also key factors. However, the Asia Pacific region is expected to witness the highest growth rate, owing to rapid industrialization, increasing adoption of digital technologies, and the growing significance of data-driven decision-making in emerging economies like China and India. Europe and Latin America are also showing promising growth potential due to increasing investments in data analytics and the rising trend of data-driven business strategies.

Offline Data Analysis is becoming increasingly relevant in the context of data preparation. While cloud-based solutions offer numerous advantages, there are scenarios where offline data analysis is preferred, particularly in industries with stringent data security requirements. Offline data analysis allows organizations to process and analyze data without relying on continuous internet connectivity, ensuring data privacy and reducing the risk of data breaches. This approach is particularly beneficial for sectors such as healthcare, finance, and government, where data sensitivity is paramount. By leveraging offline data analysis, businesses can maintain control over their data while still gaining valuable insights, making it an essential component of a comprehensive data preparation strategy.

Component Analysis

The data preparation market is segmented into tools and services based on components. Data preparation tools are software solutions that help in the collection, transformation, and organization of raw data into a usable format. These tools are essential for businesses to handle large volumes of data efficiently and derive valuable insights. The market for data preparation tools is expanding rapidly, driven by the increasing need for high-quality data to fuel advanced analytics and AI applications. These tools are becoming more sophisticated, featuring advanced capabilities such as machine learning, natural language processing, and automation to streamline data prep processes.

Data Prep Market size was valued at USD 4.02 Billion in 2024 and is projected to reach USD 16.12 Billion by 2031, growing at a CAGR of 19% from 2024 to 2031.

Global Data Prep Market Drivers

Increasing Demand for Data Analytics: Businesses across all industries are increasingly relying on data-driven decision-making, necessitating the need for clean, reliable, and useful information. This rising reliance on data increases the demand for better data preparation technologies, which are required to transform raw data into meaningful insights. Growing Volume and Complexity of Data: The increase in data generation continues unabated, with information streaming in from a variety of sources. This data frequently lacks consistency or organization, therefore effective data preparation is critical for accurate analysis. To assure quality and coherence while dealing with such a large and complicated data landscape, powerful technologies are required. Increased Use of Self-Service Data Preparation Tools: User-friendly, self-service data preparation solutions are gaining popularity because they enable non-technical users to access, clean, and prepare data. independently. This democratizes data access, decreases reliance on IT departments, and speeds up the data analysis process, making data-driven insights more available to all business units. Integration of AI and ML: Advanced data preparation technologies are progressively using AI and machine learning capabilities to improve their effectiveness. These technologies automate repetitive activities, detect data quality issues, and recommend data transformations, increasing productivity and accuracy. The use of AI and ML streamlines the data preparation process, making it faster and more reliable. Regulatory Compliance Requirements: Many businesses are subject to tight regulations governing data security and privacy. Data preparation technologies play an important role in ensuring that data meets these compliance requirements. By giving functions that help manage and protect sensitive information these technologies help firms negotiate complex regulatory climates. Cloud-based Data Management: The transition to cloud-based data storage and analytics platforms needs data preparation solutions that can work smoothly with cloud-based data sources. These solutions must be able to integrate with a variety of cloud settings to assist effective data administration and preparation while also supporting modern data infrastructure.

The Data Preparation Tools market is experiencing robust growth, projected to reach a significant market size by 2033. Driven by the exponential increase in data volume and variety across industries, coupled with the rising need for accurate, consistent data for effective business intelligence and machine learning initiatives, this sector is poised for continued expansion. The 18.5% Compound Annual Growth Rate (CAGR) signifies strong market momentum, fueled by increasing adoption across diverse sectors like IT and Telecom, Retail & E-commerce, BFSI (Banking, Financial Services, and Insurance), and Manufacturing. The preference for self-service data preparation tools empowers business users to directly access and prepare data, minimizing reliance on IT departments and accelerating analysis. Furthermore, the integration of data preparation tools with advanced analytics platforms and cloud-based solutions is streamlining workflows and improving overall efficiency. This trend is further augmented by the growing demand for robust data governance and compliance measures, necessitating sophisticated data preparation capabilities. While the market shows significant potential, challenges remain. The complexity of integrating data from multiple sources and maintaining data consistency across disparate systems present hurdles for many organizations. The need for skilled data professionals to effectively utilize these tools also contributes to market constraints. However, ongoing advancements in automation and user-friendly interfaces are mitigating these challenges. The competitive landscape is marked by established players like Microsoft, Tableau, and IBM, alongside innovative startups offering specialized solutions. This competitive dynamic fosters innovation and drives down costs, benefiting end-users. The market segmentation by application and tool type highlights the varied needs and preferences across industries, and understanding these distinctions is crucial for effective market penetration and strategic planning. Geographical expansion, particularly within rapidly developing economies in Asia-Pacific, will play a significant role in shaping the future trajectory of this thriving market.

This repository contains the input data associated with the research paper titled "The Role of Spatial Features and Adjacency in Data-driven Short-term Prediction of Trip Production: An Exploratory Study in the Netherlands". The paper is currently under review for publication in the IEEE Transactions on Intelligent Transportation Systems. The data shows the origin destination information for Mezuro zones in the Netherlands for March 2017. This information are provided in a format of a relational table in .CSV format. The details on data preparation steps are provided in the pdf file inside MiRRORS data.rar and it is titled MiRRORS description 20191220.pdf. The Mezuro zones are defined in the shapefiles directory within MiRRORS data.rar. This project's main source of data (origin destination table) is named od_day_hour_20170301_0.CSV located inside MiRRORS data.rar. This dataset is the source to investigate the travel patterns in the Netherlands.

The Data Preparation Software market is at the forefront of transforming how organizations manage and analyze their data, enabling businesses to harness the power of information effectively. This software plays a crucial role in cleaning, organizing, and transforming raw data into a usable format, making it vital fo

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Data Visualization Tools Market Size 2025-2029

The data visualization tools market size is forecast to increase by USD 7.95 billion at a CAGR of 11.2% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for business intelligence and AI-powered insights. Companies are recognizing the value of transforming complex data into easily digestible visual representations to inform strategic decision-making. However, this market faces challenges as data complexity and massive data volumes continue to escalate. Organizations must invest in advanced data visualization tools to effectively manage and analyze their data to gain a competitive edge. The ability to automate data visualization processes and integrate AI capabilities will be crucial for companies to overcome the challenges posed by data complexity and volume. By doing so, they can streamline their business operations, enhance data-driven insights, and ultimately drive growth in their respective industries.

What will be the Size of the Data Visualization Tools Market during the forecast period?

Request Free SampleIn today's data-driven business landscape, the market continues to evolve, integrating advanced capabilities to support various sectors in making informed decisions. Data storytelling and preparation are crucial elements, enabling organizations to effectively communicate complex data insights. Real-time data visualization ensures agility, while data security safeguards sensitive information. Data dashboards facilitate data exploration and discovery, offering data-driven finance, strategy, and customer experience. Big data visualization tackles complex datasets, enabling data-driven decision making and innovation. Data blending and filtering streamline data integration and analysis. Data visualization software supports data transformation, cleaning, and aggregation, enhancing data-driven operations and healthcare. On-premises and cloud-based solutions cater to diverse business needs. Data governance, ethics, and literacy are integral components, ensuring data-driven product development, government, and education adhere to best practices. Natural language processing, machine learning, and visual analytics further enrich data-driven insights, enabling interactive charts and data reporting. Data connectivity and data-driven sales fuel business intelligence and marketing, while data discovery and data wrangling simplify data exploration and preparation. The market's continuous dynamism underscores the importance of data culture, data-driven innovation, and data-driven HR, as organizations strive to leverage data to gain a competitive edge.

How is this Data Visualization Tools Industry segmented?

The data visualization tools 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. DeploymentOn-premisesCloudCustomer TypeLarge enterprisesSMEsComponentSoftwareServicesApplicationHuman resourcesFinanceOthersEnd-userBFSIIT and telecommunicationHealthcareRetailOthersGeographyNorth AmericaUSMexicoEuropeFranceGermanyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.The market has experienced notable expansion as businesses across diverse sectors acknowledge the significance of data analysis and representation to uncover valuable insights and inform strategic decisions. Data visualization plays a pivotal role in this domain. On-premises deployment, which involves implementing data visualization tools within an organization's physical infrastructure or dedicated data centers, is a popular choice. This approach offers organizations greater control over their data, ensuring data security, privacy, and adherence to data governance policies. It caters to industries dealing with sensitive data, subject to regulatory requirements, or having stringent security protocols that prohibit cloud-based solutions. Data storytelling, data preparation, data-driven product development, data-driven government, real-time data visualization, data security, data dashboards, data-driven finance, data-driven strategy, big data visualization, data-driven decision making, data blending, data filtering, data visualization software, data exploration, data-driven insights, data-driven customer experience, data mapping, data culture, data cleaning, data-driven operations, data aggregation, data transformation, data-driven healthcare, on-premises data visualization, data governance, data ethics, data discovery, natural language processing, data reporting, data visualization platforms, data-driven innovation, data wrangling, data-driven s

An Integrated Science Data Analytics Platform is an environment that enables the confluence of resources for scientific investigation. It harmonizes data, tools and computational resources to enable the research community to focus on the investigation rather than spending time on security, data preparation, management, etc. OceanWorks is a NASA technology integration project to establish a cloud-based Integrated Ocean Science Data Analytics Platform for big ocean science at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) for big ocean science. It focuses on advancement and maturity by bringing together several NASA open-source, big data projects for parallel analytics, anomaly detection, in situ to satellite data matchup, quality-screened data subsetting, search relevancy, and data discovery. Our communities are relying on data available through distributed data centers to conduct their research. In typical investigations, scientists would (1) search for data, (2) evaluate the relevance of that data, (3) download it, and (4) then apply algorithms to identify trends, anomalies, or other attributes of the data. Such a workflow cannot scale if the research involves a massive amount of data or multi-variate measurements. With the upcoming NASA Surface Water and Ocean Topography (SWOT) mission expected to produce over 20PB of observational data during its 3-year nominal mission, the volume of data will challenge all existing Earth Science data archival, distribution and analysis paradigms. This paper discusses how OceanWorks enhances the analysis of physical ocean data where the computation is done on an elastic cloud platform next to the archive to deliver fast, web-accessible services for working with oceanographic measurements.

Between November 2016 and February 2017, FORS conducted a brief survey to better understand researchers’ attitudes, practices, and needs specifically regarding data sharing and re-use. The insight gained from this survey will be used to improve existing services and prioritize new ones. Here are some key findings:

Across disciplines, around 80 percent researchers generally consider the sharing of research data to be very important.

More than half of the respondents were involved in the production of both quantitative and qualitative data.

More than two-thirds of the researchers stated that they had already shared data.

Data sharing is most common amongst colleagues inside the same institution and within researchers’ broader networks.

Conditions most cited for sharing data include being informed about the projects that other researchers carry out based on one’s data and the desire to be cited for the use of one’s data by others.

Re-using quantitative data is much more common than re-using qualitative data, at the same time most researchers indicated that they would consider re-using qualitative data for either their own research and/or for teaching purposes.

There is a large interest in and strong need for additional services and materials in the area of data management, especially for data management planning, data preparation and cleaning, and security, storage and backup.

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demograohic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor chracteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Household/families
• Individuals

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2008 Household Expenditure and Income Survey sample was designed using two-stage cluster stratified sampling method. In the first stage, the primary sampling units (PSUs), the blocks, were drawn using probability proportionate to the size, through considering the number of households in each block to be the block size. The second stage included drawing the household sample (8 households from each PSU) using the systematic sampling method. Fourth substitute households from each PSU were drawn, using the systematic sampling method, to be used on the first visit to the block in case that any of the main sample households was not visited for any reason.

To estimate the sample size, the coefficient of variation and design effect in each subdistrict were calculated for the expenditure variable from data of the 2006 Household Expenditure and Income Survey. This results was used to estimate the sample size at sub-district level, provided that the coefficient of variation of the expenditure variable at the sub-district level did not exceed 10%, with a minimum number of clusters that should not be less than 6 at the district level, that is to ensure good clusters representation in the administrative areas to enable drawing poverty pockets.

It is worth mentioning that the expected non-response in addition to areas where poor families are concentrated in the major cities were taken into consideration in designing the sample. Therefore, a larger sample size was taken from these areas compared to other ones, in order to help in reaching the poverty pockets and covering them.

Mode of data collection

Face-to-face [f2f]

Research instrument

List of survey questionnaires: (1) General Form (2) Expenditure on food commodities Form (3) Expenditure on non-food commodities Form

Cleaning operations

Raw Data The design and implementation of this survey procedures were: 1. Sample design and selection 2. Design of forms/questionnaires, guidelines to assist in filling out the questionnaires, and preparing instruction manuals 3. Design the tables template to be used for the dissemination of the survey results 4. Preparation of the fieldwork phase including printing forms/questionnaires, instruction manuals, data collection instructions, data checking instructions and codebooks 5. Selection and training of survey staff to collect data and run required data checkings 6. Preparation and implementation of the pretest phase for the survey designed to test and develop forms/questionnaires, instructions and software programs required for data processing and production of survey results 7. Data collection 8. Data checking and coding 9. Data entry 10. Data cleaning using data validation programs 11. Data accuracy and consistency checks 12. Data tabulation and preliminary results 13. Preparation of the final report and dissemination of final results

Harmonized Data - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets - The harmonization process started with cleaning all raw data files received from the Statistical Office - Cleaned data files were then all merged to produce one data file on the individual level containing all variables subject to harmonization - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables - A post-harmonization cleaning process was run on the data - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format

Data Annotation and Labeling Market Outlook

The global data annotation and labeling market size was valued at approximately USD 1.6 billion in 2023 and is projected to grow to USD 8.5 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 20.5% during the forecast period. A key growth factor driving this market is the increasing demand for high-quality labeled data to train and validate machine learning and artificial intelligence models.

The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies has significantly increased the demand for precise and accurate data annotation and labeling. As AI and ML applications become more widespread across various industries, the need for large volumes of accurately labeled data is more critical than ever. This requirement is driving investments in sophisticated data annotation tools and platforms that can deliver high-quality labeled datasets efficiently. Moreover, the complexity of data types being used in AI/ML applications—from text and images to audio and video—necessitates advanced annotation solutions that can handle diverse data formats.

Another major factor contributing to the growth of the data annotation and labeling market is the increasing adoption of automated data labeling tools. While manual annotation remains essential for ensuring high-quality outcomes, automation technologies are increasingly being integrated into annotation workflows to improve efficiency and reduce costs. These automated tools leverage AI and ML to annotate data with minimal human intervention, thus expediting the data preparation process and enabling organizations to deploy AI/ML models more rapidly. Additionally, the rise of semi-supervised learning approaches, which combine both manual and automated methods, is further propelling market growth.

The expansion of sectors such as healthcare, automotive, and retail is also fueling the demand for data annotation and labeling services. In healthcare, for instance, annotated medical images are crucial for training diagnostic algorithms, while in the automotive sector, labeled data is indispensable for developing autonomous driving systems. Retailers are increasingly relying on annotated data to enhance customer experiences through personalized recommendations and improved search functionalities. The growing reliance on data-driven decision-making across these and other sectors underscores the vital role of data annotation and labeling in modern business operations.

Regionally, North America is expected to maintain its leadership position in the data annotation and labeling market, driven by the presence of major technology companies and extensive R&D activities in AI and ML. Europe is also anticipated to witness significant growth, supported by government initiatives to promote AI technologies and increased investment in digital transformation projects. The Asia Pacific region is expected to emerge as a lucrative market, with countries like China and India making substantial investments in AI research and development. Additionally, the increasing adoption of AI/ML technologies in various industries across the Middle East & Africa and Latin America is likely to contribute to market growth in these regions.

Type Analysis

The data annotation and labeling market is segmented by type, which includes text, image/video, and audio. Text annotation is a critical segment, driven by the proliferation of natural language processing (NLP) applications. Text data annotation involves labeling words, phrases, or sentences to help algorithms understand language context, sentiment, and intent. This type of annotation is vital for developing chatbots, voice assistants, and other language-based AI applications. As businesses increasingly adopt NLP for customer service and content analysis, the demand for text annotation services is expected to rise significantly.

Image and video annotation represents another substantial segment within the data annotation and labeling market. This type involves labeling objects, features, and activities within images and videos to train computer vision models. The automotive industry's growing focus on developing autonomous vehicles is a significant driver for image and video annotation. Annotated images and videos are essential for training algorithms to recognize and respond to various road conditions, signs, and obstacles. Additionally, sectors like healthcare, where medical imaging data needs precise annotation for diagnostic AI tools, and retail, which uses visual data for inventory management and customer insigh

Link Function: information

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Big Data In Manufacturing Market Size 2024-2028

The big data in manufacturing market size is forecast to increase by USD 17.32 billion at a CAGR of 25.86% between 2023 and 2028.

The market is experiencing significant growth, driven primarily by the increasing adoption of Industry 4.0 and the emergence of artificial intelligence (AI) and machine learning (ML) technologies. These advanced technologies enable manufacturers to collect, process, and analyze vast amounts of data in real-time, leading to improved operational efficiency, enhanced product quality, and increased customer satisfaction. However, the complexity of big data analytics poses a significant challenge for manufacturers. The vast volume, velocity, and variety of data require sophisticated tools and expertise to process and derive meaningful insights. Additionally, ensuring data security and privacy are critical concerns, as manufacturers handle sensitive information related to their supply chains, production processes, and customer data.
To capitalize on the opportunities presented by the market, companies must invest in advanced analytics tools and hire data science talent. Collaborating with technology partners and service providers can also help manufacturers overcome the challenges associated with data complexity and security. By leveraging these resources, manufacturers can gain a competitive edge by making data-driven decisions, optimizing their production processes, and delivering personalized customer experiences.

What will be the Size of the Big Data In Manufacturing Market during the forecast period?

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In the dynamic and evolving world of manufacturing, big data plays a pivotal role in driving operational efficiency, energy savings, and product innovation. Seamlessly integrated data infrastructure, process control, and digital thread enable real-time monitoring and predictive analytics, enhancing production planning and quality control. Industrial automation, continuous improvement, and industrial Internet of Things (IoT) devices generate sensor data for advanced analytics, including deep learning and predictive modeling, to optimize manufacturing processes. Data cleaning and data preparation are crucial for maintaining data integrity and ensuring data privacy. Robotic automation, product design, and emissions reduction are areas where big data and advanced analytics contribute significantly.
Cloud computing, digital twin, and supply chain optimization are transforming manufacturing operations, enabling business intelligence (BI) and data-driven decision making. Data security, prescriptive analytics, and waste reduction are essential components of the digital transformation, driving cost reduction, process optimization, and demand forecasting. Lean manufacturing, six sigma, and additive manufacturing are further areas where big data and advanced technologies are making a significant impact. The continuous integration of AI, machine learning, and machine vision into manufacturing processes is revolutionizing the industry, with smart factories and augmented reality (AR) and virtual reality (VR) leading the way. Data governance, edge computing, and data integration are essential for managing and optimizing data assets, ensuring data compliance, and enabling task automation.
The ongoing evolution of manufacturing processes is shaping the future of the industry, with a focus on operational excellence, sustainability, and customer satisfaction.

How is this Big Data In Manufacturing Industry segmented?

The big data in manufacturing industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Services
  Solutions


Deployment

  On-premises
  Cloud-based
  Hybrid


Geography

  North America

    US
    Canada


  Europe

    Germany
    UK


  APAC

    China


  Rest of World (ROW)

By Type Insights

The services segment is estimated to witness significant growth during the forecast period.

In the realm of manufacturing, the utilization of big data has emerged as a game-changer. The sector generates copious amounts of data from IoT devices, sensors, production lines, and supply chain operations. This data deluge presents a rich resource for analytics and insights. Big data services play a pivotal role, empowering manufacturers to optimize costs, allocate resources effectively, and enhance operational efficiency. Predictive maintenance is a key application, minimizing downtime by anticipating equipment failures and scheduling repairs. Real-time product quality monitoring reduces defects, scrap, and rework, leading to substantial savings. Advanced analytics, including deep learning and machine learning, enable predictive modeling and continuous im

Computational analyses are playing an increasingly central role in research. Journals, funders, and researchers are calling for published research to include associated data and code. However, many involved in research have not received training in best practices and tools for sharing code and data. This course aims to address this gap in training while also providing those who support researchers with curated best practices guidance and tools. This course is unique compared to other reproducibility courses due to its practical, step-by-step design. It is comprised of hands-on exercises to prepare research code and data for computationally reproducible publication. Although the course starts with some brief introductory information about computational reproducibility, the bulk of the course is guided work with data and code. Participants move through preparing research for reuse, organization, documentation, automation, and submitting their code and data to share. Tools that support reproducibility will be introduced (Code Ocean), but all lessons will be platform agnostic. Level: Intermediate Intended audience: The course is targeted at researchers and research support staff who are involved in the preparation and publication of research materials. Anyone with an interest in reproducible publication is welcome. The course is especially useful for those looking to learn practical steps for improving the computational reproducibility of their own research.

Automated Sample Preparation Market Outlook

The global automated sample preparation market size was valued at approximately $1.5 billion in 2023, and it is projected to reach around $3.2 billion by 2032, expanding at a compound annual growth rate (CAGR) of approximately 8.8% during the forecast period. This robust growth is propelled by the increasing demand for high-throughput sample preparation processes in various applications such as clinical diagnostics, pharmaceuticals, and biotechnology. The market is witnessing significant advancements due to technological innovations that aim to enhance efficiency and accuracy in sample handling, which is driving the wider adoption of automated sample preparation solutions across diverse end-user sectors.

The growth of the automated sample preparation market is primarily driven by the necessity for improved workflow efficiency and accuracy in sample handling processes. Laboratories and research facilities are increasingly recognizing the importance of automation to minimize errors, reduce sample contamination, and enhance reproducibility. Automated systems are becoming indispensable in managing the complexities associated with handling large volumes of samples, particularly in high-throughput environments like clinical diagnostics and pharmaceutical research. Additionally, the growing demand for personalized medicine, which requires precise and reliable sample preparation for genetic, molecular, and proteomics analysis, is further bolstering the market growth, making automated solutions a vital component in the healthcare and research sectors.

Moreover, the rapid advancements in automation technologies are contributing to the market expansion. The integration of artificial intelligence and machine learning with automated sample preparation systems is revolutionizing data management and interpretation, enabling more streamlined and efficient processes. These technological innovations are enhancing the capabilities of automated systems, making them more adaptable and user-friendly. Furthermore, the increasing adoption of cloud-based solutions for data storage and analysis is providing added advantages, such as improved data accessibility and real-time monitoring, which are crucial for decision-making in critical applications. These advancements are expected to continue driving the market growth throughout the forecast period.

Another significant factor influencing the automated sample preparation market is the increasing regulatory pressure and quality standards in various industries. Stringent regulations in sectors such as pharmaceuticals, biotechnology, and environmental testing necessitate the use of precise and reliable sample preparation techniques to ensure compliance and maintain product quality. Automated systems are instrumental in achieving these standards by providing consistent and reproducible results, thereby reducing the risk of human errors. This has resulted in a growing demand for automated solutions, as companies strive to meet regulatory requirements and enhance their competitive edge in the market. The emphasis on regulatory compliance is likely to remain a key driver for market growth in the coming years.

Regionally, North America dominates the automated sample preparation market, accounting for a significant share due to the presence of well-established healthcare infrastructure, high adoption rate of advanced technologies, and a strong focus on research and development. The Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, driven by rising healthcare expenditure, increasing demand for advanced diagnostic techniques, and expanding pharmaceutical and biotechnology sectors in emerging economies like China and India. Furthermore, government initiatives to boost research activities and improve healthcare infrastructure in these regions are further fueling market growth, making Asia Pacific a key region to watch in the coming years.

Product Type Analysis

The automated sample preparation market by product type is segmented into instruments, consumables, and software. Instruments play a crucial role in the automation of sample preparation processes, and they account for a substantial share of the market. These instruments include automated liquid handlers, workstations, and various specialized equipment that are designed to streamline sample processing. The demand for these instruments is driven by their ability to enhance efficiency, reduce manual labor, and improve accuracy in laboratories and research facilities. The continuous innovation in instrument design and functionality is further encouraging a

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.     The study...

Sample Preparation Systems Market Outlook

The global sample preparation systems market size was valued at approximately $6.7 billion in 2023 and is projected to reach around $11.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.2%. This growth is driven by increasing research activities in life sciences, technological advancements in sample preparation systems, and the rising demand for precise and reproducible sample preparation techniques in various applications.

One of the major growth factors in the sample preparation systems market is the escalating demand for high-throughput sequencing and advanced genomic techniques. As researchers endeavor to understand complex biological systems, the need for efficient and reliable sample preparation methods becomes paramount. These systems ensure the integrity and quality of samples, which is crucial for obtaining accurate and reproducible results. Furthermore, innovations in automation and miniaturization of sample preparation instruments have significantly enhanced the throughput and efficiency, thus propelling the market growth.

Another significant driver is the increasing prevalence of chronic diseases and the consequent surge in diagnostic testing. With the growing burden of diseases such as cancer, diabetes, and cardiovascular diseases, there is a heightened need for advanced diagnostic tools that rely heavily on precise sample preparation. Diagnostic laboratories and research institutes are investing significantly in upgrading their sample preparation technologies to meet the rising demand for accurate and rapid diagnostics, which in turn fuels the market growth.

The expanding applications of sample preparation systems in proteomics and epigenomics also contribute to market growth. Proteomics, the large-scale study of proteins, and epigenomics, the study of changes in gene expression, require sophisticated sample preparation techniques to analyze complex biological samples. The advancements in these fields have opened new avenues for research, leading to increased adoption of sample preparation systems. Moreover, the rising funding for research activities and the growing number of research collaborations are further expected to drive market expansion.

Regionally, North America holds the largest market share due to the presence of advanced research infrastructure, significant investments in healthcare and biotechnology, and the high adoption rate of advanced technologies. Europe follows closely, with substantial growth driven by increased research activities and government funding. The Asia Pacific region is projected to witness the highest growth rate during the forecast period, attributed to the rapid development of healthcare infrastructure, growing research and development activities, and increasing awareness about advanced diagnostic techniques.

Product Type Analysis

The sample preparation systems market, segmented by product type, includes instruments, consumables, and software. Instruments form the core component of sample preparation systems and include various devices such as centrifuges, pipettes, automated liquid handling systems, and homogenizers. These instruments play a crucial role in ensuring accurate and efficient sample preparation, which is essential for downstream analytical processes. The demand for automated sample preparation instruments has been on the rise, driven by the need for high-throughput processing and reduced manual intervention, which minimizes errors and variability.

Consumables, another significant segment, encompass all the necessary materials required for sample preparation, such as reagents, kits, vials, and tubes. The consumables segment is expected to grow steadily due to the recurring nature of their use in sample preparation processes. The continuous need for high-quality consumables that ensure the integrity of the samples and the accuracy of the results drives this segment's growth. Additionally, advancements in consumable materials and the development of specialized kits for specific applications further boost the market.

Software, though a relatively smaller segment, plays a vital role in the sample preparation systems market by providing data management, process automation, and analytical capabilities. The integration of sophisticated software solutions with sample preparation instruments allows for seamless workflow automation, improved efficiency, and enhanced data accuracy. As the complexity of biological research increases, the demand for advanced software solutions that can handle large dataset

The Augmented Analytics market is experiencing robust growth, projected to reach $23.27 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 28.09% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and complexity of data necessitate automated insights generation, leading to wider adoption across diverse sectors. Businesses are increasingly focusing on data-driven decision-making, demanding faster, more accessible analytics solutions. Advancements in artificial intelligence (AI) and machine learning (ML) are significantly improving the accuracy and efficiency of augmented analytics platforms. The rising demand for self-service analytics tools empowers business users to extract valuable insights without requiring extensive technical expertise, further fueling market growth. The cloud deployment model's flexibility and scalability contribute significantly to market expansion, attracting businesses of all sizes. Key industry segments like BFSI (Banking, Financial Services, and Insurance), Telecom & IT, and Healthcare are major contributors to market revenue due to their substantial data volumes and need for sophisticated analytics. While data security concerns and the need for skilled professionals to implement and maintain these systems pose certain restraints, the overall market outlook remains highly positive. The competitive landscape is dynamic, with established players like SAS Institute, IBM, and Microsoft competing alongside emerging companies such as ThoughtSpot and Sisense. These companies are constantly innovating to enhance their offerings, focusing on features like natural language processing (NLP) for intuitive data interaction, automated data preparation, and advanced visualization tools. The market is also witnessing a rise in specialized solutions tailored to specific industry needs, further fragmenting the competitive landscape. Geographically, North America is expected to maintain a significant market share, followed by Europe and the Asia-Pacific region. The Rest of the World segment is also expected to witness substantial growth, driven by increasing digitalization and adoption of advanced analytics across emerging economies. The sustained high CAGR suggests that the augmented analytics market will continue its trajectory of rapid expansion throughout the forecast period, offering significant opportunities for market participants. Augmented Analytics Market Report: 2019-2033 This comprehensive report provides a detailed analysis of the Augmented Analytics market, projecting robust growth from [Estimated Year] 2025 to [Forecast Period] 2033. The study covers the historical period (2019-2024), providing a solid foundation for understanding the market's trajectory. It delves into key segments, including deployment (on-premise and cloud), end-user industries (BFSI, Telecom & IT, Retail, Healthcare, Manufacturing, Media & Entertainment, and others), and leading players shaping the landscape. This report leverages a robust methodology, encompassing extensive primary and secondary research to provide accurate insights for strategic decision-making. Recent developments include: May 2023: TrinityLife Sciences, a leader in global life sciences commercialization solutions, and WhizAI, a leader in AI-powered analytics for life sciences and healthcare, announced a strategic partnership that allows life sciences companies to quickly and easily generate and share AI-driven insights. WhizAI’s augmented analytics can be layered on Trinity’s enterprise reporting platforms to bring insights to more organizational stakeholders., January 2023: Seerist Inc., the leading augmented analytics solution for threat and security professionals, announced about the addition of new capabilities to elevate the value of the solution. These updates allow users to receive significant contextual intelligence, extract meaning from the data "noise" and further customize the solution to target critical areas important to an organization's operations.. Key drivers for this market are: Increasing Demand to Cater Complex Business Data, Huge Adoption of Business Intelligence Tools. Potential restraints include: Resistance Due to Replacement of Human Intelligence. Notable trends are: Retail Sector is Expected to Have a Significant Growth During the Forecast Period.

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US Test Preparation Market Size 2025-2029

The US test preparation market size is forecast to increase by USD 18.4 billion, at a CAGR of 7.9% between 2024 and 2029.

The Test Preparation Market is experiencing significant growth, driven by the increasing emphasis on online mode of test preparation and technological advances in test preparation services. With the shift towards digital learning, test preparation companies are leveraging technology to offer flexible, accessible, and personalized solutions to students. This trend is particularly prominent in regions with a high prevalence of online education and remote learning. However, regulatory hurdles impact adoption of test preparation services in certain markets, as governments and educational institutions grapple with issues of standardization and quality control. Additionally, the availability of open-source test preparation materials poses a challenge to market players, as students increasingly turn to free resources to prepare for exams. Educational institutions utilize cloud-based Learning Management Systems (LMS) like Talent LMS to deliver test prep courses.
To capitalize on market opportunities and navigate these challenges effectively, test preparation companies must focus on offering differentiated value propositions, such as personalized coaching, interactive learning tools, and comprehensive study resources. By staying abreast of regulatory developments and collaborating with educational institutions, these companies can build trust and credibility, ensuring long-term growth in the Test Preparation Market.

What will be the size of the US Test Preparation Market during the forecast period?

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The test preparation market is dynamic and diverse, encompassing various elements to help students excel in assessments. Content creation plays a pivotal role, with test preparation forums, blogs, and workshops serving as valuable resources for learners. Curriculum development in test preparation focuses on enhancing reading comprehension, mathematics skills, writing skills, cognitive skills, time management, test analysis, critical thinking, and language proficiency. Test preparation research and innovation continue to shape the industry, with an emphasis on addressing the unique needs of different learning styles. Test preparation events, seminars, and webinars offer opportunities for score improvement and stress management. The application process and study skills are also crucial aspects of test preparation, ensuring test-takers are well-equipped to navigate the challenges of various assessments.
Science skills are increasingly gaining importance in test preparation, as they are essential for success in many fields. Test preparation communities foster collaboration and knowledge sharing among learners, fostering a supportive environment for test-takers.

How is this market segmented?

The market 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.

End-user

  Higher education
  K-12


Product

  University exams
  Certifications exams
  High school exams
  Elementary exams
  Others


Learning Method

  Blended
  Online


Geography

  North America

    US

By End-user Insights

The higher education segment is estimated to witness significant growth during the forecast period. The test preparation market experiences continuous growth due to the increasing number of students aiming for entrance exams and professional certifications. In the higher education sector, there is a surging demand for specialized courses, leading companies to offer certifications in fields like medicine, nursing, law, and wealth management. Test preparation institutes employ innovative technologies such as artificial intelligence and virtual reality to enhance learning experiences. Online test preparation resources, including practice tests, diagnostic testing, and adaptive learning, are increasingly popular.

Test prep professionals provide personalized learning strategies and test-taking techniques to students. Test prep apps, courses, and software are accessible on mobile learning platforms for convenience. Education technology and data analytics are integral to improving student performance and test scores. Test prep websites and online platforms offer test prep advice and resources to students dealing with test anxiety. Test prep services and providers cater to the diverse needs of students, ensuring they are well-prepared for entrance exams and college admissions. The test preparation industry is evolving, integrating technologies and learning strategies to keep a balance between effectiveness and affordability.

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The Higher e

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

In light of the rapid socio-economic development in this era, it is necessary to make data on household expenditure and income available, as well as the relationship between those statistics and various variables with direct or indirect impact. Therefore, most of the countries are nowadays keen to periodically carry-out Household Expenditure and Income surveys. Given the continuous changes in spending patterns, income levels and prices, as well as in population both internal and external migration, it was now mandatory to update data for household income and expenditure over time. The main objective of the survey is to obtain detailed data on HH income and expenditure, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, it was well considered that the sample should be representative on the sub-district level. Hence, the data collected through the survey would also enable to achieve the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index. 2- Study the consumer expenditure pattern prevailing in the society and the impact of demograohic and socio-economic variables on those patterns. 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators. 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it. 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector. 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor chracteristics as well as drawing poverty maps.. 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

This survey was carried-out for a sample of 12678 households distributed on urban and rural areas in all the Kingdom governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size has been uniformly selected, and in the second stage, a systematic approach guaranteing a representative sample of all sub-districts (Qada) has been applied.

Mode of data collection

Face-to-face [f2f]

Research instrument

List of survey questionnaires:

(1) General Form (2) Expenditure on food commodities Form (3) Expenditure on non-food commodities Form

Cleaning operations

Raw Data

The design and implementation of this survey procedures are: 1. Sample design and selection. 2. Design of forms/questionnaires, guidelines to assist in filling out the questionnaires, and preparing instruction manuals. 3. Design the tables template to be used for the dissemination of the survey results. 4. Preparation of the fieldwork phase including printing forms/questionnaires, instruction manuals, data collection instructions, data checking instructions and codebooks. 5. Selection and training of survey staff to collect data and run required data checkings. 6. Preparation and implementation of the pretest phase for the survey designed to test and develop forms/questionnaires, instructions and software programs required for data processing and production of survey results. 7. Data collection. 8. Data checking and coding. 9. Data entry. 10. Data cleaning using data validation programs. 11. Data accuracy and consistency checks. 12. Data tabulation and preliminary results. 13. Preparation of the final report and dissemination of final results.

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Agency.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Survey and interview data from a study on the views and experiences of early career researchers (postdoctoral researchers) around the support from research organisations, funding bodies and career services and how this offer might be improved in the future. This applied to those employed inside and outside of academia. The data result from an online survey of early career social scientists (N=1048), interviews with a subset of the respondents (N=35) and with experts (N=9). The findings informed the strategy for careers advice and support provided by the Economic and Social Research Council through Doctoral Training Partnerships and Centres for Doctoral Training and the creation of new funding strands for early career researchers.

The last two generations have seen a remarkable world-wide transformation of higher education (HE) into a core social sector with continually expanding local and global reach. Most nations are moving towards, or have already become, 'high participation' HE systems in which the majority of people will be educated to tertiary level. In the UK HE is at the same time a pillar of science and the innovation system, a primary driver of productivity at work, a major employer and a mainstay of cities and regions, and a national export industry where 300,000 non-EU students generated over 7 billion in export-related earnings for the UK in 2012-13. In 2012, 60 per cent of UK school leavers were expected to graduate from tertiary education over the lifetime, 45 per cent at bachelor degree level, compared to OECD means of 53/39 per cent. Higher education and the scientific research associated with universities have never been more important to UK society and government. HE is large and inclusive with a key role in mediating the future. Yet it is poorly understood. Practice has moved ahead of social science. There has been no integrated research centre dedicated to this important part of the UK. The Centre for Engaged Global Higher Education (CEGHE), which has been funded initially for five years by the Economic and Social Research Council (ESRC), now fills that gap.

On behalf of the ESRC CEGHE conducts and disseminates research on all aspects of higher education (HE), in order to enhance student learning and the contributions of Higher Education Institutions (HEIs) to their communities; develop the economic, social and global engagement of and impacts of UK HE; and provide data resources and advice for government and stakeholder organisations in HE in the four nations of the UK and worldwide. CEGHE is organised in three closely integrated research programmes that are focused respectively on global, national-system and local aspects of HE. CEGHE's team of researchers work on roblems and issues with broad application to the improvement of HE; develop new theories about and ways of researching HE and its social and economic contributions; and respond also to new issues as they arise, within the framework of its research programmes. An important part of CEGHE's work is the preparation and provision of data, briefings and advice to national and international policy makers, for HEIs themselves, and for UK organisations committed to fostering HE and its engagement with UK communities and stakeholders. CEGHE's seminars and conferences are open to the public and it is dedicated to disseminating its research findings on a broad basis through published papers, media articles and its website and social media platform.

CEGHE is led by Professor Simon Marginson, one of the world's leading researchers on higher education matters with a special expertise in global and international aspects of the sector. It works with partner research universities in Sheffield, Lancaster, Ireland, Australia, South Africa, Netherlands, China, Hong Kong SAR, Japan and USA. Among the issues currently the subject of CEGHE research projects are inquiries into ways and means of measuring and enhancing HE's contribution to the public good, university-industry collaboration in research, the design of an optimal system of tuition loans, a survey of the effects of tuition debt on the life choices of graduates such as investment in housing and family formation, the effects of widening participation on social opportunities in HE especially for under-represented social groups, trends and developments in HE in Europe and East Asia and the implications for UK HE, the emergence of new HE providers in the private and FE sectors, the future academic workforce in the UK and the skills that will be needed, student learning and knowledge in science and engineering, and developments in online HE.