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Big Data Services Market Size 2025-2029

The big data services market size is forecast to increase by USD 604.2 billion, at a CAGR of 54.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of big data in various industries, particularly in blockchain technology. The ability to process and analyze vast amounts of data in real-time is revolutionizing business operations and decision-making processes. However, this market is not without challenges. One of the most pressing issues is the need to cater to diverse client requirements, each with unique data needs and expectations. This necessitates customized solutions and a deep understanding of various industries and their data requirements. Additionally, ensuring data security and privacy in an increasingly interconnected world poses a significant challenge. Companies must navigate these obstacles while maintaining compliance with regulations and adhering to ethical data handling practices. To capitalize on the opportunities presented by the market, organizations must focus on developing innovative solutions that address these challenges while delivering value to their clients. By staying abreast of industry trends and investing in advanced technologies, they can effectively meet client demands and differentiate themselves in a competitive landscape.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the ever-increasing volume, velocity, and variety of data being generated across various sectors. Data extraction is a crucial component of this dynamic landscape, enabling entities to derive valuable insights from their data. Human resource management, for instance, benefits from data-driven decision making, operational efficiency, and data enrichment. Batch processing and data integration are essential for data warehousing and data pipeline management. Data governance and data federation ensure data accessibility, quality, and security. Data lineage and data monetization facilitate data sharing and collaboration, while data discovery and data mining uncover hidden patterns and trends. Real-time analytics and risk management provide operational agility and help mitigate potential threats. Machine learning and deep learning algorithms enable predictive analytics, enhancing business intelligence and customer insights. Data visualization and data transformation facilitate data usability and data loading into NoSQL databases. Government analytics, financial services analytics, supply chain optimization, and manufacturing analytics are just a few applications of big data services. Cloud computing and data streaming further expand the market's reach and capabilities. Data literacy and data collaboration are essential for effective data usage and collaboration. Data security and data cleansing are ongoing concerns, with the market continuously evolving to address these challenges. The integration of natural language processing, computer vision, and fraud detection further enhances the value proposition of big data services. The market's continuous dynamism underscores the importance of data cataloging, metadata management, and data modeling for effective data management and optimization.

How is this Big Data Services Industry segmented?

The big data services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentSolutionServicesEnd-userBFSITelecomRetailOthersTypeData storage and managementData analytics and visualizationConsulting servicesImplementation and integration servicesSupport and maintenance servicesSectorLarge enterprisesSmall and medium enterprises (SMEs)GeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW).

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period.Big data services have become indispensable for businesses seeking operational efficiency and customer insight. The vast expanse of structured and unstructured data presents an opportunity for organizations to analyze consumer behaviors across multiple channels. Big data solutions facilitate the integration and processing of data from various sources, enabling businesses to gain a deeper understanding of customer sentiment towards their products or services. Data governance ensures data quality and security, while data federation and data lineage provide transparency and traceability. Artificial intelligence and machine learning algo

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

The Cognitive Analytics Market is experiencing an unprecedented surge, projected to reach a market size of approximately $40,000 million by 2025, driven by a remarkable Compound Annual Growth Rate (CAGR) of 40.00%. This exponential growth is primarily fueled by the increasing demand for advanced data analysis and decision-making capabilities across diverse industries. Key drivers include the proliferation of big data, the growing adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies, and the need for enhanced customer experiences and operational efficiencies. Businesses are actively leveraging cognitive analytics to extract deeper insights from complex datasets, enabling them to predict trends, personalize offerings, and automate processes. The market's expansion is further supported by advancements in Natural Language Processing (NLP) and Automated Reasoning, which empower systems to understand and interpret human language and make logical deductions, thus unlocking new avenues for innovation and competitive advantage. The market is segmented by deployment models, with On-Premise and On-Demand solutions catering to varied organizational needs. Component-wise, Tools and Services both play crucial roles in enabling cognitive analytics functionalities. Technology types such as NLP, Machine Learning, and Automated Reasoning form the backbone of these solutions, driving their intelligence and capabilities. Prominent end-user industries like BFSI, Manufacturing, IT & Telecommunication, Aerospace and Defense, Healthcare, and Retail are at the forefront of adopting cognitive analytics, recognizing its transformative potential. Geographically, North America and Europe are leading the adoption, followed by the rapidly growing Asia Pacific region, signaling a global shift towards data-driven strategies. Major players like IBM, Google, Microsoft, and Amazon Web Services are continuously innovating, offering sophisticated platforms and solutions that are shaping the future of the cognitive analytics landscape. Key drivers for this market are: , Rise in Adoption of Cognitive Computing Technology; Increasing Volume of Unstructured Data. Potential restraints include: , Complex Analytical Process. Notable trends are: Healthcare Segment to Witness High Growth.

Systematic reviews are the method of choice to synthesize research evidence. To identify main topics (so-called hot spots) relevant to large corpora of original publications in need of a synthesis, one must address the “three Vs” of big data (volume, velocity, and variety), especially in loosely defined or fragmented disciplines. For this purpose, text mining and predictive modeling are very helpful. Thus, we applied these methods to a compilation of documents related to digitalization in aesthetic, arts, and cultural education, as a prototypical, loosely defined, fragmented discipline, and particularly to quantitative research within it (QRD-ACE). By broadly querying the abstract and citation database Scopus with terms indicative of QRD-ACE, we identified a corpus of N = 55,553 publications for the years 2013–2017. As the result of an iterative approach of text mining, priority screening, and predictive modeling, we identified n = 8,304 potentially relevant publications of which n = 1,666 were included after priority screening. Analysis of the subject distribution of the included publications revealed video games as a first hot spot of QRD-ACE. Topic modeling resulted in aesthetics and cultural activities on social media as a second hot spot, related to 4 of k = 8 identified topics. This way, we were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. We discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE. Dataset for: Christ, A., Penthin, M., & Kröner, S. (2019). Big Data and Digital Aesthetic, Arts, and Cultural Education: Hot Spots of Current Quantitative Research. Social Science Computer Review, 089443931988845. https://doi.org/10.1177/0894439319888455

Please cite the following paper when using this dataset:

N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A.Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” arXiv [cs.CY], 2024. Available: https://doi.org/10.48550/arXiv.2406.07693

Abstract

This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.

Overview:
This collection contains three synthetic datasets produced by gpt-4o-mini for sentiment analysis and PDT (Product Desirability Toolkit) testing. Each dataset contains 1000 hypothetical software product reviews with the aim to produce a diversity of sentiment and text. The datasets were created as part of the research described in:

Hastings, J.D., Weitl-Harms, S., Doty, J., Myers, Z. L., and Thompson, W., “Utilizing Large Language Models to Synthesize Product Desirability Datasets,” in Proceedings of the 2024 IEEE International Conference
on Big Data (BigData-24), Workshop on Large Language and Foundation Models (WLLFM-24), Dec. 2024.
https://arxiv.org/abs/2411.13485.

Briefly, each row in the datasets was produced as follows:
1) Word+Review: The LLM selected a word and synthesized a review that would align with a random target sentiment.
2) Review+Word: The LLM produced a review to align with the target sentiment score, and then selected a word appropriate for the review.
3) Supply-Word: A word was supplied to the LLM which was then scored, and a review was produced to align with that score.

For sentiment analysis and PDT testing, the two columns of main interest across the datasets are likely 'Selected Word' and 'Hypothetical Review'.

License:
This data is licensed under the CC Attribution 4.0 international license, and may be taken and used freely with credit given. Cite as:

Hastings, J., Weitl-Harms, S., Doty, J., Myers, Z., & Thompson, W. (2024). Synthetic Product Desirability Datasets for Sentiment Analysis Testing (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.14188456

This dataset contains the Julia code package for the Bayesian SVM algorithm described in the ECML PKDD 2017 paper; Wenzel et al.: Bayesian Nonlinear Support Vector Machines for Big Data.Files are provided in .jl format; containing Julia language code: a high-performance dynamic programming language for numerical computing. These files can be accessed by openly available text edit software. To run the code please see the description below or the more detailed wiki BSVM.jl - contains the module to run the Bayesian SVM algorithm.AFKMC2.jl - File for the Assumption Free K MC2 algorithm (KMeans)KernelFunctions.jl - Module for the kernel typeDataAccess.jl - Module for either generating data or exporting from an existing datasetrun_test.jl and paper_experiments.jl - Modules to run on a file and compute accuracy on a nFold cross validation, also to compute the brier score and the logscoretest_functions.jl and paper_experiment_functions.jl - Sets of datatype and functions for efficient testing.ECM.jl - Module for expectation conditional maximization (ECM) for nonlinear Bayesian SVMFor datasets used in the related experiments please see https://doi.org/10.6084/m9.figshare.5443621RequirementsThe BayesianSVM only works for version of Julia > 0.5. Other necessary packages will automatically be added in the installation. It is also possible to run the package from Python, to do so please check Pyjulia. If you prefer to use R you have the possibility to use RJulia. All these are a bit technical due to the fact that Julia is still a young package.InstallationTo install the last version of the package in Julia run Pkg.clone("git://github.com/theogf/BayesianSVM.jl.git")Running the AlgorithmHere are the basic steps for using the algorithm : using BayesianSVM Model = BSVM(X_training,y_training) Model.Train() y_predic = sign(Model.Predict(X_test)) y_uncertaintypredic = Model.PredictProb(X_test) Where X_training should be a matrix of size NSamples x NFeatures, and y_training should be a vector of 1 and -1You can find a more complete description in the WikiBackgroundWe propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional features over frequentist competitors such as accurate predictive uncertainty estimates and automatic hyperparameter search.Please also check out our github repository:github.com/theogf/BayesianSVM.jl

Systematic reviews are the method of choice to synthesize research evidence. To identify main topics (so-called hot spots) relevant to large corpora of original publications in need of a synthesis, one must address the “three Vs” of big data (volume, velocity, and variety), especially in loosely defined or fragmented disciplines. For this purpose, text mining and predictive modeling are very helpful. Thus, we applied these methods to a compilation of documents related to digitalization in aesthetic, arts, and cultural education, as a prototypical, loosely defined, fragmented discipline, and particularly to quantitative research within it (QRD-ACE). By broadly querying the abstract and citation database Scopus with terms indicative of QRD-ACE, we identified a corpus of N = 55,553 publications for the years 2013–2017. As the result of an iterative approach of text mining, priority screening, and predictive modeling, we identified n = 8,304 potentially relevant publications of which n = 1,666 were included after priority screening. Analysis of the subject distribution of the included publications revealed video games as a first hot spot of QRD-ACE. Topic modeling resulted in aesthetics and cultural activities on social media as a second hot spot, related to 4 of k = 8 identified topics. This way, we were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. We discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE. Dataset for: Christ, A., Penthin, M., & Kröner, S. (2019). Big Data and Digital Aesthetic, Arts, and Cultural Education: Hot Spots of Current Quantitative Research. Social Science Computer Review, 089443931988845. https://doi.org/10.1177/0894439319888455:

This dataset is adapted from raw data with fully anonymized results on the State Examination of Dutch as a Second Language. This exam is officially administred by the Board of Tests and Examinations (College voor Toetsen en Examens, or CvTE). See cvte.nl/about-cvte. The Board of Tests and Examinations is mandated by the Dutch government.

The article accompanying the dataset:

Schepens, Job, Roeland van Hout, and T. Florian Jaeger. “Big Data Suggest Strong Constraints of Linguistic Similarity on Adult Language Learning.” Cognition 194 (January 1, 2020): 104056. https://doi.org/10.1016/j.cognition.2019.104056.

Every row in the dataset represents the first official testing score of a unique learner. The columns contain the following information as based on questionnaires filled in at the time of the exam:

"L1" - The first language of the learner "C" - The country of birth "L1L2" - The combination of first and best additional language besides Dutch "L2" - The best additional language besides Dutch "AaA" - Age at Arrival in the Netherlands in years (starting date of residence) "LoR" - Length of residence in the Netherlands in years "Edu.day" - Duration of daily education (1 low, 2 middle, 3 high, 4 very high). From 1992 until 2006, learners' education has been measured by means of a side-by-side matrix question in a learner's questionnaire. Learners were asked to mark which type of education they have had (elementary, secondary, or tertiary schooling) by means of filling in for how many years they have been enrolled, in which country, and whether or not they have graduated. Based on this information we were able to estimate how many years learners have had education on a daily basis from six years of age onwards. Since 2006, the question about learners' education has been altered and it is asked directly how many years learners have had formal education on a daily basis from six years of age onwards. Possible answering categories are: 1) 0 thru 5 years; 2) 6 thru 10 years; 3) 11 thru 15 years; 4) 16 years or more. The answers have been merged into the categorical answer. "Sex" - Gender "Family" - Language Family "ISO639.3" - Language ID code according to Ethnologue "Enroll" - Proportion of school-aged youth enrolled in secondary education according to the World Bank. The World Bank reports on education data in a wide number of countries around the world on a regular basis. We took the gross enrollment rate in secondary schooling per country in the year the learner has arrived in the Netherlands as an indicator for a country's educational accessibility at the time learners have left their country of origin. "STEX_speaking_score" - The STEX test score for speaking proficiency. "Dissimilarity_morphological" - Morphological similarity "Dissimilarity_lexical" - Lexical similarity "Dissimilarity_phonological_new_features" - Phonological similarity (in terms of new features) "Dissimilarity_phonological_new_categories" - Phonological similarity (in terms of new sounds)

A few rows of the data:

"L1","C","L1L2","L2","AaA","LoR","Edu.day","Sex","Family","ISO639.3","Enroll","STEX_speaking_score","Dissimilarity_morphological","Dissimilarity_lexical","Dissimilarity_phonological_new_features","Dissimilarity_phonological_new_categories" "English","UnitedStates","EnglishMonolingual","Monolingual",34,0,4,"Female","Indo-European","eng ",94,541,0.0094,0.083191,11,19 "English","UnitedStates","EnglishGerman","German",25,16,3,"Female","Indo-European","eng ",94,603,0.0094,0.083191,11,19 "English","UnitedStates","EnglishFrench","French",32,3,4,"Male","Indo-European","eng ",94,562,0.0094,0.083191,11,19 "English","UnitedStates","EnglishSpanish","Spanish",27,8,4,"Male","Indo-European","eng ",94,537,0.0094,0.083191,11,19 "English","UnitedStates","EnglishMonolingual","Monolingual",47,5,3,"Male","Indo-European","eng ",94,505,0.0094,0.083191,11,19

Refined DataCo Smart Supply Chain Geospatial Dataset

Optimized for Geospatial and Big Data Analysis

This dataset is a refined and enhanced version of the original DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS dataset, specifically designed for advanced geospatial and big data analysis. It incorporates geocoded information, language translations, and cleaned data to enable applications in logistics optimization, supply chain visualization, and performance analytics.

Key Features

1. Geocoded Source and Destination Data

• Accurate latitude and longitude coordinates for both source and destination locations.
• Facilitates geospatial mapping, route analysis, and distance calculations.

2. Supplementary GeoJSON Files

• src_points.geojson: Source point geometries.
• dest_points.geojson: Destination point geometries.
• routes.geojson: Line geometries representing source-destination routes.
• These files are compatible with GIS software and geospatial libraries such as GeoPandas, Folium, and QGIS.

3. Language Translation

• Key location fields (countries, states, and cities) are translated into English for consistency and global accessibility.

4. Cleaned and Consolidated Data

• Addressed missing values, removed duplicates, and corrected erroneous entries.
• Ready-to-use dataset for analysis without additional preprocessing.

5. Routes and Points Geometry

• Enables the creation of spatial visualizations, hotspot identification, and route efficiency analyses.

Applications

1. Logistics Optimization

• Analyze transportation routes and delivery performance to improve efficiency and reduce costs.

2. Supply Chain Visualization

• Create detailed maps to visualize the global flow of goods.

3. Geospatial Modeling

• Perform proximity analysis, clustering, and geospatial regression to uncover patterns in supply chain operations.

4. Business Intelligence

• Use the dataset for KPI tracking, decision-making, and operational insights.

Dataset Content

Files Included

1. DataCoSupplyChainDatasetRefined.csv

   • The main dataset containing cleaned fields, geospatial coordinates, and English translations.

2. src_points.geojson

   • GeoJSON file containing the source points for easy visualization and analysis.

3. dest_points.geojson

   • GeoJSON file containing the destination points.

4. routes.geojson

   • GeoJSON file with LineStrings representing routes between source and destination points.

Attribution

This dataset is based on the original dataset published by Fabian Constante, Fernando Silva, and António Pereira:
Constante, Fabian; Silva, Fernando; Pereira, António (2019), “DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS”, Mendeley Data, V5, doi: 10.17632/8gx2fvg2k6.5.

Refinements include geospatial processing, translation, and additional cleaning by the uploader to enhance usability and analytical potential.

Tips for Using the Dataset

• For geospatial analysis, leverage tools like GeoPandas, QGIS, or Folium to visualize routes and points.
• Use the GeoJSON files for interactive mapping and spatial queries.
• Combine this dataset with external datasets (e.g., road networks) for enriched analytics.

This dataset is designed to empower data scientists, researchers, and business professionals to explore the intersection of geospatial intelligence and supply chain optimization.

AI in Market Research Market Outlook

According to our latest research, the AI in Market Research market size reached USD 3.16 billion in 2024, with a robust compound annual growth rate (CAGR) of 21.8%. This remarkable momentum is fueled by the increasing adoption of artificial intelligence across diverse industries seeking data-driven insights and automation in research processes. By 2033, the global market is forecasted to reach USD 23.87 billion, underscoring the transformative impact of AI-powered technologies in redefining how organizations conduct market research, analyze consumer behavior, and make strategic decisions. The growth trajectory is shaped by the convergence of big data analytics, enhanced natural language processing, and the demand for real-time actionable intelligence.

One of the most significant growth factors propelling the AI in Market Research market is the exponential increase in data volume and complexity generated by digital transformation across industries. Organizations are inundated with structured and unstructured data from multiple channels, including social media, e-commerce platforms, and customer interactions. Traditional market research methods are often inadequate to process and analyze such vast datasets efficiently. AI technologies, particularly machine learning and natural language processing, enable businesses to sift through massive data pools, extract meaningful patterns, and generate actionable insights at unprecedented speed and accuracy. The ability to automate repetitive tasks, such as survey analysis and sentiment detection, further enhances efficiency and reduces human error, making AI an indispensable tool for modern market research.

Another key driver is the growing emphasis on personalized consumer experiences and competitive differentiation. As businesses strive to understand rapidly evolving customer preferences and market dynamics, AI-powered market research tools offer granular insights into consumer sentiment, purchasing behavior, and emerging trends. These tools leverage advanced algorithms to identify micro-segments, predict demand fluctuations, and optimize product offerings. The integration of AI with predictive analytics and real-time data processing empowers organizations to make informed decisions faster than ever before. Furthermore, AI's ability to continuously learn and adapt from new data ensures that market research remains relevant and forward-looking, providing a sustainable competitive edge in crowded marketplaces.

The democratization of AI-driven market research solutions is also fueling market expansion. Previously, sophisticated analytics and research tools were accessible primarily to large enterprises with significant resources. Today, cloud-based AI platforms and scalable service models are making advanced market research capabilities available to small and medium enterprises (SMEs) as well. This widespread accessibility is driving adoption across industries such as retail, BFSI, healthcare, and media, where agile decision-making and customer-centricity are critical. The proliferation of easy-to-use AI-powered dashboards and visualization tools further lowers the entry barrier, enabling organizations of all sizes to harness the power of AI for strategic growth and innovation.

From a regional perspective, North America continues to dominate the AI in Market Research market, accounting for the largest share in 2024, driven by the presence of leading technology providers, high digital maturity, and robust investment in AI research and development. Europe follows closely, with significant adoption in sectors like retail, finance, and healthcare, supported by favorable regulatory frameworks and a strong focus on data privacy. The Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, increasing smartphone penetration, and a burgeoning startup ecosystem. Latin America and the Middle East & Africa are also emerging as promising markets, as organizations in these regions recognize the value of AI-driven insights in navigating complex market environments and enhancing competitiveness.

Component Analysis

The AI in Market Research market is segmented by component into software and services, each playing a pivotal role in driving adoption and value creation. The software segment, which includes AI platforms, data analytics tools, and machine learning algorithms, dominates the market due to its ability to automate complex analytical tasks, streamli

This post contains a database of Russian numeral constructions from the RuTenTen corpus (https://www.sketchengine.co.uk/rutenten-russian-corpus/). The constructions are of the following type: paucal numeral (2, 3 or 4) followed by an adjective and a feminine noun. Abstract: With the advent of large web-based corpora, Russian linguistics steps into the era of “big data”. But how useful are large datasets in our field? What are the advantages? Which problems arise? The present study seeks to shed light on these questions based on an investigation of the Russian paucal construction in the RuTenTen corpus, a web-based corpus with more than ten billion words. The focus is on the choice between adjectives in the nominative (dve/tri/četyre starye knigi) and genitive (dve/tri/četyre staryx knigi) in paucal constructions with the numerals dve, tri or četyre and a feminine noun. Three generalizations emerge. First, the large RuTenTen dataset enables us to identify predictors that could not be explored in smaller corpora. In particular, it is shown that predicates, modifiers, prepositions and word-order affect the case of the adjective. Second, we identify situations where the RuTenTen data cannot be straightforwardly reconciled with findings from earlier studies or there appear to be discrepancies between different statistical models. In such cases, further research is called for. The effect of the numeral (dve, tri vs. četyre) and verbal government are relevant examples. Third, it is shown that adjectives in the nominative have more easily learnable predictors that cover larger classes of examples and show clearer preferences for the relevant case. It is therefore suggested that nominative adjectives have the potential to outcompete adjectives in the genitive over time. Although these three generalizations are valuable additions to our knowledge of Russian paucal constructions, three problems arise. Large internet-based corpora like the RuTenTen corpus (a) are not balanced, (b) involve a certain amount of “noise”, and (c) do not provide metadata. As a consequence of this, it is argued, it may be wise to exercise some caution with regard to conclusions based on “big data”.

Context

Oftentimes, a dataset looks complex, but once looked at carefully it begins to reveal implying representations of itself. And thus, by proper feature extraction, one can use simple, interpretable classifiers to build a prediction model.

This dataset is a didactic data to illustrate this point. While algorithms such as random forest, boosting, support vector machines, and neural-nets can certainly be applied to create a good classifier, the models that emerge from them loose interpretability to various degrees.

On the other hand, with careful feature engineering, one can build a very simple classifier such as Linear Discriminant Analysis, or Multiclass Logistic Regression classifier to build clearly interpretable and simple models that have the same predictive effectiveness.

Tasks

Build a simple linear classifier using LDA, logistic or softmax regression, etc. to classify each row of data into a tri-valued categorical space. The input space X comprises of (x1, x2), and the target space, y, is the column 't' in the dataset.

Note

After you have built a few linear classifiers, compare its accuracy with one of the black-box algorithms such as random forest, SVM, etc. You should be able to achieve comparable accuracy with careful feature engineering.

Final words

This exercise shows you the power of feature engineering, and carefully thinking over the data.

Text Analytics Market Outlook

According to our latest research, the global Text Analytics market size reached USD 9.7 billion in 2024, and is projected to grow at a robust CAGR of 18.2% during the forecast period. By 2033, the market is expected to reach an impressive USD 45.1 billion, propelled by increasing digital transformation initiatives and the exponential growth of unstructured data worldwide. The surging demand for advanced analytics solutions across industries, coupled with the need for actionable insights from textual information, is a key driver behind this substantial market expansion.

One of the primary growth factors for the text analytics market is the overwhelming proliferation of unstructured data generated from various digital channels such as emails, social media, customer feedback, and online reviews. Organizations are increasingly recognizing the value of leveraging this data to gain a competitive edge, enhance customer experience, and streamline operations. The integration of artificial intelligence (AI) and machine learning (ML) with text analytics tools has further amplified their capabilities, enabling more accurate sentiment analysis, entity recognition, and trend identification. As businesses strive to make data-driven decisions, the adoption of text analytics solutions is witnessing unprecedented momentum, especially in sectors like BFSI, healthcare, and retail, where customer engagement and risk management are paramount.

Another significant driver is the rising emphasis on customer experience management and personalized marketing strategies. Enterprises are utilizing text analytics to decode customer sentiments, preferences, and pain points, thereby tailoring their products and services to meet evolving demands. The ability to monitor brand reputation in real-time and respond proactively to customer feedback has become a strategic imperative. Moreover, regulatory compliance requirements in industries such as finance and healthcare are pushing organizations to adopt robust text analytics platforms for risk and compliance management. This trend is further supported by the growing availability of cloud-based analytics solutions, which offer scalability, cost-effectiveness, and ease of integration with existing business processes.

The expansion of digital transformation in emerging economies is also fueling the growth of the text analytics market. Governments and enterprises in regions like Asia Pacific and Latin America are investing heavily in advanced analytics infrastructure to enhance operational efficiency and drive innovation. The increasing penetration of internet and mobile devices has led to a surge in data generation, creating new opportunities for text analytics vendors. Furthermore, the ongoing advancements in natural language processing (NLP) and big data technologies are enabling more sophisticated analysis of multilingual and domain-specific content, broadening the applicability of text analytics across diverse industry verticals.

The integration of Content Analytics, Discovery, and Cognitive Software is playing a transformative role in the text analytics market. These technologies are enabling organizations to extract deeper insights from vast amounts of unstructured data, facilitating more informed decision-making processes. Content Analytics, in particular, allows businesses to analyze and interpret data from various sources, including social media, customer feedback, and online reviews, providing a comprehensive understanding of market trends and consumer behavior. Discovery tools help in identifying hidden patterns and correlations within the data, while Cognitive Software enhances the ability to process and understand natural language, making analytics more intuitive and accessible. As these technologies continue to evolve, they are expected to drive significant advancements in the capabilities of text analytics solutions, offering organizations new opportunities to innovate and compete in the digital age.

From a regional perspective, North America continues to dominate the text analytics market, accounting for the largest share in 2024, owing to the presence of leading technology providers and early adoption of advanced analytics solutions. Europe follows closely, driven by stringent data privacy regulations

Chinese civilization has a long history, and the Central Plains, with Henan as the core, is one of its birthplaces. Understanding the psycho-linguistic changes in Henan is of great significance for understanding the evolution and formation of national cultural psychology. The traditional method is mainly qualitative or speculative, which makes the consistency among the research results low. Moreover, because most of the research are conducted in a specific period or on a specific figure, they are relatively scattered and unsystematic. To systematically and quantitatively study the psychological changes in the Central Plains represented by Henan Province, this article aimed to examine the self-reported discourses of historical celebrities in Henan in official history and their psycho-linguistic changes based on the classical Chinese Linguistic Inquiry and Word Count (classical Chinese LIWC, CC-LIWC) psycholinguistic dictionary and the classical Chinese words segmentation system, using big data. The research found that the frequency of male words in Henan historical celebrities (F = 2.938, p < 0.05), of differ words (F = 4.767, p < 0.01), of motion words (F = 4.042, p < 0.01), and of time words (F = 5.412, p < 0.01) are significantly different among the five dynasties. The conclusion is that during the Spring and Autumn and the Warring States periods, 100 schools of thought contended, and the status of “scholars” in Henan rose. The frequency of male and differ words was at that point significantly higher than during the other dynasties. From “The Contention of a Hundred Schools of Thought” to “The Supremacy of Confucianism,” the scholars in Henan were in decline, and differential cognitive tendencies had diminished since the Han dynasty. During the period of the Three Kingdoms and Jin and Southern and Northern Dynasties, political powers and territories changed, and the historical celebrities in Henan show a remarkable tendency that were related to time and space. The psycho-linguistic changes found in this study are highly consistent with the development of social history, which indicates that the guidance of the social, political, and cultural environment has an important influence on the psycho-linguistic changes of social class. This is the first time that the text analysis system has been applied in classical Chinese to carry out quantitative research on the psycho-linguistic changes of ancient Chinese people, which provides new ideas and new methods for humanistic research.

[instructions for use] 1. This data set is manually edited by Yidu cloud medicine according to the real medical record distribution; 2. This dataset is an example of the yidu-n7k dataset on openkg. Yidu-n7k dataset can only be used for academic research of natural language processing, not for commercial purposes. ———————————————— Yidu-n4k data set is derived from chip 2019 evaluation task 1, that is, the data set of "clinical terminology standardization task". The standardization of clinical terms is an indispensable task in medical statistics. Clinically, there are often hundreds of different ways to write about the same diagnosis, operation, medicine, examination, test and symptoms. The problem to be solved in Standardization (normalization) is to find the corresponding standard statement for various clinical statements. With the basis of terminology standardization, researchers can carry out subsequent statistical analysis of EMR. In essence, the task of clinical terminology standardization is also a kind of semantic similarity matching task. However, due to the diversity of original word expressions, a single matching model is difficult to achieve good results. Yidu cloud, a leading medical artificial intelligence technology company in the industry, is also the first Unicorn company to drive medical innovation solutions with data intelligence. With the mission of "data intelligence and green medical care" and the goal of "improving the relationship between human beings and diseases", Yidu cloud uses data artificial intelligence to help the government, hospitals and the whole industry fully tap the intelligent political and civil value of medical big data, and build a big data ecological platform for the medical industry that can cover the whole country, make overall utilization and unified access. Since its establishment in 2013, Yidu cloud has gathered world-renowned scientists and the best people in the professional field to form a strong talent team. The company has invested hundreds of millions of yuan in R & D and service system establishment every year, built a medical data intelligent platform with large data processing capacity, high data integrity and transparent development process, and has obtained more than dozens of software copyrights and national invention patents.

The Large-Scale Model Training Machine market is experiencing explosive growth, fueled by the increasing demand for advanced artificial intelligence (AI) applications across diverse sectors. The market, estimated at $15 billion in 2025, is projected to witness a robust Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated $75 billion by 2033. This surge is driven by several factors, including the proliferation of big data, advancements in deep learning algorithms, and the growing need for efficient model training in applications such as natural language processing (NLP), computer vision, and recommendation systems. Key market segments include the Internet, telecommunications, and government sectors, which are heavily investing in AI infrastructure to enhance their services and operational efficiency. The CPU+GPU segment dominates the market due to its superior performance in handling complex computations required for large-scale model training. Leading companies like Google, Amazon, Microsoft, and NVIDIA are at the forefront of innovation, constantly developing more powerful hardware and software solutions to address the evolving needs of this rapidly expanding market. The market's growth trajectory is shaped by several trends. The increasing adoption of cloud-based solutions for model training is significantly lowering the barrier to entry for smaller companies. Simultaneously, the development of specialized hardware like Tensor Processing Units (TPUs) and Field-Programmable Gate Arrays (FPGAs) is further optimizing performance and reducing costs. Despite this positive outlook, challenges remain. High infrastructure costs, the complexity of managing large datasets, and the shortage of skilled AI professionals are significant restraints on the market's expansion. However, ongoing technological advancements and increased investment in AI research are expected to mitigate these challenges, paving the way for sustained growth in the Large-Scale Model Training Machine market. Regional analysis indicates North America and Asia Pacific (particularly China) as the leading markets, with strong growth anticipated in other regions as AI adoption accelerates globally.

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Augmented Intelligence Market Size 2024-2028

The augmented intelligence market size is forecast to increase by USD 61.3 billion at a CAGR of 33.1% between 2023 and 2028.

Augmented Intelligence (IA) is revolutionizing business operations by amplifying human intelligence with advanced technologies such as Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), and Virtual Assistants. IA is increasingly being adopted by enterprises to enhance decision-making capabilities and improve business outcomes. The implementation of IA in Business Intelligence (BI) tools is a significant trend, enabling organizations to derive insights from Big Data and perform predictive analytics.
However, the shortage of IA experts poses a challenge to the widespread adoption of these technologies. ML and DL algorithms are integral to IA, enabling systems to learn and make decisions autonomously. NLP is used to understand human language and interact with virtual assistants, while Big Data and Data Analytics provide the foundation for IA applications. Predictive analytics is a key benefit of IA, enabling organizations to anticipate future trends and make informed decisions. IA is transforming various industries, including healthcare, finance, and retail, by augmenting human intelligence and automating routine tasks.

What will be the Size of the Market During the Forecast Period?

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Augmented Intelligence (IA), also known as Intelligence Amplification, refers to the use of advanced technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP) to support and enhance human intelligence. IA systems are designed to process vast amounts of data and provide insights that would be difficult or impossible for humans to identify on their own. Machine Learning and Deep Learning are at the core of IA systems. ML algorithms learn from data and improve their performance over time, while DL algorithms can identify complex patterns and relationships within data.
Additionally, NLP enables computers to understand human language, enabling more effective communication between humans and machines. IA is being adopted across various industries, including streaming video services, factory automation, political think tanks, medical analysis, and more. In factory automation, IA systems are used to optimize production processes and improve quality control. In medical analysis, IA is used to analyze patient data and provide doctors with accurate diagnoses and treatment recommendations. In political think tanks, IA is used to analyze large datasets and identify trends and patterns. IA systems rely on big data and data analytics to function effectively.
However, predictive analytics is a key application of IA, allowing organizations to make informed decisions based on data trends and patterns. Data scientists are essential in developing and implementing IA systems, ensuring that they are accurate, unbiased, and free from fatigue or distraction. Decision-making: IA systems are designed to augment human decision-making by providing accurate and relevant information in real-time. Autonomous systems and reactive machines are examples of IA applications that can make decisions based on data and environmental inputs. However, it is important to note that IA systems are not infallible and have an error rate that must be considered in decision-making.
In conclusion, cybernetics, the study of communication and control in machines and living beings, plays a crucial role in IA development. Algorithms are used to process data and provide insights, and IA systems are designed to learn and adapt over time, improving their performance and accuracy. Limitations: IA systems are not without limitations. Bias in data can lead to inaccurate or unfair outcomes, and user viewing habits can influence the performance of recommendation systems. It is essential to address these limitations and ensure that IA systems are designed to augment human intelligence in a symbiotic relationship, rather than replacing it.

How is this market segmented and which is the largest segment?

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

Technology

  Machine learning
  NLP
  Computer vision
  Others


Geography

  North America

    US


  Europe

    UK


  APAC

    China
    India
    Japan


  South America



  Middle East and Africa

By Technology Insights

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

Augmented Intelligence, also known as Intelligence Amplification, is a technology that enhances human intelligence by integrating Machine Learning (ML), Deep Learning, Natural Language Pr

Apache Hadoop is the central software project, beside Apache SOLR, and Apache Lucene (SW, software). Companies which offer Hadoop distributions and Hadoop based solutions are the central companies in the scope of the study (HV, hardware vendors). Other companies started very early with Hadoop related projects as early adopters (EA). Global players (GP) are affected by this emerging market, its opportunities and the new competitors (NC). Some new but highly relevant companies like Talend or LucidWorks have been selected because of their obvious commitment to the open source ideas. Widely adopted technologies with a relation to the selected research topic are represented by the group TEC.