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Big Data Market Size 2024-2028

The big data market size is forecast to increase by USD 508.73 billion at a CAGR of 21.46% between 2023 and 2028.

The market is experiencing significant growth due to the growth in data generation from various sources, including IoT platforms and digital transformation services. This data deluge presents opportunities for businesses to leverage advanced analytics tools for applications such as fraud detection and prevention, workforce analytics, and business intelligence. However, the increasing adoption of big data implementation also brings challenges, including the need for data security and privacy measures. Quantum computing and blockchain technology are emerging trends In the big data landscape, offering potential solutions to complex data processing and security issues. In healthcare analytics, data protection regulations are driving the need for secure data management and sharing.
Additionally, supply chain optimization is another area where big data can bring significant value, enabling real-time monitoring and predictive analytics. Overall, the market is poised for continued growth, driven by the need to extract valuable insights from the vast amounts of data being generated.

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

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The market is experiencing growth as businesses increasingly leverage information from vast datasets to drive strategic decision-making, enhance customer experiences, and improve operational efficiency. The digital revolution has led to an exponential increase in data creation, fueling demand for advanced analytics capabilities, real-time processing, and data protection and privacy solutions. Hardware and software companies offer on-premise and cloud-based systems to accommodate various industry needs, including customer analytics in retail and e-commerce, supply chain analytics in manufacturing, marketing analytics, pricing analytics, spatial analytics, workforce analytics, risk and credit analytics, transportation analytics, healthcare, energy and utilities, and IT and telecom. Big data applications span numerous sectors, enabling organizations to gain valuable insights from their data to optimize operations, mitigate risks, and innovate new products and services.

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

The big data industry 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.

Deployment

  On-premises
  Cloud-based
  Hybrid


Type

  Services
  Software


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period. On-premises big data software solutions involve the installation of hardware and software by the end-user, granting them complete control over the system. Despite the high upfront costs, on-premises solutions offer advantages such as full ownership and operational efficiency. In contrast, cloud-based solutions require recurring monthly payments and involve data storage on companies' servers, increasing security concerns. Advanced analytics, real-time processing, and integrated analytics are key features driving the market. Data creation from digital transformation, customer experiences, and various industries like retail, healthcare, and finance, fuel the demand for scalable infrastructure and user-friendly interfaces. Technologies such as quantum computing, blockchain, AI-driven analytics platforms, and automation are transforming business intelligence solutions.

Ensuring data protection and privacy, accessibility, and seamless data transactions are crucial in this data-driven era. Key technologies include distributed computing, visualization tools, and social media. Target audiences range from decision-makers to various industries, including transportation, energy, and consumer engagement.

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The On-premises segment was valued at USD 86.53 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 47% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market in North America is experiencing significant growth due to digital transformation initiatives by enterprises in sectors such as healthcare, retail

The open-source big data tools market is experiencing robust growth, driven by the increasing need for scalable, cost-effective data management and analysis solutions across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the rising volume and velocity of data generated across industries, from banking and finance to manufacturing and government, necessitate powerful and adaptable tools. Secondly, the cost-effectiveness and flexibility of open-source solutions compared to proprietary alternatives are major drawcards, especially for smaller organizations and startups. The ease of customization and community support further enhance their appeal. Growth is also being propelled by technological advancements such as the development of more sophisticated data analytics tools, improved cloud integration, and increased adoption of containerization technologies like Docker and Kubernetes for deployment and management. The market's segmentation across application (banking, manufacturing, etc.) and tool type (data collection, storage, analysis) reflects the diverse range of uses and specialized tools available. Key restraints to market growth include the complexity associated with implementing and managing open-source solutions, requiring skilled personnel and ongoing maintenance. Security concerns and the need for robust data governance frameworks also pose challenges. However, the growing maturity of the open-source ecosystem, coupled with the emergence of managed services providers offering support and expertise, is mitigating these limitations. The continued advancements in artificial intelligence (AI) and machine learning (ML) are further integrating with open-source big data tools, creating synergistic opportunities for growth in predictive analytics and advanced data processing. This integration, alongside the ever-increasing volume of data needing analysis, will undoubtedly drive continued market expansion over the forecast period.

This statistic shows the size of the global big data analytics services market related to healthcare in 2016 and a forecast for 2025, by application. It is predicted that by 2025 the market for health-related financial analytics services using big data will increase to over 13 billion U.S. dollars.

The need for advanced analytical approaches to provide HPDA solutions is driving the market growth of High Performance Data Analytics (HPDA). According to the analyst from Verified Market Research, The High Performance Data Analytics (HPDA) Market is estimated to reach a valuation of USD 597.06 Billion over the forecast period 2031, by subjugating around USD 113.23 Billion in 2023.

The adoption of an open-source framework for big data analytics is driving market growth. This surge in demand enables the market to grow at a CAGR of 23.1% from 2024 to 2031.

High Performance Data Analytics (HPDA) Market: Definition/ Overview

HPDA refers to big data analytics that uses High-Performance Computing (HPC) techniques. Big data analytics has always relied on high-performance computing (HPC), but as data grows exponentially, new forms of high-performance computing will be required to access previously unimaginable volumes of data. The combination of big data analytics and high-performance computing is called “high-performance data analytics.” High-performance data analytics is the process of quickly finding insights from large data sets by running powerful analytical tools in parallel on high-performance computing systems.

Furthermore, high-performance data analytics infrastructure is a rapidly expanding market for government and commercial organizations that need to combine high-performance computing with data-intensive analysis. For complex modeling and simulations, big data analytics techniques like Hadoop and Spark have long required high-performance computing, which they lack.

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.

As reported by a survey conducted in 2024 on digital news consumption, over 70 percent of respondents from India stated that they sourced their news online, which included social media, making it a popular form of accessing news. In comparison, 40 percent of respondents stated that they used print media as a news source during that period.

We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive—the largest publicly available archive of FOSS source code with accompanying development history—all versions of files whose names are commonly used to convey licensing terms to software users and developers. The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing. Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared. The dataset is released as open data as an archive file containing all deduplicated license blobs, plus several portable CSV files for metadata, referencing blobs via cryptographic checksums.

For more details see the included README file and companion paper:

Stefano Zacchiroli. A Large-scale Dataset of (Open Source) License Text Variants. In proceedings of the 2022 Mining Software Repositories Conference (MSR 2022). 23-24 May 2022 Pittsburgh, Pennsylvania, United States. ACM 2022.

If you use this dataset for research purposes, please acknowledge its use by citing the above paper.

A survey from 2022 found that around 61 percent of adults in the United States with a household income less than 40,000 U.S. dollars a year stated their personal finances were a major source of stress for them. This statistic shows the percentage of adults in the United States who stated select issues were a major source of stress for them as of 2022, by household income.

The Data Analytics in Retail Industry is segmented by Application (Merchandising and Supply Chain Analytics, Social Media Analytics, Customer Analytics, Operational Intelligence, Other Applications), by Business Type (Small and Medium Enterprises, Large-scale Organizations), and Geography. The market size and forecasts are provided in terms of value (USD billion) for all the above segments.

According to a survey held among adults in the United States in February 2022, ABC and CBS were considered to be the most credible news sources in the country, with 61 percent of respondents believing the organizations to be very or somewhat credible. Sources which fared less well were MSNBC, Fox News, National Public Radio, and HuffPost, with less than 50 percent of adults agreeing that they found these to be reliable news outlets. The credibility of all the news sources in the ranking was higher in 2022 than in the previous year, though the figures in 2021 were particularly low.

Trust and bias in news Finding trustworthy, impartial news sources can be difficult for audiences in a world where fake news is in constant circulation and bias in news is a growing concern. More than 50 percent of total respondents to a survey held in early 2020 believed that there was a fair amount or great deal of bias in the news sources they used most often. The same study found that close to 70 percent of respondents were more concerned with bias in news that other people may consume than with their own news source.

A report exploring trust in news found that radio, network news, and newspapers were the most trusted news sources in the United States, whereas social media was not considered reliable in this regard. The lack of trust in news on social media has yet to affect consumption – social networks are the most used source of news among many consumers, particularly younger generations. In fact, some news consumers are moving away from official news platforms altogether and getting their updates from influencers rather than journalists.

The Industrial Analytics Market size was valued at USD 25.11 Billion in the year 2024, and it is expected to reach USD 97.38 Billion in 2031, at a CAGR of 18.46% from 2024 to 2031.

Key Market Drivers
Rise of Industry 4.0: The rise of Industry 4.0 is leading to the creation of vast amounts of data from sensors, machines, and other industrial equipment. This data is then analyzed by industrial analytics solutions to optimize processes, improve efficiency, and gain valuable insights into operations.
Proliferation of IoT and IIoT: The proliferation of IoT and IIoT devices is resulting in a massive amount of data generation. Industrial analytics solutions are being employed to analyze this data.
Big Data Adoption: Big data is increasingly being recognized by businesses as a valuable asset for informed decision-making. Industrial analytics plays a critical role in processing and analyzing large datasets from various industrial sources, thereby enabling data-driven decision-making for improved performance.
Cloud Technology Advancement: The advancement of cloud technology is offering scalability, flexibility, and cost-effectiveness for businesses. This growth in cloud computing is facilitating the widespread use of industrial analytics, making it accessible to a wider range of organizations.

The global big data in healthcare market size is estimated to grow from USD 78 billion in 2024 to USD 540 billion by 2035, representing a CAGR of 19.20% during the forecast period till 2035.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Big Spring, TX, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 12,942, while the mean income for the highest quintile (20% of households with the highest income) is 170,892. This indicates that the top earners earn 13 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 273,156, which is 159.84% higher compared to the highest quintile, and 2110.62% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Big Spring median household income. You can refer the same here

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.

Multiclass COVID-19 detection by utilizing ESN-MDFS: Extreme Smart Network using mean dropout feature selection technique.

In software development, it’s common to reuse existing source code by copying and pasting, resulting in the proliferation of numerous code clones—similar or identical code fragments—that detrimentally affect software quality and maintainability. Although several techniques for code clone detection exist, many encounter challenges in effectively identifying semantic clones due to their inability to extract syntax and semantics information. Fewer techniques leverage low-level source code representations like bytecode or assembly for clone detection. This work introduces a novel code representation for identifying syntactic and semantic clones in Java source code. It integrates high-level features extracted from the Abstract Syntax Tree with low-level features derived from intermediate representations generated by static analysis tools, like the Soot framework. Leveraging this combined representation, fifteen machine-learning models are trained to effectively detect code clones. Evaluation on a large dataset demonstrates the models’ efficacy in accurately identifying semantic clones. Among these classifiers, ensemble classifiers, such as the LightGBM classifier, exhibit exceptional accuracy. Linearly combining features enhances the effectiveness of the models compared to multiplication and distance combination techniques. The experimental findings indicate that the proposed method can outperform the current clone detection techniques in detecting semantic clones.

According the most recent survey conducted in Norway in 2019, TV and online newspapers are the sources of choice when searching for information on major crisis situations. Radio came second, with 21 percent of respondents stating to use it as information source.

OEQ data for major sources and batch plants hosted layer

Use of JSATS can generate a large volume of data. To manage and visualize the data, an integrated suite of science-based tools known as the Hydropower Biological Evaluation Toolset (HBET) can be used.

Introduction: With the increasing fluctuations in the current domestic and international economic situation and the rapid iteration of macroeconomic regulation and control demands, the inadequacy of the existing economic data statistical system in terms of agility has been exposed. It has become a primary task to closely track and accurately predict the domestic and international economic situation using effective tools and measures to compensate for the inadequate economic early warning system and promote stable and orderly industrial production.Methods: Against this background, this paper takes industrial added value as the forecasting object, uses electricity consumption to predict industrial added value, selects factors influencing industrial added value based on grounded theory, and constructs a big data forecasting model using a combination of “expert interviews + big data technology” for economic forecasting.Results: The forecasting accuracy on four provincial companies has reached over 90%.Discussion: The final forecast results can be submitted to government departments to provide suggestions for guiding macroeconomic development.