In 2021, the United States is the leading country in the big data and business analytics (BDA) market, with 51 percent market share. The following four leading counties all hover around 5 percent market share. Global BDA spending is forecast to reach almost 216 billion U.S. dollars in 2021, with the majority to be spent on IT services and software.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

Question Paper Solutions of chapter MapReduce workflows of Big Data Analysis, 8th Semester , Information Technology

The market size of the Big Data Analytics In Healthcare Market is categorized based on Type (Software, Service) and Application (Hospitals & Clinics, Finance & Insurance Agencies, Research Organization) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

This report provides insights into the market size and forecasts the value of the market, expressed in USD million, across these defined segments.

As of March 2024, there were a reported 5,381 data centers in the United States, the most of any country worldwide. A further 521 were located in Germany, while 514 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These centers can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

As per newly released data by Future Market Insights (FMI), the global tourism industry and big data analytics market is estimated at US$ 225.4 billion in 2023 and is projected to reach US$ 486.6 billion by 2033, at a CAGR of 8% from 2023 to 2033.

2018 to 2022 Global Tourism Industry Big Data Analytics Demand Outlook Compared to 2023 to 2033 Forecast

Regional Analysis

Country-wise Insights

Category-wise Insights

A survey conducted among manufacturing companies in the fourth quarter of 2020 showed that 21 percent implemented usage of big data analytics for their regular manufacturing activities. Big data analytics mainly helped manufacturing companies to improve supply chain management, and enterprise resource planning. In Industry 4.0 era, adaptation of big data analytics would become increasingly common in all sectors of manufacturing industry.

The global Big Data Analytics in Energy Market size was valued at USD 26.53 billion in 2025 and is projected to grow from USD 33.08 billion in 2026 to USD 80.81 billion by 2033, exhibiting a CAGR of 9.8% during the forecast period. The market growth is attributed to the increasing need for efficient energy management, rising adoption of smart grids, and advancements in data analytics technologies. The market is segmented based on analytics type, deployment model, application sector, end user, and region. By analytics type, the market is divided into descriptive analytics, predictive analytics, prescriptive analytics, and diagnostic analytics. By deployment model, the market is classified into on-premises, cloud-based, and hybrid. By application sector, the market is segmented into utility management, renewable energy management, energy trading and risk management, and energy consumption optimization. By end user, the market is categorized into residential, commercial, and industrial. By region, the market is segmented into North America, South America, Europe, the Middle East & Africa, and Asia Pacific. Key drivers for this market are: 1. Predictive maintenance solutions 2. Renewable energy integration 3. Enhanced asset management 4. Real-time data analytics 5. Regulatory compliance support. Potential restraints include: 1. Growing energy data volume 2. Enhanced operational efficiency 3. Regulatory compliance pressures 4. Demand for predictive analytics 5. Rising focus on renewable energy.

Global Big Data Market was valued at USD 221.98 billion in 2023 and is expected to reach USD 431.77 billion by 2029 with a CAGR of 11.56% during the forecast period.

The main project aims were to examine the human rights implications of rapidly developing technologies. As noted above, in an increasingly digitised world, technological developments and the collection, storage and use of 'big data' pose unprecedented challenges for the protection of human rights. The aim of the project was to examine the intersection of such technological developments and the ideals of human rights protection. The work focused on both positive and negative aspects of this relationship. As noted above, the core research aims were organised on these issues that cut across the threats and opportunities:1) How is the use of ICT and big data shaping the content and scope of rights? (2) How does the use of ICT and big data shape operational practices across state and non-state activities? What new theoretical questions and implications for human rights are generated? (3) What methodologies are needed to identify and document the misuse of modern technologies and the failure to comply with rights-based obligations? (4) How can the use of ICT and big data best support evidence-based approaches to human rights protection and advocacy? (5) What possibilities and limitations exist for regulating the collection, storage and use of ICT and big data by states and non-state actors? The deposited data largely focuses on interviews with law enforcement and security agency representatives about uses of digital technology. We found that an enthusiastic embrace of technnology often existed yet this was not always accompanied by the development of codes of practice, regulatory frameworks and operational guidence on how they should be used. In addition to a potential regulatory vacuum, such disconnects also placed additional burdens on law enforcement themselves as they sought to apply existing rules and regulations. This is something we have described in publications as 'surveillance arbitration'. We also include interviews with civil society actors and lawyers that interrogate these issues and associated digital rights campaigning matters in more detail.

The data and analytics software market is poised to experience significant growth, expanding from USD 108.69 billion in 2025 to a projected USD 248.84 billion by 2033, exhibiting a CAGR of 9.72% during the forecast period. This growth is fueled by the increasing adoption of big data and cloud computing, as well as the rising demand for data-driven insights to improve decision-making and gain a competitive edge in various industries. Major market drivers include the growing volume and complexity of data, technological advancements in data management and analytics, and the need for real-time insights to optimize operations and customer experiences. Market trends include the rise of artificial intelligence (AI) and machine learning (ML), which enable more advanced data analysis and predictive modeling. The adoption of cloud-based data analytics solutions is also gaining traction, offering flexibility, cost-effectiveness, and scalability. Some market restraints include data security and privacy concerns, the lack of skilled data analytics professionals, and the integration challenges associated with diverse data sources. The market is highly competitive, with established vendors such as Qlik, Informatica, Oracle, Microsoft, and Teradata, along with emerging players like Databricks, Amazon Web Services (AWS), and Google Cloud Platform (GCP) vying for market share. Key drivers for this market are: 1. Self-service analytics tools 2. Integration with other cloud applications 3. Prescriptive and predictive analytics 4. Artificial intelligence and machine 5. learning Data storytelling. Potential restraints include: Cloud adoption real-time analytics artificial intelligence.

The big data pharmaceutical advertising market is segmented into four major product segments: software, services, training, and consulting. The software segment is the largest segment of the market, followed by the services segment. The software segment is expected to continue to dominate the market during the forecast period. Recent developments include: Bright Bytes is a platform dealing in data management that was acquired by Microsoft in February 2019. the agenda behind this acquiring was to initiate the collection, integration, as well as, report of information across various online platforms related to both the applications and services, for the target audience. , To increase its global footprint, IBM announced and launched IBM Cloud Multizone Region (MZR) in August 2019. This launch is expected to help the clients in adopting critical workloads prevalent in a hybrid cloud environment. , The big data pharmaceutical advertising market Research shares a brief analysis on the segmentation, drivers leading to the adoption of technology, and the opportunities in the highly competitive industry. It shares a detailed study on strategic analysis, market structure, market growth, competitive analysis, joint ventures, strategic alliance, recent developments new product developments, research and development, and merger and acquisition in the field of study. The report also briefs about the regions where the market is studied i.e. North America, Europe, Asia-Pacific, Middle East, and the Rest of the World (ROW).. Key drivers for this market are: Increasing adoption of big data analytics by pharmaceutical companies. Growing demand for personalized marketing.. Potential restraints include: Lack of expertise in big data analytics. Data privacy concerns.. Notable trends are: Use of artificial intelligence (AI) and machine learning (ML). Development of new data management and analysis tools..

This statistic displays the results of a survey conducted on Norwegian population representatives in 2018 and their views on the impact of big data analysis and artificial intelligence on their job place in the future. The majority of respondents (37 percent) thought there would be no significant change. Four percent of respondents thought their job would get excessive.

This dataset records Cladophora and associated benthic algae, collectively Cladophora community or submerged aquatic vegetation (SAV), biomass collected during the growing season of 2022 at stations located along the U.S. shoreline of Lakes Michigan, Huron, Erie, and Ontario. It also records a variety of supporting data collected at Cladophora measurement stations. These supporting data include: - seasonal time series of light, currents, wave action, temperature, specific conductivity, turbidity, pH, phycocyanin, chlorophyll, and dissolved oxygen from moored sensors at a subset of stations; - measurements of Secchi disk depth and water chemistry; - water column profiles of PAR, temperature, specific conductivity, turbidity, pH, phycocyanin, chlorophyll, and dissolved oxygen; - diver observations of SAV, dreissenid mussels, round goby abundance, and substrate properties; - measurements of dreissenid mussel abundance and size class distribution coincident with SAV biomass; - nutrient content of SAV, dreissenid mussels, and sediments; - and information about sampling locations and operations. Similar data were collected at several of the same transects within four Great Lakes in 2018, 2019, 2020, and 2021 are available at (2018) https://doi.org/10.5066/P9E570JS, (2019) https://doi.org/10.5066/P99O4QXB, (2020) https://doi.org/10.5066/P9O9FSTT, and (2021) https://doi.org/10.5066/P9449EUF.

International Journal of Engineering and Advanced Technology Publication fee - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level

The technological development in the new economic era has brought challenges to enterprises. Enterprises need to use massive and effective consumption information to provide customers with high-quality customized services. Big data technology has strong mining ability. The relevant theories of computer data mining technology are summarized to optimize the marketing strategy of enterprises. The application of data mining in precision marketing services is analyzed. Extreme Gradient Boosting (XGBoost) has shown strong advantages in machine learning algorithms. In order to help enterprises to analyze customer data quickly and accurately, the characteristics of XGBoost feedback are used to reverse the main factors that can affect customer activation cards, and effective analysis is carried out for these factors. The data obtained from the analysis points out the direction of effective marketing for potential customers to be activated. Finally, the performance of XGBoost is compared with the other three methods. The characteristics that affect the top 7 prediction results are tested for differences. The results show that: (1) the accuracy and recall rate of the proposed model are higher than other algorithms, and the performance is the best. (2) The significance p values of the features included in the test are all less than 0.001. The data shows that there is a very significant difference between the proposed features and the results of activation or not. The contributions of this paper are mainly reflected in two aspects. 1. Four precision marketing strategies based on big data mining are designed to provide scientific support for enterprise decision-making. 2. The improvement of the connection rate and stickiness between enterprises and customers has played a huge driving role in overall customer marketing.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

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: