Big Data Processing and Distribution Software Market Outlook

The global big data processing and distribution software market size was valued at approximately USD 42.6 billion in 2023 and is projected to reach USD 105.8 billion by 2032, showcasing a stark compound annual growth rate (CAGR) of 10.8% during the forecast period. The significant growth of this market can be attributed to the increasing adoption of big data analytics across various industry verticals, coupled with the rising need for businesses to manage and analyze vast amounts of unstructured data. As organizations continue to integrate advanced analytics into their operational strategies, the demand for sophisticated big data processing and distribution solutions is anticipated to escalate further, thereby driving market expansion.

The proliferation of the Internet of Things (IoT) and the burgeoning amount of data generated by connected devices have been pivotal growth factors for the big data processing and distribution software market. With billions of devices continuously generating massive datasets, organizations are striving to harness this information to gain actionable insights. The capability to process and analyze these large volumes of data efficiently allows companies to improve decision-making, enhance customer experiences, and optimize operations. Moreover, advancements in artificial intelligence and machine learning have further augmented data processing capabilities, facilitating the extraction of deeper insights and patterns from complex datasets. Consequently, the accelerating pace of digital transformation across industries is a major catalyst propelling market growth.

Another significant driver is the increasing emphasis on regulatory compliance and data security. With the exponential growth of data, organizations face mounting pressure to comply with stringent data protection regulations such as GDPR and CCPA. This has led to a surge in demand for robust data processing and distribution software that ensures data privacy while providing comprehensive analytics capabilities. Additionally, sectors such as healthcare and finance, which handle sensitive personal information, are particularly keen on adopting advanced software solutions to safeguard data integrity and security. This trend is expected to continue, further fueling the market's upward trajectory as businesses seek to balance data-driven innovation with compliance requirements.

The rising trend of cloud computing is also playing a crucial role in the growth of the big data processing and distribution software market. As businesses increasingly shift their operations to the cloud, the demand for cloud-based data processing solutions has escalated. Cloud platforms offer scalability, cost-efficiency, and flexibility, allowing enterprises to process vast datasets without the need for substantial infrastructure investments. Furthermore, the integration of big data analytics with cloud services enables real-time data processing and analysis, enhancing agility and fostering innovation. This migration towards cloud-based solutions is expected to drive market growth, particularly among small and medium enterprises (SMEs) looking to leverage big data capabilities without incurring high costs.

Component Analysis

The big data processing and distribution software market can be segmented into two primary components: Software and Services. The software segment encompasses various tools and platforms designed to collect, store, process, and analyze large data sets. This segment is poised for substantial growth as enterprises increasingly rely on sophisticated software solutions to derive meaningful insights from data. Key products include data integration tools, Hadoop distributions, and real-time data processing platforms. As businesses across industries continue to prioritize data-driven decision-making, the demand for advanced software solutions is expected to remain robust, driving the overall market expansion.

Hadoop Distributions play a pivotal role in the big data processing and distribution software market. These distributions provide the necessary framework for storing and processing large datasets across clusters of computers. By leveraging Hadoop, organizations can efficiently manage and analyze vast amounts of data, enabling them to gain valuable insights and make data-driven decisions. The flexibility and scalability offered by Hadoop Distributions make them an ideal choice for businesses looking to harness the power of big data without

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.

Feature selection is an important technique for data mining before a machine learning algorithm is applied. Despite its importance, most studies of feature selection are restricted to batch learning. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale applications. Most existing studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of Online Feature Selection (OFS) in which an online learner is only allowed to maintain a classifier involved only a small and fixed number of features. The key challenge of Online Feature Selection is how to make accurate prediction using a small and fixed number of active features. This is in contrast to the classical setup of online learning where all the features can be used for prediction. We attempt to tackle this challenge by studying sparsity regularization and truncation techniques. Specifically, this article addresses two different tasks of online feature selection: (1) learning with full input where an learner is allowed to access all the features to decide the subset of active features, and (2) learning with partial input where only a limited number of features is allowed to be accessed for each instance by the learner. We present novel algorithms to solve each of the two problems and give their performance analysis. We evaluate the performance of the proposed algorithms for online feature selection on several public datasets, and demonstrate their applications to real-world problems including image classification in computer vision and microarray gene expression analysis in bioinformatics. The encouraging results of our experiments validate the efficacy and efficiency of the proposed techniques.Related Publication: Hoi, S. C., Wang, J., Zhao, P., & Jin, R. (2012). Online feature selection for mining big data. In Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (pp. 93-100). ACM. http://dx.doi.org/10.1145/2351316.2351329 Full text available in InK: http://ink.library.smu.edu.sg/sis_research/2402/ Wang, J., Zhao, P., Hoi, S. C., & Jin, R. (2014). Online feature selection and its applications. IEEE Transactions on Knowledge and Data Engineering, 26(3), 698-710. http://dx.doi.org/10.1109/TKDE.2013.32 Full text available in InK: http://ink.library.smu.edu.sg/sis_research/2277/

France Big Data Market was valued at USD 14.51 Billion in 2023 and is expected to reach USD 26.09 Billion by 2029 with a CAGR of 10.11% during the forecast period.

In 2020, Daejeon Metropolitan City Hall and 5 autonomous districts and 58 public data youth interns (in alphabetical order, surnames are omitted. / Oh Jin, Yujin, Kisik, Minah, Wantae, Eunji, Hyewon, Yeonhee, Jiyoung, Mijin, Jongwoong, Jieun, Jihoon, Jinhee, Hyun, Hyejin, Geungi, Harin, Baekheon, Joohyun, Yookyung, Hyunkyung, Hyungwon, Kyumin, Seokhyun, Arim, Seokyung, Seohee, Gyeongnam, Bomi, Sangmi, Suyeon, Seungrim, Seungjun, Sieon, Inkigayo, Jisoo, Chungseok, Hankyu, Hyeonu, Subin, Jeonghee, Jongseon, Jiyeon, Mihee, Minyoung, Youngjin, Hanik, Minjeong, Sujin, Yunyoung, Eunseong, Heeju, Mirim, Jaeyoung, Jonghoon, Taeseok, Yuji) surveyed 31,000 small business owners together. This is research material.

Cloud Based Big Data Market Outlook

The global market size for Cloud Based Big Data was valued at approximately USD 45 billion in 2023 and is projected to reach around USD 285 billion by 2032, growing at a compound annual growth rate (CAGR) of 22.3% during the forecast period. This rapid expansion is driven by the increasing adoption of cloud technologies across various sectors, the rising need for data analytics, and advancements in artificial intelligence and machine learning algorithms that require robust big data platforms.

One primary growth factor for the Cloud Based Big Data market is the exponential increase in data generation from various sources such as social media, IoT devices, and enterprise applications. As data continues to proliferate, organizations are compelled to seek efficient and scalable solutions for data storage, processing, and analysis. Cloud-based platforms provide the necessary infrastructure and tools to manage such vast amounts of data, making them indispensable for modern businesses. Additionally, the flexibility and scalability of cloud solutions enable organizations to handle peak loads dynamically, further driving their adoption.

Another significant factor contributing to market growth is the substantial cost savings associated with cloud-based solutions. Traditional on-premise big data infrastructure requires significant capital investment in hardware and software, as well as ongoing maintenance costs. In contrast, cloud-based solutions operate on a pay-as-you-go model, allowing organizations to scale their resources up or down based on demand. This economic advantage is particularly appealing to small and medium enterprises (SMEs) that may lack the financial resources to invest in large-scale infrastructure.

Furthermore, the integration of advanced data analytics capabilities with cloud platforms is revolutionizing how organizations derive insights from their data. Cloud-based big data solutions now come equipped with machine learning, artificial intelligence, and data visualization tools that enable real-time analytics and decision-making. These advanced capabilities are transforming industries by providing actionable insights that drive business growth, enhance customer experiences, and optimize operations. The continuous improvement and innovation in these technologies are significant drivers of market expansion.

Big Data Consulting services are becoming increasingly vital as organizations strive to harness the full potential of their data. These services offer expert guidance on implementing big data strategies, selecting the right technologies, and optimizing data processes to align with business goals. By leveraging Big Data Consulting, companies can navigate the complexities of data management, ensuring that they not only store and process data efficiently but also derive actionable insights. This expertise is particularly crucial in today's rapidly evolving digital landscape, where staying competitive requires a deep understanding of data-driven decision-making.

From a regional perspective, North America holds a significant share of the Cloud Based Big Data market due to the early adoption of advanced technologies and the presence of key market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digital transformation in countries like China and India, coupled with government initiatives promoting cloud adoption, is propelling the market in this region. Additionally, the growing awareness of the benefits of big data analytics among enterprises in this region is further fueling market growth.

Component Analysis

The Cloud Based Big Data market can be segmented by component into two primary categories: Software and Services. Software solutions encompass a wide range of tools and applications designed for data storage, processing, analysis, and visualization. These include big data platforms, data integration tools, business intelligence software, and advanced analytics applications. The demand for these software solutions is driven by the need for efficient data management and the ability to derive actionable insights from vast datasets. Innovations in machine learning and AI integrated within these software solutions are further enhancing their capabilities and attractiveness to enterprises.

Services, on the other hand, include various support and maintenance services, consulting

The Latin America Big Data Analytics Market Report is Segmented by Organization Size (Small and Medium Scale, and Large-Scale Organizations), End-User Vertical (IT & Telecom, BFSI, Retail & Consumer Goods, Manufacturing, Healthcare & Life Sciences, Government, and Other End-User Verticals), and Country. The Report Offers the Market Size in Value Terms in (USD) for all the Abovementioned Segments.

Big Data Analytics In Agriculture Market size was valued at USD 1.25 Billion in 2023 and is projected to reach USD 2.16 Billion by 2031, growing at a CAGR of 7.66% during the forecast period 2024-2031.

Global Big Data Analytics In Agriculture Market Drivers

The market for Big Data Analytics in Agriculture is driven by several key factors:

Rising Demand for Food Production: With the global population increasing, there is a growing demand for food production. Big Data analytics helps in optimizing agricultural practices, improving crop yields, and ensuring food security.

Adoption of Precision Farming: Precision farming involves using technology to monitor and manage field variability in crops. Big Data analytics provides insights into soil conditions, weather patterns, and crop health, enabling farmers to make data-driven decisions that enhance productivity and reduce costs.

Global Big Data Analytics In Agriculture Market Restraints

The Big Data Analytics in Agriculture Market faces several restraints that could limit its growth and adoption. These market restraints include:

High Implementation Costs: The initial cost of setting up big data analytics infrastructure is substantial. This includes the costs of hardware, software, and skilled personnel, which can be prohibitive, especially for small and medium-sized farms.

Data Privacy and Security Concerns: Farmers and agricultural enterprises are increasingly concerned about the privacy and security of their data. Unauthorized access, data breaches, and misuse of sensitive agricultural data could deter adoption.

Big Data As A Service Market Outlook

The global Big Data As A Service (BDaaS) market size was valued at approximately USD 15 billion in 2023 and is expected to reach around USD 70 billion by 2032, growing at a compound annual growth rate (CAGR) of 18.2% during the forecast period. The growth of the BDaaS market can be attributed to the increasing adoption of big data analytics across various industries, coupled with the growing demand for cost-effective and flexible data management solutions.

Several factors are contributing to the robust growth of the BDaaS market. One of the primary growth drivers is the exponential increase in data generation from various sources, including social media platforms, internet of things (IoT) devices, and enterprise applications. As organizations seek to leverage this data to gain insights, improve decision-making, and enhance customer experiences, the demand for big data analytics solutions is surging. Moreover, the complexity and volume of data are pushing companies to adopt BDaaS, as it provides scalable and efficient data processing capabilities without the need for significant capital investments in infrastructure.

Another significant growth factor is the rising trend of digital transformation across industries. As businesses increasingly move towards digitization, the need for advanced analytics and data management solutions becomes paramount. BDaaS offers organizations the ability to analyze large datasets in real time, enabling them to make data-driven decisions quickly. This is particularly important in industries such as BFSI and healthcare, where timely insights can lead to improved operational efficiency, better patient outcomes, and enhanced customer satisfaction. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) with big data analytics is driving the adoption of BDaaS, as these technologies provide more accurate and actionable insights.

The cost-effectiveness and flexibility offered by BDaaS are also key factors driving its market growth. Traditional data management solutions require substantial investments in hardware, software, and skilled personnel. In contrast, BDaaS eliminates the need for such investments by providing data storage, processing, and analytics capabilities on a subscription basis. This pay-as-you-go model allows organizations to scale their data management capabilities according to their needs, making it an attractive option for both small and medium enterprises (SMEs) and large enterprises. Additionally, BDaaS providers offer various deployment models, including public, private, and hybrid clouds, providing organizations with the flexibility to choose the model that best suits their requirements.

As organizations continue to navigate the complexities of big data, solutions like DBeq are becoming increasingly relevant. DBeq offers a unique approach to data management by providing a seamless integration of data processing and analytics capabilities. This solution is designed to handle large volumes of data efficiently, enabling organizations to derive meaningful insights without the need for extensive infrastructure investments. By leveraging DBeq, companies can streamline their data workflows, reduce operational costs, and enhance decision-making processes. The flexibility and scalability of DBeq make it an attractive option for businesses looking to optimize their data management strategies in a rapidly evolving digital landscape.

From a regional perspective, North America holds the largest share of the BDaaS market, driven by the presence of major technology companies and a high adoption rate of advanced analytics solutions. The region's well-established infrastructure, coupled with substantial investments in research and development, further supports market growth. Europe and Asia Pacific are also significant markets for BDaaS, with increasing digital transformation initiatives and growing awareness of the benefits of big data analytics. The Asia Pacific region, in particular, is expected to witness the highest growth rate during the forecast period, fueled by rapid economic development, expanding internet penetration, and the rising adoption of cloud-based solutions.

Component Analysis

The BDaaS market is segmented by component into solutions and services. Solutions comprise the core platforms and tools necessary f

The Big Data Security Market report segments the industry into By Component (Solutions, Services), By Organization Size (Small & Medium Enterprises, Large Enterprises), By End-user Industry (Banking, Financial Services, & Insurance (BFSI), Manufacturing, IT & Telecommunication, Aerospace & Defense, Healthcare, Other End-users), and By Geography (North America, Europe, Asia-Pacific, Latin America, Middle-East & Africa).

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

The Big Data Consulting Market Report is Segmented by Service Type (Strategic Consulting, Implementation Services, Analytics and Insights, Managed Services, Training and Support), Deployment Model (On-Premise, Cloud-Based, Hybrid), Organization Size (Small and Medium Enterprises (SMEs), Large Enterprises), Application (Customer Analytics, Operational Analytics, Risk and Fraud Management, Supply Chain Management, Marketing and Sales Analytics, Predictive Maintenance, Financial Analytics, Other Applications), and Geography (North America, Europe, Asia Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

The Big Data Engineering Services Market report segments the industry into By Type (Data Modelling, Data Integration, Data Quality, Analytics), By Business Function (Marketing and Sales, Finance, Operations, and more), By Organization Size (Small and Medium Enterprizes, Large Enterprises), By Deployement Type (Cloud, On-premise), By End-user Industry (BFSI, Government, and more), and Geography (North America, Europe, and more).

The Brazilian Big Data Analytics Market is Segmented by Organization Size (Small, Medium, and Large-Scale Organizations) and End-User Vertical (IT and Telecom, BFSI, Retail and Consumer Goods, Manufacturing, Healthcare and Life Sciences, Government, and Other End-User Verticals). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Hadoop Big Data Analytics Solution Market Outlook

In 2023, the global Hadoop Big Data Analytics Solution market size was valued at approximately USD 45 billion and is projected to reach around USD 145 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.5% during the forecast period. This significant growth is driven by the increasing adoption of big data technologies across various industries, advancements in data analytics, and the rising need for cost-effective and scalable data management solutions.

One of the primary growth factors for the Hadoop Big Data Analytics Solution market is the exponential increase in data generation. With the proliferation of digital devices and the internet, vast amounts of data are being produced every second. This data, often referred to as big data, contains valuable insights that can drive business decisions and innovation. Organizations across sectors are increasingly recognizing the potential of big data analytics in enhancing operational efficiency, optimizing business processes, and gaining a competitive edge. Consequently, the demand for advanced analytics solutions like Hadoop, which can handle and process large datasets efficiently, is witnessing a substantial rise.

Another significant growth driver is the ongoing digital transformation initiatives undertaken by businesses globally. As organizations strive to become more data-driven, they are investing heavily in advanced analytics solutions to harness the power of their data. Hadoop, with its ability to store and process vast volumes of structured and unstructured data, is becoming a preferred choice for businesses aiming to leverage big data for strategic decision-making. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) with Hadoop platforms is further augmenting their analytical capabilities, making them indispensable tools for modern enterprises.

The cost-effectiveness and scalability of Hadoop solutions also contribute to their growing popularity. Traditional data storage and processing systems often struggle to handle the sheer volume and variety of big data. In contrast, Hadoop offers a more flexible and scalable architecture, allowing organizations to store and analyze large datasets without incurring prohibitive costs. Moreover, the open-source nature of Hadoop software reduces the total cost of ownership, making it an attractive option for organizations of all sizes, including small and medium enterprises (SMEs).

From a regional perspective, North America is expected to dominate the Hadoop Big Data Analytics Solution market during the forecast period. The region's strong technological infrastructure, coupled with the presence of major market players and early adopters of advanced analytics solutions, drives market growth. Additionally, the increasing focus on data-driven decision-making and the high adoption rates of digital technologies in sectors like BFSI, healthcare, and retail further bolster the market in North America. Conversely, the Asia Pacific region is anticipated to witness the highest growth rate, driven by rapid digitalization, government initiatives promoting big data analytics, and the expanding e-commerce industry.

MapReduce Services play a pivotal role in the Hadoop ecosystem by enabling the processing of large data sets across distributed clusters. As businesses continue to generate vast amounts of data, the need for efficient data processing frameworks becomes increasingly critical. MapReduce, with its ability to break down complex data processing tasks into smaller, manageable units, allows organizations to analyze data at scale. This service is particularly beneficial for industries dealing with high-volume data streams, such as finance, healthcare, and retail, where timely insights can drive strategic decisions. The integration of MapReduce Services with Hadoop platforms enhances their data processing capabilities, making them indispensable tools for modern enterprises seeking to leverage big data for competitive advantage.

Component Analysis

When analyzing the Hadoop Big Data Analytics Solution market by component, it becomes evident that software, hardware, and services are the three main segments. The software segment encompasses the core Hadoop components like Hadoop Distributed File System (HDFS) and MapReduce, along with various tools and platforms designed to enhance its capabilities. The growing complexity and volume of data necessitate robust s

The Latin American Big Data Analytics market, valued at $7.84 billion in 2025, is projected to experience robust growth, fueled by a Compound Annual Growth Rate (CAGR) of 7.67% from 2025 to 2033. This expansion is driven by the increasing adoption of cloud-based analytics solutions, the burgeoning need for data-driven decision-making across various sectors, and the rising availability of affordable data storage and processing capabilities. Key growth drivers include the expanding digital economy, government initiatives promoting data analytics adoption, and the increasing prevalence of mobile devices generating substantial data volumes. The BFSI (Banking, Financial Services, and Insurance), IT and Telecommunications, and Retail and Consumer Goods sectors are leading adopters, leveraging big data analytics for improved customer relationship management, fraud detection, risk assessment, and supply chain optimization. However, challenges remain, including a lack of skilled data professionals, data security concerns, and high implementation costs, potentially hindering wider market penetration, particularly in smaller organizations. Nevertheless, the long-term outlook remains positive, with significant growth opportunities across all segments, driven by continued technological advancements and increasing business demand. The market segmentation reveals substantial variations in adoption rates across different industries and organizational sizes. Large-scale organizations are currently the dominant consumers of big data analytics solutions due to their higher investment capacity and complex data management needs. However, small and medium-scale enterprises are exhibiting rapid growth in adoption, driven by the availability of cost-effective cloud-based solutions and increasing awareness of the benefits of data-driven decision-making. Geographically, Brazil, Mexico, and Argentina are the key markets within Latin America, contributing a significant portion of the overall market revenue. The presence of established IT infrastructure and a relatively developed digital economy in these countries fosters a favorable environment for big data analytics adoption. Future growth will likely be influenced by government regulations concerning data privacy and security, as well as the continued evolution of big data technologies. Competitive dynamics are shaped by both international players like Qliktech, Splunk, and Salesforce, and regional vendors who cater to the specific needs of the Latin American market. Recent developments include: June 2023 - Belvo, an open financial data and payments platform in Latin America, and FICO, a prominent global provider of analytical software and a recognized innovator in AI decision-making platforms, announced a strategic partnership to enhance credit availability in the region. The two companies are creating a machine learning model that can be understood and explained, generating a customer score from transaction-level data that the customer has authorized., April 2023 - Telecentro Argentina stated that it chose Nokia for the operator's network transformation, extending the network's security against DDoS threats and attacks while supplying the traffic capacity expansion required for future services and subscriber base development. Deepfield Defender offers a comprehensive view of DDoS assaults across the whole network, accurately identifying attacks as they happen for the highest levels of protection and mitigation.. Key drivers for this market are: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making Among Large-scale Enterprises, Rapid Increase in the Generation of Data Coupled with Availability of Several End-user-specific Tools Due to the Growth in the Local Landscape; Growing Demand in Enterprise, Government, and Telecom Verticals. Potential restraints include: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making Among Large-scale Enterprises, Rapid Increase in the Generation of Data Coupled with Availability of Several End-user-specific Tools Due to the Growth in the Local Landscape; Growing Demand in Enterprise, Government, and Telecom Verticals. Notable trends are: IT & Telecommunication Sector to Hold Significant Market Share.

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.

In 2018, large enterprises were the biggest investors in big data analytics in Italy, contributing for 88 percent of the total spending. Small and medium-sized enterprises, by contrast, contributed for 12 percent. The breakdown by sector shows that the banking industry was the biggest spender.