IoT Analytics Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Analytics Type (Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics), By Service Types (Managed Services, Professional Services), By End User (Manufacturing, Healthcare, Energy and Utilities, Transportation and Logistics, Retail, Information Technology and Telecommunications, Others), By Region & Competition, 2020-2030F

Market Overview

The Global IoT Analytics Market was valued at USD 27.28 billion in 2024 and is expected to reach USD 101.82 billion by 2030 with a CAGR of 24.36% during the forecast period.

The Internet of Things Analytics Market, commonly referred to as the IoT Analytics Market, encompasses the collection, processing, and analysis of vast volumes of data generated by connected devices, sensors, and IoT systems across multiple industries. IoT analytics enables organizations to transform raw data from smart devices into actionable insights, supporting improved decision-making, operational efficiency, predictive maintenance, and enhanced customer experiences. The market covers a wide array of solutions, including platforms for data management, analytics software, and specialized applications, as well as services such as managed analytics, consulting, and system integration.

Deployment models for IoT analytics span on-premise, cloud, and hybrid infrastructures, providing flexibility for diverse enterprise needs. The market is witnessing accelerated growth driven by several factors. Increasing adoption of Industry 4.0 practices, smart manufacturing, and connected industrial operations is generating immense volumes of data that require sophisticated analytics to optimize production, reduce downtime, and improve quality control. Similarly, sectors such as healthcare are leveraging IoT analytics for patient monitoring, predictive diagnostics, and operational management, while energy and utilities are using analytics to enhance grid management and energy efficiency. The integration of artificial intelligence and machine learning with IoT analytics platforms is further enhancing predictive and prescriptive capabilities, enabling businesses to anticipate failures, optimize resource allocation, and implement automated decision-making.

Key Market Drivers

Proliferation of Connected Devices Driving the IoT Analytics Market

In the contemporary business landscape, the exponential proliferation of connected devices stands as a pivotal force propelling the IoT Analytics Market forward, enabling organizations to harness vast streams of data for strategic decision-making and operational efficiency. As enterprises across sectors such as manufacturing, healthcare, transportation, and retail increasingly integrate Internet of Things ecosystems into their core operations, the sheer volume of generated data necessitates sophisticated analytics solutions to derive actionable insights, mitigate risks, and optimize resource allocation.

This driver is underpinned by the seamless interconnectivity facilitated by advancements in sensor technologies and wireless communication protocols, which allow for real-time data capture from diverse endpoints, ranging from industrial machinery to consumer wearables. Businesses are leveraging this connectivity to enhance predictive maintenance capabilities, where analytics platforms process device-generated data to forecast equipment failures, thereby reducing downtime and extending asset lifespans. Furthermore, in supply chain management, the integration of connected devices enables granular tracking of goods, improving inventory accuracy and minimizing losses through data-driven visibility. The IoT Analytics Market benefits immensely from this trend, as companies seek to transform raw data into competitive advantages, such as personalized customer experiences in retail through behavioral analysis or energy optimization in smart buildings via consumption patterns.

Regulatory pressures for sustainability also amplify this driver, compelling firms to utilize analytics for monitoring environmental impacts, like carbon emissions from fleets of connected vehicles. Economically, the cost reductions achieved through proactive interventions—such as averting costly breakdowns—translate into higher profitability margins, making investments in analytics infrastructure a strategic imperative. Moreover, the convergence with emerging technologies like 5G networks accelerates data transmission speeds, allowing for more complex analytics models that incorporate machine learning algorithms to identify anomalies and trends in large datasets.

This not only fosters innovation but also opens new revenue streams, such as data monetization services where aggregated insights are sold to third parties. Challenges such as data silos and interoperability issues are being addressed through standardized protocols, ensuring that the proliferation of devices does not overwhelm analytics capabilities but rather enhances them. In healthcare, for instance, connected medical devices provide continuous patient monitoring, with analytics enabling early detection of health deteriorations, thus improving outcomes and reducing hospitalization costs.

According to the Organisation for Economic Co-operation and Development (OECD) on measuring the Internet of Things, the total number of IoT connections reached 11.7 billion globally in 2020, with projections estimating 29.3 billion networked devices by 2023, of which 14.7 billion are machine-to-machine connections representing 50 percent of the total. Additionally, machine-to-machine SIM card subscriptions in the OECD area stood at 385 million as of June 2021, up from 132 million in 2015, highlighting a significant adoption surge. The report also notes that low-power wide-area connections for machine-to-machine grew to 223 million in 2018, expected to reach 1.9 billion by 2023, comprising 14 percent of all devices. This proliferation contributes to an economic impact of 0.99 percent annual average addition to gross domestic product growth from 2018 to 2030, equating to approximately 849 billion United States dollars per year in 2018 prices.

Advancements in Artificial Intelligence and Machine Learning Propelling the IoT Analytics Market

The integration of advancements in artificial intelligence and machine learning represents a transformative driver in the IoT Analytics Market, empowering businesses to unlock deeper insights from complex data sets generated by interconnected systems. Organizations are increasingly adopting these technologies to automate data processing, enhance pattern recognition, and enable predictive modeling, thereby elevating the value proposition of IoT deployments. In manufacturing, for example, machine learning algorithms analyze sensor data to optimize production lines, reducing waste and improving quality control through anomaly detection.

This driver is fueled by the evolution of neural networks and deep learning frameworks that can handle unstructured data from diverse IoT sources, such as video feeds from surveillance cameras or audio from smart assistants. Businesses benefit from improved decision-making, where AI-driven analytics forecast market trends based on consumer behavior captured via connected devices, allowing for agile inventory management and targeted marketing campaigns. In the energy sector, artificial intelligence optimizes grid operations by predicting demand fluctuations from smart meter data, leading to efficient resource distribution and cost savings. The IoT Analytics Market is further bolstered by open-source platforms that democratize access to these technologies, enabling small and medium enterprises to compete with larger players.

Security enhancements through machine learning, such as behavioral analysis for threat detection, address vulnerabilities in IoT networks, fostering greater adoption. Economically, these advancements drive revenue growth by enabling new business models like as-a-service offerings, where analytics provide ongoing value. Challenges like data privacy are mitigated through federated learning techniques that process information locally, complying with regulations while maintaining efficacy. In agriculture, AI analyzes soil and weather data from IoT sensors to recommend precise irrigation, boosting yields and sustainability.

The convergence with big data tools amplifies this driver, allowing for scalable processing of terabytes of information in real time. Talent development in AI skills is crucial, as companies invest in upskilling to fully leverage these capabilities. Overall, advancements in artificial intelligence and machine learning are catalyzing innovation in the IoT Analytics Market, transforming raw data into strategic assets for competitive advantage.

The European Union  on technological trends and policies indicates that the market for artificial intelligence, closely integrated with IoT, is projected to grow at a compound annual growth rate of 26.5 percent from 2018 to 2023, reaching 96 billion euros by 2023. Furthermore, 36 percent of companies in the EU27 were using IoT in 2019, with high willingness to invest in the next 12 months, driven by its role in fueling AI applications. The OECD notes that IoT-related patent growth was close to 20 percent per year from 2010 to 2018, with smart connected objects patents increasing at 20 percent annually from 2000 to 2018. Venture capital investment in IoT reached 8 billion United States dollars in 2020, with the United States accounting for 4.5 billion dollars.

Rising Demand for Predictive Maintenance Accelerating the IoT Analytics Market

The rising demand for predictive maintenance emerges as a critical driver in the IoT Analytics Market, allowing businesses to shift from reactive to proactive strategies in asset management, thereby minimizing operational disruptions and extending equipment longevity. By utilizing analytics to interpret data from IoT sensors embedded in machinery, companies can predict failures before they occur, scheduling maintenance only when necessary and avoiding unnecessary costs. This approach is particularly valuable in industries like aviation and oil and gas, where downtime can result in significant financial losses.

The IoT Analytics Market thrives on this demand, as advanced algorithms process historical and real-time data to identify wear patterns and performance degradation. Benefits include reduced maintenance expenses, improved safety, and enhanced reliability, contributing to overall business resilience. In transportation, fleet operators use predictive analytics to monitor vehicle health, optimizing routes and fuel efficiency.

Regulatory compliance in sectors like pharmaceuticals ensures that equipment analytics maintain quality standards. The integration with digital twins further enhances this driver, simulating scenarios for better forecasting. Economically, predictive maintenance drives cost efficiencies, with businesses reporting substantial returns on investment. Challenges such as data accuracy are addressed through sensor calibration and AI refinement. Overall, this driver positions the IoT Analytics Market as essential for modern industrial operations.

The OECD highlights that in European manufacturing, 25 percent of IoT usage is for condition-based maintenance, contributing to productivity gains of 92 billion United States dollars in 2018, representing 53 percent of global benefits from IoT. Firms adopting IoT for maintenance see an average 18 percent cost reduction. In Germany, IoT-driven digitalization could save 61 megatons of CO2 by 2030 in industrial emissions. The EU Parliament brief notes that implementing Industry 4.0, supported by IoT analytics, could increase productivity by 30 percent. Additionally, venture capital in IoT-related mergers and acquisitions totaled 163 billion United States dollars for 782 deals from 2014 to 2020.

Expansion of 5G Networks Fueling the IoT Analytics Market

The expansion of 5G networks serves as a fundamental driver for the IoT Analytics Market, providing the high-speed, low-latency connectivity required to handle massive data volumes from distributed devices in real time. Businesses are capitalizing on 5G's capabilities to deploy denser IoT networks, enabling applications like autonomous vehicles and smart factories where instantaneous data analysis is crucial. This driver enhances analytics by facilitating edge computing, reducing reliance on centralized clouds and improving response times.

In telecommunications, 5G supports enhanced mobile broadband for IoT, driving data-intensive services. The IoT Analytics Market grows as 5G enables more sophisticated models, incorporating video analytics and augmented reality. Economic benefits include new market opportunities in remote operations and telemedicine. Challenges like coverage gaps are being overcome through infrastructure investments. Overall, 5G expansion is revolutionizing the IoT Analytics Market by enabling unprecedented data throughput and innovation.

According to the OECD , mobile machine-to-machine connections were 1.2 billion in 2018, projected to reach 4.4 billion by 2023, supported by 5G advancements. In Germany, 59 percent of industrial IoT projects plan to use 5G within 24 months as of 2021. The EU report emphasizes 5G rollout as a driver for greater data flows in IoT, with the AI-IoT market growing at 26.5 percent CAGR to 96 billion euros by 2023. Short-range semiconductor shipments for IoT multiplied over seven times from 2004 to 2020, reaching 6.6 billion units. Semiconductors for IoT were valued at 21 billion United States dollars in 2017, comprising 5 to 7 percent of the worldwide market.

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Key Market Challenges

Data Security and Privacy Concerns

One of the most significant challenges facing the Internet of Things Analytics Market is ensuring data security and privacy. IoT systems generate enormous volumes of sensitive information across multiple sectors, including healthcare, financial services, manufacturing, energy, and government operations. This data often includes personally identifiable information, operational metrics, and strategic business insights, all of which require robust protection. Cybersecurity breaches, unauthorized access, and data leaks can result in substantial financial losses, reputational damage, and regulatory penalties for organizations. As IoT devices proliferate, the attack surface expands, making it increasingly difficult for enterprises to maintain comprehensive security across all connected devices and networks.

The complexity of securing IoT analytics platforms is compounded by the diverse and heterogeneous nature of devices, communication protocols, and data formats. Many IoT devices are designed for functionality and cost-effectiveness rather than security, leaving them vulnerable to hacking or malware attacks. Additionally, data is frequently transmitted and stored across cloud-based infrastructures, creating potential exposure points. Organizations must invest in advanced encryption techniques, secure authentication mechanisms, and continuous monitoring systems to mitigate risks. Furthermore, regulatory compliance adds another layer of complexity. Different regions enforce varying data protection regulations, such as the General Data Protection Regulation in Europe, which imposes strict requirements on data collection, storage, and processing. Enterprises operating across multiple regions must navigate these regulatory frameworks while ensuring seamless analytics operations.

Addressing security and privacy concerns is not merely a technological challenge but also a strategic and operational consideration. Companies must balance the need for real-time data processing and actionable insights with the imperative to safeguard sensitive information. The development and adoption of comprehensive security frameworks, including data anonymization, end-to-end encryption, and secure device management, are critical to sustaining trust and long-term growth in the Internet of Things Analytics Market. Failure to address these concerns may slow market adoption, particularly in sectors such as healthcare and financial services, where data sensitivity is paramount. As cyber threats evolve and the volume of IoT-generated data continues to expand exponentially, maintaining robust security and privacy standards will remain a persistent and pressing challenge for stakeholders in the market.

Integration Complexity and Interoperability Challenges

Another major challenge for the Internet of Things Analytics Market is the complexity associated with integrating diverse systems and ensuring interoperability across devices, platforms, and analytics applications. The Internet of Things ecosystem comprises a wide range of devices from different manufacturers, each employing unique communication protocols, data formats, and software standards. Harmonizing these disparate components to enable seamless data collection, processing, and analysis is a critical hurdle for enterprises seeking to derive actionable insights from IoT data. In industrial, healthcare, and smart city applications, the integration of legacy systems with modern IoT infrastructure further complicates deployment and operational efficiency.

The interoperability challenge extends beyond hardware and communication protocols to analytics software and platforms. Enterprises often deploy multiple analytics solutions for distinct business functions, which may not be natively compatible with each other or with existing IoT infrastructure. This lack of standardization can result in data silos, inconsistent analytics outputs, and delays in decision-making. Moreover, the growing use of cloud computing, edge analytics, and hybrid deployment models introduces additional layers of complexity, requiring sophisticated middleware and data orchestration mechanisms to ensure real-time insights and operational continuity. Organizations must also contend with the rapid evolution of IoT standards and technologies, which necessitates continuous adaptation and investment in flexible integration frameworks.

Addressing integration and interoperability challenges demands significant technical expertise, strategic planning, and financial resources. Enterprises must adopt scalable, modular, and standards-based architectures that facilitate connectivity across heterogeneous devices and systems while maintaining data integrity and performance. The development of universal standards and collaboration among technology providers can also help mitigate these challenges, enabling smoother integration and faster time-to-value for IoT analytics initiatives. Failure to overcome integration complexity may result in suboptimal analytics performance, reduced return on investment, and slower adoption of IoT analytics solutions. As organizations increasingly seek comprehensive, enterprise-wide analytics capabilities, achieving seamless integration and interoperability will remain a central challenge in driving sustained growth in the Internet of Things Analytics Market.

Key Market Trends

Increasing Adoption of Artificial Intelligence and Machine Learning

A significant trend shaping the Internet of Things Analytics Market is the increasing integration of artificial intelligence and machine learning technologies into analytics platforms. Organizations across sectors are leveraging these technologies to process massive volumes of data generated by connected devices and transform it into actionable insights. Traditional analytics methods are often insufficient for handling the high velocity, variety, and volume of data in modern IoT ecosystems. Artificial intelligence and machine learning enable predictive and prescriptive analytics, allowing enterprises to anticipate failures, optimize operations, and make proactive business decisions.

In industrial sectors, predictive maintenance powered by machine learning models allows manufacturers to detect anomalies in equipment before they lead to costly downtime, thereby enhancing operational efficiency and reducing maintenance costs. Similarly, in healthcare, artificial intelligence-driven analytics enable real-time monitoring of patient health, early detection of critical conditions, and optimization of treatment plans. The financial sector is also benefiting, with artificial intelligence algorithms analyzing transaction data to detect fraud, assess credit risk, and enhance customer experience. Furthermore, the combination of IoT analytics with machine learning accelerates decision-making in smart cities, enabling efficient traffic management, energy optimization, and resource allocation.

Enterprises are increasingly investing in AI-enabled IoT analytics solutions that support autonomous decision-making and self-learning systems. These capabilities not only improve operational efficiency but also enhance the quality and accuracy of insights, which is critical for strategic planning and risk management. The trend towards integrating artificial intelligence and machine learning within the Internet of Things Analytics Market is expected to continue, driven by the growing demand for intelligent automation, real-time monitoring, and actionable predictive insights. Companies that successfully adopt these technologies will gain competitive advantages by improving operational performance, reducing costs, and delivering superior services to customers, thereby reinforcing the market’s rapid growth trajectory.

Expansion of Edge Computing in IoT Analytics

Edge computing is emerging as a critical trend in the Internet of Things Analytics Market, driven by the need for real-time data processing and reduced latency. Traditionally, data collected from IoT devices was transmitted to centralized cloud platforms for analysis, which often resulted in delays and high bandwidth costs. With edge computing, data processing occurs closer to the source of data generation, enabling faster decision-making and reducing the dependency on centralized cloud infrastructure. This trend is particularly relevant for sectors requiring instantaneous insights, such as autonomous vehicles, industrial automation, smart grids, and healthcare monitoring.

By deploying edge analytics, organizations can process large volumes of sensor and device data locally, extracting actionable insights in real time. This capability is crucial in environments where latency or downtime can have significant operational or safety implications. For example, in manufacturing, edge-enabled IoT analytics can immediately detect equipment anomalies, prevent production line failures, and optimize energy consumption. In healthcare, edge computing allows continuous monitoring of critical patients, ensuring timely interventions without delays associated with cloud-based processing. Furthermore, edge analytics enhances data security and privacy by reducing the need to transmit sensitive information over external networks, mitigating the risk of cyberattacks or data breaches.

The adoption of edge computing is also supported by advancements in hardware, such as powerful microprocessors, and software frameworks capable of handling complex analytics at the edge. Enterprises are increasingly investing in hybrid architectures that combine edge and cloud analytics, providing scalability while maintaining real-time responsiveness. This trend is expected to drive growth in the Internet of Things Analytics Market, as organizations seek to optimize operational efficiency, improve data security, and achieve near-instantaneous insights, making edge-enabled solutions a cornerstone of future IoT analytics strategies.

Growing Focus on Industry-Specific IoT Analytics Solutions

Another prominent trend in the Internet of Things Analytics Market is the development of industry-specific analytics solutions tailored to the unique requirements of different sectors. Organizations are increasingly recognizing that generic analytics platforms may not fully address the operational challenges and regulatory requirements of specific industries. Consequently, technology providers are offering customized solutions that combine domain expertise with advanced analytics capabilities to deliver actionable insights that align with sector-specific objectives.

In the healthcare industry, IoT analytics solutions are being designed to monitor patient health metrics, optimize hospital operations, and support telemedicine applications. In manufacturing, specialized analytics platforms provide real-time visibility into production lines, predictive maintenance, and supply chain optimization. Energy and utility companies are leveraging IoT analytics to monitor grid performance, enhance energy efficiency, and manage renewable energy sources more effectively. Similarly, the transportation and logistics sector is utilizing industry-focused solutions to track fleet performance, optimize route planning, and improve delivery efficiency.

Industry-specific solutions also address compliance and regulatory requirements, enabling organizations to meet local and international standards while gaining operational insights. These solutions often integrate advanced analytics with artificial intelligence, machine learning, and edge computing to provide predictive and prescriptive capabilities tailored to industry needs. By delivering actionable insights specific to business operations, organizations can improve decision-making, reduce operational costs, enhance customer experiences, and gain a competitive edge in their respective markets.

The trend towards industry-focused IoT analytics solutions is expected to accelerate, driven by increasing demand for precise, actionable insights and the need to optimize sector-specific processes. Providers that offer highly specialized and customizable analytics platforms are likely to witness significant adoption, thereby contributing to sustained growth in the Internet of Things Analytics Market and reinforcing its strategic importance across multiple industry verticals.

Segmental Insights

Analytics Type Insights

In 2024, the Predictive Analytics segment dominated the Internet of Things Analytics Market and is expected to maintain its leading position throughout the forecast period due to its ability to deliver actionable insights that drive operational efficiency, cost reduction, and strategic decision-making across industries. Predictive analytics leverages historical and real-time data from connected devices, sensors, and industrial equipment to forecast potential outcomes, identify patterns, and anticipate system failures before they occur.

This capability is particularly critical for sectors such as manufacturing, energy and utilities, healthcare, and transportation, where downtime, inefficiencies, or service disruptions can lead to significant financial losses and operational setbacks. In manufacturing, predictive analytics enables proactive maintenance of machinery, optimizing production schedules and minimizing unplanned downtime, thereby improving overall equipment effectiveness. Similarly, in the energy and utilities sector, predictive models are utilized to forecast energy demand, monitor grid performance, and detect anomalies in real time, enhancing operational resilience and energy efficiency. In healthcare, predictive analytics facilitates patient monitoring, early detection of critical conditions, and resource allocation, improving patient outcomes and operational management.

Furthermore, the rapid adoption of artificial intelligence and machine learning in predictive analytics platforms enhances the accuracy of forecasts and the ability to process large volumes of heterogeneous data from multiple IoT sources. Companies are increasingly investing in predictive analytics to gain competitive advantages, optimize supply chains, improve customer experiences, and reduce operational risks. The growing reliance on data-driven decision-making, coupled with the rising complexity of IoT ecosystems and the need for proactive management of assets and resources, ensures that the Predictive Analytics segment will continue to dominate the Internet of Things Analytics Market during the forecast period, reinforcing its strategic importance across multiple industry verticals.

Service Types Insights

In 2024, the Managed Services segment dominated the Internet of Things Analytics Market and is expected to maintain its leadership position throughout the forecast period due to the growing complexity of IoT ecosystems and the increasing demand for end-to-end analytics solutions that reduce operational burden on enterprises. Managed services encompass outsourced offerings that include monitoring, maintenance, and management of IoT analytics platforms, allowing organizations to focus on core business operations while ensuring optimal performance of their connected infrastructure.

The rapid proliferation of Internet of Things devices across industries such as manufacturing, healthcare, energy and utilities, transportation, and information technology has led to massive volumes of heterogeneous data, which require continuous management and specialized expertise to process, analyze, and secure effectively. Enterprises increasingly prefer managed services as they provide scalable, cost-effective, and reliable solutions without the need for significant in-house infrastructure or specialized personnel. In addition, managed services providers offer advanced capabilities such as real-time monitoring, predictive maintenance, and data visualization, which enhance operational efficiency and decision-making accuracy.

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Regional Insights

Largest Region

In 2024, North America dominated the Internet of Things Analytics Market and is expected to maintain its leading position throughout the forecast period, driven by the region’s advanced technological infrastructure, early adoption of Internet of Things solutions, and strong presence of key market players. The United States and Canada have been at the forefront of integrating connected devices across various sectors, including manufacturing, healthcare, energy and utilities, transportation, and information technology, creating a high demand for sophisticated analytics platforms capable of processing and deriving insights from massive volumes of heterogeneous data. North American enterprises increasingly leverage Internet of Things analytics to enhance operational efficiency, reduce costs, enable predictive maintenance, and improve decision-making processes, particularly through the integration of artificial intelligence and machine learning technologies.

Government initiatives and investments in smart infrastructure projects, such as smart cities and intelligent transportation systems, are also driving the adoption of Internet of Things analytics solutions, further consolidating the region’s market leadership. Moreover, the region benefits from a well-established ecosystem of analytics solution providers, cloud computing services, and managed service providers, which facilitates seamless deployment, real-time monitoring, and scalable integration of Internet of Things platforms. The regulatory environment, which emphasizes data privacy, security, and compliance standards, has encouraged enterprises to adopt robust analytics solutions that ensure secure data management while supporting strategic objectives.

Emerging Region

During the forecast period, the Asia Pacific region is emerging as a high-growth market for the Internet of Things Analytics Market, driven by rapid industrialization, increasing adoption of connected devices, and substantial investments in digital infrastructure. Countries such as China, India, Japan, and South Korea are witnessing significant expansion in manufacturing, healthcare, energy, transportation, and information technology sectors, all of which are increasingly leveraging Internet of Things analytics to optimize operations, enhance decision-making, and improve service delivery. The growing emphasis on smart cities, intelligent transportation systems, and industrial automation initiatives across the region is further accelerating demand for real-time data analytics and predictive insights.

Additionally, the proliferation of cloud computing, 5G connectivity, and edge computing technologies is enabling faster processing and analysis of vast volumes of data generated by Internet of Things devices, making analytics solutions more scalable and accessible to enterprises of all sizes. Small and medium-sized enterprises are also beginning to adopt Internet of Things analytics solutions, recognizing the benefits of improved operational efficiency, cost savings, and enhanced customer experiences. Government policies and incentives aimed at promoting digital transformation and smart infrastructure projects are playing a critical role in supporting market growth.

Recent Development

• In June 2024, Cisco launched a USD 1 billion global investment fund to advance secure and reliable artificial intelligence solutions. As part of this initiative, the company strategically invested in Cohere, Mistral AI, and Scale AI to strengthen customer readiness, computing infrastructure, and foundational models. Additionally, at Cisco Live 2024, Cisco unveiled a range of artificial intelligence-powered innovations, showcasing enhancements in networking, security, and observability solutions, reflecting its commitment to driving intelligent, next-generation technologies across enterprise operations.
• In May 2025, Cisco introduced a prototype chip engineered to network quantum computers and announced the launch of a new quantum computing laboratory in Santa Monica, California. This strategic initiative underscores Cisco’s commitment to pioneering quantum technologies and positions the company alongside leading technology firms investing in next-generation computing. The lab will focus on advancing quantum networking, enabling faster and more secure data transfer between quantum systems, and supporting research and development efforts that aim to transform computational capabilities for enterprise and scientific applications.
• In June 2024, Cisco launched a USD1 billion global investment fund to advance secure and reliable artificial intelligence solutions. The company strategically invested in Cohere, Mistral AI, and Scale AI to strengthen customer readiness, computing infrastructure, and foundational models. At Cisco Live 2024, Cisco also unveiled a suite of artificial intelligence-powered innovations, highlighting advancements in networking, security, and observability solutions. These initiatives demonstrate Cisco’s commitment to driving intelligent technologies and enhancing enterprise capabilities through cutting-edge artificial intelligence applications.
• In June 2024, Hitachi Energy announced a strategic plan to invest an additional USD4.5 billion by 2027 to accelerate the global clean energy transition. This investment targets key areas including manufacturing, engineering, digital technologies, research and development, and strategic partnerships. By enhancing its capabilities across these domains, Hitachi Energy aims to support sustainable energy solutions, improve operational efficiency, and drive innovation in the renewable energy sector, reinforcing its commitment to advancing the global shift toward a cleaner and more resilient energy infrastructure.

Key Market Players

• Microsoft Corporation
• IBM Corporation
• Oracle Corporation
• Cisco Systems, Inc.
• SAP SE
• Amazon Web Services, Inc.
• Hitachi, Ltd.
• Siemens AG
• Hewlett Packard Enterprise
• Intel Corporation

Report Scope:

In this report, the Global IoT Analytics Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

•  IoT Analytics Market, By Analytics Type:

o   Descriptive Analytics

o   Diagnostic Analytics

o   Predictive Analytics

o   Prescriptive Analytics

• IoT Analytics Market, By Service Types:

o   Managed Services

o   Professional Services

• IoT Analytics Market, By End User:

o   Manufacturing

o   Healthcare

o   Energy and Utilities

o   Transportation and Logistics

o   Retail

o   Information Technology and Telecommunications

o   Others

• IoT Analytics Market, By Region:

o   North America

§  United States

§  Canada

§  Mexico

o   Europe

§  Germany

§  France

§  United Kingdom

§  Italy

§  Spain

o   South America

§  Brazil

§  Argentina

§  Colombia

o   Asia-Pacific

§  China

§  India

§  Japan

§  South Korea

§  Australia

o   Middle East & Africa

§  Saudi Arabia

§  UAE

§  South Africa

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global IoT Analytics Market.

Available Customizations:

Global IoT Analytics Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

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