Connected Device Analytics Market by Offering (Services, Software), Deployment Mode (Cloud, On Premise), End User Industry - Global Forecast 2025-2032
Description
The Connected Device Analytics Market was valued at USD 34.29 billion in 2024 and is projected to grow to USD 42.39 billion in 2025, with a CAGR of 24.02%, reaching USD 192.04 billion by 2032.
Framing the strategic importance of connected device analytics for transforming operational efficiency, product innovation, and customer engagement across enterprises
Connected device analytics has moved from experimental pilots to an operational imperative across multiple industries. Organizations increasingly rely on telemetry, embedded sensors, and edge computation to extract actionable intelligence that optimizes products, services, and operational flows. As device ecosystems proliferate, analytics capabilities determine competitive differentiation by enabling real-time decisioning, proactive maintenance, and personalized experiences.
This analysis examines how analytics architectures, data strategies, and governance models intersect with business objectives. It focuses on the ways in which organizations integrate device-derived data into enterprise systems, the evolving role of cloud and edge platforms in processing flows, and how privacy and compliance requirements shape data collection and usage. By bridging technical capabilities with strategic priorities, the discussion aims to equip leaders with the context needed to prioritize investments, design resilient architectures, and align internal teams around measurable outcomes.
Throughout the report, attention is paid to cross-functional collaboration, vendor selection dynamics, and the operational changes required to scale pilot initiatives into mission-critical services. The intention is to present a clear, actionable foundation for leaders who must translate device signals into sustained business value while navigating a complex regulatory and geopolitical environment.
Understanding the pivotal technological, supply chain, and regulatory forces reshaping connected device analytics and redefining enterprise strategies
The landscape for connected device analytics is undergoing profound transformation driven by three converging forces: technological maturation, shifting supply chain dynamics, and heightened regulatory focus. Advances in edge compute, more efficient machine learning models, and expanded low-power wireless protocols enable analytics closer to the source, reducing latency and lowering costs for real-time applications. At the same time, increased modularity in software stacks and standardized telemetry formats are accelerating integration across heterogeneous device fleets.
Supply chain evolution is prompting enterprises to rethink procurement and component sourcing strategies, which in turn affects device lifecycles and upgrade pathways. As a result, product teams are prioritizing software-defined features and remote updateability to extend device value. Regulatory and privacy pressures are also reshaping data collection practices; organizations are investing in data minimization, enhanced consent mechanisms, and privacy-preserving analytics techniques to maintain consumer trust and comply with jurisdictional requirements.
These shifts are not isolated. They create feedback loops where improved device intelligence informs more efficient supply chains and where regulatory clarity enables broader deployment of analytics across industries. Consequently, leaders must balance rapid innovation with robust governance, ensuring architectures remain adaptable while safeguarding data integrity and user rights.
Assessing how cumulative tariff-driven procurement shifts have affected device sourcing, supply chain resilience, and the architecture of analytics pipelines
Policy interventions and tariff adjustments in recent years have altered the economics and operational calculus for device manufacturing and component sourcing. Cumulative tariff impacts have incentivized manufacturers and integrators to reassess supplier portfolios, diversify sourcing geographies, and accelerate software-centric product strategies that reduce reliance on high-cost hardware refresh cycles. These responses have practical implications for analytics initiatives, as device heterogeneity and component substitution affect telemetry consistency and lifecycle support practices.
In practice, organizations are adapting by enhancing device abstraction layers within analytics pipelines and by investing in validation suites that detect and normalize telemetry variations across alternate hardware revisions. Procurement teams increasingly evaluate supplier resilience and logistical agility as part of analytics program planning, knowing that component lead times and duties can influence deployment timelines. Furthermore, regional incentives and trade policies have encouraged some manufacturers to relocate assembly or final integration steps, creating new nodes in the global device ecosystem that require updated compliance and data transfer strategies.
Consequently, analytics leaders must account for procurement volatility when defining device health metrics, remote update policies, and long-term maintenance commitments. By embedding flexibility into device firmware and analytics ingestion layers, organizations can maintain continuity of insight even as underlying hardware and supply arrangements evolve.
Delivering nuanced segmentation insights that clarify how offerings, deployment modes, and industry-specific demands dictate analytics strategies and investment priorities
Segmentation-based insights reveal where analytics investments deliver the most differentiated value. Based on Offering, market is studied across Services and Software, which highlights a growing emphasis on managed analytics services that complement core analytics platforms and a parallel demand for modular software components that accelerate time to deployment. Services-oriented engagements tend to prioritize integration, ongoing optimization, and operational support, whereas software investments focus on extensibility, API-driven interoperability, and developer enablement.
Based on Deployment Mode, market is studied across Cloud and On Premise, reflecting divergent enterprise priorities between centralized analytics and low-latency edge processing. Cloud deployments offer scalable processing and integrative capabilities for enterprise data lakes, while on premise architectures or edge-first approaches are chosen when data residency, latency, or bandwidth constraints demand localized computation. Hybrid strategies are increasingly common, blending centralized model training with edge inference to balance performance and governance.
Based on End User Industry, market is studied across Consumer Electronics, Healthcare, Manufacturing, Retail, and Transportation, each presenting distinct use cases, regulatory constraints, and integration complexities. Consumer electronics emphasize user personalization and OTA update mechanisms. Healthcare requires stringent privacy controls and validated analytics for clinical contexts. Manufacturing prioritizes predictive maintenance and process optimization. Retail focuses on customer experience and inventory analytics. Transportation stresses safety, telemetry fidelity, and cross-jurisdictional regulatory compliance. Understanding these segmentation dimensions enables tailored go-to-market strategies and guides product roadmaps toward the most pressing operational needs.
Analyzing regional variations in infrastructure, regulation, and commercial dynamics that influence deployment choices and governance models across global markets
Regional dynamics shape both the adoption patterns and operational design of connected device analytics. The Americas frequently emphasize rapid innovation cycles, commercial scale cloud adoption, and a broad set of vendor ecosystems that support enterprise integration and advanced analytics services. Regulatory frameworks and consumer expectations in the region drive investment in privacy-enhancing technologies and robust consent management within analytics pipelines.
In Europe, Middle East & Africa, data protection regulations and a mosaic of national compliance requirements influence architecture choices and often encourage localized data processing or stricter data governance. Vendor selection in this region places premium value on demonstrable compliance expertise and the ability to operate under diverse legal regimes. Meanwhile, digital infrastructure gaps and varied market maturity across countries require adaptable deployment strategies that balance centralization with local operational realities.
Asia-Pacific presents a heterogeneous landscape where high-volume manufacturing hubs and fast-growing consumer device markets coexist with aggressive national digital strategies. The region combines advanced edge deployments with innovative payment and service models. Cross-border trade flows and regional partnerships have important implications for component sourcing and multinational rollout plans. Together, these regional contrasts underscore the need for flexible analytics architectures that accommodate differing infrastructure, regulatory, and commercial environments.
Identifying vendor strategies and competitive differentiators that influence enterprise selection criteria and partnerships in connected device analytics
Competitive dynamics in connected device analytics are shaped by a mix of established platform providers, specialized analytics software vendors, systems integrators, and an expanding cohort of startups delivering verticalized solutions. Established providers compete on breadth of integration, partner ecosystems, and enterprise-grade security controls, while smaller vendors differentiate through domain-specific models, lightweight deployment footprints, and rapid customization capabilities.
Strategic behaviors to watch include platform consolidation via partnerships and focused M&A activity aimed at closing gaps in edge orchestration, model lifecycle management, and device fleet security. Vendors that embed explainable AI capabilities and provide robust model governance tooling are gaining preference among regulated industries. At the same time, open standards and extensible APIs are becoming key selection criteria, enabling enterprises to avoid vendor lock-in and to stitch best-of-breed components into coherent stacks.
From a buyer perspective, procurement decisions increasingly prioritize vendors that can demonstrate operational success through case studies, rigorous validation frameworks, and measurable improvements in uptime, latency, or customer experience. The ability to support hybrid deployments, deliver ongoing managed services, and maintain transparent security and privacy practices remains a decisive differentiator.
Actionable recommendations for executives to operationalize analytics, strengthen resilience, and ensure compliant, scalable deployment across device fleets
Leaders must align analytics initiatives with clear operational outcomes and governance guardrails to unlock sustained value. Begin by defining prioritized use cases tied to measurable business metrics and by establishing cross-functional ownership that includes product, IT, security, and legal stakeholders. This alignment reduces friction during scaling and ensures insights translate into operational decisions.
Invest in modular architectures that separate concerns between data ingestion, feature engineering, model training, and inference so teams can iterate without disrupting production flows. Embrace hybrid processing patterns: centralize heavy-weight analytics tasks while deploying inference at the edge for latency-sensitive applications. Complement architectural discipline with robust CI/CD for models and firmware to enable controlled rollouts and rapid remediation.
Strengthen supplier resilience by diversifying component sources, negotiating clear support SLAs, and validating alternate hardware configurations within analytics pipelines. Prioritize privacy-preserving techniques such as federated learning or differential privacy where regulations or trust considerations constrain raw data movement. Finally, make continuous upskilling a priority so operational teams can interpret device signals, maintain models, and embed analytics-driven processes into day-to-day decision-making.
Explaining a rigorous, multi-method research approach that combines executive interviews, technical validation, and secondary analysis to derive practical insights
The research approach blends primary qualitative engagement with secondary technical synthesis to produce a comprehensive view of device analytics practice and adoption. Primary research included structured interviews with technology leaders, solutions architects, and operations managers across multiple industries, focusing on real-world deployment barriers, governance practices, and vendor selection criteria. These interviews provided insight into operational trade-offs and the human processes required to scale analytics programs.
Secondary analysis synthesized public technical documentation, standards initiatives, and industry case studies to contextualize architectural patterns, data governance models, and emerging best practices. Technical validation involved reviewing telemetry schemas, model lifecycle artifacts, and edge orchestration patterns to assess consistency and integration complexity. Data triangulation was applied to reconcile interview findings with documented technical capabilities, ensuring conclusions reflect both strategic intent and operational reality.
Throughout the work, attention was paid to ethical data handling and privacy compliance. Methodological safeguards included anonymizing interview transcripts, validating claims through multiple independent sources, and documenting limitations where proprietary data or rapidly evolving implementations constrained definitive conclusions.
Concluding strategic perspectives on how enterprises can harness connected device analytics to drive operational excellence, resilience, and customer-centric innovation
Connected device analytics represents a confluence of technological capability and strategic necessity. As organizations scale device deployments, the capacity to collect, normalize, and act on telemetry becomes a core competency that touches product design, operations, and customer experience. The path to maturity requires not only technological investment but also organizational alignment around data governance, model operations, and resilient supply practices.
The cumulative effect of geopolitical policy shifts and regional regulatory divergence has accelerated the need for adaptable architectures and supplier strategies that prioritize continuity and compliance. Yet these challenges also create opportunities: they favor software-defined features, remote manageability, and analytic approaches that extract ongoing value from heterogeneous device fleets. By focusing on modular architectures, clear use cases, and robust governance, leaders can turn complexity into a competitive advantage.
Ultimately, success depends on integrating analytics into the operational fabric of the organization-moving beyond proof-of-concept experiments to sustained, measurable improvements in reliability, efficiency, and customer outcomes. The recommendations and insights in this report are intended to guide that transition and to equip decision-makers with the frameworks necessary to scale analytics responsibly and effectively.
Note: PDF & Excel + Online Access - 1 Year
Framing the strategic importance of connected device analytics for transforming operational efficiency, product innovation, and customer engagement across enterprises
Connected device analytics has moved from experimental pilots to an operational imperative across multiple industries. Organizations increasingly rely on telemetry, embedded sensors, and edge computation to extract actionable intelligence that optimizes products, services, and operational flows. As device ecosystems proliferate, analytics capabilities determine competitive differentiation by enabling real-time decisioning, proactive maintenance, and personalized experiences.
This analysis examines how analytics architectures, data strategies, and governance models intersect with business objectives. It focuses on the ways in which organizations integrate device-derived data into enterprise systems, the evolving role of cloud and edge platforms in processing flows, and how privacy and compliance requirements shape data collection and usage. By bridging technical capabilities with strategic priorities, the discussion aims to equip leaders with the context needed to prioritize investments, design resilient architectures, and align internal teams around measurable outcomes.
Throughout the report, attention is paid to cross-functional collaboration, vendor selection dynamics, and the operational changes required to scale pilot initiatives into mission-critical services. The intention is to present a clear, actionable foundation for leaders who must translate device signals into sustained business value while navigating a complex regulatory and geopolitical environment.
Understanding the pivotal technological, supply chain, and regulatory forces reshaping connected device analytics and redefining enterprise strategies
The landscape for connected device analytics is undergoing profound transformation driven by three converging forces: technological maturation, shifting supply chain dynamics, and heightened regulatory focus. Advances in edge compute, more efficient machine learning models, and expanded low-power wireless protocols enable analytics closer to the source, reducing latency and lowering costs for real-time applications. At the same time, increased modularity in software stacks and standardized telemetry formats are accelerating integration across heterogeneous device fleets.
Supply chain evolution is prompting enterprises to rethink procurement and component sourcing strategies, which in turn affects device lifecycles and upgrade pathways. As a result, product teams are prioritizing software-defined features and remote updateability to extend device value. Regulatory and privacy pressures are also reshaping data collection practices; organizations are investing in data minimization, enhanced consent mechanisms, and privacy-preserving analytics techniques to maintain consumer trust and comply with jurisdictional requirements.
These shifts are not isolated. They create feedback loops where improved device intelligence informs more efficient supply chains and where regulatory clarity enables broader deployment of analytics across industries. Consequently, leaders must balance rapid innovation with robust governance, ensuring architectures remain adaptable while safeguarding data integrity and user rights.
Assessing how cumulative tariff-driven procurement shifts have affected device sourcing, supply chain resilience, and the architecture of analytics pipelines
Policy interventions and tariff adjustments in recent years have altered the economics and operational calculus for device manufacturing and component sourcing. Cumulative tariff impacts have incentivized manufacturers and integrators to reassess supplier portfolios, diversify sourcing geographies, and accelerate software-centric product strategies that reduce reliance on high-cost hardware refresh cycles. These responses have practical implications for analytics initiatives, as device heterogeneity and component substitution affect telemetry consistency and lifecycle support practices.
In practice, organizations are adapting by enhancing device abstraction layers within analytics pipelines and by investing in validation suites that detect and normalize telemetry variations across alternate hardware revisions. Procurement teams increasingly evaluate supplier resilience and logistical agility as part of analytics program planning, knowing that component lead times and duties can influence deployment timelines. Furthermore, regional incentives and trade policies have encouraged some manufacturers to relocate assembly or final integration steps, creating new nodes in the global device ecosystem that require updated compliance and data transfer strategies.
Consequently, analytics leaders must account for procurement volatility when defining device health metrics, remote update policies, and long-term maintenance commitments. By embedding flexibility into device firmware and analytics ingestion layers, organizations can maintain continuity of insight even as underlying hardware and supply arrangements evolve.
Delivering nuanced segmentation insights that clarify how offerings, deployment modes, and industry-specific demands dictate analytics strategies and investment priorities
Segmentation-based insights reveal where analytics investments deliver the most differentiated value. Based on Offering, market is studied across Services and Software, which highlights a growing emphasis on managed analytics services that complement core analytics platforms and a parallel demand for modular software components that accelerate time to deployment. Services-oriented engagements tend to prioritize integration, ongoing optimization, and operational support, whereas software investments focus on extensibility, API-driven interoperability, and developer enablement.
Based on Deployment Mode, market is studied across Cloud and On Premise, reflecting divergent enterprise priorities between centralized analytics and low-latency edge processing. Cloud deployments offer scalable processing and integrative capabilities for enterprise data lakes, while on premise architectures or edge-first approaches are chosen when data residency, latency, or bandwidth constraints demand localized computation. Hybrid strategies are increasingly common, blending centralized model training with edge inference to balance performance and governance.
Based on End User Industry, market is studied across Consumer Electronics, Healthcare, Manufacturing, Retail, and Transportation, each presenting distinct use cases, regulatory constraints, and integration complexities. Consumer electronics emphasize user personalization and OTA update mechanisms. Healthcare requires stringent privacy controls and validated analytics for clinical contexts. Manufacturing prioritizes predictive maintenance and process optimization. Retail focuses on customer experience and inventory analytics. Transportation stresses safety, telemetry fidelity, and cross-jurisdictional regulatory compliance. Understanding these segmentation dimensions enables tailored go-to-market strategies and guides product roadmaps toward the most pressing operational needs.
Analyzing regional variations in infrastructure, regulation, and commercial dynamics that influence deployment choices and governance models across global markets
Regional dynamics shape both the adoption patterns and operational design of connected device analytics. The Americas frequently emphasize rapid innovation cycles, commercial scale cloud adoption, and a broad set of vendor ecosystems that support enterprise integration and advanced analytics services. Regulatory frameworks and consumer expectations in the region drive investment in privacy-enhancing technologies and robust consent management within analytics pipelines.
In Europe, Middle East & Africa, data protection regulations and a mosaic of national compliance requirements influence architecture choices and often encourage localized data processing or stricter data governance. Vendor selection in this region places premium value on demonstrable compliance expertise and the ability to operate under diverse legal regimes. Meanwhile, digital infrastructure gaps and varied market maturity across countries require adaptable deployment strategies that balance centralization with local operational realities.
Asia-Pacific presents a heterogeneous landscape where high-volume manufacturing hubs and fast-growing consumer device markets coexist with aggressive national digital strategies. The region combines advanced edge deployments with innovative payment and service models. Cross-border trade flows and regional partnerships have important implications for component sourcing and multinational rollout plans. Together, these regional contrasts underscore the need for flexible analytics architectures that accommodate differing infrastructure, regulatory, and commercial environments.
Identifying vendor strategies and competitive differentiators that influence enterprise selection criteria and partnerships in connected device analytics
Competitive dynamics in connected device analytics are shaped by a mix of established platform providers, specialized analytics software vendors, systems integrators, and an expanding cohort of startups delivering verticalized solutions. Established providers compete on breadth of integration, partner ecosystems, and enterprise-grade security controls, while smaller vendors differentiate through domain-specific models, lightweight deployment footprints, and rapid customization capabilities.
Strategic behaviors to watch include platform consolidation via partnerships and focused M&A activity aimed at closing gaps in edge orchestration, model lifecycle management, and device fleet security. Vendors that embed explainable AI capabilities and provide robust model governance tooling are gaining preference among regulated industries. At the same time, open standards and extensible APIs are becoming key selection criteria, enabling enterprises to avoid vendor lock-in and to stitch best-of-breed components into coherent stacks.
From a buyer perspective, procurement decisions increasingly prioritize vendors that can demonstrate operational success through case studies, rigorous validation frameworks, and measurable improvements in uptime, latency, or customer experience. The ability to support hybrid deployments, deliver ongoing managed services, and maintain transparent security and privacy practices remains a decisive differentiator.
Actionable recommendations for executives to operationalize analytics, strengthen resilience, and ensure compliant, scalable deployment across device fleets
Leaders must align analytics initiatives with clear operational outcomes and governance guardrails to unlock sustained value. Begin by defining prioritized use cases tied to measurable business metrics and by establishing cross-functional ownership that includes product, IT, security, and legal stakeholders. This alignment reduces friction during scaling and ensures insights translate into operational decisions.
Invest in modular architectures that separate concerns between data ingestion, feature engineering, model training, and inference so teams can iterate without disrupting production flows. Embrace hybrid processing patterns: centralize heavy-weight analytics tasks while deploying inference at the edge for latency-sensitive applications. Complement architectural discipline with robust CI/CD for models and firmware to enable controlled rollouts and rapid remediation.
Strengthen supplier resilience by diversifying component sources, negotiating clear support SLAs, and validating alternate hardware configurations within analytics pipelines. Prioritize privacy-preserving techniques such as federated learning or differential privacy where regulations or trust considerations constrain raw data movement. Finally, make continuous upskilling a priority so operational teams can interpret device signals, maintain models, and embed analytics-driven processes into day-to-day decision-making.
Explaining a rigorous, multi-method research approach that combines executive interviews, technical validation, and secondary analysis to derive practical insights
The research approach blends primary qualitative engagement with secondary technical synthesis to produce a comprehensive view of device analytics practice and adoption. Primary research included structured interviews with technology leaders, solutions architects, and operations managers across multiple industries, focusing on real-world deployment barriers, governance practices, and vendor selection criteria. These interviews provided insight into operational trade-offs and the human processes required to scale analytics programs.
Secondary analysis synthesized public technical documentation, standards initiatives, and industry case studies to contextualize architectural patterns, data governance models, and emerging best practices. Technical validation involved reviewing telemetry schemas, model lifecycle artifacts, and edge orchestration patterns to assess consistency and integration complexity. Data triangulation was applied to reconcile interview findings with documented technical capabilities, ensuring conclusions reflect both strategic intent and operational reality.
Throughout the work, attention was paid to ethical data handling and privacy compliance. Methodological safeguards included anonymizing interview transcripts, validating claims through multiple independent sources, and documenting limitations where proprietary data or rapidly evolving implementations constrained definitive conclusions.
Concluding strategic perspectives on how enterprises can harness connected device analytics to drive operational excellence, resilience, and customer-centric innovation
Connected device analytics represents a confluence of technological capability and strategic necessity. As organizations scale device deployments, the capacity to collect, normalize, and act on telemetry becomes a core competency that touches product design, operations, and customer experience. The path to maturity requires not only technological investment but also organizational alignment around data governance, model operations, and resilient supply practices.
The cumulative effect of geopolitical policy shifts and regional regulatory divergence has accelerated the need for adaptable architectures and supplier strategies that prioritize continuity and compliance. Yet these challenges also create opportunities: they favor software-defined features, remote manageability, and analytic approaches that extract ongoing value from heterogeneous device fleets. By focusing on modular architectures, clear use cases, and robust governance, leaders can turn complexity into a competitive advantage.
Ultimately, success depends on integrating analytics into the operational fabric of the organization-moving beyond proof-of-concept experiments to sustained, measurable improvements in reliability, efficiency, and customer outcomes. The recommendations and insights in this report are intended to guide that transition and to equip decision-makers with the frameworks necessary to scale analytics responsibly and effectively.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
185 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Deployment of federated learning models to enhance cross-device privacy in analytics
- 5.2. Integration of edge computing capabilities to reduce latency in device telemetry analysis
- 5.3. Implementation of unified data governance frameworks across diverse connected device ecosystems
- 5.4. Adoption of real-time anomaly detection powered by deep learning in networked sensors
- 5.5. Use of blockchain ledgers to ensure secure provenance tracking for distributed device data
- 5.6. Expansion of cross-vertical analytics platforms integrating smart home and industrial IoT streams
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Connected Device Analytics Market, by Offering
- 8.1. Services
- 8.2. Software
- 9. Connected Device Analytics Market, by Deployment Mode
- 9.1. Cloud
- 9.2. On Premise
- 10. Connected Device Analytics Market, by End User Industry
- 10.1. Consumer Electronics
- 10.2. Healthcare
- 10.3. Manufacturing
- 10.4. Retail
- 10.5. Transportation
- 11. Connected Device Analytics Market, by Region
- 11.1. Americas
- 11.1.1. North America
- 11.1.2. Latin America
- 11.2. Europe, Middle East & Africa
- 11.2.1. Europe
- 11.2.2. Middle East
- 11.2.3. Africa
- 11.3. Asia-Pacific
- 12. Connected Device Analytics Market, by Group
- 12.1. ASEAN
- 12.2. GCC
- 12.3. European Union
- 12.4. BRICS
- 12.5. G7
- 12.6. NATO
- 13. Connected Device Analytics Market, by Country
- 13.1. United States
- 13.2. Canada
- 13.3. Mexico
- 13.4. Brazil
- 13.5. United Kingdom
- 13.6. Germany
- 13.7. France
- 13.8. Russia
- 13.9. Italy
- 13.10. Spain
- 13.11. China
- 13.12. India
- 13.13. Japan
- 13.14. Australia
- 13.15. South Korea
- 14. Competitive Landscape
- 14.1. Market Share Analysis, 2024
- 14.2. FPNV Positioning Matrix, 2024
- 14.3. Competitive Analysis
- 14.3.1. Adobe Inc.
- 14.3.2. Amazon Web Services, Inc.
- 14.3.3. Cisco Systems, Inc.
- 14.3.4. General Electric Company
- 14.3.5. Google LLC by Alphabet Inc.
- 14.3.6. Guavus, Inc.
- 14.3.7. Hewlett Packard Enterprise Development LP
- 14.3.8. Infor
- 14.3.9. International Business Machines Corporation
- 14.3.10. Microsoft Corporation
- 14.3.11. Oracle Corporation
- 14.3.12. PTC Inc.
- 14.3.13. Salesforce, Inc.
- 14.3.14. SAP SE
- 14.3.15. SAS Institute Inc.
- 14.3.16. Software AG
- 14.3.17. Teradata Corporation
- 14.3.18. Cloudera, Inc. by Clayton, Dubilier & Rice and KKR
- 14.3.19. Splunk Inc.
- 14.3.20. QlikTech International AB
- 14.3.21. Arundo Analytics, Inc.
- 14.3.22. Datadog, Inc.
- 14.3.23. Exosite LLC
- 14.3.24. MathWorks, Inc.
- 14.3.25. Atom by NTT Data
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