Data Fusion Market by Components (Services, Software), Business Function (Customer Service, IT, Marketing), Distribution Channel, Deployment, Organization Size, End User Industry - Global Forecast 2025-2032
Description
The Data Fusion Market was valued at USD 5.99 billion in 2024 and is projected to grow to USD 6.26 billion in 2025, with a CAGR of 5.11%, reaching USD 8.92 billion by 2032.
A strategic introduction describing how data fusion integrates diverse data sources and analytics to enable resilient enterprise decisions and operational speed
Data fusion has emerged as a foundational capability for organizations seeking to convert disparate data into coherent, operational intelligence. As enterprises contend with growing data volumes, heterogeneous sources, and rising expectations for real-time insight, the integration of data ingestion, contextualization, and analytics has moved from experimental projects to strategic initiatives. Leaders now view data fusion not only as a technical enabler but also as a business imperative that underpins decision velocity, risk reduction, and customer experience improvements.
Consequently, technology choices must align with broader enterprise priorities such as governance, privacy, and scalability. Modern architectures increasingly combine cloud-native services, edge processing, and on-premise systems to balance performance with compliance. Moreover, organizations are prioritizing modular platforms and interoperable tools that support iterative deployment and continuous improvement. As a result, cross-functional teams spanning IT, analytics, security, and business units must coordinate to translate fused data into measurable actions, with defined metrics and accountable ownership models that sustain long-term value.
Analysis of transformative shifts in the data fusion landscape highlighting AI-driven automation, privacy-centric architectures, and cross-domain interoperability
The landscape for data fusion is undergoing several transformative shifts driven by technological advances, regulatory pressure, and evolving business models. Artificial intelligence and machine learning are accelerating the automation of data preparation, entity resolution, and inferencing, which reduces time to insight and lowers manual effort. At the same time, privacy-preserving techniques such as federated learning and differential privacy are reshaping architectures so that organizations can extract value while minimizing exposure and meeting regulatory obligations.
Interoperability has become a competitive requirement rather than an optional feature, with open standards and API-first designs enabling cross-domain workflows and integration with legacy systems. Cloud-native paradigms continue to democratize compute and analytics, yet hybrid deployments persist where data residency, latency, or security constraints demand on-premise or edge processing. In parallel, operationalization-the shift from proofs of concept to production-grade pipelines-now requires robust observability, model governance, and performance SLAs. Taken together, these shifts reframe how leaders prioritize investments and design operating models that embed fused data into routine decision-making.
Assessment of the cumulative impacts of proposed and enacted United States tariff measures in 2025 on supply chains, sourcing strategies, and cost pass-through
United States tariff activity in 2025 has introduced practical frictions that reverberate through sourcing decisions, supply chain design, and cost structures. Tariff measures increase the cost of imported components and finished goods, prompting many procurement teams to reevaluate supplier relationships, contract terms, and total landed cost calculations. In response, organizations are accelerating supplier diversification, seeking alternative geographies, and in some cases adjusting product specifications to substitute tariff-exposed inputs.
These adjustments have downstream operational implications. Logistics routing and inventory strategies change as companies rebalance onshore, nearshore, and offshore sourcing to mitigate tariff exposure and delivery risk. Meanwhile, finance and commercial teams reassess pricing strategies and margin protection mechanisms, which often leads to selective cost pass-through to customers or temporary adjustments in promotional activity. Additionally, regulatory compliance and customs classification have grown in importance, requiring closer collaboration between trade, legal, and procurement functions to minimize penalties and optimize duty treatment. Overall, tariff-driven dynamics compel an integrated approach that connects sourcing, operations, and commercial planning to preserve competitiveness.
Comprehensive segmentation insights revealing deployment and component choices, end-user industry behaviors, organization size effects, and channel dynamics
Segmentation reveals how deployment models, component mixes, end-user industries, organization size, business function priorities, and distribution channels shape adoption pathways and value realization. Deployment choices split across Cloud, Hybrid, and On Premise footprints, and each path carries distinct trade-offs: cloud deployments accelerate time-to-insight through managed services and elasticity, hybrid architectures balance latency and compliance, while on premise continues to serve environments with strict data residency or deterministic performance needs.
Components divide between Services and Software. Services encompass Managed Services and Professional Services, with managed offerings supporting operations at scale while professional engagements address bespoke integration and domain expertise. Software breaks down into Platform and Tools: platform capabilities focus on orchestration, governance, and shared services, whereas tools emphasize point solutions for specific tasks such as entity matching or stream processing. End user industries span Banking Financial Services Insurance, Healthcare Life Sciences, IT Telecom, and Retail E Commerce, each bringing unique data types, regulatory demands, and performance expectations that influence solution design.
Organization size matters: Large Enterprises typically require enterprise-grade integrations, multi-region compliance, and extensive vendor management, while Small Medium Enterprises favor consumption-based models, pre-integrated solutions, and ease of deployment. Business functions vary in priority and use case: Customer Service emphasizes unified customer views and real-time personalization; IT focuses on reliability and security; Marketing demands enriched audience data and measurement; Operations requires real-time telemetry and anomaly detection; Sales needs timely prospect insights and pipeline analytics. Distribution channels span Direct, Indirect, and Online routes; the Indirect route further splits across Distributors and Resellers, each offering different go-to-market leverage, implementation support, and contractual frameworks that affect procurement and post-sale engagement.
Regional analysis showing how Americas, Europe Middle East & Africa, and Asia-Pacific dynamics influence adoption, regulation, investment, and partnership models
Regional dynamics materially influence how organizations adopt and operationalize data fusion capabilities, driven by local regulation, talent availability, infrastructure maturity, and commercial ecosystems. In the Americas, enterprises often prioritize cloud-led strategies and rapid innovation cycles, supported by mature service providers and a strong investment appetite. This environment favors scalable analytics and commercial experimentation, yet companies must still manage data sovereignty considerations and evolving privacy expectations.
Europe, Middle East & Africa present a more heterogeneous landscape where regulatory stringency-particularly around data protection and cross-border flows-shapes architectural decisions. Organizations in this region often emphasize privacy-by-design and strong governance frameworks while also navigating diverse language and market structures that affect solution localization. In contrast, Asia-Pacific demonstrates pronounced variation between advanced economies with robust cloud ecosystems and emerging markets that emphasize local hosting, cost efficiency, and tailored channel partnerships. Taken together, these regional differences inform investment priorities, partnership models, and implementation timelines, requiring tailored approaches to deployment and stakeholder engagement that reflect local constraints and opportunities.
Company-level insights into product direction, partnership strategies, investment priorities, and competitive positioning shaping the data fusion ecosystem
Companies operating in the data fusion ecosystem are differentiating along multiple vectors including product breadth, service delivery models, partnership networks, and investment focus. Some vendors emphasize platform strategies that centralize integration, governance, and model management to serve complex enterprise requirements, while others prioritize point solutions optimized for specific use cases such as real-time streaming, identity resolution, or data lineage. Strategic partnerships-spanning cloud providers, systems integrators, and specialist consultancies-play a pivotal role in enabling end-to-end deployments and accelerating time to value.
Investment patterns reveal a dual focus on product engineering and customer success: teams are allocating resources to scalable SaaS architectures, low-code orchestration, and operational tooling that enables observability and continuous model improvement. At the same time, go-to-market approaches vary from direct enterprise engagements to channel-led distribution that leverages distributors and resellers for broader market reach. Competitive positioning increasingly blends technical capability with domain expertise and outcome-based commercial models, as buyers demand demonstrable business impact and reduction of operational burden. These dynamics encourage vendors to articulate clear value propositions, referenceable deployments, and modular commercial terms that align with buyer risk profiles.
Actionable recommendations translating analysis into practical priorities for leaders focused on governance, integration, talent, and partnership strategies
Leaders should translate insights into prioritized actions that address governance, integration, talent, risk, and partner selection. First, establish clear governance with defined ownership for data quality, model validation, and privacy controls to reduce operational risk and enable accountable decision-making. Second, adopt an integration-first mindset that favors interoperable platforms, well-defined APIs, and reusable data contracts so that new data sources and analytics capabilities can be onboarded with minimal friction.
Third, invest in talent and capability building through targeted hires, cross-functional training, and collaboration with external specialists to bridge gaps in data engineering, ML operations, and domain knowledge. Fourth, implement pragmatic risk mitigation by combining automated monitoring, scenario-based stress tests, and well-documented rollback procedures to maintain service continuity. Finally, be deliberate in partner selection: prioritize vendors and channel partners that demonstrate technical parity, proven integrations, and the ability to deliver outcomes rather than point technical features. By sequencing these initiatives-starting with governance and integration, followed by capability building and partner alignment-leaders can accelerate impact while reducing operational disruption.
Research methodology overview detailing data collection approaches, validation protocols, analytical frameworks, and synthesis techniques used to ensure rigor
This research employed a structured approach that combined primary engagement with domain experts and secondary synthesis of publicly available technical literature and regulatory documentation. Primary inputs included interviews with practitioners across IT, analytics, procurement, and line-of-business stakeholders to capture real-world constraints, success factors, and implementation trade-offs. These dialogues informed use case selection, architectural patterns, and operational requirements that underpin the analysis.
Secondary work encompassed a systematic review of vendor materials, white papers, standards publications, and regional regulatory guidance to validate technical claims and identify recurring themes. Analytical frameworks such as capability mapping, gap analysis, and scenario planning were applied to organize findings and surface actionable insights. Data validation and triangulation occurred through cross-referencing interview inputs with published sources and, where possible, multiple independent confirmations of claims. Throughout the process, emphasis remained on transparency in assumptions, repeatable methods for collection and synthesis, and documentation of limitations to support informed interpretation by readers.
Conclusion synthesizing implications of data fusion trends, tariff impacts, segmentation insights, and regional differentials to inform executive strategy
The report synthesizes how emerging capabilities, regulatory shifts, segmentation realities, and regional differences converge to shape strategic priorities for organizations adopting data fusion. Key themes include the centrality of governance and interoperability, the need for hybrid deployment patterns to reconcile performance and compliance, and the growing influence of tariffs and trade dynamics on sourcing and operational strategies. Together, these themes highlight that technical excellence must be paired with operational discipline and cross-functional alignment to realize sustained value.
As organizations consider next steps, they should balance near-term operational fixes with longer-term capability building. This means addressing immediate constraints such as supplier exposure or data silos while investing in durable platforms, skills, and partnerships that support continuous improvement. Ultimately, successful adoption depends on clear objectives, measurable success criteria, and an iterative approach that scales from targeted pilots to enterprise-wide systems. By aligning technology choices with business outcomes, organizations can transform fragmented data into a strategic asset that supports resilient, informed decision-making.
Note: PDF & Excel + Online Access - 1 Year
A strategic introduction describing how data fusion integrates diverse data sources and analytics to enable resilient enterprise decisions and operational speed
Data fusion has emerged as a foundational capability for organizations seeking to convert disparate data into coherent, operational intelligence. As enterprises contend with growing data volumes, heterogeneous sources, and rising expectations for real-time insight, the integration of data ingestion, contextualization, and analytics has moved from experimental projects to strategic initiatives. Leaders now view data fusion not only as a technical enabler but also as a business imperative that underpins decision velocity, risk reduction, and customer experience improvements.
Consequently, technology choices must align with broader enterprise priorities such as governance, privacy, and scalability. Modern architectures increasingly combine cloud-native services, edge processing, and on-premise systems to balance performance with compliance. Moreover, organizations are prioritizing modular platforms and interoperable tools that support iterative deployment and continuous improvement. As a result, cross-functional teams spanning IT, analytics, security, and business units must coordinate to translate fused data into measurable actions, with defined metrics and accountable ownership models that sustain long-term value.
Analysis of transformative shifts in the data fusion landscape highlighting AI-driven automation, privacy-centric architectures, and cross-domain interoperability
The landscape for data fusion is undergoing several transformative shifts driven by technological advances, regulatory pressure, and evolving business models. Artificial intelligence and machine learning are accelerating the automation of data preparation, entity resolution, and inferencing, which reduces time to insight and lowers manual effort. At the same time, privacy-preserving techniques such as federated learning and differential privacy are reshaping architectures so that organizations can extract value while minimizing exposure and meeting regulatory obligations.
Interoperability has become a competitive requirement rather than an optional feature, with open standards and API-first designs enabling cross-domain workflows and integration with legacy systems. Cloud-native paradigms continue to democratize compute and analytics, yet hybrid deployments persist where data residency, latency, or security constraints demand on-premise or edge processing. In parallel, operationalization-the shift from proofs of concept to production-grade pipelines-now requires robust observability, model governance, and performance SLAs. Taken together, these shifts reframe how leaders prioritize investments and design operating models that embed fused data into routine decision-making.
Assessment of the cumulative impacts of proposed and enacted United States tariff measures in 2025 on supply chains, sourcing strategies, and cost pass-through
United States tariff activity in 2025 has introduced practical frictions that reverberate through sourcing decisions, supply chain design, and cost structures. Tariff measures increase the cost of imported components and finished goods, prompting many procurement teams to reevaluate supplier relationships, contract terms, and total landed cost calculations. In response, organizations are accelerating supplier diversification, seeking alternative geographies, and in some cases adjusting product specifications to substitute tariff-exposed inputs.
These adjustments have downstream operational implications. Logistics routing and inventory strategies change as companies rebalance onshore, nearshore, and offshore sourcing to mitigate tariff exposure and delivery risk. Meanwhile, finance and commercial teams reassess pricing strategies and margin protection mechanisms, which often leads to selective cost pass-through to customers or temporary adjustments in promotional activity. Additionally, regulatory compliance and customs classification have grown in importance, requiring closer collaboration between trade, legal, and procurement functions to minimize penalties and optimize duty treatment. Overall, tariff-driven dynamics compel an integrated approach that connects sourcing, operations, and commercial planning to preserve competitiveness.
Comprehensive segmentation insights revealing deployment and component choices, end-user industry behaviors, organization size effects, and channel dynamics
Segmentation reveals how deployment models, component mixes, end-user industries, organization size, business function priorities, and distribution channels shape adoption pathways and value realization. Deployment choices split across Cloud, Hybrid, and On Premise footprints, and each path carries distinct trade-offs: cloud deployments accelerate time-to-insight through managed services and elasticity, hybrid architectures balance latency and compliance, while on premise continues to serve environments with strict data residency or deterministic performance needs.
Components divide between Services and Software. Services encompass Managed Services and Professional Services, with managed offerings supporting operations at scale while professional engagements address bespoke integration and domain expertise. Software breaks down into Platform and Tools: platform capabilities focus on orchestration, governance, and shared services, whereas tools emphasize point solutions for specific tasks such as entity matching or stream processing. End user industries span Banking Financial Services Insurance, Healthcare Life Sciences, IT Telecom, and Retail E Commerce, each bringing unique data types, regulatory demands, and performance expectations that influence solution design.
Organization size matters: Large Enterprises typically require enterprise-grade integrations, multi-region compliance, and extensive vendor management, while Small Medium Enterprises favor consumption-based models, pre-integrated solutions, and ease of deployment. Business functions vary in priority and use case: Customer Service emphasizes unified customer views and real-time personalization; IT focuses on reliability and security; Marketing demands enriched audience data and measurement; Operations requires real-time telemetry and anomaly detection; Sales needs timely prospect insights and pipeline analytics. Distribution channels span Direct, Indirect, and Online routes; the Indirect route further splits across Distributors and Resellers, each offering different go-to-market leverage, implementation support, and contractual frameworks that affect procurement and post-sale engagement.
Regional analysis showing how Americas, Europe Middle East & Africa, and Asia-Pacific dynamics influence adoption, regulation, investment, and partnership models
Regional dynamics materially influence how organizations adopt and operationalize data fusion capabilities, driven by local regulation, talent availability, infrastructure maturity, and commercial ecosystems. In the Americas, enterprises often prioritize cloud-led strategies and rapid innovation cycles, supported by mature service providers and a strong investment appetite. This environment favors scalable analytics and commercial experimentation, yet companies must still manage data sovereignty considerations and evolving privacy expectations.
Europe, Middle East & Africa present a more heterogeneous landscape where regulatory stringency-particularly around data protection and cross-border flows-shapes architectural decisions. Organizations in this region often emphasize privacy-by-design and strong governance frameworks while also navigating diverse language and market structures that affect solution localization. In contrast, Asia-Pacific demonstrates pronounced variation between advanced economies with robust cloud ecosystems and emerging markets that emphasize local hosting, cost efficiency, and tailored channel partnerships. Taken together, these regional differences inform investment priorities, partnership models, and implementation timelines, requiring tailored approaches to deployment and stakeholder engagement that reflect local constraints and opportunities.
Company-level insights into product direction, partnership strategies, investment priorities, and competitive positioning shaping the data fusion ecosystem
Companies operating in the data fusion ecosystem are differentiating along multiple vectors including product breadth, service delivery models, partnership networks, and investment focus. Some vendors emphasize platform strategies that centralize integration, governance, and model management to serve complex enterprise requirements, while others prioritize point solutions optimized for specific use cases such as real-time streaming, identity resolution, or data lineage. Strategic partnerships-spanning cloud providers, systems integrators, and specialist consultancies-play a pivotal role in enabling end-to-end deployments and accelerating time to value.
Investment patterns reveal a dual focus on product engineering and customer success: teams are allocating resources to scalable SaaS architectures, low-code orchestration, and operational tooling that enables observability and continuous model improvement. At the same time, go-to-market approaches vary from direct enterprise engagements to channel-led distribution that leverages distributors and resellers for broader market reach. Competitive positioning increasingly blends technical capability with domain expertise and outcome-based commercial models, as buyers demand demonstrable business impact and reduction of operational burden. These dynamics encourage vendors to articulate clear value propositions, referenceable deployments, and modular commercial terms that align with buyer risk profiles.
Actionable recommendations translating analysis into practical priorities for leaders focused on governance, integration, talent, and partnership strategies
Leaders should translate insights into prioritized actions that address governance, integration, talent, risk, and partner selection. First, establish clear governance with defined ownership for data quality, model validation, and privacy controls to reduce operational risk and enable accountable decision-making. Second, adopt an integration-first mindset that favors interoperable platforms, well-defined APIs, and reusable data contracts so that new data sources and analytics capabilities can be onboarded with minimal friction.
Third, invest in talent and capability building through targeted hires, cross-functional training, and collaboration with external specialists to bridge gaps in data engineering, ML operations, and domain knowledge. Fourth, implement pragmatic risk mitigation by combining automated monitoring, scenario-based stress tests, and well-documented rollback procedures to maintain service continuity. Finally, be deliberate in partner selection: prioritize vendors and channel partners that demonstrate technical parity, proven integrations, and the ability to deliver outcomes rather than point technical features. By sequencing these initiatives-starting with governance and integration, followed by capability building and partner alignment-leaders can accelerate impact while reducing operational disruption.
Research methodology overview detailing data collection approaches, validation protocols, analytical frameworks, and synthesis techniques used to ensure rigor
This research employed a structured approach that combined primary engagement with domain experts and secondary synthesis of publicly available technical literature and regulatory documentation. Primary inputs included interviews with practitioners across IT, analytics, procurement, and line-of-business stakeholders to capture real-world constraints, success factors, and implementation trade-offs. These dialogues informed use case selection, architectural patterns, and operational requirements that underpin the analysis.
Secondary work encompassed a systematic review of vendor materials, white papers, standards publications, and regional regulatory guidance to validate technical claims and identify recurring themes. Analytical frameworks such as capability mapping, gap analysis, and scenario planning were applied to organize findings and surface actionable insights. Data validation and triangulation occurred through cross-referencing interview inputs with published sources and, where possible, multiple independent confirmations of claims. Throughout the process, emphasis remained on transparency in assumptions, repeatable methods for collection and synthesis, and documentation of limitations to support informed interpretation by readers.
Conclusion synthesizing implications of data fusion trends, tariff impacts, segmentation insights, and regional differentials to inform executive strategy
The report synthesizes how emerging capabilities, regulatory shifts, segmentation realities, and regional differences converge to shape strategic priorities for organizations adopting data fusion. Key themes include the centrality of governance and interoperability, the need for hybrid deployment patterns to reconcile performance and compliance, and the growing influence of tariffs and trade dynamics on sourcing and operational strategies. Together, these themes highlight that technical excellence must be paired with operational discipline and cross-functional alignment to realize sustained value.
As organizations consider next steps, they should balance near-term operational fixes with longer-term capability building. This means addressing immediate constraints such as supplier exposure or data silos while investing in durable platforms, skills, and partnerships that support continuous improvement. Ultimately, successful adoption depends on clear objectives, measurable success criteria, and an iterative approach that scales from targeted pilots to enterprise-wide systems. By aligning technology choices with business outcomes, organizations can transform fragmented data into a strategic asset that supports resilient, informed decision-making.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
187 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. AI-powered data fusion platforms automating cross-source analytics workflows for faster decision making
- 5.2. Real-time streaming data integration and fusion for predictive maintenance and operational efficiency improvements
- 5.3. Federated learning frameworks enabling secure multi-party data fusion across distributed enterprises and edge nodes
- 5.4. Integration of IoT sensor streams with legacy enterprise systems to drive data-driven operational optimization
- 5.5. Cloud-native data fabric architectures scaling multi-cloud data fusion and governance for enterprise analytics workloads
- 5.6. Use of graph database technologies to map complex relationships and enable advanced analytics in data fusion projects
- 5.7. Adoption of open source data virtualization and federation tools for unified access to disparate data repositories
- 5.8. Edge computing architectures combining on-device analytics and central data fusion for low-latency decision support
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Data Fusion Market, by Components
- 8.1. Services
- 8.1.1. Managed Services
- 8.1.2. Professional Services
- 8.2. Software
- 8.2.1. Platform
- 8.2.2. Tools
- 9. Data Fusion Market, by Business Function
- 9.1. Customer Service
- 9.2. IT
- 9.3. Marketing
- 9.4. Operations
- 9.5. Sales
- 10. Data Fusion Market, by Distribution Channel
- 10.1. Direct
- 10.2. Indirect
- 10.2.1. Distributors
- 10.2.2. Resellers
- 10.3. Online
- 11. Data Fusion Market, by Deployment
- 11.1. Cloud
- 11.2. Hybrid
- 11.3. On Premise
- 12. Data Fusion Market, by Organization Size
- 12.1. Large Enterprises
- 12.2. Small Medium Enterprises
- 13. Data Fusion Market, by End User Industry
- 13.1. Banking Financial Services Insurance
- 13.2. Healthcare Life Sciences
- 13.3. IT Telecom
- 13.4. Retail E Commerce
- 14. Data Fusion Market, by Region
- 14.1. Americas
- 14.1.1. North America
- 14.1.2. Latin America
- 14.2. Europe, Middle East & Africa
- 14.2.1. Europe
- 14.2.2. Middle East
- 14.2.3. Africa
- 14.3. Asia-Pacific
- 15. Data Fusion Market, by Group
- 15.1. ASEAN
- 15.2. GCC
- 15.3. European Union
- 15.4. BRICS
- 15.5. G7
- 15.6. NATO
- 16. Data Fusion Market, by Country
- 16.1. United States
- 16.2. Canada
- 16.3. Mexico
- 16.4. Brazil
- 16.5. United Kingdom
- 16.6. Germany
- 16.7. France
- 16.8. Russia
- 16.9. Italy
- 16.10. Spain
- 16.11. China
- 16.12. India
- 16.13. Japan
- 16.14. Australia
- 16.15. South Korea
- 17. Competitive Landscape
- 17.1. Market Share Analysis, 2024
- 17.2. FPNV Positioning Matrix, 2024
- 17.3. Competitive Analysis
- 17.3.1. Amazon Web Services Inc.
- 17.3.2. BAE Systems plc
- 17.3.3. Cybersixgill Ltd.
- 17.3.4. Fujitsu Limited
- 17.3.5. General Dynamics Corporation
- 17.3.6. Google LLC
- 17.3.7. Hitachi Ltd.
- 17.3.8. Huawei Technologies Co., Ltd.
- 17.3.9. IBM Corporation
- 17.3.10. Lockheed Martin Corporation
- 17.3.11. Microsoft Corporation
- 17.3.12. NEC Corporation
- 17.3.13. Northrop Grumman Corporation
- 17.3.14. Oracle Corporation
- 17.3.15. Palantir Technologies Inc.
- 17.3.16. Raytheon Technologies Corporation
- 17.3.17. Recorded Future Inc.
- 17.3.18. SAP SE
- 17.3.19. SAS Institute Inc.
- 17.3.20. Schneider Electric SE
- 17.3.21. Siemens AG
- 17.3.22. Thales Group
- 17.3.23. WorkFusion Inc.
- 17.3.24. ZeroFox Inc.
- 17.3.25. ZTE Corporation
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