Network Analytics Market by Product Type (Services, Software), Pricing Model (License, Pay As You Go, Subscription), Application, Deployment Mode, End User - Global Forecast 2025-2032
Description
The Network Analytics Market was valued at USD 4.33 billion in 2024 and is projected to grow to USD 4.92 billion in 2025, with a CAGR of 14.94%, reaching USD 13.21 billion by 2032.
A strategic introduction that positions modern network analytics as a critical enterprise capability enabling operational resilience, security, and customer experience optimization
Network analytics has evolved from a niche telemetry capability into an enterprise-level strategic discipline that underpins digital resilience, service quality, and security posture. Over the past several years, advancements in data processing, streaming telemetry, and machine learning have shifted expectations for what network analytics must deliver: real-time situational awareness, predictive insights that reduce mean time to repair, and contextualized intelligence that informs both business and engineering decisions. Consequently, executives must view analytics not merely as an operational toolset but as a cross-functional capability that ties capacity planning to customer experience objectives and cyber risk management.
Leadership priorities are increasingly centered on turning raw network data into actionable decision support. This requires investment in unified data architectures, integration between analytics platforms and orchestration layers, and disciplined governance to ensure data quality and lineage. As organizations progress along their analytics maturity curves, the focus moves from isolated dashboards to embedded analytics that drive automation and continuous optimization. Therefore, an effective introduction to this subject emphasizes strategic alignment, the need for interoperable toolchains, and an outcome-driven roadmap that accelerates measurable improvements in reliability, performance, and cost efficiency.
A detailed analysis of convergent technological and operational shifts that are rapidly elevating expectations for speed, scale, interoperability, and security within network analytics
The landscape of network analytics is being reshaped by several convergent shifts that together create new expectations for speed, scale, and insight fidelity. First, the adoption of streaming telemetry and higher-frequency instrumentation has moved analytics away from batch-oriented snapshots toward continuous, event-driven detection and response. This transition allows analytics engines to surface anomalies and evolving degradations in near real time, reducing the window for impact and enabling faster remediation. Second, the integration of machine learning models into analytics pipelines has shifted emphasis from descriptive reporting to predictive and prescriptive guidance, enabling organizations to anticipate capacity constraints and automate routine corrective actions.
In addition, cloud-native architectures and containerized network functions are driving demand for analytics that understand ephemeral topologies and microservice-level performance. As applications become more distributed, analytics solutions must reconcile metrics from on-premises, hybrid, and cloud environments to present a coherent operational picture. Finally, regulatory attention and a heightened threat environment have elevated security analytics as a critical component of the analytics stack. Taken together, these shifts demand that vendors and adopters prioritize interoperability, model explainability, and scalable data management to extract consistent value across increasingly complex networking environments.
An assessment of how evolving US tariff measures are influencing procurement strategies, supplier risk management, and the shift toward cloud-centric and software-led network analytics approaches
Recent policy changes and tariff adjustments in the United States have introduced additional layers of complexity for global procurement and supply chains supporting network analytics deployments. Increased duties on certain hardware components and ancillary devices can extend lead times for on-premises appliance refreshes and drive organizations to evaluate alternative sourcing strategies. In response, many technology buyers are reassessing total cost of ownership by factoring in import-related delays, potential supplier concentration risk, and logistical contingency plans that preserve deployment timelines for critical observability upgrades.
Furthermore, these trade measures are accelerating consideration of cloud-first and software-centric approaches that reduce reliance on imported hardware. Organizations are increasingly weighing the trade-offs between managed services and in-house professional services to maintain control while minimizing exposure to cross-border supply disruptions. At the same time, vendor strategies are adapting through regionalized manufacturing, diversified component sourcing, and enhanced local support offerings to mitigate tariff-driven friction. For decision-makers, the immediate priority is to model procurement scenarios that incorporate tariff sensitivity, optimize inventory buffers for essential spares, and negotiate contractual terms that allocate risk across supplier relationships to maintain operational continuity.
Comprehensive segmentation analysis linking product capabilities, application domains, end-user priorities, deployment models, pricing frameworks, and channel dynamics to buyer behavior
Understanding the competitive and adoption dynamics requires careful segmentation across product, application, end-user, deployment, pricing, and sales channel dimensions. Product differentiation separates services and software, where the services domain includes both managed services and professional services, with the latter further delineated into consulting, integration, and support offerings. On the software side, capabilities cluster into historical analytics that enable trend analysis, predictive analytics that forecast future behavior, and real-time analytics that support immediate operational decision-making. Application-level segmentation highlights the distinct needs of network monitoring, performance management, security analytics, and traffic analysis; within performance management, the focus narrows to capacity planning and QoS monitoring while security analytics concentrates on anomaly detection and threat detection to protect distributed environments.
End-user segmentation bifurcates enterprise and telecom needs, and enterprise verticals such as banking, financial services and insurance, healthcare, IT, and retail exhibit divergent priorities in compliance, latency, and customer experience. Deployment choices between cloud and on-premises environments continue to shape architecture decisions, with cloud offerings differentiated into hybrid cloud, private cloud, and public cloud models that offer varying trade-offs in control, scalability, and operational overhead. Pricing models vary from perpetual licensing to usage-based pay-as-you-go and subscription formats, the latter commonly structured as annual or monthly subscriptions to align vendor economics with customer consumption. Finally, sales channels span channel partners, direct sales, and distributors, and channel partner ecosystems often include system integrators and value added resellers that add integration and verticalization capabilities. By synthesizing these segmentation lenses, stakeholders can better align product roadmaps, go-to-market strategies, and service portfolios to the nuanced requirements of distinct buyer cohorts.
In-depth regional insights revealing how regulatory, operational, and commercial forces across the Americas, Europe Middle East & Africa, and Asia-Pacific shape adoption and vendor approaches
Regional dynamics continue to shape adoption trajectories and vendor strategies across the Americas, Europe, Middle East & Africa, and Asia-Pacific, each presenting distinct regulatory, operational, and commercial considerations. In the Americas, demand is being driven by digital transformation initiatives and the need to modernize legacy networks to support cloud migration and edge services, while procurement cycles reflect a balance between capital investments and operational expense models. Transitional initiatives emphasize interoperability, observability across hybrid estates, and consolidation toward unified analytics platforms to reduce tool sprawl and accelerate root cause analysis.
In Europe, Middle East & Africa, regulatory compliance and data sovereignty are prominent drivers that influence deployment modes and vendor selection, leading many organizations to prefer private or hybrid cloud configurations and to demand stronger on-premises integration capabilities. Local service ecosystems and regional engineering support play a decisive role in adoption, especially for critical infrastructure and telecom operators. Across Asia-Pacific, rapid digitalization and mobile-first architectures create robust demand for scalable real-time analytics and security analytics that protect expanding digital services. Vendor strategies in this region commonly focus on partnerships with local system integrators and flexible pricing to accommodate a broad spectrum of enterprise maturities. Across these regions, operators and enterprises prioritize solutions that balance performance, compliance, and operational agility to meet both current and emerging demands.
Key corporate insights into how leading vendors and service providers are differentiating through platform interoperability, ML-driven capabilities, and regional service ecosystems
Key companies operating in the network analytics ecosystem are differentiating through investments in data engineering, machine learning model development, and cloud-native architecture. Leading technology providers are enhancing platform interoperability to support hybrid and multi-cloud environments while prioritizing explainability and governance for embedded AI capabilities. At the same time, specialized vendors are focusing on domain expertise-particularly in security analytics and traffic analysis-where deep protocol awareness and high-fidelity telemetry parsing provide a competitive advantage. Service providers and system integrators are expanding managed offerings that combine analytics platforms with orchestration and remediation playbooks, enabling faster time-to-value for customers that lack extensive in-house operations teams.
Strategic partnerships between analytics vendors and cloud service providers are increasingly common, enabling tighter integration with cloud-native observability toolchains and streamlined consumption models. Additionally, global suppliers are investing in regional delivery centers and localized professional services to address procurement friction and reduce deployment timelines. For buyers, vendor selection decisions hinge on demonstrated ability to integrate with existing toolsets, the maturity of ML-driven detection capabilities, and the strength of services ecosystems that can support rapid onboarding and continuous optimization. Ultimately, corporate strategy in this space requires a balanced approach that considers product innovation, ecosystem partnerships, and the capacity to scale managed services across diverse customer segments.
Actionable recommendations for executives to pragmatically build analytics capabilities through outcome-driven use cases, modular architectures, partnerships, and workforce transformation
Industry leaders should adopt a pragmatic, phased approach to implementing or expanding network analytics capabilities, aligning investments with measurable operational and business outcomes. Start by defining high-priority use cases that deliver tangible improvements in service availability, customer experience, or security posture, and then map required data sources and integration points. Next, establish data governance and quality controls to ensure the reliability of analytics outputs, and invest in modular architectures that support incremental deployment across cloud and on-premises environments. This staged approach reduces risk while enabling continuous learning and progressive automation of remediation workflows.
Moreover, leaders must actively cultivate partnerships with channel and systems integrator ecosystems to accelerate deployment and to bring vertical-specific expertise. Emphasize vendor selection criteria that include model explainability, open APIs, and pre-built connectors for common infrastructure and observability tools. Simultaneously, build internal capabilities by upskilling network and security operations teams with data science fundamentals and analytics-driven incident management practices. Finally, negotiate commercial terms that provide flexibility-such as hybrid pricing models and outcome-based clauses-to align vendor incentives with operational results and to ensure adaptability as organizational requirements evolve.
Transparent description of the research approach combining primary practitioner interviews, technical documentation review, and comparative architectural analysis to ensure rigorous findings
This research synthesizes qualitative and quantitative inputs drawn from vendor documentation, technical white papers, product release notes, and interviews with practitioners across enterprise and telecom environments to construct a comprehensive landscape of capabilities and adoption patterns. Data collection emphasized primary engagements with network architects, security operations leaders, and procurement specialists to capture real-world implementation challenges and success factors. Secondary sources included publicly available regulatory documents, standards releases related to telemetry and observability, and technical benchmarks that validate performance claims.
Analytical methods combined taxonomy-driven segmentation with cross-sectional analysis to identify capability gaps and alignment opportunities. The research team applied comparative evaluations of architectural approaches-cloud-first, hybrid, and on-premises-assessing each against criteria such as operational overhead, integration complexity, and suitability for real-time analytics. Interviews were thematically coded to surface recurrent operational pain points and best practices, while vendor feature sets were evaluated for maturity, interoperability, and demonstrable automation capabilities. Throughout, attention was given to ensuring reproducibility of conclusions by documenting sources and by applying consistent evaluation rubrics across all assessed dimensions.
A decisive conclusion emphasizing the strategic imperative to treat network analytics as a continuous enterprise capability that drives resilience, automation, and business outcomes
In closing, network analytics is now a strategic enabler for organizations seeking resilience, operational excellence, and improved security posture in increasingly distributed environments. The convergence of streaming telemetry, machine learning, and cloud-native deployment models creates both opportunity and complexity; organizations that adopt a disciplined, use-case-driven approach will extract the most value. Key considerations for success include investing in data governance, selecting interoperable platforms, leveraging managed services where appropriate, and evolving workforce skills to operate and interpret analytics outputs effectively.
Looking forward, continued innovation will focus on tighter integration between analytics and automation systems, enhanced model explainability to support trust and compliance, and more flexible commercial models that align consumption with outcomes. For decision-makers, the imperative is to treat analytics as a continuous capability rather than a one-off project, ensuring investments are adaptable as architectures and business needs evolve. By doing so, organizations can turn network telemetry into a strategic asset that supports both technical excellence and broader business objectives.
Note: PDF & Excel + Online Access - 1 Year
A strategic introduction that positions modern network analytics as a critical enterprise capability enabling operational resilience, security, and customer experience optimization
Network analytics has evolved from a niche telemetry capability into an enterprise-level strategic discipline that underpins digital resilience, service quality, and security posture. Over the past several years, advancements in data processing, streaming telemetry, and machine learning have shifted expectations for what network analytics must deliver: real-time situational awareness, predictive insights that reduce mean time to repair, and contextualized intelligence that informs both business and engineering decisions. Consequently, executives must view analytics not merely as an operational toolset but as a cross-functional capability that ties capacity planning to customer experience objectives and cyber risk management.
Leadership priorities are increasingly centered on turning raw network data into actionable decision support. This requires investment in unified data architectures, integration between analytics platforms and orchestration layers, and disciplined governance to ensure data quality and lineage. As organizations progress along their analytics maturity curves, the focus moves from isolated dashboards to embedded analytics that drive automation and continuous optimization. Therefore, an effective introduction to this subject emphasizes strategic alignment, the need for interoperable toolchains, and an outcome-driven roadmap that accelerates measurable improvements in reliability, performance, and cost efficiency.
A detailed analysis of convergent technological and operational shifts that are rapidly elevating expectations for speed, scale, interoperability, and security within network analytics
The landscape of network analytics is being reshaped by several convergent shifts that together create new expectations for speed, scale, and insight fidelity. First, the adoption of streaming telemetry and higher-frequency instrumentation has moved analytics away from batch-oriented snapshots toward continuous, event-driven detection and response. This transition allows analytics engines to surface anomalies and evolving degradations in near real time, reducing the window for impact and enabling faster remediation. Second, the integration of machine learning models into analytics pipelines has shifted emphasis from descriptive reporting to predictive and prescriptive guidance, enabling organizations to anticipate capacity constraints and automate routine corrective actions.
In addition, cloud-native architectures and containerized network functions are driving demand for analytics that understand ephemeral topologies and microservice-level performance. As applications become more distributed, analytics solutions must reconcile metrics from on-premises, hybrid, and cloud environments to present a coherent operational picture. Finally, regulatory attention and a heightened threat environment have elevated security analytics as a critical component of the analytics stack. Taken together, these shifts demand that vendors and adopters prioritize interoperability, model explainability, and scalable data management to extract consistent value across increasingly complex networking environments.
An assessment of how evolving US tariff measures are influencing procurement strategies, supplier risk management, and the shift toward cloud-centric and software-led network analytics approaches
Recent policy changes and tariff adjustments in the United States have introduced additional layers of complexity for global procurement and supply chains supporting network analytics deployments. Increased duties on certain hardware components and ancillary devices can extend lead times for on-premises appliance refreshes and drive organizations to evaluate alternative sourcing strategies. In response, many technology buyers are reassessing total cost of ownership by factoring in import-related delays, potential supplier concentration risk, and logistical contingency plans that preserve deployment timelines for critical observability upgrades.
Furthermore, these trade measures are accelerating consideration of cloud-first and software-centric approaches that reduce reliance on imported hardware. Organizations are increasingly weighing the trade-offs between managed services and in-house professional services to maintain control while minimizing exposure to cross-border supply disruptions. At the same time, vendor strategies are adapting through regionalized manufacturing, diversified component sourcing, and enhanced local support offerings to mitigate tariff-driven friction. For decision-makers, the immediate priority is to model procurement scenarios that incorporate tariff sensitivity, optimize inventory buffers for essential spares, and negotiate contractual terms that allocate risk across supplier relationships to maintain operational continuity.
Comprehensive segmentation analysis linking product capabilities, application domains, end-user priorities, deployment models, pricing frameworks, and channel dynamics to buyer behavior
Understanding the competitive and adoption dynamics requires careful segmentation across product, application, end-user, deployment, pricing, and sales channel dimensions. Product differentiation separates services and software, where the services domain includes both managed services and professional services, with the latter further delineated into consulting, integration, and support offerings. On the software side, capabilities cluster into historical analytics that enable trend analysis, predictive analytics that forecast future behavior, and real-time analytics that support immediate operational decision-making. Application-level segmentation highlights the distinct needs of network monitoring, performance management, security analytics, and traffic analysis; within performance management, the focus narrows to capacity planning and QoS monitoring while security analytics concentrates on anomaly detection and threat detection to protect distributed environments.
End-user segmentation bifurcates enterprise and telecom needs, and enterprise verticals such as banking, financial services and insurance, healthcare, IT, and retail exhibit divergent priorities in compliance, latency, and customer experience. Deployment choices between cloud and on-premises environments continue to shape architecture decisions, with cloud offerings differentiated into hybrid cloud, private cloud, and public cloud models that offer varying trade-offs in control, scalability, and operational overhead. Pricing models vary from perpetual licensing to usage-based pay-as-you-go and subscription formats, the latter commonly structured as annual or monthly subscriptions to align vendor economics with customer consumption. Finally, sales channels span channel partners, direct sales, and distributors, and channel partner ecosystems often include system integrators and value added resellers that add integration and verticalization capabilities. By synthesizing these segmentation lenses, stakeholders can better align product roadmaps, go-to-market strategies, and service portfolios to the nuanced requirements of distinct buyer cohorts.
In-depth regional insights revealing how regulatory, operational, and commercial forces across the Americas, Europe Middle East & Africa, and Asia-Pacific shape adoption and vendor approaches
Regional dynamics continue to shape adoption trajectories and vendor strategies across the Americas, Europe, Middle East & Africa, and Asia-Pacific, each presenting distinct regulatory, operational, and commercial considerations. In the Americas, demand is being driven by digital transformation initiatives and the need to modernize legacy networks to support cloud migration and edge services, while procurement cycles reflect a balance between capital investments and operational expense models. Transitional initiatives emphasize interoperability, observability across hybrid estates, and consolidation toward unified analytics platforms to reduce tool sprawl and accelerate root cause analysis.
In Europe, Middle East & Africa, regulatory compliance and data sovereignty are prominent drivers that influence deployment modes and vendor selection, leading many organizations to prefer private or hybrid cloud configurations and to demand stronger on-premises integration capabilities. Local service ecosystems and regional engineering support play a decisive role in adoption, especially for critical infrastructure and telecom operators. Across Asia-Pacific, rapid digitalization and mobile-first architectures create robust demand for scalable real-time analytics and security analytics that protect expanding digital services. Vendor strategies in this region commonly focus on partnerships with local system integrators and flexible pricing to accommodate a broad spectrum of enterprise maturities. Across these regions, operators and enterprises prioritize solutions that balance performance, compliance, and operational agility to meet both current and emerging demands.
Key corporate insights into how leading vendors and service providers are differentiating through platform interoperability, ML-driven capabilities, and regional service ecosystems
Key companies operating in the network analytics ecosystem are differentiating through investments in data engineering, machine learning model development, and cloud-native architecture. Leading technology providers are enhancing platform interoperability to support hybrid and multi-cloud environments while prioritizing explainability and governance for embedded AI capabilities. At the same time, specialized vendors are focusing on domain expertise-particularly in security analytics and traffic analysis-where deep protocol awareness and high-fidelity telemetry parsing provide a competitive advantage. Service providers and system integrators are expanding managed offerings that combine analytics platforms with orchestration and remediation playbooks, enabling faster time-to-value for customers that lack extensive in-house operations teams.
Strategic partnerships between analytics vendors and cloud service providers are increasingly common, enabling tighter integration with cloud-native observability toolchains and streamlined consumption models. Additionally, global suppliers are investing in regional delivery centers and localized professional services to address procurement friction and reduce deployment timelines. For buyers, vendor selection decisions hinge on demonstrated ability to integrate with existing toolsets, the maturity of ML-driven detection capabilities, and the strength of services ecosystems that can support rapid onboarding and continuous optimization. Ultimately, corporate strategy in this space requires a balanced approach that considers product innovation, ecosystem partnerships, and the capacity to scale managed services across diverse customer segments.
Actionable recommendations for executives to pragmatically build analytics capabilities through outcome-driven use cases, modular architectures, partnerships, and workforce transformation
Industry leaders should adopt a pragmatic, phased approach to implementing or expanding network analytics capabilities, aligning investments with measurable operational and business outcomes. Start by defining high-priority use cases that deliver tangible improvements in service availability, customer experience, or security posture, and then map required data sources and integration points. Next, establish data governance and quality controls to ensure the reliability of analytics outputs, and invest in modular architectures that support incremental deployment across cloud and on-premises environments. This staged approach reduces risk while enabling continuous learning and progressive automation of remediation workflows.
Moreover, leaders must actively cultivate partnerships with channel and systems integrator ecosystems to accelerate deployment and to bring vertical-specific expertise. Emphasize vendor selection criteria that include model explainability, open APIs, and pre-built connectors for common infrastructure and observability tools. Simultaneously, build internal capabilities by upskilling network and security operations teams with data science fundamentals and analytics-driven incident management practices. Finally, negotiate commercial terms that provide flexibility-such as hybrid pricing models and outcome-based clauses-to align vendor incentives with operational results and to ensure adaptability as organizational requirements evolve.
Transparent description of the research approach combining primary practitioner interviews, technical documentation review, and comparative architectural analysis to ensure rigorous findings
This research synthesizes qualitative and quantitative inputs drawn from vendor documentation, technical white papers, product release notes, and interviews with practitioners across enterprise and telecom environments to construct a comprehensive landscape of capabilities and adoption patterns. Data collection emphasized primary engagements with network architects, security operations leaders, and procurement specialists to capture real-world implementation challenges and success factors. Secondary sources included publicly available regulatory documents, standards releases related to telemetry and observability, and technical benchmarks that validate performance claims.
Analytical methods combined taxonomy-driven segmentation with cross-sectional analysis to identify capability gaps and alignment opportunities. The research team applied comparative evaluations of architectural approaches-cloud-first, hybrid, and on-premises-assessing each against criteria such as operational overhead, integration complexity, and suitability for real-time analytics. Interviews were thematically coded to surface recurrent operational pain points and best practices, while vendor feature sets were evaluated for maturity, interoperability, and demonstrable automation capabilities. Throughout, attention was given to ensuring reproducibility of conclusions by documenting sources and by applying consistent evaluation rubrics across all assessed dimensions.
A decisive conclusion emphasizing the strategic imperative to treat network analytics as a continuous enterprise capability that drives resilience, automation, and business outcomes
In closing, network analytics is now a strategic enabler for organizations seeking resilience, operational excellence, and improved security posture in increasingly distributed environments. The convergence of streaming telemetry, machine learning, and cloud-native deployment models creates both opportunity and complexity; organizations that adopt a disciplined, use-case-driven approach will extract the most value. Key considerations for success include investing in data governance, selecting interoperable platforms, leveraging managed services where appropriate, and evolving workforce skills to operate and interpret analytics outputs effectively.
Looking forward, continued innovation will focus on tighter integration between analytics and automation systems, enhanced model explainability to support trust and compliance, and more flexible commercial models that align consumption with outcomes. For decision-makers, the imperative is to treat analytics as a continuous capability rather than a one-off project, ensuring investments are adaptable as architectures and business needs evolve. By doing so, organizations can turn network telemetry into a strategic asset that supports both technical excellence and broader business objectives.
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. Implementation of intent-based networking analytics to automate service assurance
- 5.2. Integration of edge computing and network analytics for localized traffic insights
- 5.3. Use of network behavior anomaly detection with machine learning for threat prevention
- 5.4. Deployment of programmable telemetry and streaming analytics for continuous monitoring
- 5.5. Convergence of 5G network slicing analytics with QoS optimization for enterprise services
- 5.6. Leveraging network digital twins and simulation analytics for capacity planning accuracy
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Network Analytics Market, by Product Type
- 8.1. Services
- 8.1.1. Managed Services
- 8.1.2. Professional Services
- 8.1.2.1. Consulting
- 8.1.2.2. Integration
- 8.1.2.3. Support
- 8.2. Software
- 8.2.1. Historical Analytics
- 8.2.2. Predictive Analytics
- 8.2.3. Real Time Analytics
- 9. Network Analytics Market, by Pricing Model
- 9.1. License
- 9.2. Pay As You Go
- 9.3. Subscription
- 9.3.1. Annual Subscription
- 9.3.2. Monthly Subscription
- 10. Network Analytics Market, by Application
- 10.1. Network Monitoring
- 10.2. Performance Management
- 10.2.1. Capacity Planning
- 10.2.2. QoS Monitoring
- 10.3. Security Analytics
- 10.3.1. Anomaly Detection
- 10.3.2. Threat Detection
- 10.4. Traffic Analysis
- 11. Network Analytics Market, by Deployment Mode
- 11.1. Cloud
- 11.1.1. Hybrid Cloud
- 11.1.2. Private Cloud
- 11.1.3. Public Cloud
- 11.2. On Premises
- 12. Network Analytics Market, by End User
- 12.1. Enterprise
- 12.1.1. BFSI
- 12.1.2. Healthcare
- 12.1.3. IT
- 12.1.4. Retail
- 12.2. Telecom
- 13. Network Analytics Market, by Region
- 13.1. Americas
- 13.1.1. North America
- 13.1.2. Latin America
- 13.2. Europe, Middle East & Africa
- 13.2.1. Europe
- 13.2.2. Middle East
- 13.2.3. Africa
- 13.3. Asia-Pacific
- 14. Network Analytics Market, by Group
- 14.1. ASEAN
- 14.2. GCC
- 14.3. European Union
- 14.4. BRICS
- 14.5. G7
- 14.6. NATO
- 15. Network Analytics Market, by Country
- 15.1. United States
- 15.2. Canada
- 15.3. Mexico
- 15.4. Brazil
- 15.5. United Kingdom
- 15.6. Germany
- 15.7. France
- 15.8. Russia
- 15.9. Italy
- 15.10. Spain
- 15.11. China
- 15.12. India
- 15.13. Japan
- 15.14. Australia
- 15.15. South Korea
- 16. Competitive Landscape
- 16.1. Market Share Analysis, 2024
- 16.2. FPNV Positioning Matrix, 2024
- 16.3. Competitive Analysis
- 16.3.1. Alteryx
- 16.3.2. Apixio, Inc.
- 16.3.3. Arcadia Solutions, LLC
- 16.3.4. Athenahealth, Inc.
- 16.3.5. Cisco Systems, Inc.
- 16.3.6. CitiusTech Inc.
- 16.3.7. Clarify Health Solutions, Inc.
- 16.3.8. ClosedLoop.ai Inc.
- 16.3.9. Cloudticity, L.L.C
- 16.3.10. Cotiviti, Inc.
- 16.3.11. Epic Systems Corporation
- 16.3.12. GE HealthCare Technologies Inc.
- 16.3.13. Google LLC by Alphabet Inc.
- 16.3.14. Health Catalyst, Inc.
- 16.3.15. HealthVerity, Inc.
- 16.3.16. HOKUTO Inc.
- 16.3.17. Inovalon Holdings, Inc.
- 16.3.18. International Business Machines Corporation
- 16.3.19. IQVIA Inc.
- 16.3.20. McKesson Corporation
- 16.3.21. MedeAnalytics, Inc.
- 16.3.22. Microsoft Corporation
- 16.3.23. Optum, Inc.
- 16.3.24. Oracle Corporation
- 16.3.25. RIB Datapine GmbH
- 16.3.26. SAP SE
- 16.3.27. SAS Institute, Inc.
- 16.3.28. Veradigm LLC
- 16.3.29. Verinovum
- 16.3.30. Virgin Pulse
- 16.3.31. Wipro
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