IT Operation Analytics Market by Solution Type (Services, Software Tools), Analytics Type (Predictive Analytics, Prescriptive Analytics), Data Source, Deployment Mode, Organization Size, Application, Industry Vertical - Global Forecast 2025-2032
Description
The IT Operation Analytics Market was valued at USD 21.77 billion in 2024 and is projected to grow to USD 24.56 billion in 2025, with a CAGR of 13.71%, reaching USD 60.87 billion by 2032.
Uncovering How IT Operation Analytics Drive Enterprise Efficiency Resilience and Strategic Decision Making Across Complex Dynamic Technology Ecosystems
The landscape of IT operation analytics has emerged as a pivotal cornerstone for enterprises striving to achieve digital resilience, operational efficiency, and strategic foresight. As organizations grapple with increasingly complex technology environments and the imperative to deliver seamless user experiences, the ability to correlate vast volumes of operational data while deriving actionable insights has become mission-critical. This introduction establishes the foundational context for understanding how advanced analytics capabilities are reshaping decision-making processes and driving value creation across infrastructures.
Against a backdrop of accelerated cloud adoption, hybrid deployments, and the proliferation of connected devices, IT operation analytics solutions are evolving into unified platforms that offer real-time visibility, predictive intelligence, and automated remediation. By integrating data from diverse sources-ranging from server metrics and application logs to network and security feeds-enterprises can detect anomalies, optimize resource utilization, and anticipate disruptions before they impact end users. This section underscores the strategic significance of adopting a holistic analytics framework that empowers organizations to transition from reactive incident management to proactive operational optimization.
Exploring The Progressive Evolution Of Hybrid Cloud Adoption and Security-Driven Observability Shaping Modern It Operations Analytics
Over the past several years, the IT operations landscape has undergone transformative shifts fueled by digital transformation initiatives and evolving architectural paradigms. The migration of core workloads to cloud environments has blurred traditional perimeter boundaries, demanding analytics platforms that can seamlessly instrument and monitor hybrid and multi-cloud deployments. At the same time, regulatory pressures and heightened security risks have elevated the importance of integrating security analytics into operational workflows, enabling faster threat detection and compliance monitoring.
Simultaneously, the adoption of microservices and containerization has fragmented visibility across ephemeral infrastructure components, compelling organizations to embrace distributed tracing and advanced correlation techniques. This shift toward decoupled architectures has catalyzed the emergence of unified observability frameworks that converge metrics, logs, and traces into a cohesive analytical view. As enterprises strive to maintain agility and scale, the evolution of open source tooling and vendor-agnostic standards is accelerating innovation while driving cost efficiencies. These combined trends are redefining how organizations architect, deploy, and govern their IT analytics capabilities to support continuous delivery and real-time operational insights.
Assessing The Broad Repercussions Of 2025 United States Trade Tariffs On It Operations Analytics Supply Chain and Deployment Strategies
The introduction of new United States tariffs in early 2025 has imparted a cumulative impact on the IT operation analytics market, reshaping supply chains and influencing vendor strategies. Hardware components critical to on-premises deployments have experienced increased duties, prompting solution providers to adjust pricing models and accelerate the shift toward subscription-based cloud offerings. As multinational enterprises reassess total cost of ownership calculations, there is growing momentum behind hybrid and fully hosted analytics solutions that mitigate import cost exposure.
In parallel, regional localization strategies have gained traction, with analytics vendors expanding partnerships with data center operators across North America to circumvent logistical bottlenecks. This dynamic has also catalyzed investments in software-defined instrumentation, reducing reliance on specialized appliances. Moreover, the tariff-induced realignment of procurement cycles has driven enterprises to adopt more flexible deployment architectures, enabling rapid scaling and redeployment of analytics nodes in geographically strategic locations. Ultimately, the 2025 tariff adjustments have underscored the need for adaptable solution portfolios and agile vendor ecosystems that can navigate evolving trade regulations while safeguarding service continuity.
Illustrating Comprehensive Segmentation Of It Operation Analytics Market Across Solutions Analytics Types and Industry-Specific Applications
A nuanced understanding of the market can be achieved by segmenting based on solution type, analytics type, data source, deployment mode, organization size, application, and industry vertical. In terms of solution type, the market differentiates between services-encompassing both managed services and professional services-and software tools that are available in cloud, hybrid, and on-premises formats. When viewed through the lens of analytics type, offerings divide into predictive analytics that anticipate performance deviations and prescriptive analytics that recommend corrective actions.
Turning to data source, IT operation analytics draws intelligence from server and network metrics, application logs, security logs, and increasingly from IoT device data, enabling a holistic operational perspective. Deployment mode segmentation highlights the distinction between cloud-native solutions and those deployed on-premises, each delivering unique benefits in terms of control, scalability, and compliance. Large enterprises typically leverage comprehensive, end-to-end analytics suites, while small and medium enterprises often select lighter, cost-effective packages. Across diverse use cases-ranging from application performance management and IT automation orchestration to log management, network monitoring, and security analytics-the market exhibits distinct value propositions. Finally, adoption varies by industry vertical: sectors such as banking, financial services and insurance, energy and utilities, government and public sector, healthcare, IT and telecom, manufacturing, retail and e-commerce, and transportation and logistics each demonstrate unique drivers and maturity levels.
Evaluating Regional Variances In Cloud-Native Adoption and Regulatory Influences Driving Differentiated Analytics Growth Trajectories
Regional dynamics play a pivotal role in shaping the progression of IT operation analytics adoption. In the Americas, rapid cloud migration and a robust ecosystem of managed service providers have driven demand for advanced analytics capabilities. North American enterprises, in particular, are embracing unified observability platforms that offer seamless integration with leading cloud infrastructures and facilitate compliance with stringent data privacy regulations.
Meanwhile, Europe, the Middle East & Africa presents a multifaceted landscape defined by diverse regulatory frameworks and varying levels of technological maturity. In Western Europe, momentum is building around cross-border data flows and collaborative innovation hubs, whereas public sector initiatives across the Middle East and Africa are catalyzing investments in secure analytics solutions tailored to government and critical infrastructure environments.
Across Asia-Pacific, rapid growth in digital transformation projects and an expanding base of IoT deployments have elevated the importance of scalable analytics architectures. Emerging economies are prioritizing cost-effective, cloud-based analytics offerings to optimize resource allocation, while established markets in Japan, South Korea, and Australia are pioneering AI-driven predictive and prescriptive analytics initiatives to drive operational excellence and competitive differentiation.
Revealing Strategic Collaborations And Innovation Approaches Employed By Leading Analytics Providers To Gain Competitive Differentiation
Leading technology vendors are intensifying efforts to expand their analytics portfolios through strategic partnerships, acquisitions, and proprietary innovation. Many of the top players have integrated AI and machine learning engines into their platforms, enabling real-time anomaly detection, root cause analysis, and automated remediation across complex infrastructure stacks. Collaboration with cloud hyperscalers has become a focal point, ensuring seamless interoperability and rapid provisioning of analytics services.
At the same time, a number of specialized niche providers are carving out market share by offering focused solutions for high-growth domains such as security analytics and network performance monitoring. These companies are distinguishing themselves through deep domain expertise, flexible pricing models, and open APIs that facilitate integration with existing toolchains. Additionally, service providers are increasingly offering bundled packages that combine professional services with managed operations to accelerate time to value and reduce the burden on internal IT teams. Collectively, these corporate strategies underscore a competitive ecosystem where innovation, customer alignment, and ecosystem collaboration drive differentiation.
Outlining Actionable Steps For Operational Leaders To Build Predictive Analytics-Driven Infrastructure And Strengthen Strategic Partnerships
To capitalize on emerging opportunities and mitigate evolving risks, industry leaders should prioritize the development of unified observability frameworks that converge metrics, logs, and traces into a single pane of glass. By investing in predictive and prescriptive capabilities, organizations can move beyond reactive firefighting toward anticipatory operations, thereby reducing downtime and optimizing resource utilization.
Equally critical is the establishment of strategic partnerships with cloud service providers, system integrators, and niche analytics specialists to ensure access to best-in-class technologies and domain expertise. Leaders must also adopt hybrid deployment models that balance the control and compliance benefits of on-premises solutions with the scalability and flexibility of cloud architectures. Furthermore, upskilling IT teams in data science methodologies and automation tools will be essential for sustaining innovation and embedding analytics into daily workflows. Finally, instituting strong governance practices around data quality, privacy, and ethical AI usage will build stakeholder trust and future-proof analytics initiatives against regulatory changes.
Describing A Multi-Stage Research Framework Integrating Secondary Analysis Primary Interviews And Quantitative Validation For Robust Insights
This research was conducted using a rigorous multi-stage approach combining secondary, primary, and quantitative analyses. The secondary research phase involved examining industry publications, vendor collateral, regulatory filings, and technology white papers to establish a foundational understanding of market dynamics and emerging trends. Following this, a series of primary interviews were conducted with executives, product managers, and end-user organizations to capture firsthand perspectives and validate key hypotheses.
Quantitative data was collected through a structured survey of IT professionals across various geographies and organization sizes, ensuring statistical relevance and representativeness. Data triangulation techniques were employed to cross-verify findings from multiple sources, enhancing the robustness of insights. Additionally, a bottom-up analysis was conducted to align product capabilities and usage patterns with end-user requirements. Throughout the research process, an iterative review mechanism was utilized to refine assumptions, incorporate stakeholder feedback, and maintain methodological integrity.
Summarizing The Strategic Imperatives And Technological Convergence Required To Achieve Proactive It Operations Excellence
As the complexity of IT infrastructures continues to escalate, the imperative for advanced operational analytics has never been clearer. By unifying disparate data sources and applying machine learning techniques, organizations can transition from reactive issue resolution to proactive operational optimization. The interplay of tariff pressures, regional variances, and competitive innovations underscores the dynamic nature of the market, demanding agility and strategic foresight.
Leveraging segmentation insights across solution types, analytics approaches, data sources, deployment modes, organization sizes, applications, and industry verticals will enable stakeholders to tailor their strategies and capture targeted value. The recommendations outlined herein provide a roadmap for investments and partnerships that will drive sustainable differentiation. Ultimately, the synergy of technology convergence, data governance, and organizational readiness will dictate success in harnessing the full potential of IT operation analytics.
Note: PDF & Excel + Online Access - 1 Year
Uncovering How IT Operation Analytics Drive Enterprise Efficiency Resilience and Strategic Decision Making Across Complex Dynamic Technology Ecosystems
The landscape of IT operation analytics has emerged as a pivotal cornerstone for enterprises striving to achieve digital resilience, operational efficiency, and strategic foresight. As organizations grapple with increasingly complex technology environments and the imperative to deliver seamless user experiences, the ability to correlate vast volumes of operational data while deriving actionable insights has become mission-critical. This introduction establishes the foundational context for understanding how advanced analytics capabilities are reshaping decision-making processes and driving value creation across infrastructures.
Against a backdrop of accelerated cloud adoption, hybrid deployments, and the proliferation of connected devices, IT operation analytics solutions are evolving into unified platforms that offer real-time visibility, predictive intelligence, and automated remediation. By integrating data from diverse sources-ranging from server metrics and application logs to network and security feeds-enterprises can detect anomalies, optimize resource utilization, and anticipate disruptions before they impact end users. This section underscores the strategic significance of adopting a holistic analytics framework that empowers organizations to transition from reactive incident management to proactive operational optimization.
Exploring The Progressive Evolution Of Hybrid Cloud Adoption and Security-Driven Observability Shaping Modern It Operations Analytics
Over the past several years, the IT operations landscape has undergone transformative shifts fueled by digital transformation initiatives and evolving architectural paradigms. The migration of core workloads to cloud environments has blurred traditional perimeter boundaries, demanding analytics platforms that can seamlessly instrument and monitor hybrid and multi-cloud deployments. At the same time, regulatory pressures and heightened security risks have elevated the importance of integrating security analytics into operational workflows, enabling faster threat detection and compliance monitoring.
Simultaneously, the adoption of microservices and containerization has fragmented visibility across ephemeral infrastructure components, compelling organizations to embrace distributed tracing and advanced correlation techniques. This shift toward decoupled architectures has catalyzed the emergence of unified observability frameworks that converge metrics, logs, and traces into a cohesive analytical view. As enterprises strive to maintain agility and scale, the evolution of open source tooling and vendor-agnostic standards is accelerating innovation while driving cost efficiencies. These combined trends are redefining how organizations architect, deploy, and govern their IT analytics capabilities to support continuous delivery and real-time operational insights.
Assessing The Broad Repercussions Of 2025 United States Trade Tariffs On It Operations Analytics Supply Chain and Deployment Strategies
The introduction of new United States tariffs in early 2025 has imparted a cumulative impact on the IT operation analytics market, reshaping supply chains and influencing vendor strategies. Hardware components critical to on-premises deployments have experienced increased duties, prompting solution providers to adjust pricing models and accelerate the shift toward subscription-based cloud offerings. As multinational enterprises reassess total cost of ownership calculations, there is growing momentum behind hybrid and fully hosted analytics solutions that mitigate import cost exposure.
In parallel, regional localization strategies have gained traction, with analytics vendors expanding partnerships with data center operators across North America to circumvent logistical bottlenecks. This dynamic has also catalyzed investments in software-defined instrumentation, reducing reliance on specialized appliances. Moreover, the tariff-induced realignment of procurement cycles has driven enterprises to adopt more flexible deployment architectures, enabling rapid scaling and redeployment of analytics nodes in geographically strategic locations. Ultimately, the 2025 tariff adjustments have underscored the need for adaptable solution portfolios and agile vendor ecosystems that can navigate evolving trade regulations while safeguarding service continuity.
Illustrating Comprehensive Segmentation Of It Operation Analytics Market Across Solutions Analytics Types and Industry-Specific Applications
A nuanced understanding of the market can be achieved by segmenting based on solution type, analytics type, data source, deployment mode, organization size, application, and industry vertical. In terms of solution type, the market differentiates between services-encompassing both managed services and professional services-and software tools that are available in cloud, hybrid, and on-premises formats. When viewed through the lens of analytics type, offerings divide into predictive analytics that anticipate performance deviations and prescriptive analytics that recommend corrective actions.
Turning to data source, IT operation analytics draws intelligence from server and network metrics, application logs, security logs, and increasingly from IoT device data, enabling a holistic operational perspective. Deployment mode segmentation highlights the distinction between cloud-native solutions and those deployed on-premises, each delivering unique benefits in terms of control, scalability, and compliance. Large enterprises typically leverage comprehensive, end-to-end analytics suites, while small and medium enterprises often select lighter, cost-effective packages. Across diverse use cases-ranging from application performance management and IT automation orchestration to log management, network monitoring, and security analytics-the market exhibits distinct value propositions. Finally, adoption varies by industry vertical: sectors such as banking, financial services and insurance, energy and utilities, government and public sector, healthcare, IT and telecom, manufacturing, retail and e-commerce, and transportation and logistics each demonstrate unique drivers and maturity levels.
Evaluating Regional Variances In Cloud-Native Adoption and Regulatory Influences Driving Differentiated Analytics Growth Trajectories
Regional dynamics play a pivotal role in shaping the progression of IT operation analytics adoption. In the Americas, rapid cloud migration and a robust ecosystem of managed service providers have driven demand for advanced analytics capabilities. North American enterprises, in particular, are embracing unified observability platforms that offer seamless integration with leading cloud infrastructures and facilitate compliance with stringent data privacy regulations.
Meanwhile, Europe, the Middle East & Africa presents a multifaceted landscape defined by diverse regulatory frameworks and varying levels of technological maturity. In Western Europe, momentum is building around cross-border data flows and collaborative innovation hubs, whereas public sector initiatives across the Middle East and Africa are catalyzing investments in secure analytics solutions tailored to government and critical infrastructure environments.
Across Asia-Pacific, rapid growth in digital transformation projects and an expanding base of IoT deployments have elevated the importance of scalable analytics architectures. Emerging economies are prioritizing cost-effective, cloud-based analytics offerings to optimize resource allocation, while established markets in Japan, South Korea, and Australia are pioneering AI-driven predictive and prescriptive analytics initiatives to drive operational excellence and competitive differentiation.
Revealing Strategic Collaborations And Innovation Approaches Employed By Leading Analytics Providers To Gain Competitive Differentiation
Leading technology vendors are intensifying efforts to expand their analytics portfolios through strategic partnerships, acquisitions, and proprietary innovation. Many of the top players have integrated AI and machine learning engines into their platforms, enabling real-time anomaly detection, root cause analysis, and automated remediation across complex infrastructure stacks. Collaboration with cloud hyperscalers has become a focal point, ensuring seamless interoperability and rapid provisioning of analytics services.
At the same time, a number of specialized niche providers are carving out market share by offering focused solutions for high-growth domains such as security analytics and network performance monitoring. These companies are distinguishing themselves through deep domain expertise, flexible pricing models, and open APIs that facilitate integration with existing toolchains. Additionally, service providers are increasingly offering bundled packages that combine professional services with managed operations to accelerate time to value and reduce the burden on internal IT teams. Collectively, these corporate strategies underscore a competitive ecosystem where innovation, customer alignment, and ecosystem collaboration drive differentiation.
Outlining Actionable Steps For Operational Leaders To Build Predictive Analytics-Driven Infrastructure And Strengthen Strategic Partnerships
To capitalize on emerging opportunities and mitigate evolving risks, industry leaders should prioritize the development of unified observability frameworks that converge metrics, logs, and traces into a single pane of glass. By investing in predictive and prescriptive capabilities, organizations can move beyond reactive firefighting toward anticipatory operations, thereby reducing downtime and optimizing resource utilization.
Equally critical is the establishment of strategic partnerships with cloud service providers, system integrators, and niche analytics specialists to ensure access to best-in-class technologies and domain expertise. Leaders must also adopt hybrid deployment models that balance the control and compliance benefits of on-premises solutions with the scalability and flexibility of cloud architectures. Furthermore, upskilling IT teams in data science methodologies and automation tools will be essential for sustaining innovation and embedding analytics into daily workflows. Finally, instituting strong governance practices around data quality, privacy, and ethical AI usage will build stakeholder trust and future-proof analytics initiatives against regulatory changes.
Describing A Multi-Stage Research Framework Integrating Secondary Analysis Primary Interviews And Quantitative Validation For Robust Insights
This research was conducted using a rigorous multi-stage approach combining secondary, primary, and quantitative analyses. The secondary research phase involved examining industry publications, vendor collateral, regulatory filings, and technology white papers to establish a foundational understanding of market dynamics and emerging trends. Following this, a series of primary interviews were conducted with executives, product managers, and end-user organizations to capture firsthand perspectives and validate key hypotheses.
Quantitative data was collected through a structured survey of IT professionals across various geographies and organization sizes, ensuring statistical relevance and representativeness. Data triangulation techniques were employed to cross-verify findings from multiple sources, enhancing the robustness of insights. Additionally, a bottom-up analysis was conducted to align product capabilities and usage patterns with end-user requirements. Throughout the research process, an iterative review mechanism was utilized to refine assumptions, incorporate stakeholder feedback, and maintain methodological integrity.
Summarizing The Strategic Imperatives And Technological Convergence Required To Achieve Proactive It Operations Excellence
As the complexity of IT infrastructures continues to escalate, the imperative for advanced operational analytics has never been clearer. By unifying disparate data sources and applying machine learning techniques, organizations can transition from reactive issue resolution to proactive operational optimization. The interplay of tariff pressures, regional variances, and competitive innovations underscores the dynamic nature of the market, demanding agility and strategic foresight.
Leveraging segmentation insights across solution types, analytics approaches, data sources, deployment modes, organization sizes, applications, and industry verticals will enable stakeholders to tailor their strategies and capture targeted value. The recommendations outlined herein provide a roadmap for investments and partnerships that will drive sustainable differentiation. Ultimately, the synergy of technology convergence, data governance, and organizational readiness will dictate success in harnessing the full potential of IT operation analytics.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
181 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. Emerging trends in automated root cause analysis techniques enhancing accuracy and speed in IT operation analytics
- 5.2. Shift toward real-time data monitoring for enhanced IT operational efficiency
- 5.3. Increasing adoption of predictive analytics to prevent IT system downtimes
- 5.4. Integration of machine learning algorithms for smarter IT infrastructure management
- 5.5. Role of cloud-based analytics platforms in optimizing IT operations
- 5.6. Emerging trends in automated root cause analysis within IT operation analytics
- 5.7. Impact of big data analytics on ensuring cybersecurity in IT environments
- 5.8. Growing importance of user experience analytics in IT operational strategies
- 5.9. Rise of edge computing analytics to support decentralized IT operations
- 5.10. Developments in anomaly detection techniques to improve IT system reliability
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. IT Operation Analytics Market, by Solution Type
- 8.1. Services
- 8.1.1. Managed Services
- 8.1.2. Professional Services
- 8.2. Software Tools
- 8.2.1. Cloud
- 8.2.2. Hybrid
- 8.2.3. On-Premises
- 9. IT Operation Analytics Market, by Analytics Type
- 9.1. Predictive Analytics
- 9.2. Prescriptive Analytics
- 10. IT Operation Analytics Market, by Data Source
- 10.1. Application Logs
- 10.2. IoT Device Data
- 10.3. Network Metrics
- 10.4. Security Logs
- 10.5. Server Metrics
- 11. IT Operation Analytics Market, by Deployment Mode
- 11.1. Cloud
- 11.2. On Premises
- 12. IT Operation Analytics Market, by Organization Size
- 12.1. Large Enterprises
- 12.2. Small And Medium Enterprises
- 13. IT Operation Analytics Market, by Application
- 13.1. Application Performance Management
- 13.2. IT Automation & Orchestration
- 13.3. Log Management
- 13.4. Network Monitoring
- 13.5. Security Analytics
- 14. IT Operation Analytics Market, by Industry Vertical
- 14.1. Banking, Financial Services, & Insurance
- 14.2. Energy & Utilities
- 14.3. Government & Public Sector
- 14.4. Healthcare
- 14.5. IT & Telecom
- 14.6. Manufacturing
- 14.7. Retail & E Commerce
- 14.8. Transportation & Logistics
- 15. IT Operation Analytics Market, by Region
- 15.1. Americas
- 15.1.1. North America
- 15.1.2. Latin America
- 15.2. Europe, Middle East & Africa
- 15.2.1. Europe
- 15.2.2. Middle East
- 15.2.3. Africa
- 15.3. Asia-Pacific
- 16. IT Operation Analytics Market, by Group
- 16.1. ASEAN
- 16.2. GCC
- 16.3. European Union
- 16.4. BRICS
- 16.5. G7
- 16.6. NATO
- 17. IT Operation Analytics Market, by Country
- 17.1. United States
- 17.2. Canada
- 17.3. Mexico
- 17.4. Brazil
- 17.5. United Kingdom
- 17.6. Germany
- 17.7. France
- 17.8. Russia
- 17.9. Italy
- 17.10. Spain
- 17.11. China
- 17.12. India
- 17.13. Japan
- 17.14. Australia
- 17.15. South Korea
- 18. Competitive Landscape
- 18.1. Market Share Analysis, 2024
- 18.2. FPNV Positioning Matrix, 2024
- 18.3. Competitive Analysis
- 18.3.1. Cisco Systems, Inc.
- 18.3.2. Broadcom Inc.
- 18.3.3. BMC Software, Inc.
- 18.3.4. Apica
- 18.3.5. Hewlett Packard Enterprise Company
- 18.3.6. Dell Technologies Inc.
- 18.3.7. BigPanda, Inc.
- 18.3.8. Cloud Software Group, Inc.
- 18.3.9. Devo Technology Inc.
- 18.3.10. Diamanti, Inc.
- 18.3.11. Dynatrace, Inc.
- 18.3.12. Elasticsearch, Inc.
- 18.3.13. Evolven Software, Inc.
- 18.3.14. ExtraHop Networks, Inc.
- 18.3.15. HCL Technologies
- 18.3.16. Hitachi, Ltd.
- 18.3.17. International Business Machines Corporation
- 18.3.18. Ivanti Software, Inc.
- 18.3.19. Microsoft Corporation
- 18.3.20. NetApp, Inc.
- 18.3.21. New Relic, Inc.
- 18.3.22. Nexthink SA
- 18.3.23. Open Text Corporation
- 18.3.24. Oracle Corporation
- 18.3.25. SAP SE
- 18.3.26. SAS Institute Inc.
- 18.3.27. ServiceNow, Inc.
- 18.3.28. Sumo Logic, Inc.
- 18.3.29. Veritas Technology LLC
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