Healthcare Cloud Based Analytics Market by Component (Services, Software), Analytics Type (Descriptive, Predictive, Prescriptive), Deployment Mode, Application, End User - Global Forecast 2025-2032
Description
The Healthcare Cloud Based Analytics Market was valued at USD 47.94 billion in 2024 and is projected to grow to USD 52.22 billion in 2025, with a CAGR of 8.89%, reaching USD 94.81 billion by 2032.
An executive-oriented introduction to cloud-enabled healthcare analytics that explains strategic priorities, operational tensions, and governance considerations
Cloud-based analytics is rapidly becoming the backbone of healthcare transformation, reshaping how data is collected, analyzed, and operationalized across clinical and administrative domains. Organizations increasingly view analytics not as an adjunct capability but as a strategic imperative that underpins care coordination, population health management, revenue cycle optimization, and supply chain resilience. As a result, leaders are prioritizing architectures and operating models that enable secure, scalable, and interoperable analytics workflows that extend from edge devices to centralized cloud platforms.
Transitioning to cloud-native analytics environments involves reconciling multiple priorities: preserving patient privacy, ensuring regulatory compliance, and sustaining high availability while delivering meaningful insights at the point of care. This introduction frames the strategic context for stakeholders who must balance clinical imperatives with fiscal responsibility, acknowledging that successful programs blend technology adoption with governance, talent development, and process redesign. From this vantage point, investment in cloud-enabled analytics becomes both a catalyst for improved outcomes and a vehicle for organizational resilience.
A forward-looking analysis of the fundamental industry shifts driving cloud analytics adoption, interoperability advances, and governance imperatives for healthcare systems
The healthcare analytics landscape is undergoing transformative shifts driven by technological maturation, policy evolution, and changing care delivery models that demand near-real-time intelligence. Modern analytics stacks now integrate advanced machine learning with streaming data pipelines, enabling predictive alerts and prescriptive recommendations that support clinicians and care teams. Simultaneously, standards-based interoperability and API-first designs are lowering barriers to multi-vendor compositions, allowing health systems to orchestrate best-of-breed solutions rather than relying solely on monolithic platforms.
In addition, economic and regulatory forces are reshaping procurement and deployment decisions. Value-based care commitments and quality-driven reimbursement models are incentivizing investments in analytics that demonstrate outcome improvement and cost containment. At the same time, cyber risk and privacy concerns have elevated the importance of robust data governance, encryption, and identity management strategies. Together, these shifts compel leaders to prioritize architectures that are flexible, auditable, and tightly aligned to clinical workflows so that analytic insights translate into measurable improvements at scale.
An in-depth look at how the United States tariffs announced for 2025 reshape procurement, supply-chain strategies, and cloud-versus-on-premises decisions in healthcare analytics
Policy changes and trade measures can alter the economics and timing of technology adoption, and the tariff landscape announced for 2025 in the United States has introduced new considerations for healthcare organizations and their technology partners. Tariffs affecting imported hardware and specialized medical devices can increase the total cost of ownership for on-premises infrastructure, making cloud migration or hybrid models more attractive for organizations seeking capital flexibility. Moreover, supply-chain disruptions tied to tariff-driven sourcing adjustments have led many vendors to re-evaluate manufacturing footprints and procurement strategies, with downstream effects on delivery schedules and integration timelines.
Consequently, healthcare leaders are weighing short-term supply-chain volatility against the longer-term benefits of cloud-based consumption models that shift capital outlays to operational expenses. Providers and payers must also consider contractual protections and procurement clauses that mitigate exposure to sudden cost increases, while technology vendors are taking steps to localize critical components and diversify supplier bases. In practical terms, the tariff environment has accelerated evaluation of hybrid deployments, contract renegotiations, and contingency planning so organizations can maintain service continuity amid shifting trade dynamics.
A nuanced segmentation framework revealing how components, deployment choices, analytics modality, application domains, and end-user needs shape strategic decisions for analytics programs
Strategic segmentation clarifies where value is created and how deployment choices affect outcomes across distinct use cases. Based on Component, market is studied across Services and Software. The Services is further studied across Managed Services and Professional Services. This distinction underscores that procurement decisions are not solely about software capabilities but also about the depth and continuity of services required to implement, operate, and scale analytic solutions. Organizations that underinvest in services frequently encounter delayed time-to-value and inconsistent adoption across clinical teams.
Deployment options materially influence architecture and governance trade-offs. Based on Deployment Mode, market is studied across Hybrid, Private, and Public. Hybrid approaches often emerge as the pragmatic choice for health systems that need to balance data residency and latency requirements with the scalability advantages of public cloud. Analytics modalities shape the nature of analytic outputs and their operational integration. Based on Analytics Type, market is studied across Descriptive, Predictive, and Prescriptive. Descriptive capabilities remain foundational, while predictive models and prescriptive engines are increasingly embedded into care pathways and operational processes to enable proactive interventions.
Application-level segmentation directs attention to the primary operational domains where analytics drives impact. Based on Application, market is studied across Claims Management, Financial Management, Patient Management, Remote Monitoring, and Supply Chain Management. Each application domain demands tailored data models, domain-specific ontologies, and workflow integration strategies. Finally, end-user segmentation clarifies stakeholder needs and adoption vectors. Based on End User, market is studied across Life Sciences, Payers, and Providers. Life sciences organizations prioritize analytics for trial optimization and real-world evidence, payers focus on risk stratification and fraud detection, and providers concentrate on clinical decision support and operational efficiency. Together, these segment lenses provide a multidimensional framework for prioritizing investments and aligning vendor selection with desired outcomes.
A regional analysis describing how adoption patterns, regulatory regimes, and local ecosystems in Americas, Europe, Middle East & Africa, and Asia-Pacific influence analytics deployment and partnerships
Geographic dynamics play a pivotal role in adoption patterns, regulatory expectations, and partnership ecosystems. In the Americas, large integrated delivery networks and forward-looking payers are driving demand for scalable analytics that support population health initiatives and value-based contracts, while cross-border data flows and regional privacy regimes shape how cloud solutions are deployed. In Europe, Middle East & Africa, the regulatory mosaic and emphasis on data sovereignty require flexible deployment options and strong local partnerships, with several markets prioritizing standards-based interoperability and national digital health strategies that incentivize analytics-driven care models.
Across Asia-Pacific, the pace of digital transformation varies markedly, but large public and private systems are rapidly adopting remote monitoring and AI-assisted workflows to address capacity constraints and aging populations. Regional centers of excellence and vendor ecosystems are emerging that combine cloud infrastructure with localized clinical models. These geographic insights indicate that successful commercialization strategies must adapt to regulatory complexity, cultural expectations around data use, and the maturity of local health IT ecosystems, while also leveraging regional strengths in innovation and scale.
Insights into vendor strategies, partnerships, and service innovations that determine how infrastructure providers and specialty analytics firms deliver clinical and operational value across health systems
Vendor dynamics reveal a bifurcated market where hyperscale cloud providers and specialized healthcare analytics firms coexist and collaborate to meet complex customer needs. Leading infrastructure providers are focused on compliance, scalability, and platform services that enable rapid model deployment, while specialist vendors emphasize domain expertise, validated algorithms, and clinical workflow integration. Partnerships and strategic alliances are increasingly common, allowing organizations to combine the security and reach of major cloud platforms with the clinical depth and regulatory experience of niche solution providers.
In addition, companies are differentiating through investment in certified data models, interoperability toolkits, and validated clinical content that reduce implementation risk and accelerate adoption. Service models are maturing, with managed service offerings that bundle ongoing model management, performance monitoring, and regulatory reporting. This evolution reflects a market where customers value both the technical robustness of the platform and the operational discipline required to sustain analytic programs over time, prompting vendors to compete on outcomes and long-term support rather than feature sets alone.
A tactical set of actionable recommendations for healthcare and payer leaders to accelerate analytics adoption, strengthen governance, and secure resilient vendor and procurement arrangements
Leaders seeking to realize the full potential of cloud-based analytics should adopt a pragmatic, outcome-driven roadmap that balances quick wins with sustainable capability building. Begin by aligning analytics initiatives to specific clinical and operational problems where measurable outcomes can be demonstrated, and ensure executive sponsorship and clinical leadership are engaged to drive adoption. Investing in a robust data governance framework, including clear ownership, standardized metadata, and privacy-protecting architectures, will reduce friction during deployment and increase stakeholder confidence in analytic outputs.
Operationally, organizations should favor modular architectures that support hybrid deployment, enabling sensitive data to remain on-premises while leveraging public cloud scalability for compute-intensive workloads. Strengthening vendor contracts to include service level guarantees, model governance support, and provisions that address supply-chain or tariff-related disruptions will provide additional resiliency. Finally, cultivate multidisciplinary teams that combine clinical subject-matter expertise with data engineering and product management skills to ensure models are maintained, validated, and improved in production environments, thereby converting analytic insight into sustained clinical impact.
A clear exposition of the mixed-methods research approach combining primary interviews, case studies, secondary analysis, and data triangulation to validate practical recommendations
The research methodology blends qualitative and quantitative approaches to ensure findings are validated and actionable for decision makers. Primary research includes in-depth interviews with health system executives, payer strategists, clinical leaders, and technology architects to capture first-hand perspectives on deployment challenges, vendor selection criteria, and operational priorities. These interviews are complemented by case study analyses of implementations that highlight both successes and lessons learned, enabling comparative insights across deployment models and application domains.
Secondary research involves systematic review of standards, regulatory guidance, technical white papers, and industry publications that inform interoperability, security, and deployment best practices. Data triangulation is used to reconcile divergent perspectives and to ensure conclusions are robust across multiple evidence streams. Throughout the process, methodological rigor is maintained via clear documentation of assumptions, validation of key findings with subject-matter experts, and attention to ethical considerations in the use of clinical and administrative datasets.
A concise conclusion that synthesizes the strategic implications of cloud-based healthcare analytics and the priority actions needed to convert capability into measurable clinical and operational outcomes
In summary, cloud-based analytics represents a strategic lever for organizations aiming to improve clinical outcomes, operational efficiency, and financial resilience. The convergence of advanced analytics, standards-based interoperability, and evolving procurement practices has created an environment in which carefully designed cloud strategies can deliver persistent value. However, realizing that value requires deliberate attention to governance, workforce capabilities, and resilient sourcing strategies that mitigate policy and supply-chain uncertainty.
Decision makers should therefore prioritize high-impact use cases, adopt hybrid deployment postures that respect data sovereignty, and structure vendor relationships to emphasize long-term outcomes and operational support. With these elements in place, healthcare organizations and payers will be better positioned to convert analytic potential into measurable improvements in care delivery and system performance.
Note: PDF & Excel + Online Access - 1 Year
An executive-oriented introduction to cloud-enabled healthcare analytics that explains strategic priorities, operational tensions, and governance considerations
Cloud-based analytics is rapidly becoming the backbone of healthcare transformation, reshaping how data is collected, analyzed, and operationalized across clinical and administrative domains. Organizations increasingly view analytics not as an adjunct capability but as a strategic imperative that underpins care coordination, population health management, revenue cycle optimization, and supply chain resilience. As a result, leaders are prioritizing architectures and operating models that enable secure, scalable, and interoperable analytics workflows that extend from edge devices to centralized cloud platforms.
Transitioning to cloud-native analytics environments involves reconciling multiple priorities: preserving patient privacy, ensuring regulatory compliance, and sustaining high availability while delivering meaningful insights at the point of care. This introduction frames the strategic context for stakeholders who must balance clinical imperatives with fiscal responsibility, acknowledging that successful programs blend technology adoption with governance, talent development, and process redesign. From this vantage point, investment in cloud-enabled analytics becomes both a catalyst for improved outcomes and a vehicle for organizational resilience.
A forward-looking analysis of the fundamental industry shifts driving cloud analytics adoption, interoperability advances, and governance imperatives for healthcare systems
The healthcare analytics landscape is undergoing transformative shifts driven by technological maturation, policy evolution, and changing care delivery models that demand near-real-time intelligence. Modern analytics stacks now integrate advanced machine learning with streaming data pipelines, enabling predictive alerts and prescriptive recommendations that support clinicians and care teams. Simultaneously, standards-based interoperability and API-first designs are lowering barriers to multi-vendor compositions, allowing health systems to orchestrate best-of-breed solutions rather than relying solely on monolithic platforms.
In addition, economic and regulatory forces are reshaping procurement and deployment decisions. Value-based care commitments and quality-driven reimbursement models are incentivizing investments in analytics that demonstrate outcome improvement and cost containment. At the same time, cyber risk and privacy concerns have elevated the importance of robust data governance, encryption, and identity management strategies. Together, these shifts compel leaders to prioritize architectures that are flexible, auditable, and tightly aligned to clinical workflows so that analytic insights translate into measurable improvements at scale.
An in-depth look at how the United States tariffs announced for 2025 reshape procurement, supply-chain strategies, and cloud-versus-on-premises decisions in healthcare analytics
Policy changes and trade measures can alter the economics and timing of technology adoption, and the tariff landscape announced for 2025 in the United States has introduced new considerations for healthcare organizations and their technology partners. Tariffs affecting imported hardware and specialized medical devices can increase the total cost of ownership for on-premises infrastructure, making cloud migration or hybrid models more attractive for organizations seeking capital flexibility. Moreover, supply-chain disruptions tied to tariff-driven sourcing adjustments have led many vendors to re-evaluate manufacturing footprints and procurement strategies, with downstream effects on delivery schedules and integration timelines.
Consequently, healthcare leaders are weighing short-term supply-chain volatility against the longer-term benefits of cloud-based consumption models that shift capital outlays to operational expenses. Providers and payers must also consider contractual protections and procurement clauses that mitigate exposure to sudden cost increases, while technology vendors are taking steps to localize critical components and diversify supplier bases. In practical terms, the tariff environment has accelerated evaluation of hybrid deployments, contract renegotiations, and contingency planning so organizations can maintain service continuity amid shifting trade dynamics.
A nuanced segmentation framework revealing how components, deployment choices, analytics modality, application domains, and end-user needs shape strategic decisions for analytics programs
Strategic segmentation clarifies where value is created and how deployment choices affect outcomes across distinct use cases. Based on Component, market is studied across Services and Software. The Services is further studied across Managed Services and Professional Services. This distinction underscores that procurement decisions are not solely about software capabilities but also about the depth and continuity of services required to implement, operate, and scale analytic solutions. Organizations that underinvest in services frequently encounter delayed time-to-value and inconsistent adoption across clinical teams.
Deployment options materially influence architecture and governance trade-offs. Based on Deployment Mode, market is studied across Hybrid, Private, and Public. Hybrid approaches often emerge as the pragmatic choice for health systems that need to balance data residency and latency requirements with the scalability advantages of public cloud. Analytics modalities shape the nature of analytic outputs and their operational integration. Based on Analytics Type, market is studied across Descriptive, Predictive, and Prescriptive. Descriptive capabilities remain foundational, while predictive models and prescriptive engines are increasingly embedded into care pathways and operational processes to enable proactive interventions.
Application-level segmentation directs attention to the primary operational domains where analytics drives impact. Based on Application, market is studied across Claims Management, Financial Management, Patient Management, Remote Monitoring, and Supply Chain Management. Each application domain demands tailored data models, domain-specific ontologies, and workflow integration strategies. Finally, end-user segmentation clarifies stakeholder needs and adoption vectors. Based on End User, market is studied across Life Sciences, Payers, and Providers. Life sciences organizations prioritize analytics for trial optimization and real-world evidence, payers focus on risk stratification and fraud detection, and providers concentrate on clinical decision support and operational efficiency. Together, these segment lenses provide a multidimensional framework for prioritizing investments and aligning vendor selection with desired outcomes.
A regional analysis describing how adoption patterns, regulatory regimes, and local ecosystems in Americas, Europe, Middle East & Africa, and Asia-Pacific influence analytics deployment and partnerships
Geographic dynamics play a pivotal role in adoption patterns, regulatory expectations, and partnership ecosystems. In the Americas, large integrated delivery networks and forward-looking payers are driving demand for scalable analytics that support population health initiatives and value-based contracts, while cross-border data flows and regional privacy regimes shape how cloud solutions are deployed. In Europe, Middle East & Africa, the regulatory mosaic and emphasis on data sovereignty require flexible deployment options and strong local partnerships, with several markets prioritizing standards-based interoperability and national digital health strategies that incentivize analytics-driven care models.
Across Asia-Pacific, the pace of digital transformation varies markedly, but large public and private systems are rapidly adopting remote monitoring and AI-assisted workflows to address capacity constraints and aging populations. Regional centers of excellence and vendor ecosystems are emerging that combine cloud infrastructure with localized clinical models. These geographic insights indicate that successful commercialization strategies must adapt to regulatory complexity, cultural expectations around data use, and the maturity of local health IT ecosystems, while also leveraging regional strengths in innovation and scale.
Insights into vendor strategies, partnerships, and service innovations that determine how infrastructure providers and specialty analytics firms deliver clinical and operational value across health systems
Vendor dynamics reveal a bifurcated market where hyperscale cloud providers and specialized healthcare analytics firms coexist and collaborate to meet complex customer needs. Leading infrastructure providers are focused on compliance, scalability, and platform services that enable rapid model deployment, while specialist vendors emphasize domain expertise, validated algorithms, and clinical workflow integration. Partnerships and strategic alliances are increasingly common, allowing organizations to combine the security and reach of major cloud platforms with the clinical depth and regulatory experience of niche solution providers.
In addition, companies are differentiating through investment in certified data models, interoperability toolkits, and validated clinical content that reduce implementation risk and accelerate adoption. Service models are maturing, with managed service offerings that bundle ongoing model management, performance monitoring, and regulatory reporting. This evolution reflects a market where customers value both the technical robustness of the platform and the operational discipline required to sustain analytic programs over time, prompting vendors to compete on outcomes and long-term support rather than feature sets alone.
A tactical set of actionable recommendations for healthcare and payer leaders to accelerate analytics adoption, strengthen governance, and secure resilient vendor and procurement arrangements
Leaders seeking to realize the full potential of cloud-based analytics should adopt a pragmatic, outcome-driven roadmap that balances quick wins with sustainable capability building. Begin by aligning analytics initiatives to specific clinical and operational problems where measurable outcomes can be demonstrated, and ensure executive sponsorship and clinical leadership are engaged to drive adoption. Investing in a robust data governance framework, including clear ownership, standardized metadata, and privacy-protecting architectures, will reduce friction during deployment and increase stakeholder confidence in analytic outputs.
Operationally, organizations should favor modular architectures that support hybrid deployment, enabling sensitive data to remain on-premises while leveraging public cloud scalability for compute-intensive workloads. Strengthening vendor contracts to include service level guarantees, model governance support, and provisions that address supply-chain or tariff-related disruptions will provide additional resiliency. Finally, cultivate multidisciplinary teams that combine clinical subject-matter expertise with data engineering and product management skills to ensure models are maintained, validated, and improved in production environments, thereby converting analytic insight into sustained clinical impact.
A clear exposition of the mixed-methods research approach combining primary interviews, case studies, secondary analysis, and data triangulation to validate practical recommendations
The research methodology blends qualitative and quantitative approaches to ensure findings are validated and actionable for decision makers. Primary research includes in-depth interviews with health system executives, payer strategists, clinical leaders, and technology architects to capture first-hand perspectives on deployment challenges, vendor selection criteria, and operational priorities. These interviews are complemented by case study analyses of implementations that highlight both successes and lessons learned, enabling comparative insights across deployment models and application domains.
Secondary research involves systematic review of standards, regulatory guidance, technical white papers, and industry publications that inform interoperability, security, and deployment best practices. Data triangulation is used to reconcile divergent perspectives and to ensure conclusions are robust across multiple evidence streams. Throughout the process, methodological rigor is maintained via clear documentation of assumptions, validation of key findings with subject-matter experts, and attention to ethical considerations in the use of clinical and administrative datasets.
A concise conclusion that synthesizes the strategic implications of cloud-based healthcare analytics and the priority actions needed to convert capability into measurable clinical and operational outcomes
In summary, cloud-based analytics represents a strategic lever for organizations aiming to improve clinical outcomes, operational efficiency, and financial resilience. The convergence of advanced analytics, standards-based interoperability, and evolving procurement practices has created an environment in which carefully designed cloud strategies can deliver persistent value. However, realizing that value requires deliberate attention to governance, workforce capabilities, and resilient sourcing strategies that mitigate policy and supply-chain uncertainty.
Decision makers should therefore prioritize high-impact use cases, adopt hybrid deployment postures that respect data sovereignty, and structure vendor relationships to emphasize long-term outcomes and operational support. With these elements in place, healthcare organizations and payers will be better positioned to convert analytic potential into measurable improvements in care delivery and system performance.
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. Optimizing predictive clinical decision support through AI-driven cloud analytics for patient outcome improvement
- 5.2. Ensuring HIPAA-compliant multi-cloud data governance frameworks for healthcare analytics scalability
- 5.3. Enabling real-time remote patient monitoring and telehealth data integration on cloud platforms
- 5.4. Aggregating and analyzing unstructured EHR and medical imaging data in cloud-native data lakes for insights
- 5.5. Leveraging FHIR-based interoperability to streamline cross-institution healthcare data exchange in the cloud
- 5.6. Implementing advanced security protocols such as homomorphic encryption for protecting sensitive health analytics
- 5.7. Integrating patient-generated wearable and IoT data streams into cloud analytics for personalized care
- 5.8. Utilizing predictive population health analytics on cloud platforms to identify high-risk patient cohorts
- 5.9. Adopting edge computing in conjunction with cloud analytics to reduce latency in critical care monitoring
- 5.10. Driving value-based care reimbursement through real-time cloud analytics of cost and quality performance metrics
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Healthcare Cloud Based Analytics Market, by Component
- 8.1. Services
- 8.1.1. Managed Services
- 8.1.2. Professional Services
- 8.2. Software
- 9. Healthcare Cloud Based Analytics Market, by Analytics Type
- 9.1. Descriptive
- 9.2. Predictive
- 9.3. Prescriptive
- 10. Healthcare Cloud Based Analytics Market, by Deployment Mode
- 10.1. Hybrid
- 10.2. Private
- 10.3. Public
- 11. Healthcare Cloud Based Analytics Market, by Application
- 11.1. Claims Management
- 11.2. Financial Management
- 11.3. Patient Management
- 11.4. Remote Monitoring
- 11.5. Supply Chain Management
- 12. Healthcare Cloud Based Analytics Market, by End User
- 12.1. Life Sciences
- 12.2. Payers
- 12.3. Providers
- 13. Healthcare Cloud Based 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. Healthcare Cloud Based Analytics Market, by Group
- 14.1. ASEAN
- 14.2. GCC
- 14.3. European Union
- 14.4. BRICS
- 14.5. G7
- 14.6. NATO
- 15. Healthcare Cloud Based 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. Allscripts Healthcare Solutions, Inc.
- 16.3.2. Amazon Web Services, Inc.
- 16.3.3. Athenahealth, Inc.
- 16.3.4. Cerner Corporation
- 16.3.5. Change Healthcare Inc.
- 16.3.6. Cognizant Technology Solutions Corporation
- 16.3.7. Domo, Inc.
- 16.3.8. Epic Systems Corporation
- 16.3.9. Google LLC
- 16.3.10. Health Catalyst, Inc.
- 16.3.11. IBM Corporation
- 16.3.12. Inovalon Holdings, Inc.
- 16.3.13. Microsoft Corporation
- 16.3.14. Oracle Corporation
- 16.3.15. SAS Institute Inc.
- 16.3.16. Siemens Healthineers AG
- 16.3.17. Tableau Software, LLC
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