Cloud Business Analytics Market by Component (Services, Solution), End Users (Banking Financial Services Insurance, Healthcare Life Sciences, IT & Telecom), Application, Deployment Model, Organization Size - Global Forecast 2025-2032
Description
The Cloud Business Analytics Market was valued at USD 40.98 billion in 2024 and is projected to grow to USD 49.90 billion in 2025, with a CAGR of 21.72%, reaching USD 197.50 billion by 2032.
An essential overview of how cloud business analytics is redefining organizational decision making by embedding real time insights into core processes
Cloud business analytics continues to evolve as organizations seek to convert growing volumes of data into timely, defensible decisions. Adoption extends beyond technical pilots into operational workflows, where analytics informs customer engagement, risk management, fraud detection, and supply chain optimization. Increasingly, analytics is embedded within applications and processes, shifting expectations from retrospective dashboards to real-time, prescriptive insights that directly influence business outcomes.
Technology advances such as wider availability of managed analytics platforms, the maturation of feature stores for ML models, and integrated observability tooling enable more consistent model governance and faster time-to-value. At the same time, organizational dynamics are adapting: data teams collaborate more closely with product, operations, and compliance functions to prioritize use cases that deliver measurable outcomes. This confluence of capabilities and organizational change positions cloud business analytics as a strategic competency rather than a discretionary IT investment.
Emerging technology, governance expectations, and architecture portability are driving a fundamental transformation in cloud analytics operational models
The landscape for cloud analytics is experiencing transformative shifts driven by a convergence of technology, regulation, and buyer expectations. First, generative AI and advanced machine learning have moved from experimental projects to production-grade components of analytics stacks, enabling more sophisticated natural language interfaces and automated insight generation. These capabilities accelerate adoption but also raise governance and explainability demands that organizations must address to maintain trust and regulatory compliance.
Second, the rise of hybrid and multicloud architectures is changing how analytics platforms are designed and consumed. Teams now expect seamless portability of data and models across on-premises and cloud environments, which compels vendors to offer stronger interoperability and standardized APIs. Third, sustainability concerns and energy efficiency priorities are influencing technology selection, with buyers favoring providers that disclose energy use and offer carbon-aware compute options. Finally, the talent pipeline is shifting; organizations increasingly combine specialized data scientists with citizen analysts supported by low-code tools to scale analytics while retaining central governance. Together, these shifts create both opportunity and complexity, requiring firms to balance innovation with robust operational controls.
How the 2025 tariff measures have reshaped procurement choices and accelerated cloud adoption while forcing vendors to rethink supply chain localization
The imposition of tariffs in 2025 has reshaped cost and procurement dynamics for cloud analytics solutions in tangible ways. Increased duties on certain hardware components and on-premises systems have accelerated the migration to cloud-native managed services for organizations seeking to avoid upfront capital expenditures and the logistics of cross-border hardware procurement. Consequently, procurement strategies have shifted toward consumption-based models and flexible contracts that de-risk exposure to tariff volatility.
Tariff-driven cost adjustments have also impacted vendors’ cost structures, prompting many suppliers to localize aspects of their supply chains and partner ecosystems to mitigate customs exposure. This localization has reduced lead times for on-premises deployments but has introduced regional variability in available configurations and pricing. Simultaneously, organizations have reassessed total cost of ownership considerations, placing greater emphasis on lifecycle costs such as data egress, storage efficiency, and model retraining overheads rather than only initial procurement price.
Finally, the tariffs have influenced strategic sourcing decisions. Enterprises that previously preferred hardware ownership are increasingly balancing control with agility, moving non-differentiating workloads to cloud providers while keeping sensitive or latency-critical workloads closer to the user base. This rebalancing affects architecture patterns, procurement cycles, and vendor negotiations across the analytics ecosystem.
In depth segmentation insights exposing how deployment choices, component mixes, enterprise size, vertical demands, and application priorities shape analytics adoption
Segmentation analysis reveals how deployment patterns, component preferences, organizational characteristics, industry verticals, and application use cases shape adoption pathways and solution requirements. Based on deployment model, organizations evaluate trade-offs between hybrid cloud for latency-sensitive and regulated workloads, private cloud for enhanced control and data residency, and public cloud for scalability and operational simplicity, each choice informing integration and governance needs. Based on component, buyers weigh the balance between services and solution investments, often prioritizing managed services to accelerate adoption while leveraging professional services for complex integrations and managed services for ongoing operations.
Based on organization size, large enterprises emphasize enterprise-grade security, cross-border compliance, and integration with legacy systems, whereas small and medium enterprises prioritize rapid time-to-value, cost efficiency, and simplified tooling that reduces reliance on specialized talent. Based on end users, sectors such as banking, financial services and insurance demand high-assurance models and auditability, healthcare and life sciences require strict data privacy and longitudinal analytics capabilities, IT and telecom prioritize scalable streaming analytics and network telemetry, manufacturing focuses on operations and predictive maintenance, and retail and e-commerce concentrate on customer analytics and personalization. Finally, based on application, purchasers differentiate among customer analytics for personalization and lifetime value optimization, fraud analytics for anomaly detection and real-time interdiction, operations analytics for process optimization and predictive maintenance, risk analytics for stress-testing and regulatory reporting, and sales analytics for pipeline management and pricing optimization. These segmentation lenses collectively drive product roadmaps, go-to-market strategies, and implementation frameworks across vendors and adopters.
How regional regulatory diversity, infrastructure maturity, and partnership ecosystems are steering cloud analytics deployment and vendor choice globally
Regional dynamics significantly influence technology selection, regulatory compliance, and partnership strategies across the cloud analytics landscape. In the Americas, demand centers often prioritize scale, advanced analytics capabilities, and rapid innovation cycles, with strong interest in cloud-native managed platforms and vendor-native AI integrations. Data governance regimes in certain jurisdictions require careful handling of cross-border flows, but commercial appetite for consumption-based pricing and advanced cloud services remains robust.
In Europe, Middle East & Africa, regulatory complexity and data residency requirements are pronounced, compelling many organizations to adopt hybrid models that keep sensitive data on-premises or within region-specific clouds. This region also demonstrates heightened sensitivity to privacy and ethical AI considerations, which influences vendor selection and the structure of contractual safeguards. In the Asia-Pacific region, diversity in infrastructure maturity and regulatory regimes leads to a mix of strategies: some markets leapfrog directly to fully cloud-native deployments, while others retain significant private cloud footprints due to sovereignty concerns. Across regions, local partnerships, data center footprints, and compliance certifications are decisive factors in technology adoption and deployment timelines.
Competitive dynamics and vendor differentiation in cloud analytics shaped by platform breadth, vertical specialization, and operational readiness
The competitive environment in cloud business analytics combines global cloud providers, specialist analytics platforms, systems integrators, and niche vertical solution vendors, each presenting distinct value propositions. Global cloud providers offer broad platform services and managed analytics capabilities that reduce operational burden and enable rapid experimentation, whereas specialist analytics vendors deliver domain-specific tooling, advanced model management, and tailored connectors that address industry nuances. Systems integrators and professional services firms translate platform capability into operational reality by managing complex migrations, data harmonization, and governance frameworks.
Market leaders differentiate through investments in model explainability, integrated MLOps pipelines, and partnerships that extend data acquisition and enrichment capabilities. Emerging vendors carve out space with focused offerings for verticals such as healthcare, financial services, and manufacturing, embedding regulatory controls and lineage tracking relevant to those industries. Additionally, an ecosystem of middleware and observability tools supports higher levels of operational maturity, enabling organizations to monitor model drift, ensure reproducibility, and automate compliance reporting. Competitive dynamics favor vendors that balance innovation with enterprise readiness, offering extensible platforms that interoperate with established data infrastructures and third-party operational tooling.
Actionable strategic steps to accelerate analytics adoption while ensuring governance, portability, and measurable business outcomes in cloud environments
Leaders should prioritize governance, interoperability, and outcome-focused adoption to realize the full potential of cloud analytics. First, embed robust data and model governance early in the transformation; establishing lineage, access controls, and explainability requirements reduces risk and accelerates stakeholder confidence. Implementing governance as code and integrating it into CI/CD pipelines ensures that controls scale with increasing model proliferation.
Second, adopt an interoperability-first architecture that supports hybrid deployments and avoids vendor lock-in. Designing data contracts, leveraging standardized APIs, and investing in portable model formats enable teams to move workloads fluidly across environments in response to cost, compliance, or performance considerations. Third, align analytics initiatives to measurable business outcomes by defining KPIs tied to revenue, cost, or risk mitigation, and maintain a portfolio approach that balances quick wins with strategic investments. Additionally, upskill internal teams through cross-functional training and promote the use of low-code tooling to expand analytic capacity without sacrificing governance.
Finally, reassess procurement practices to favor flexible contracts and local sourcing where tariffs and supply chain variability pose risks. Encourage vendors to commit to transparency in pricing, energy usage, and compliance certifications, and incorporate those metrics into procurement scorecards. By implementing these actions, leaders can accelerate adoption while managing operational, regulatory, and financial exposure.
A transparent mixed methods approach combining primary interviews, secondary technical sources, and systematic triangulation to validate cloud analytics insights
The research synthesized secondary literature, vendor documentation, regulatory texts, and interviews with technology leaders and practitioners to construct a robust evidence base. Secondary sources included white papers, technical specifications, and publicly available policy statements that clarify regulatory obligations and product capabilities. Primary research consisted of structured interviews with chief data officers, cloud architects, heads of analytics, and procurement executives across a range of industry sectors to capture decision criteria, implementation challenges, and vendor evaluation practices.
Qualitative findings were triangulated across sources to validate themes such as governance priorities, hybrid architecture adoption, and the influence of tariffs on procurement. The methodology emphasized reproducibility and transparency: interview guides, coding schemas, and criteria for inclusion were standardized to reduce bias. Wherever possible, technical claims were cross-checked against vendor documentation and independent technical reviews. Limitations of the study include the evolving nature of regulatory landscapes and the rapid pace of technological change, which can alter vendor capabilities and buyer preferences; to mitigate this, the research incorporated the most recent public disclosures and first-hand practitioner feedback available at the time of analysis.
Concluding synthesis on the strategic imperatives for operationalizing cloud analytics to convert insight into sustained business value
Cloud business analytics stands at an inflection point where technology capability, regulatory scrutiny, and commercial pragmatism converge to reshape enterprise decisioning. Organizations that proactively embed governance, adopt interoperable architectures, and align analytics initiatives with measurable business outcomes will secure competitive advantage. Conversely, those that treat analytics as an isolated IT initiative risk fragmentation, model sprawl, and compliance exposure that undercut potential value.
The interplay of tariffs, regional regulations, and evolving vendor offerings requires a dynamic approach to procurement and deployment. By adopting flexible contracts, prioritizing local partnerships where necessary, and focusing on lifecycle costs, organizations can remain resilient to supply chain shocks and regulatory shifts. Ultimately, success will be determined by the ability to operationalize insights at scale-through repeatable MLOps practices, cross-functional collaboration, and continued investment in explainability and observability-thus turning analytic capability into sustained business impact.
Please Note: PDF & Excel + Online Access - 1 Year
An essential overview of how cloud business analytics is redefining organizational decision making by embedding real time insights into core processes
Cloud business analytics continues to evolve as organizations seek to convert growing volumes of data into timely, defensible decisions. Adoption extends beyond technical pilots into operational workflows, where analytics informs customer engagement, risk management, fraud detection, and supply chain optimization. Increasingly, analytics is embedded within applications and processes, shifting expectations from retrospective dashboards to real-time, prescriptive insights that directly influence business outcomes.
Technology advances such as wider availability of managed analytics platforms, the maturation of feature stores for ML models, and integrated observability tooling enable more consistent model governance and faster time-to-value. At the same time, organizational dynamics are adapting: data teams collaborate more closely with product, operations, and compliance functions to prioritize use cases that deliver measurable outcomes. This confluence of capabilities and organizational change positions cloud business analytics as a strategic competency rather than a discretionary IT investment.
Emerging technology, governance expectations, and architecture portability are driving a fundamental transformation in cloud analytics operational models
The landscape for cloud analytics is experiencing transformative shifts driven by a convergence of technology, regulation, and buyer expectations. First, generative AI and advanced machine learning have moved from experimental projects to production-grade components of analytics stacks, enabling more sophisticated natural language interfaces and automated insight generation. These capabilities accelerate adoption but also raise governance and explainability demands that organizations must address to maintain trust and regulatory compliance.
Second, the rise of hybrid and multicloud architectures is changing how analytics platforms are designed and consumed. Teams now expect seamless portability of data and models across on-premises and cloud environments, which compels vendors to offer stronger interoperability and standardized APIs. Third, sustainability concerns and energy efficiency priorities are influencing technology selection, with buyers favoring providers that disclose energy use and offer carbon-aware compute options. Finally, the talent pipeline is shifting; organizations increasingly combine specialized data scientists with citizen analysts supported by low-code tools to scale analytics while retaining central governance. Together, these shifts create both opportunity and complexity, requiring firms to balance innovation with robust operational controls.
How the 2025 tariff measures have reshaped procurement choices and accelerated cloud adoption while forcing vendors to rethink supply chain localization
The imposition of tariffs in 2025 has reshaped cost and procurement dynamics for cloud analytics solutions in tangible ways. Increased duties on certain hardware components and on-premises systems have accelerated the migration to cloud-native managed services for organizations seeking to avoid upfront capital expenditures and the logistics of cross-border hardware procurement. Consequently, procurement strategies have shifted toward consumption-based models and flexible contracts that de-risk exposure to tariff volatility.
Tariff-driven cost adjustments have also impacted vendors’ cost structures, prompting many suppliers to localize aspects of their supply chains and partner ecosystems to mitigate customs exposure. This localization has reduced lead times for on-premises deployments but has introduced regional variability in available configurations and pricing. Simultaneously, organizations have reassessed total cost of ownership considerations, placing greater emphasis on lifecycle costs such as data egress, storage efficiency, and model retraining overheads rather than only initial procurement price.
Finally, the tariffs have influenced strategic sourcing decisions. Enterprises that previously preferred hardware ownership are increasingly balancing control with agility, moving non-differentiating workloads to cloud providers while keeping sensitive or latency-critical workloads closer to the user base. This rebalancing affects architecture patterns, procurement cycles, and vendor negotiations across the analytics ecosystem.
In depth segmentation insights exposing how deployment choices, component mixes, enterprise size, vertical demands, and application priorities shape analytics adoption
Segmentation analysis reveals how deployment patterns, component preferences, organizational characteristics, industry verticals, and application use cases shape adoption pathways and solution requirements. Based on deployment model, organizations evaluate trade-offs between hybrid cloud for latency-sensitive and regulated workloads, private cloud for enhanced control and data residency, and public cloud for scalability and operational simplicity, each choice informing integration and governance needs. Based on component, buyers weigh the balance between services and solution investments, often prioritizing managed services to accelerate adoption while leveraging professional services for complex integrations and managed services for ongoing operations.
Based on organization size, large enterprises emphasize enterprise-grade security, cross-border compliance, and integration with legacy systems, whereas small and medium enterprises prioritize rapid time-to-value, cost efficiency, and simplified tooling that reduces reliance on specialized talent. Based on end users, sectors such as banking, financial services and insurance demand high-assurance models and auditability, healthcare and life sciences require strict data privacy and longitudinal analytics capabilities, IT and telecom prioritize scalable streaming analytics and network telemetry, manufacturing focuses on operations and predictive maintenance, and retail and e-commerce concentrate on customer analytics and personalization. Finally, based on application, purchasers differentiate among customer analytics for personalization and lifetime value optimization, fraud analytics for anomaly detection and real-time interdiction, operations analytics for process optimization and predictive maintenance, risk analytics for stress-testing and regulatory reporting, and sales analytics for pipeline management and pricing optimization. These segmentation lenses collectively drive product roadmaps, go-to-market strategies, and implementation frameworks across vendors and adopters.
How regional regulatory diversity, infrastructure maturity, and partnership ecosystems are steering cloud analytics deployment and vendor choice globally
Regional dynamics significantly influence technology selection, regulatory compliance, and partnership strategies across the cloud analytics landscape. In the Americas, demand centers often prioritize scale, advanced analytics capabilities, and rapid innovation cycles, with strong interest in cloud-native managed platforms and vendor-native AI integrations. Data governance regimes in certain jurisdictions require careful handling of cross-border flows, but commercial appetite for consumption-based pricing and advanced cloud services remains robust.
In Europe, Middle East & Africa, regulatory complexity and data residency requirements are pronounced, compelling many organizations to adopt hybrid models that keep sensitive data on-premises or within region-specific clouds. This region also demonstrates heightened sensitivity to privacy and ethical AI considerations, which influences vendor selection and the structure of contractual safeguards. In the Asia-Pacific region, diversity in infrastructure maturity and regulatory regimes leads to a mix of strategies: some markets leapfrog directly to fully cloud-native deployments, while others retain significant private cloud footprints due to sovereignty concerns. Across regions, local partnerships, data center footprints, and compliance certifications are decisive factors in technology adoption and deployment timelines.
Competitive dynamics and vendor differentiation in cloud analytics shaped by platform breadth, vertical specialization, and operational readiness
The competitive environment in cloud business analytics combines global cloud providers, specialist analytics platforms, systems integrators, and niche vertical solution vendors, each presenting distinct value propositions. Global cloud providers offer broad platform services and managed analytics capabilities that reduce operational burden and enable rapid experimentation, whereas specialist analytics vendors deliver domain-specific tooling, advanced model management, and tailored connectors that address industry nuances. Systems integrators and professional services firms translate platform capability into operational reality by managing complex migrations, data harmonization, and governance frameworks.
Market leaders differentiate through investments in model explainability, integrated MLOps pipelines, and partnerships that extend data acquisition and enrichment capabilities. Emerging vendors carve out space with focused offerings for verticals such as healthcare, financial services, and manufacturing, embedding regulatory controls and lineage tracking relevant to those industries. Additionally, an ecosystem of middleware and observability tools supports higher levels of operational maturity, enabling organizations to monitor model drift, ensure reproducibility, and automate compliance reporting. Competitive dynamics favor vendors that balance innovation with enterprise readiness, offering extensible platforms that interoperate with established data infrastructures and third-party operational tooling.
Actionable strategic steps to accelerate analytics adoption while ensuring governance, portability, and measurable business outcomes in cloud environments
Leaders should prioritize governance, interoperability, and outcome-focused adoption to realize the full potential of cloud analytics. First, embed robust data and model governance early in the transformation; establishing lineage, access controls, and explainability requirements reduces risk and accelerates stakeholder confidence. Implementing governance as code and integrating it into CI/CD pipelines ensures that controls scale with increasing model proliferation.
Second, adopt an interoperability-first architecture that supports hybrid deployments and avoids vendor lock-in. Designing data contracts, leveraging standardized APIs, and investing in portable model formats enable teams to move workloads fluidly across environments in response to cost, compliance, or performance considerations. Third, align analytics initiatives to measurable business outcomes by defining KPIs tied to revenue, cost, or risk mitigation, and maintain a portfolio approach that balances quick wins with strategic investments. Additionally, upskill internal teams through cross-functional training and promote the use of low-code tooling to expand analytic capacity without sacrificing governance.
Finally, reassess procurement practices to favor flexible contracts and local sourcing where tariffs and supply chain variability pose risks. Encourage vendors to commit to transparency in pricing, energy usage, and compliance certifications, and incorporate those metrics into procurement scorecards. By implementing these actions, leaders can accelerate adoption while managing operational, regulatory, and financial exposure.
A transparent mixed methods approach combining primary interviews, secondary technical sources, and systematic triangulation to validate cloud analytics insights
The research synthesized secondary literature, vendor documentation, regulatory texts, and interviews with technology leaders and practitioners to construct a robust evidence base. Secondary sources included white papers, technical specifications, and publicly available policy statements that clarify regulatory obligations and product capabilities. Primary research consisted of structured interviews with chief data officers, cloud architects, heads of analytics, and procurement executives across a range of industry sectors to capture decision criteria, implementation challenges, and vendor evaluation practices.
Qualitative findings were triangulated across sources to validate themes such as governance priorities, hybrid architecture adoption, and the influence of tariffs on procurement. The methodology emphasized reproducibility and transparency: interview guides, coding schemas, and criteria for inclusion were standardized to reduce bias. Wherever possible, technical claims were cross-checked against vendor documentation and independent technical reviews. Limitations of the study include the evolving nature of regulatory landscapes and the rapid pace of technological change, which can alter vendor capabilities and buyer preferences; to mitigate this, the research incorporated the most recent public disclosures and first-hand practitioner feedback available at the time of analysis.
Concluding synthesis on the strategic imperatives for operationalizing cloud analytics to convert insight into sustained business value
Cloud business analytics stands at an inflection point where technology capability, regulatory scrutiny, and commercial pragmatism converge to reshape enterprise decisioning. Organizations that proactively embed governance, adopt interoperable architectures, and align analytics initiatives with measurable business outcomes will secure competitive advantage. Conversely, those that treat analytics as an isolated IT initiative risk fragmentation, model sprawl, and compliance exposure that undercut potential value.
The interplay of tariffs, regional regulations, and evolving vendor offerings requires a dynamic approach to procurement and deployment. By adopting flexible contracts, prioritizing local partnerships where necessary, and focusing on lifecycle costs, organizations can remain resilient to supply chain shocks and regulatory shifts. Ultimately, success will be determined by the ability to operationalize insights at scale-through repeatable MLOps practices, cross-functional collaboration, and continued investment in explainability and observability-thus turning analytic capability into sustained business impact.
Please Note: PDF & Excel + Online Access - 1 Year
Table of Contents
190 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. Integration of real-time IoT data streams into cloud-based analytics platforms for predictive maintenance and operational optimization
- 5.2. Adoption of AI-driven augmented analytics tools to automate data preparation and insight generation at scale
- 5.3. Migration of legacy on-premises data warehouses to hybrid multi-cloud analytics ecosystems for flexibility and cost savings
- 5.4. Implementation of privacy-preserving federated learning in cloud analytics to comply with data sovereignty regulations
- 5.5. Development of industry-specific cloud analytics solutions leveraging vertical data models and domain ontologies
- 5.6. Increasing reliance on edge computing analytics to process data near source and reduce latency in cloud environments
- 5.7. Convergence of cloud-based business intelligence with natural language processing for conversational data exploration
- 5.8. Emergence of no-code/low-code cloud analytics platforms empowering citizen analysts to build dashboards independently
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Cloud Business Analytics Market, by Component
- 8.1. Services
- 8.1.1. Managed Services
- 8.1.2. Professional Services
- 8.2. Solution
- 9. Cloud Business Analytics Market, by End Users
- 9.1. Banking Financial Services Insurance
- 9.2. Healthcare Life Sciences
- 9.3. IT & Telecom
- 9.4. Manufacturing
- 9.5. Retail eCommerce
- 10. Cloud Business Analytics Market, by Application
- 10.1. Customer Analytics
- 10.2. Fraud Analytics
- 10.3. Operations Analytics
- 10.4. Risk Analytics
- 10.5. Sales Analytics
- 11. Cloud Business Analytics Market, by Deployment Model
- 11.1. Hybrid Cloud
- 11.2. Private Cloud
- 11.3. Public Cloud
- 12. Cloud Business Analytics Market, by Organization Size
- 12.1. Large Enterprises
- 12.2. Small & Medium Enterprises
- 13. Cloud Business 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. Cloud Business Analytics Market, by Group
- 14.1. ASEAN
- 14.2. GCC
- 14.3. European Union
- 14.4. BRICS
- 14.5. G7
- 14.6. NATO
- 15. Cloud Business 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. Alibaba Cloud Computing Ltd.
- 16.3.2. Alteryx, Inc.
- 16.3.3. Amazon Web Services, Inc.
- 16.3.4. Domo, Inc.
- 16.3.5. Google LLC
- 16.3.6. International Business Machines Corporation
- 16.3.7. Microsoft Corporation
- 16.3.8. MicroStrategy Incorporated
- 16.3.9. Oracle Corporation
- 16.3.10. Pyramid Analytics B.V.
- 16.3.11. QlikTech International AB
- 16.3.12. Salesforce, Inc.
- 16.3.13. SAP SE
- 16.3.14. SAS Institute Inc.
- 16.3.15. Sisense Ltd.
- 16.3.16. Tableau Software, LLC
- 16.3.17. Teradata Corporation
- 16.3.18. ThoughtSpot, Inc.
- 16.3.19. TIBCO Software Inc.
- 16.3.20. Zoho Corporation Pvt. Ltd.
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