2026 Global: Connected Device Analytics Market-Competitive Review (2032) report
Description
The 2026 Global: Connected Device Analytics Market-Competitive Review (2031) report features the global market size and projected growth/decline data for the period 2021 through 2032. The report primarily provides an examination of the business strategies for the ten largest global companies in the market and how their strategies differ.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for connected device analytics market by geography and historical trend. The scope of the report extends to sizing of the connected device analytics market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
AWS (Amazon Web Services), Microsoft Azure, Google Cloud, Cisco, Siemens, IBM, PTC, Ericsson, Verizon, and Oracle are widely recognized as ten major companies shaping the Connected Device Analytics market due to their complementary strengths in cloud infrastructure, edge services, networking, and industry-specific analytics. AWS provides a broad IoT stack—IoT Core, Greengrass, and managed streaming/analytics services—that enables large-scale device ingestion, real-time processing, and ML model deployment across cloud and edge environments. Microsoft Azure pairs its IoT Hub and Azure IoT Edge with Fabric and Power BI to deliver integrated telemetry, time-series and real-time intelligence for enterprise workflows and digital-twin scenarios. Google Cloud combines Pub/Sub, Dataflow and BigQuery for streaming pipelines and sub-second analytics that power operational dashboards and product telemetry at scale. Cisco and Ericsson contribute essential networking, private wireless and edge orchestration capabilities that reduce latency and secure device connectivity for industrial and retail deployments. Siemens and PTC focus on industrial IoT and operations technology: Siemens’ MindSphere and PTC’s ThingWorx supply OT-grade telemetry, asset modeling and application ecosystems for manufacturing analytics and predictive maintenance workflows. IBM emphasizes hybrid cloud analytics and asset lifecycle intelligence, leveraging its enterprise services, Watson AI capabilities and edge solutions to help regulated industries operationalize device data. Verizon and other telecom incumbents extend analytics value by coupling pervasive low-latency connectivity, device management and vertical SaaS offerings—Verizon’s Sensor Insights exemplifies turnkey device-to-insight products for equipment monitoring and near-real-time intelligence. Oracle brings converged database analytics and edge options that support high-throughput telemetry ingest and enterprise governance for IoT data pipelines.
These companies succeed because they combine scalable streaming and OLAP analytics, device management, security, and edge compute into cohesive platforms that address the full device lifecycle and varied vertical requirements. Cloud-native streaming platforms (for example, managed Flink, Kafka/Confluent, and serverless pipelines) and high-performance OLAP stores (ClickHouse, BigQuery, Databricks lakehouse) are core to processing telemetry at scale, while vendor-specific services provide provisioning, firmware management, private networking, and edge orchestration needed by enterprises. Industry-focused vendors such as Siemens, PTC and Ericsson add domain models, OT connectors, and deterministic SLAs that accelerate adoption in manufacturing, utilities and critical infrastructure. Telecom and connectivity specialists (Verizon, Ericsson) pair SIM/eSIM management, private 5G and WAN services with analytics packages so customers can deploy distributed sensors and ingest data with predictable network behavior and security.
Adoption drivers include the need for real-time operational intelligence, predictive maintenance, and autonomous process control, which push enterprises to choose platforms that offer observability, fast ingest, managed streaming, and edge inferencing. Buyers also evaluate ecosystem breadth—prebuilt connectors, specialized vertical apps, and marketplaces—because integrating device telemetry with ERP, MES and AI models determines time-to-value. Cost, data residency, and hybrid deployment flexibility influence vendor selection, leading many organizations to a multi-vendor architecture that combines hyperscaler analytics with specialized OT and connectivity providers to meet performance, compliance, and domain-specific analytics requirements.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for connected device analytics market by geography and historical trend. The scope of the report extends to sizing of the connected device analytics market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
AWS (Amazon Web Services), Microsoft Azure, Google Cloud, Cisco, Siemens, IBM, PTC, Ericsson, Verizon, and Oracle are widely recognized as ten major companies shaping the Connected Device Analytics market due to their complementary strengths in cloud infrastructure, edge services, networking, and industry-specific analytics. AWS provides a broad IoT stack—IoT Core, Greengrass, and managed streaming/analytics services—that enables large-scale device ingestion, real-time processing, and ML model deployment across cloud and edge environments. Microsoft Azure pairs its IoT Hub and Azure IoT Edge with Fabric and Power BI to deliver integrated telemetry, time-series and real-time intelligence for enterprise workflows and digital-twin scenarios. Google Cloud combines Pub/Sub, Dataflow and BigQuery for streaming pipelines and sub-second analytics that power operational dashboards and product telemetry at scale. Cisco and Ericsson contribute essential networking, private wireless and edge orchestration capabilities that reduce latency and secure device connectivity for industrial and retail deployments. Siemens and PTC focus on industrial IoT and operations technology: Siemens’ MindSphere and PTC’s ThingWorx supply OT-grade telemetry, asset modeling and application ecosystems for manufacturing analytics and predictive maintenance workflows. IBM emphasizes hybrid cloud analytics and asset lifecycle intelligence, leveraging its enterprise services, Watson AI capabilities and edge solutions to help regulated industries operationalize device data. Verizon and other telecom incumbents extend analytics value by coupling pervasive low-latency connectivity, device management and vertical SaaS offerings—Verizon’s Sensor Insights exemplifies turnkey device-to-insight products for equipment monitoring and near-real-time intelligence. Oracle brings converged database analytics and edge options that support high-throughput telemetry ingest and enterprise governance for IoT data pipelines.
These companies succeed because they combine scalable streaming and OLAP analytics, device management, security, and edge compute into cohesive platforms that address the full device lifecycle and varied vertical requirements. Cloud-native streaming platforms (for example, managed Flink, Kafka/Confluent, and serverless pipelines) and high-performance OLAP stores (ClickHouse, BigQuery, Databricks lakehouse) are core to processing telemetry at scale, while vendor-specific services provide provisioning, firmware management, private networking, and edge orchestration needed by enterprises. Industry-focused vendors such as Siemens, PTC and Ericsson add domain models, OT connectors, and deterministic SLAs that accelerate adoption in manufacturing, utilities and critical infrastructure. Telecom and connectivity specialists (Verizon, Ericsson) pair SIM/eSIM management, private 5G and WAN services with analytics packages so customers can deploy distributed sensors and ingest data with predictable network behavior and security.
Adoption drivers include the need for real-time operational intelligence, predictive maintenance, and autonomous process control, which push enterprises to choose platforms that offer observability, fast ingest, managed streaming, and edge inferencing. Buyers also evaluate ecosystem breadth—prebuilt connectors, specialized vertical apps, and marketplaces—because integrating device telemetry with ERP, MES and AI models determines time-to-value. Cost, data residency, and hybrid deployment flexibility influence vendor selection, leading many organizations to a multi-vendor architecture that combines hyperscaler analytics with specialized OT and connectivity providers to meet performance, compliance, and domain-specific analytics requirements.
Table of Contents
32 Pages
- 1.0 Scope of Report and Methodology
- 2.0 Market SWOT Analysis and Players
- 2.1 Market Definition
- 2.2 Market Segments
- 2.3 Market Strengths
- 2.4 Market Weaknesses
- 2.5 Market Threats
- 2.6 Market Opportunities
- 2.7 Major Players
- 3.0 Competitive Analysis
- 3.1 Market Player 1
- 3.2 Market Player 2
- 3.3 Market Player 3
- 3.4 Market Player 4
- 3.5 Market Player 5
- 3.6 Market Player 6
- 3.7 Market Player 7
- 3.8 Market Player 8
- 3.9 Market Player 9
- 3.10 Market Player 10
- 4.0 Comparative Business Strategies
- 4.1 Comparative Business Strategies of Player 1 and 2
- 4.2 Comparative Business Strategies of Player 1 and 3
- 4.3 Comparative Business Strategies of Player 1 and 4
- 4.4 Comparative Business Strategies of Player 2 and 3
- 4.5 Comparative Business Strategies of Player 2 and 4
- 4.6 Comparative Business Strategies of Player 3 and 4
- 5.0 Appendix
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