2026 Global: Big Data Analytics In Manufacturing Market-Competitive Review (2032) report
Description
The 2026 Global: Big Data Analytics In Manufacturing 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 big data analytics in manufacturing market by geography and historical trend. The scope of the report extends to sizing of the big data analytics in manufacturing 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:
NVIDIA, Siemens, Amazon Web Services (AWS), IBM, Rockwell Automation, Microsoft, Oracle, SAS, Databricks, and Teradata are ten major companies shaping the Big Data Analytics in Manufacturing market through platforms that combine industrial IoT, AI/ML, and cloud-scale data processing. NVIDIA provides accelerated computing and AI toolkits that manufacturers use for predictive maintenance, quality inspection, and digital twins by leveraging GPUs and software stacks such as NVIDIA Omniverse and Isaac for simulation and vision analytics. Siemens couples its Xcelerator portfolio with industrial controls and edge-to-cloud data flows to enable process optimization, digital twins, and lifecycle analytics across discrete and process manufacturing environments. AWS supplies scalable cloud infrastructure and managed analytics services — including streaming (Kinesis), data warehousing, machine learning (SageMaker) and factory-specific solutions — which manufacturers adopt to aggregate machine telemetry, run time-series analytics, and operationalize ML at scale. IBM brings enterprise analytics and Watson AI into manufacturing for cognitive quality inspection, demand forecasting, and asset performance management, integrating on-premise OT data with hybrid cloud analytics to meet regulated-industry requirements.
Rockwell Automation and Microsoft act as complementary ecosystem leaders: Rockwell combines automation hardware and FactoryTalk analytics to deliver shop-floor data ingestion, contextualization, and analytics tailored to industrial control systems, improving OEE and reliability. Microsoft delivers Azure cloud and Azure Databricks, IoT Edge, and Power BI capabilities that manufacturers use for end-to-end data pipelines, real-time analytics, and visualization across supply chain and production operations. Oracle offers manufacturing analytics within its Cloud Infrastructure and ERP suite, enabling manufacturers to unify transactional, operational, and sensor data for holistic KPIs, predictive maintenance, and supply-chain analytics. SAS continues to serve advanced analytics and statistical modeling demands in manufacturing for process control, yield optimization, and anomaly detection through mature, explainable analytics platforms that integrate with industrial data sources.
Databricks, Teradata, and several specialist platforms focus on large-scale data engineering and analytics for manufacturing customers seeking unified data lakes and ML model operationalization. Databricks provides a unified data and AI platform built on Apache Spark that manufacturers use for large-scale sensor data processing, feature engineering, and collaborative model development to drive predictive maintenance and process optimization. Teradata supplies enterprise data warehouse and analytics solutions optimized for high-volume manufacturing use cases—supporting complex reporting, near-real-time analytics, and integration of ERP/PLM data for strategic decision-making. Collectively these ten companies form an ecosystem mix of industrial automation expertise, cloud infrastructure, AI/ML platforms, and advanced analytics tools widely adopted by manufacturers to reduce downtime, improve quality, and accelerate digital transformation across factories and supply chains.
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 big data analytics in manufacturing market by geography and historical trend. The scope of the report extends to sizing of the big data analytics in manufacturing 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:
NVIDIA, Siemens, Amazon Web Services (AWS), IBM, Rockwell Automation, Microsoft, Oracle, SAS, Databricks, and Teradata are ten major companies shaping the Big Data Analytics in Manufacturing market through platforms that combine industrial IoT, AI/ML, and cloud-scale data processing. NVIDIA provides accelerated computing and AI toolkits that manufacturers use for predictive maintenance, quality inspection, and digital twins by leveraging GPUs and software stacks such as NVIDIA Omniverse and Isaac for simulation and vision analytics. Siemens couples its Xcelerator portfolio with industrial controls and edge-to-cloud data flows to enable process optimization, digital twins, and lifecycle analytics across discrete and process manufacturing environments. AWS supplies scalable cloud infrastructure and managed analytics services — including streaming (Kinesis), data warehousing, machine learning (SageMaker) and factory-specific solutions — which manufacturers adopt to aggregate machine telemetry, run time-series analytics, and operationalize ML at scale. IBM brings enterprise analytics and Watson AI into manufacturing for cognitive quality inspection, demand forecasting, and asset performance management, integrating on-premise OT data with hybrid cloud analytics to meet regulated-industry requirements.
Rockwell Automation and Microsoft act as complementary ecosystem leaders: Rockwell combines automation hardware and FactoryTalk analytics to deliver shop-floor data ingestion, contextualization, and analytics tailored to industrial control systems, improving OEE and reliability. Microsoft delivers Azure cloud and Azure Databricks, IoT Edge, and Power BI capabilities that manufacturers use for end-to-end data pipelines, real-time analytics, and visualization across supply chain and production operations. Oracle offers manufacturing analytics within its Cloud Infrastructure and ERP suite, enabling manufacturers to unify transactional, operational, and sensor data for holistic KPIs, predictive maintenance, and supply-chain analytics. SAS continues to serve advanced analytics and statistical modeling demands in manufacturing for process control, yield optimization, and anomaly detection through mature, explainable analytics platforms that integrate with industrial data sources.
Databricks, Teradata, and several specialist platforms focus on large-scale data engineering and analytics for manufacturing customers seeking unified data lakes and ML model operationalization. Databricks provides a unified data and AI platform built on Apache Spark that manufacturers use for large-scale sensor data processing, feature engineering, and collaborative model development to drive predictive maintenance and process optimization. Teradata supplies enterprise data warehouse and analytics solutions optimized for high-volume manufacturing use cases—supporting complex reporting, near-real-time analytics, and integration of ERP/PLM data for strategic decision-making. Collectively these ten companies form an ecosystem mix of industrial automation expertise, cloud infrastructure, AI/ML platforms, and advanced analytics tools widely adopted by manufacturers to reduce downtime, improve quality, and accelerate digital transformation across factories and supply chains.
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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