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Investing in the Currency of the Future: Big Data for the Manufacturing Domain

Investing in the Currency of the Future: Big Data for the Manufacturing Domain

Big Data for the Manufacturing Domain: Key Findings, Global, 2014

The four building blocks of Big Data include i) Data Storage: to store and manage a large volume of multiple datatypes, ii) Data integration: to integrate and process data in a form that is fit for analysis, iii) Data Analytics: to derive actionable insights from the data, and iv) Data Visualization: to present the data in a suitable fashion to the user.

The influx of large volumes of structured and unstructured data owing to the increase in connected assets has resulted in a push towards a scalable framework (such as the Hadoop cluster—a $􂀀 billion opportunity by 2021) to manage, integrate, and process Big Data.

As manufacturing end users look for proactive ways to improve asset uptime and streamline their maintenance activities, the demand for predictive and prescriptive analytical platforms is expected to spike (􂀀-year compound annual growth rate [CAGR] of 􂀀% from 2014 to 2021).

From a vertical market standpoint, greater opportunities are expected across discrete industries (that account for 􂀀% of the global stored data) such as life sciences, automotive and aerospace, food and beverage, hi-tech, and general manufacturing.

In addition to traditional applications and markets for Big Data and analytics, there is tremendous potential for emerging applications such as the energy management platform (valued at $􂀀 billion in 2014 and expected to reach $􂀀in 2021) and new markets such as waste disposal (a $􂀀 billion opportunity in 2014 in the United States alone).

About this report

As part of the Internet of Industrial Things (IOIT) research portfolio offering from the Industrial Automation and Process Control practice, this strategic research service provides a detailed assessment of the key opportunities for Big Data and Analytics in the manufacturing domain from an application, technology, and market standpoint. While the deliverable encompasses a combination of both qualitative trends and quantitative data points, some of the key focal areas here include new storage requirements for high volume multiple data types , the role of analytics in manufacturing, emerging applications within the facility , new markets for sustainable growth, and innovative company initiatives that are gaining wide-scale acceptance.


  • Executive Summary
    • Key Findings
  • Internet of Industrial Things-A Research Perspective
    • Internet of Industrial Things-The Four Functional Facets
    • Frost & Sullivan's Offering
  • Research Scope and Objectives
    • Key Questions This Study Will Answer
  • Examining the Functional Components of a Big Data Solution and Unearthing its Potential for Manufacturing
    • Business Case for the Implementation of a Big Data Solution
    • Snapshot of Manufacturing vs. Other Sectors
    • Building Blocks of a Big Data Solution for Manufacturing
    • Data Storage and Integration-Analyzing the Foundation Layers
    • On-Premise vs. Cloud Based-Choosing the Right Deployment Model
    • Cloud-based Models-Acute Focus on Data Sensitivity
    • Hadoop and NoSQL Databases-Extension to Existing Infrastructure
    • Manufacturing Analytics-A Gold Mine of Opportunities
    • Risk Reward Matrices-Production Line and Plant Level
    • Evolution of Data Analytics for Maintenance-related Activities
    • Analytics for the Extended Value Chain-Supply Chain Optimization
    • Data Visualization-Customized Dashboards for Individual Personnel
    • Data Driven Workforce-Dissolving Barriers Across the Enterprise
  • Seizing Lucrative Big Data Opportunities to Gain a First-Mover Advantage-"ATM" Framework
    • Opportunity Mapping Using the ATM Framework
    • Maintenance Analytics-Increased Equipment Uptime and Performance
      • Table Big Data for the Manufacturing Domain: Revenue Forecast, Global, 2014-2021
    • Era of Advanced Machine Learning-A Proactive Approach
    • Curtailing the 7 Wastes in Manufacturing to Form a Lean Enterprise
    • Push Towards Energy Optimization
    • Influx of Unstructured Data to Stir Demand for NoSQL Databases
    • Majority of Big Data Projects are Expected to be Built on Hadoop
    • M2M Application Development Platforms
    • Regional Outlook-Key Dynamics Across Global Hotspots
    • Vertical Market Analysis-Discrete vs. Process Manufacturing
    • New Emerging Markets-Manufacturing Pollution Control
    • Size of the Pie-Summary of Key Market Opportunities
  • Market Direction-Innovative Companies Developing Value-added Products, Solutions, and Services
    • Mtell
    • ThingWorx (a PTC Company)
    • Sisense Inc.
    • MongoDB Inc.
    • Hortonworks Inc.

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