Global Data Analytics for Industries Report, 2017–2023
Global Data Analytics for Industries Report, 2017–2023
Rise of Progressive Analytic Techniques Expected to Induce New Business Models in Manufacturing
04-Apr-2017
Global
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Description
Analytics have been around for a long time in different forms. Starting with manual data collection, it has progressed to reporting systems and then to statistical-based modelling systems. The progressive improvement in computer prowess simultaneously backed by electronic advancement in sensor technology has led to an era of convergence. This has created innumerable data points and allows technology to make sense of it. Businesses are racing to convert raw machine and process data into actionable and useful insights in real-time. While predictive analytics allows problem identification before its occurrence, prescriptive analytics educates the user on the best solution after taking into the process constraints and organization policies.
Data analytics in this research service have been defined as advanced analytics such as hardware, software, solutions, and technology involved in creating, communicating, collating raw data into processed information, insights, and visualisations relevant to the user.
This research service can be useful in understanding the central framework of analytics and its incremental influence in shifting the global industrial strategy over various time periods. In addition, it also explores potential applications and use cases of analytics in diverse fields and also simultaneously maps changing workforce requirements to incline with a connected asset setup. The trend of various vertical industries such as oil and gas, transportation, and manufacturing towards adopting analytics and their future prospects has also been captured in depth in this research service. This study also carries a detailed analysis of future opportunities in various process, discrete and hybrid industries. The business impact of analytics and a strategy to derive the most out of it are some of the highlights featured. This study also discusses major economic trends and technological drivers and their impact on market demand during the study period. The technology and business trends shaping the future of data analytics in industries has also been discussed in detail. This research service also aims to ascertain the importance and role of data analytics in manufacturing; it also analyses market forces, challenges, and opportunities in the space. With ground-breaking innovation in artificial intelligence and cognitive algorithms, the factory environment is set to witness a massive transformation.
The key questions answered in this study are:
• How attractive is data analytics to process and discrete manufacturers? What is the current industry scenario?
• What are the diverse applications of analytics in an industrial setup? What are the growth opportunities of upcoming analytic extensions?
• Who are the movers and shakers in this industry? How is the market poised to grow during the forecast period?
• Who are the major market players in the value stream of Big Data and advanced analytics? What are the exciting growth opportunities?
Table of Contents
Key Findings
IIoT—Emerging Themes in the Industrial Environment
IIoT—Key Attributes of a Smart Factory
Frost & Sullivan’s Value Proposition in IIoT
Data Analytics for Process and Discrete Manufacturing
The Paradigm Shift Towards Being Predictive
The Five Strategic Shifts of Industrie 4.0
Lifecycle Mapping and Key Workforce Requirements
On-Time with Analytics—Public Transport Train Systems
Manufacturing with Analytics—Polymer Plant
Discerning Customer Needs
Manufacturing Leaning Towards Analytics
Prescribing the Next Move with Analytics
Progressing Business Scenarios
Movers and Shakers—TrendMiner
Movers and Shakers—RiverLogic
The Boom of Big Data Analytics
Transformation in the Big Data and Analytics Market
Growth Opportunity 1—Data Socialisation
Growth Opportunity 2—Self Healing Machines
Growth Opportunity 3—Analytics in Augmented Reality
Growth Opportunity 4—Virtual Plant
Big Bets of the Future—Key Growth Prospects
Strategic Imperatives for Advanced Analytics Market
3 Big Predictions
Legal Disclaimer
Market Engineering Methodology
- 1. Data Analytics for Industries: Key Findings, Global, 2016
- 2. Data Analytics for Industries: Key Questions this Study will Answer, Global, 2016
- 3. Data Analytics for Industries: Key Questions this Section will Answer, Global, 2016
- 4. Data Analytics for Industries: Data Analytics for Process and Discrete Manufacturing, Global, 2016
- 5. Data Analytics for Industries: The Paradigm Shift Towards Being Predictive, Global, 2016
- 6. Data Analytics for Industries: The Five Strategic Shifts of Industrie 4.0, Global, 2016
- 7. Data Analytics for Industries: Lifecycle Mapping and Key Workforce Requirements, Global, 2016
- 8. Data Analytics for Industries: Key Questions this Section will Answer, Global, 2016
- 9. Data Analytics for Industries: On-Time with Analytics—Public Transport Train Systems, Global, 2016
- 10. Data Analytics for Industries: Manufacturing with Analytics—Polymer Plant, Global, 2016
- 11. Data Analytics for Industries: Discerning Customer Needs, Global, 2016
- 12. Data Analytics for Industries: Manufacturing Leaning Towards Analytics, Global, 2016
- 13. Data Analytics for Industries: Prescribing the Next Move with Analytics, Global, 2016
- 14. Data Analytics for Industries: Progressing Business Scenarios, Global, 2016
- 15. Data Analytics for Industries: Company Profile TrendMiner, Global, 2016
- 16. Data Analytics for Industries: Company Profile—RiverLogic, Global, 2016
- 17. Data Analytics for Industries: The Boom of Big Data Analytics, Global, 2016
- 18. Data Analytics for Industries: Key Questions this Section will Answer, Global, 2016
- 19. Data Analytics for Industries: Transformation in the Big Data and Analytics Market, Global, 2016
- 20. Data Analytics for Industries: Big Bets of the Future—Key Growth Prospects, Global, 2016
- 21. Data Analytics for Industries: Strategic Imperatives for Advanced Analytics Market, Global, 2016
Related Research
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Table of Contents | | Executive Summary~ || Key Findings~ | Industrial Internet of Things—A Research Perspective~ || IIoT—Emerging Themes in the Industrial Environment~ || IIoT—Key Attributes of a Smart Factory~ || Frost & Sullivan’s Value Proposition in IIoT~ | Research Scope and Objectives~ | Overview of Big Data and Advanced Analytics~ || Data Analytics for Process and Discrete Manufacturing~ || The Paradigm Shift Towards Being Predictive~ || The Five Strategic Shifts of Industrie 4.0~ || Lifecycle Mapping and Key Workforce Requirements~ | The Know-How of Advanced Analytics~ || On-Time with Analytics—Public Transport Train Systems~ || Manufacturing with Analytics—Polymer Plant~ || Discerning Customer Needs~ || Manufacturing Leaning Towards Analytics~ || Prescribing the Next Move with Analytics~ || Progressing Business Scenarios~ || Movers and Shakers—TrendMiner~ || Movers and Shakers—RiverLogic~ || The Boom of Big Data Analytics~ | Growth Opportunities and Call to Action~ || Transformation in the Big Data and Analytics Market~ || Growth Opportunity 1—Data Socialisation~ || Growth Opportunity 2—Self Healing Machines~ || Growth Opportunity 3—Analytics in Augmented Reality~ || Growth Opportunity 4—Virtual Plant~ || Big Bets of the Future—Key Growth Prospects~ || Strategic Imperatives for Advanced Analytics Market~ | The Last Word~ || 3 Big Predictions~ || Legal Disclaimer~ | Appendix~ || Market Engineering Methodology~ |
List of Charts and Figures | 1. Data Analytics for Industries: Key Findings, Global, 2016~ 2. Data Analytics for Industries: Key Questions this Study will Answer, Global, 2016~ 3. Data Analytics for Industries: Key Questions this Section will Answer, Global, 2016~ 4. Data Analytics for Industries: Data Analytics for Process and Discrete Manufacturing, Global, 2016~ 5. Data Analytics for Industries: The Paradigm Shift Towards Being Predictive, Global, 2016~ 6. Data Analytics for Industries: The Five Strategic Shifts of Industrie 4.0, Global, 2016~ 7. Data Analytics for Industries: Lifecycle Mapping and Key Workforce Requirements, Global, 2016~ 8. Data Analytics for Industries: Key Questions this Section will Answer, Global, 2016~ 9. Data Analytics for Industries: On-Time with Analytics—Public Transport Train Systems, Global, 2016~ 10. Data Analytics for Industries: Manufacturing with Analytics—Polymer Plant, Global, 2016~ 11. Data Analytics for Industries: Discerning Customer Needs, Global, 2016~ 12. Data Analytics for Industries: Manufacturing Leaning Towards Analytics, Global, 2016~ 13. Data Analytics for Industries: Prescribing the Next Move with Analytics, Global, 2016~ 14. Data Analytics for Industries: Progressing Business Scenarios, Global, 2016~ 15. Data Analytics for Industries: Company Profile TrendMiner, Global, 2016~ 16. Data Analytics for Industries: Company Profile—RiverLogic, Global, 2016~ 17. Data Analytics for Industries: The Boom of Big Data Analytics, Global, 2016~ 18. Data Analytics for Industries: Key Questions this Section will Answer, Global, 2016~ 19. Data Analytics for Industries: Transformation in the Big Data and Analytics Market, Global, 2016~ 20. Data Analytics for Industries: Big Bets of the Future—Key Growth Prospects, Global, 2016~ 21. Data Analytics for Industries: Strategic Imperatives for Advanced Analytics Market, Global, 2016~ |
Author | Rahul Vijayaraghavan |
Industries | Industrial Automation |
WIP Number | K096-01-00-00-00 |
Is Prebook | No |