Overview: Technology and market advances in four separate, yet related, areas are poised to cause disintermediation as well as many market opportunities for companies across a broad spectrum within telecom and digital technologies. Machine-to-Machine (M2M) has already made a big impact on wireless communications as network operators seek to leverage revenue opportunities beyond human interaction reliant services. The evolution of automated processes due to the Internet of Things (IoT) will accelerate this impact. The Cloud supports storage of huge amount of data gathered by M2M applications and also ensures real-time availability of data for further processing and analysis. Without the processing power and number crunching ability of Big Data and Analytics, the full potential of M2M and IoT would never be realized. These four factors working in alignment will enable new business opportunities and provide additional benefits to enterprise, which in turn will be passed on to end-consumers. This research evaluates each of these individually as well as in conjunction with each. This report uniquely focuses on the mutual and conjoint benefits of M2M, Cloud, Big Data and Analytics. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report. Report Benefits:Case StudiesCXO positions on Big DataBig Data Security and PrivacyChallenges of M2M and Big DataApplications of M2M and Big DataFactors Driving M2M Analytics OpportunityBarriers and Challenges to Cloud AdoptionBusinesses Impact of Big Data and AnalyticsTarget Audience:Network operatorsM2M / IoT platform providersM2M infrastructure providersM2M / IoT application developersCloud and telecom security companiesAnalytics and Data reporting companiesTelecommunications infrastructure providersM2M equipment and service providers of all typesCloud infrastructure and support service providersData aggregators, storage and management providersBig Data solution (Infrastructure, Software, Service) vendors

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M2M/IoT, Cloud, Big Data and Analytics: Market Dynamics and
Opportunities
Published on July 2014
Report Summary
Overview:
Technology and market advances in four separate, yet related, areas are poised to cause disintermediation as well as many market
opportunities for companies across a broad spectrum within telecom and digital technologies. Machine-to-Machine (M2M) has
already made a big impact on wireless communications as network operators seek to leverage revenue opportunities beyond human
interaction reliant services.
The evolution of automated processes due to the Internet of Things (IoT) will accelerate this impact. The Cloud supports storage of
huge amount of data gathered by M2M applications and also ensures real-time availability of data for further processing and analysis.
Without the processing power and number crunching ability of Big Data and Analytics, the full potential of M2M and IoT would never
be realized.
These four factors working in alignment will enable new business opportunities and provide additional benefits to enterprise, which in
turn will be passed on to end-consumers. This research evaluates each of these individually as well as in conjunction with each. This
report uniquely focuses on the mutual and conjoint benefits of M2M, Cloud, Big Data and Analytics.
All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the
business issues you're addressing. This needs to be used within three months of purchasing the report.
Report Benefits:
Case Studies
CXO positions on Big Data
Big Data Security and Privacy
Challenges of M2M and Big Data
Applications of M2M and Big Data
Factors Driving M2M Analytics Opportunity
Barriers and Challenges to Cloud Adoption
Businesses Impact of Big Data and Analytics
Target Audience:
Network operators
M2M / IoT platform providers
M2M infrastructure providers
M2M / IoT application developers
Cloud and telecom security companies
Analytics and Data reporting companies
Telecommunications infrastructure providers
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M2M equipment and service providers of all types
Cloud infrastructure and support service providers
Data aggregators, storage and management providers
Big Data solution (Infrastructure, Software, Service) vendors
Table of Content
EXECUTIVE SUMMARY
1.0 INTRODUCTION 1
2.0 ASPECTS OF M2M APPLICATION 2
2.1 Wireless Connectivity 2
2.2 Big Data 2
2.3 The Cloud 2
3.0 BIG DATA 3
3.1 CXOs' take on Big Data 4
4.0 USING DATA AS A POWERFUL TOOL 9
4.1 Social Sensor Cloud (SSC) 9
4.2 Virtual Sensors 10
5.0 BUSINESSES IMPACT OF BIG DATA AND ANALYTICS 12
5.1 Big Data: The Road to Decision-making not the Destination 12
5.2 Correlation of Data from Different Sources 12
5.3 Big Data Myth-busters for Management 13
6.0 M2M AND BIG DATA APPLICATIONS 16
6.1 Wireless Carriers 16
6.2 Smart Cars 16
6.3 Auto Insurance 17
6.4 Insurance 18
6.5 Smart Homes 18
6.6 Healthcare 19
6.7 Utility 20
6.8 Energy Management 20
6.9 Robotics 21
6.10 Logistics 21
6.11 Asset Tracking 21
6.12 Manufacturing 22
6.13 Supply Chain for Auto Manufacturers 22
6.14 Security and Surveillance 22
6.15 Enterprise in any Sector 22
7.0 CHALLENGES OF M2M AND BIG DATA 24
7.1 Privacy and Data Ownership 24
7.2 Authenticity and Security 24
7.3 Specialized Skill-set Required 25
7.4 Change in approach 25
8.0 BIG DATA STRATEGIES 26
8.1 Do-it-Yourself (DIY) Model 26
8.2 Database as a service (DBaaS) 26
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8.3 Managed Service Providers (MSP) 27
8.4 One-button-Deploy Technology 27
9.0 BIG DATA SECURITY AND PRIVACY 28
9.1 Security Updates 28
9.2 Data Encryption 28
9.3 Choosing the Right Encryption Solution 29
9.4 Big Data to Detect Malicious Behavior 30
10.0 CLOUD 31
11.0 CLOUD COMPUTING MODEL 32
11.1 Services 32
11.1.1 IaaS 33
11.1.2 PaaS 34
11.1.3 SaaS 35
11.1.4 MaaS 36
11.1.5 CaaS 36
11.1.6 XaaS 36
11.2 Characteristics 37
11.2.1 On-demand Self-service 38
11.2.2 Broad Network Access 38
11.2.3 Resource Pooling 38
11.2.4 Rapid Elasticity 38
11.2.5 Measured Service 38
11.3 Deployment Modes 39
11.3.1 Private Cloud 39
11.3.2 Public Cloud 39
11.3.3 Community Cloud 39
11.3.4 Hybrid Cloud 39
11.4 Benefits of Cloud Computing 40
11.5 Strategic fit for Cloud Adoption 41
11.6 M2M and Cloud Integration 42
11.7 Analysis 44
12.0 BARRIERS AND CHALLENGES TO CLOUD ADOPTION 45
12.1 Reluctance to Change 45
12.2 Outsourcing Data Security 45
12.2.1 Loss of Control 45
12.3 Security Concerns 46
12.4 Cyber Attacks 46
12.4.1 Severe Budget Restrictions of SMEs 46
12.4.2 Prolific use of Internet 46
12.5 Unclear SLAs 47
12.5.1 Unclear SLA Terms for Downtime 47
12.5.2 Secondary CSPs 47
12.5.3 Entitlement to Credit for Downtime 47
12.5.4 Calculating Up-time 47
12.5.5 Different Cloud Services have Different SLAs 48
12.6 Complexity restricts Adoption 48
12.6.1 Inherent Complexity in the Cloud Computing Environment 48
12.6.2 Integration of Processes is a Complex Task 48
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12.6.3 Integration Problems with SAAS Deployment 48
12.6.4 API Management 48
12.6.5 Determine the Best way to Integrate Data 49
12.7 Cloud Interoperability 49
12.7.1 Option of cloud interoperability 49
12.7.2 Moving Applications between Clouds 49
12.8 Audit of Service Provider 49
12.8.1 Industry Best Practices are still Developing 49
12.8.2 Resistance to Audit Signals Caution 49
12.8.3 Resistance of CSPs to allow Elaborate Tests 50
12.8.4 Audits build Confidence among Reluctant SMEs 50
12.9 Viability of Third-party Providers 50
12.10 Acceptance Issues 50
12.11 Cost Considerations 51
12.12 Lack of Integration Features in the Public Cloud 51
13.0 DATA ANALYTICS 52
13.1 Factors Driving M2M Analytics Opportunity 52
13.1.1 M2M Data Growth 52
13.1.2 New Analytical Technologies 53
13.1.3 Enhanced Business Models through Data Analysis 53
13.2 Important Factors for success in M2M Analytics Market 54
13.3 Competitive Vendor Analysis 55
13.3.1 Device Manufacturers 55
13.3.2 SI and Professional Services 56
13.3.3 Management Platform Providers 56
13.3.4 Software and Application Developers 56
13.3.5 Communication Service Providers 56
14.0 ADVANCED ANALYTICS TOOLS 57
15.0 ADVANCED ANALYTICS CASE STUDIES 60
15.1 Case One: Using analytics to Identify Interdependencies among different Process Parameters 60
15.1.1 The Challenge 60
15.1.2 The Solution 60
15.1.3 The Result 60
15.2 Case Two: Use of Neural Networks Tools 60
15.2.1 The Challenge 60
15.2.2 The Solution 61
15.2.3 The Result 61
15.3 Case Three: Use Production Data to identify Gaps 61
15.3.1 The Challenge 61
15.3.2 The Solution 62
15.3.3 The Result 62
16.0 CONCLUSIONS 63
17.0 APPENDIX 64
LIST OF FIGURES
Figure 1: Aspects of M2M Application 2
Figure 2: Five Factors of Big Data 3
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Figure 3: New Interest over Time: Big Data vs. Cloud Computing 4
Figure 4: Adoption Index 7
Figure 5: Risk of Forest Fires 11
Figure 6: Google's Self-driving Car 17
Figure 7: Snapshot device by Progressive Insurance 18
Figure 8: Model of Smart City Sangdo 19
Figure 9: Data Reliability/Security Diminishing over Time 24
Figure 10: The M2M Cloud 32
Figure 11: IaaS 33
Figure 12: PaaS 35
Figure 13: SaaS 36
Figure 14: Cloud Computing Service by Deployment Mode 40
Figure 15: Cloud Application and Services Priorities 43
Figure 16: Barriers and Challenges to Cloud Adoption 45
Figure 17: Factors Driving M2M Analytics Opportunity 52
Figure 18: Value Creation Mechanisms through M2M Analytics 54
Figure 19: Competitive Vendor Analysis 55
Figure 20: Example of Histogram 57
Figure 21: Examples of Correlation Analysis 58
Figure 22: Examples of Significance Testing 58
Figure 23: Example of Artificial Neural Network 59
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