Market Of Big Data In Industry Verticals 2013 - 2018


ReportsnReports.com offers “Big Data in ICT and Telecom: Transforming Industry Verticals 2013 - 2018” research report in its store


Published on 18 February 2014


by Priyank Tiwari

(WireNews+Co)

Dallas, TX

Big Data is much more than its technical definition implies: A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tool. From a business perspective, Big Data represents a major inflection point for the ICT and Telecom sectors as it will transform business asset utility and value forever more. Every large corporation collects and maintains a huge amount of data associated with its customers including their preferences, purchases, habits, travels, and other personal information. However, value realization and the implications for using this data is often little understood and underappreciated.

This research (http://www.reportsnreports.com/reports/274940-big-data-in-ict-and-telecom-transforming-industry-verticals-2013-2018.html) evaluates Big Data challenges including management, mining, and analytics as well as the impact on telecom and ICT systems. This research also analyzes Big Data in key industry verticals including retail, financial services, healthcare, government, manufacturing, energy, and transportation. This report also assesses Big Data within government including homeland security, defense, and law enforcement. The report includes a market value assessment and forecasts for 2013 - 2018 for Big Data in telecommunications and industry verticals.

This report includes a comprehensive assessment of select companies in the Big Data ecosystem including: Accenture, CSC, Fujitsu, Hewlett Packard, Informatica, Mu Sigma, Opera Solutions, Oracle, Tata Consultancy Services. The assessment for the above companies includes the following areas:

    Company Overview

    Offering Analysis

    Strategies and Plans

    Mergers and Acquisitions

    Partnerships and Alliances

    Financial and Operational Review

    Key Contract Wins Assessment

    Analysis and Conclusions

Target Audience:

    Any governmental agency

    Big Data and analytics companies

    Data as a Service (DaaS) companies

    Fortune 1000 corporations of all types

    Cloud-based service providers of all types

    Data processing and management companies

    Application Programmer Interface (API) companies

    Data aggregators, storage and management providers

    Telecommunications infrastructure and service providers

Report Benefits (buy a copy of this report at http://www.reportsnreports.com/Purchase.aspx?name=274940 ):

    Big Data market forecasts 2013 – 2018

    Big Data market assessment for select industry verticals

    Assessment of leading companies in Big Data ecosystem

    Identify drivers for Big Data infrastructure, spending, and services

    Understand the market dynamics for Big Data in various industries

    Identify sources of Big (unstructured) Data and associated challenges

    Identify key emerging trends such as Data as a Service (DaaS) and others

    Understand Big Data operations including data capture and management

Table of Contents for Big Data in ICT and Telecom: Transforming Industry Verticals 2013 – 2018 Report include:

1.0 Executive Summary 14

2.0 Introduction 15

2.1 Data Types 15

2.1.1 Internal Data 15

2.1.2 External Data 15

2.2 Big Data 17

2.2.1 Key Characteristics Of Big Data 17

2.2.2 Distinguishing Characteristics Of Data Size 19

3.0 The Importance Of Big Data 20

3.1 Why Big Data? 21

3.2 Big Data Benefits 21

3.2.1 Better Investment Decision And Operational Changes 21

3.2.2 Real Time Customization 22

3.2.3 Improved Performance And Risk Management 22

3.2.4 New Business Models 23

4.0 The Big Data Environment 24

4.1 The Current State Of Industry Data And Analytics 24

4.1.1 Heterogeneity And Incompleteness 26

4.1.2 Scale 26

4.1.3 Timeliness 27

4.1.4 Privacy 27

4.1.5 Human Collaboration 28

4.2 Big Data Allows Enterprise To Uncover Opportunities 28

4.3 What Data Is Meaningful? 29

4.3.1 Operations Management Data: 29

4.3.2 Sales And Marketing Data: 29

4.3.3 Accounting And Finance Data: 30

5.0 Big Data In Telecom And Ict 31

5.1 How Much Data Is There In Telecom And Ict? 32

5.1.1 Exponential Growth 32

5.1.2 Putting The Amount Of Data Into Perspective 33

5.2 Opportunities To Telecom Carriers 34

5.2.1 Direct Benefits To Telecom 35

5.2.2 Benefits To Industry Verticals 37

5.2.3 Specific Opportunities 37

5.3 Challenges To Telecom Carriers 38

5.3.1 Planning For Big Data 38

5.3.2 Implementing Improved Technologies To Manage Data 38

5.4 Sources Of Data In Telecom 39

5.4.1 Subscriber Data 39

5.4.2 Network Data 40

5.4.3 Specific Carrier Systems 43

5.4.4 Sourcing Telecom Data And Privacy Concerns 45

5.5 Accessing Data Via Telecom Api 45

5.5.1 What Is An Api? 45

5.5.2 What Is A Telecom Api? 46

5.5.3 Accessing Data Over A Telecom Api 46

5.5.4 Future Of Carriers And Telecom Apis 47

6.0 Big Data Architecture 49

6.1 Traditional Information Architecture Capabilities 49

6.2 Big Data And The Cloud 49

6.3 Adding Big Data Capabilities 50

7.0 Big Data Technologies, Techniques, And Solutions 52

7.1 Technologies 52

7.1.1 Sensors And Sensor Networks 52

7.1.2 Networks Connection 52

7.1.3 Data Storage 52

7.1.4 Data Mining 52

7.1.5 Advanced Computing Systems 53

7.1.6 Data Analysis Algorithms 53

7.2 Big Data Techniques 54

7.2.1 A/B Testing 54

7.2.2 Association Rule Learning 54

7.2.3 Classification 54

7.2.4 Cluster Analysis 54

7.2.5 Crowd Sourcing 54

7.2.6 Data Fusion And Data Integration 54

7.2.7 Data Mining 55

7.2.8 Other Techniques 55

7.3 Big Data Solutions 55

7.3.1 Hadoop 55

7.3.2 Nosql 61

7.3.3 Mpp Databases 62

7.3.4 Other And Emerging Solutions 63

8.0 Big Data Sources, Capture, And Management 64

8.1 Acquiring Data 64

8.2 Big Data Sources 64

8.2.1 Entertainment Systems 68

8.2.2 Communications Systems 69

8.2.3 Social Networks 69

8.2.4 Shopping Activities 71

8.2.5 Sensor And Sensor Networks 72

8.2.6 Gamification 73

8.3 Capturing Big Data 74

8.3.1 Capturing Big Data In Commerce Activities 74

8.3.2 Capturing Big Data In Social Activities 74

8.3.1 Capturing Big Data In Lifestyle Activities 75

8.4 Big Data Management 76

8.4.1 Aggregation 76

8.4.2 Storage 77

8.4.3 Processing 77

9.0 Big Data Ecosystem And Value Chain 80

9.1 Big Data Value Chain 81

9.1.1 A Fragmented Big Data Value Chain 81

9.1.2 Value Chain Functions 82

9.1.3 Value Chain Goals 82

9.2 Big Data Ecosystem And Landscape 83

9.2.1 Emerging Data As A Service (Daas) Ecosystem 86

9.3 Leading Companies In Big Data 86

9.3.1 1010data 87

9.3.2 Actian 88

9.3.3 Agilis International 89

9.3.4 Alteryx 89

9.3.5 Amanzitel 89

9.3.6 Amazon 90

9.3.7 Apache Software Foundation 90

9.3.8 Aptean 90

9.3.9 Attivio 91

9.3.10 Cataphora 91

9.3.11 Cisco 92

9.3.12 Cloudera 92

9.3.13 Csc 93

9.3.14 Cvidya 93

9.3.15 Datameer 94

9.3.16 Dell 94

9.3.17 Emc 95

9.3.18 Eplorys 95

9.3.19 Fujitsu 96

9.3.20 Fusion-Io 96

9.3.21 Gooddata 96

9.3.22 Google 97

9.3.23 Guavus 97

9.3.24 Hitachi Data Systems 97

9.3.25 Hortonworks 97

9.3.26 Hp 98

9.3.27 Humedica 98

9.3.28 Hitachi 98

9.3.29 Ibm 99

9.3.30 Informatica 99

9.3.31 Intel 100

9.3.32 Intersystems 100

9.3.33 Jaspersoft 101

9.3.34 Marklogic 101

9.3.35 Microsoft 101

9.3.36 Mongodb 102

9.3.37 Mu Sigma 102

9.3.38 Netapp 102

9.3.39 Opera Solutions 103

9.3.40 Oracle 103

9.3.41 Paraccel 103

9.3.42 Parstream 104

9.3.43 Pentaho 104

9.3.44 Pervasive 104

9.3.45 Platfora 104

9.3.46 Qliktech 105

9.3.47 Quantum 105

9.3.48 Rackspace 105

9.3.49 Revolution Analytics 105

9.3.50 Salesforce 105

9.3.51 Sap 106

9.3.52 Sas 106

9.3.53 Sisense 106

9.3.54 Software Ag/Terracotta 107

9.3.55 Splunk 107

9.3.56 Sqrrl 107

9.3.57 Subex 107

9.3.58 Supermicro 108

9.3.59 Tableau 108

9.3.60 Teoco 108

9.3.61 Teradata 109

9.3.62 Think Big Analytics 110

9.3.63 Tidemark Systems 110

9.3.64 Tibco 110

9.3.65 Vmware (Emc) 111

9.3.66 Wedo Technologies 111

10.0 Obstacles To Implementing And Operating Big Data 113

10.1 Organizational Challenges 113

10.1.1 Human Capital: The Need For Data Scientists 114

10.2 Data Challenges 114

10.2.1 Data Quality 115

10.2.2 Timely Data Delivery 115

10.2.3 Data Storage Capacity 115

10.3 Process Challenges 115

10.4 Privacy And Public Policy Issues 116

10.4.1 Commercialization Of Private Data 116

10.4.2 Privacy Rules And Regulations 116

10.4.3 Learning From Past Mistakes 117

10.5 Big Data Standardization 118

10.5.1 National Institute Of Standards And Technology 119

10.5.2 Alliance For Telecommunication Industry Solutions 119

10.5.3 Cloud Security Alliance 120

10.5.4 International Telecommunications Union 120

10.5.5 Open Mobile Alliance 120

10.5.6 De Facto Standardization Driven By Leading Companies 120

11.0 Big Data And Telecom Analytics 121

11.1 What Is Big Data And Telecom Analytics 121

11.1.1 Pattern Discovery 122

11.1.2 Predictive Analytics 122

11.1.1 Decision Making 124

11.1.2 Process Innovation 125

11.2 The Importance Of Analytics 125

11.2.1 From Analytics To Business Intelligence 126

11.2.2 The Importance Of Analytics In Telecom 126

11.2.3 Telecom Analytics Solutions 126

11.3 Challenges In Big Data Analysis 127

11.3.1 Heterogeneity And Incompleteness 128

11.3.2 Scale 129

11.3.3 Timeliness 129

11.3.4 Privacy 130

11.3.5 Human Collaboration 130

11.4 Evaluation Of Analytics Companies 131

11.4.1 Analytics Companies 131

11.4.2 Swot Analysis Of Analytics Providers 131

12.0 Other Aspects Of Big Data 136

12.1 Big Data Vs. Api Strategies 136

12.1.1 Structured And Unstructured Solutions: Apis 136

12.2 Big Data Vs. Small Data 138

12.2.1 What Is “Small Data”? 139

12.2.2 Why Pursue A Small Data Strategy? 139

12.2.3 Big Data Vs. Small Data Differentiation 139

12.2.4 Decision Parameters For Big Vs. Small Data 140

12.2.5 Big Data Vs. Small Data: The Key Differences 141

12.2.1 Small Data Driven Emerging Business Models 143

13.0 Big Data In Industry Verticals 148

13.1 Big Data And The Internet 150

13.1.1 Search 150

13.1.2 Digital Marketing And Commerce 151

13.2 Big Data In Financial Services 154

13.2.1 Why Big Data In Financial Services? 154

13.2.2 How Banks Are Leveraging Data 157

13.2.3 Big Data Challenges And Opportunities In Financial Services 158

13.2.4 Big Data In Financial Services Case Analysis 165

13.3 Big Data In Retail Sales And Customer Relationship Management 167

13.3.1 The Current State Of Retail 167

13.3.2 Emerging Technology Trends In Retail 168

13.3.3 Big Data In Retail 171

13.3.4 Big Data In Customer Relationship Management (Crm) 181

13.4 Big Data In Healthcare 187

13.4.1 Why Big Data In Healthcare? 187

13.4.2 Healthcare Data 189

13.4.3 Emerging Business Models With Big Data In Healthcare 190

13.4.4 Big Data Deployment Challenges In Healthcare 191

13.4.5 Big Data In Healthcare Stakeholders 192

13.5 Big Data In Manufacturing 194

13.5.1 Manufacturing Overview 195

13.5.2 Value Chain And Challenges Of Manufacturing 196

13.5.3 Market Drivers And Barriers For Big Data Applications In Manufacturing 198

13.5.4 Performance Measurement 199

13.5.5 Applications & Processes In Manufacturing 200

13.5.6 Current State Of Manufacturing 207

13.6 Big Data In Transportation Sector 213

13.6.1 Intelligent Transportation Systems 213

13.6.2 Intelligent Automobiles 214

13.6.3 Automobile Development, Assembly, And Distribution 215

13.7 Big Data In Energy And Smartgrid 216

13.7.1 Analyzing Data In The Energy Sector 216

13.7.2 Big Data And Smartgrid 217

13.7.3 Big Companies Help Utilities Solve Big Problems With Big Data 219

13.7.4 Identifying Problems Is The Start To Solving Them 220

14.0 Big Data In Government 223

14.1 Steps The Government Is Taking Towards Leveraging Big Data 223

14.1.1 Recognizing Problems And Opportunities 223

14.1.2 Launching Initiatives 224

14.1.3 Identifying Inputs To The Problems 224

14.1.4 Identifying Solutions 225

14.1.5 Recognizing Challenges 226

14.2 Big Data Government Applications 226

14.2.1 Homeland Security 227

14.2.2 Department Of Defense 227

14.2.3 Crime Prevention 228

14.2.4 Public Services Administration 229

15.0 Big Data Market Drivers And Constraints 231

15.1 Growth Drivers 231

15.1.1 Overall Growth Drivers For Big Data 231

15.1.2 Data Volume And Variety 231

15.1.3 Increasing Adoption Of Big Data By Enterprises And Telecom 231

15.1.4 Maturation Of Big Data Software 232

15.1.5 Continued Investments In Big Data By Web Giants 232

15.2 Market Barriers 232

15.2.1 Privacy And Security: The “Big” Barrier To Bdaas 232

15.2.2 Workforce Re-Skilling And Organizational Resistance 232

15.2.3 Lack Of Clear Big Data Strategies 232

15.2.4 Technical Challenges: Scalability & Maintenance 232

16.0 Big Data Trends And Forecasts 234

16.1 Overall Big Data Trends 234

16.2 Big Data Trends By Industry Sector 234

16.2.1 Industrial Internet & M2m 236

16.2.2 Retail & Hospitality 237

16.2.3 Media 237

16.2.4 Utilities 237

16.2.5 Financial Services 238

16.2.6 Healthcare & Pharmaceutical 238

16.2.7 Telecommunications And Ict 239

16.2.8 Government & Homeland Security 239

16.2.9 Other Sectors 240

16.3 Big Data Forecasts 241

16.3.1 Big Data Revenue By Functional Area: 2013 - 2018 241

16.3.2 Big Data Revenue By Region 2013 - 2018 241

16.3.3 Big Data Revenue By Industry Vertical 2013 - 2018 242

17.0 Future Of Big Data In Telecom And Ict 244

17.1 Emerging Commercial Benefits 244

17.1.1 Improving Subscriber Experience 244

17.1.2 Building And Maintaining Smarter Networks 244

17.1.3 Churn/Risk Reduction And New Revenue Streams 245

17.1.4 Carrier Case Studies 246

17.2 Future Solutions, Approaches, And Challenges 246

17.2.1 Parallel Technology Advance 246

17.2.2 Multi-Platform 246

17.2.3 Self-Serve 246

17.2.4 Collaboration 246

17.2.5 More Real-Time 247

17.2.6 Privacy And Security Issues 247

17.2.7 Expanding In Mobile Market 247

17.2.8 Location-Based Information 247

17.3 Future Converged Technologies And Solutions 247

17.3.1 Self-Organizing Networks + Real-Time Data And Analytics 247

17.3.2 Big Data And The Internet Of Things (Iot) 248

17.3.3 M2m, Iot, And Big Data 248

17.4 Big Data Becomes A Part Of Everything 249

17.4.1 At Some Point It’s Not “Big” It’s Just “Data” 249

18.0 Conclusions And Recommendation 251

18.1 Recommendations For Telecom Carriers 251

18.1.1 Leverage New Technologies And Solutions 251

18.1.2 Improved Data Handling 251

19.0 Appendix 253

19.1 Big Data And The Evolution Of Everything To The Cloud 253

19.2 Relationship Between Big Data And The Internet Of Things (Iot) 254

19.3 Data Mining And Management 255

19.4 Comprehensive Assessment Of Select Companies In Big Data 256

19.4.1 Accenture 256

19.4.2 Computer Science Corporation 273

19.4.3 Fujitsu Ltd. 289

19.4.4 Hewlett-Packard Company 308

19.4.5 International Business Machines Corp. 322

19.4.6 Informatica Corporation 348

19.4.7 Mu Sigma Inc. 367

19.4.8 Opera Solutions, Llc 377

19.4.9 Oracle Corporation 383

19.4.10 Tata Consultancy Services 414

List Of Tables

Table 1: Telecom Analytic Company: Competitors Summary 131

Table 2: Telecom Analytic Company: SWOT Summarization Table - Part One 134

Table 3: Telecom Analytic Company: SWOT Summarization Table - Part Two 135

Table 4: Decision Points for using Big Data 140

Table 5: How Small Data is Different 142

Table 6: Top 8 Countries’ Value-added in Manufacturing in GDP 2008-2012 195

List Of Figures

Figure 1: Traditional Information Architecture Capabilities 15

Figure 2: Big Data Information Architecture Capabilities 16

Figure 3: Top Ten Challenges Preventing Big Data in Business 25

Figure 4: Ensuring a Data-rich Future for Social Sciences 28

Figure 5: Big Data and Adoption Cycles 31

Figure 6: Data Generated by Various Industry Sectors 34

Figure 7: Big Data Sources 43

Figure 8: Big Data as a Service (BDaaS) 44

Figure 9: Telecom APIs facilitate many Services and Huge Amounts of Data 47

Figure 10: Big Data Capabilities Integration 51

Figure 11: Hadoop in the Enterprise 56

Figure 12: Hadoop Data Detection and Protection 58

Figure 13: MapReduce Framework 59

Figure 14: Hadoop and NoSQL Vendor Revenue Share 2011-2013 61

Figure 15: Stored Data in Organizations 65

Figure 16: New Data Stored Across Geographies 66

Figure 17: Type of Data Generated by Industry Verticals 67

Figure 13: Big Data Domains 74

Figure 19: Big Data Capture and Analysis for Telecom 76

Figure 20: Data Management 78

Figure 21: Big Data Value Chain 81

Figure 22: Big Data Ecosystem by Internet Business Type 83

Figure 23: Big Data Ecosystem Layered Topology View 84

Figure 23: Big Data Landscape 85

Figure 25: Big Data Revenue Share by Vendor Solutions 2013 87

Figure 26: Big Data and Analytics 121

Figure 27: Predictive Analytics 123

Figure 28: Effectiveness of Critical Data in Decision Making 125

Figure 29: Top Challenges to Realizing Value from Big Data 128

Figure 30: Ensuring a Data-rich Future within Social Sciences 130

Figure 31: Data Volume in Old and New world 136

Figure 32: Shifting Control to the Enterprise 137

Figure 28: API Growth 138

Figure 34: Small Data Implementation Framework 146

Figure 35: Stored Data in Organizations 148

Figure 36: Stored Data by Location 149

Figure 37: Big Data and Mobile Commerce/Marketing 153

Figure 38: Big Data as Competitive Differentiator for Financial Services 156

Figure 39: Big Data in Financial Services 157

Figure 40: Financial Big Data Management Paradigm 159

Figure 41: Big Data Approaches for Financial Services 161

Figure 42: Big Data Functional Levels 162

Figure 43: Big Data for Crime Prediction 165

Figure 44: Online Sales Forecast 177

Figure 45: CRM Aspects to Consider with Big Data 183

Figure 46: Traditional CRM and Social CRM 185

Figure 47: Samsung Internal Value Chain 197

Figure 48: Measurement Pyramid 199

Figure 49: United States Manufacturing 207

Figure 50: Big Data Use Case Scenarios 209

Figure 51: Attributes of Big Data Value and Usability 210

Figure 52: Big Data and IT Systems 211

Figure 53: Big Data and the Supply Chain 212

Figure 44: Big Data and ERP 212

Figure 55: ICT and the Smartgrid 221

Figure 56: Big Data Value Anticipated in Industry 235

Figure 57: Big Services Revenue by IT Segment 2010 - 2013 236

Figure 58: Big Data Revenue by Functional Area: 2013 – 2018 241

Figure 59: Big Data Revenue by Region: 2013 – 2018 242

Figure 60: Big Data Revenue by Industry Vertical: 2013 – 2018 243

Figure 61: Big Data in Everyday Life 249

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Posted 2014-02-18 12:18:00