The Big Data Market: 2018 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals & Forecasts

Table of Contents

1 Chapter 1: Introduction 28
1.1 Executive Summary 28
1.2 Topics Covered 30
1.3 Forecast Segmentation 31
1.4 Key Questions Answered 33
1.5 Key Findings 34
1.6 Methodology 35
1.7 Target Audience 36
1.8 Companies & Organizations Mentioned 37

2 Chapter 2: An Overview of Big Data 41
2.1 What is Big Data? 41
2.2 Key Approaches to Big Data Processing 41
2.2.1 Hadoop 42
2.2.2 NoSQL 44
2.2.3 MPAD (Massively Parallel Analytic Databases) 44
2.2.4 In-Memory Processing 45
2.2.5 Stream Processing Technologies 45
2.2.6 Spark 46
2.2.7 Other Databases & Analytic Technologies 46
2.3 Key Characteristics of Big Data 47
2.3.1 Volume 47
2.3.2 Velocity 47
2.3.3 Variety 47
2.3.4 Value 48
2.4 Market Growth Drivers 48
2.4.1 Awareness of Benefits 48
2.4.2 Maturation of Big Data Platforms 48
2.4.3 Continued Investments by Web Giants, Governments & Enterprises 49
2.4.4 Growth of Data Volume, Velocity & Variety 49
2.4.5 Vendor Commitments & Partnerships 49
2.4.6 Technology Trends Lowering Entry Barriers 50
2.5 Market Barriers 50
2.5.1 Lack of Analytic Specialists 50
2.5.2 Uncertain Big Data Strategies 50
2.5.3 Organizational Resistance to Big Data Adoption 51
2.5.4 Technical Challenges: Scalability & Maintenance 51
2.5.5 Security & Privacy Concerns 51

3 Chapter 3: Big Data Analytics 52
3.1 What are Big Data Analytics? 52
3.2 The Importance of Analytics 52
3.3 Reactive vs. Proactive Analytics 53
3.4 Customer vs. Operational Analytics 53
3.5 Technology & Implementation Approaches 54
3.5.1 Grid Computing 54
3.5.2 In-Database Processing 54
3.5.3 In-Memory Analytics 55
3.5.4 Machine Learning & Data Mining 55
3.5.5 Predictive Analytics 56
3.5.6 NLP (Natural Language Processing) 56
3.5.7 Text Analytics 57
3.5.8 Visual Analytics 57
3.5.9 Graph Analytics 58
3.5.10 Social Media, IT & Telco Network Analytics 58

4 Chapter 4: Big Data in Automotive, Aerospace & Transportation 60
4.1 Overview & Investment Potential 60
4.2 Key Applications 60
4.2.1 Autonomous & Semi-Autonomous Driving 60
4.2.2 Streamlining Vehicle Recalls & Warranty Management 61
4.2.3 Fleet Management 62
4.2.4 Intelligent Transportation 62
4.2.5 UBI (Usage Based Insurance) 63
4.2.6 Predictive Aircraft Maintenance & Fuel Optimization 63
4.2.7 Air Traffic Control 64
4.3 Case Studies 64
4.3.1 Boeing: Making Flying More Efficient with Big Data 64
4.3.2 BMW: Eliminating Defects in New Vehicle Models with Big Data 65
4.3.3 Dash Labs: Turning Regular Cars into Data-Driven Smart Cars with Big Data 67
4.3.4 Ford Motor Company: Making Efficient Transportation Decisions with Big Data 68
4.3.5 Groupe Renault: Boosting Driver Safety with Big Data 70
4.3.6 Honda Motor Company: Improving F1 Performance & Fuel Efficiency with Big Data 71

5 Chapter 5: Big Data in Banking & Securities 73
5.1 Overview & Investment Potential 73
5.2 Key Applications 73
5.2.1 Customer Retention & Personalized Products 73
5.2.2 Risk Management 74
5.2.3 Fraud Detection 74
5.2.4 Credit Scoring 74
5.3 Case Studies 75
5.3.1 HSBC Group: Avoiding Regulatory Penalties with Big Data 75
5.3.2 JPMorgan Chase & Co.: Improving Business Processes with Big Data 76
5.3.3 OTP Bank: Reducing Loan Defaults with Big Data 77
5.3.4 CBA (Commonwealth Bank of Australia): Providing Personalized Services with Big Data 78

6 Chapter 6: Big Data in Defense & Intelligence 79
6.1 Overview & Investment Potential 79
6.2 Key Applications 79
6.2.1 Intelligence Gathering 79
6.2.2 Battlefield Analytics 80
6.2.3 Energy Saving Opportunities in the Battlefield 80
6.2.4 Preventing Injuries on the Battlefield 81
6.3 Case Studies 82
6.3.1 U.S. Air Force: Providing Actionable Intelligence to Warfighters with Big Data 82
6.3.2 Royal Navy: Empowering Submarine Warfare with Big Data 83
6.3.3 NSA (National Security Agency): Capitalizing on Big Data to Detect Threats 84
6.3.4 Ministry of State Security, China: Predictive Policing with Big Data 85
6.3.5 French DGSE (General Directorate for External Security): Enhancing Intelligence with Big Data 86

7 Chapter 7: Big Data in Education 87
7.1 Overview & Investment Potential 87
7.2 Key Applications 87
7.2.1 Information Integration 87
7.2.2 Identifying Learning Patterns 88
7.2.3 Enabling Student-Directed Learning 88
7.3 Case Studies 89
7.3.1 Purdue University: Improving Academic Performance with Big Data 89
7.3.2 Nottingham Trent University: Successful Student Outcomes with Big Data 90
7.3.3 Edith Cowen University: Increasing Student Retention with Big Data 91

8 Chapter 8: Big Data in Healthcare & Pharma 92
8.1 Overview & Investment Potential 92
8.2 Key Applications 92
8.2.1 Drug Discovery, Design & Development 92
8.2.2 Clinical Development & Trials 93
8.2.3 Population Health Management 94
8.2.4 Personalized Healthcare & Targeted Treatments 95
8.2.5 Proactive & Remote Patient Monitoring 95
8.2.6 Preventive Care & Health Interventions 96
8.3 Case Studies 96
8.3.1 AstraZeneca: Analytics-Driven Drug Development with Big Data 96
8.3.2 Bangkok Hospital Group: Transforming the Patient Experience with Big Data 97
8.3.3 Novartis: Digitizing Healthcare with Big Data 99
8.3.4 Pfizer: Developing Effective and Targeted Therapies with Big Data 100
8.3.5 Sanofi: Proactive Diabetes Care with Big Data 101
8.3.6 UnitedHealth Group: Enhancing Patient Care & Value with Big Data 103

9 Chapter 9: Big Data in Smart Cities & Intelligent Buildings 105
9.1 Overview & Investment Potential 105
9.2 Key Applications 105
9.2.1 Energy Optimization & Fault Detection 106
9.2.2 Intelligent Building Analytics 106
9.2.3 Urban Transportation Management 106
9.2.4 Optimizing Energy Production 107
9.2.5 Water Management 107
9.2.6 Urban Waste Management 107
9.3 Case Studies 108
9.3.1 Singapore: Building a Smart Nation with Big Data 108
9.3.2 Glasgow City Council: Promoting Smart City Efforts with Big Data 109
9.3.3 OVG Real Estate: Powering the World’s Most Intelligent Building with Big Data 110

10 Chapter 10: Big Data in Insurance 111
10.1 Overview & Investment Potential 111
10.2 Key Applications 111
10.2.1 Claims Fraud Mitigation 112
10.2.2 Customer Retention & Profiling 112
10.2.3 Risk Management 112
10.3 Case Studies 113
10.3.1 Zurich Insurance Group: Enhancing Risk Management with Big Data 113
10.3.2 RSA Group: Improving Customer Relations with Big Data 114
10.3.3 Primerica: Improving Insurance Sales Force Productivity with Big Data 115

11 Chapter 11: Big Data in Manufacturing & Natural Resources 116
11.1 Overview & Investment Potential 116
11.2 Key Applications 116
11.2.1 Asset Maintenance & Downtime Reduction 116
11.2.2 Quality & Environmental Impact Control 117
11.2.3 Optimized Supply Chain 117
11.2.4 Exploration & Identification of Natural Resources 117
11.3 Case Studies 118
11.3.1 Intel Corporation: Cutting Manufacturing Costs with Big Data 118
11.3.2 Dow Chemical Company: Optimizing Chemical Manufacturing with Big Data 119
11.3.3 Michelin: Improving the Efficiency of Supply Chain and Manufacturing with Big Data 120
11.3.4 Brunei: Saving Natural Resources with Big Data 121

12 Chapter 12: Big Data in Web, Media & Entertainment 122
12.1 Overview & Investment Potential 122
12.2 Key Applications 122
12.2.1 Audience & Advertising Optimization 123
12.2.2 Channel Optimization 123
12.2.3 Recommendation Engines 123
12.2.4 Optimized Search 123
12.2.5 Live Sports Event Analytics 124
12.2.6 Outsourcing Big Data Analytics to Other Verticals 124
12.3 Case Studies 124
12.3.1 Twitter: Cracking Down on Abusive Content with Big Data 124
12.3.2 Netflix: Improving Viewership with Big Data 126
12.3.3 NFL (National Football League): Improving Stadium Experience with Big Data 127
12.3.4 Baidu: Reshaping Search Capabilities with Big Data 128
12.3.5 Constant Contact: Effective Marketing with Big Data 129

13 Chapter 13: Big Data in Public Safety & Homeland Security 130
13.1 Overview & Investment Potential 130
13.2 Key Applications 130
13.2.1 Cyber Crime Mitigation 131
13.2.2 Crime Prediction Analytics 131
13.2.3 Video Analytics & Situational Awareness 131
13.3 Case Studies 132
13.3.1 DHS (Department of Homeland Security): Identifying Threats with Big Data 132
13.3.2 Dubai Police: Locating Wanted Vehicles More Efficiently with Big Data 133
13.3.3 Memphis Police Department: Crime Reduction with Big Data 134

14 Chapter 14: Big Data in Public Services 135
14.1 Overview & Investment Potential 135
14.2 Key Applications 135
14.2.1 Public Sentiment Analysis 135
14.2.2 Tax Collection & Fraud Detection 136
14.2.3 Economic Analysis 136
14.2.4 Predicting & Mitigating Disasters 136
14.3 Case Studies 137
14.3.1 ONS (Office for National Statistics): Exploring the UK Economy with Big Data 137
14.3.2 New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data 138
14.3.3 Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data 139
14.3.4 City of Chicago: Improving Government Productivity with Big Data 140
14.3.5 FDNY (Fire Department of the City of New York): Fighting Fires with Big Data 141
14.3.6 Ambulance Victoria: Improving Patient Survival Rates with Big Data 142

15 Chapter 15: Big Data in Retail, Wholesale & Hospitality 143
15.1 Overview & Investment Potential 143
15.2 Key Applications 143
15.2.1 Customer Sentiment Analysis 144
15.2.2 Customer & Branch Segmentation 144
15.2.3 Price Optimization 144
15.2.4 Personalized Marketing 144
15.2.5 Optimizing & Monitoring the Supply Chain 145
15.2.6 In-Field Sales Analytics 145
15.3 Case Studies 146
15.3.1 Walmart: Making Smarter Stocking Decision with Big Data 146
15.3.2 Tesco: Reducing Supermarket Energy Bills with Big Data 147
15.3.3 The Walt Disney Company: Theme Park Management with Big Data 148
15.3.4 Marriott International: Elevating Guest Services with Big Data 150
15.3.5 JJ Food Service: Predictive Wholesale Shopping Lists with Big Data 151

16 Chapter 16: Big Data in Telecommunications 152
16.1 Overview & Investment Potential 152
16.2 Key Applications 152
16.2.1 Network Performance & Coverage Optimization 152
16.2.2 Customer Churn Prevention 153
16.2.3 Personalized Marketing 153
16.2.4 Tailored Location Based Services 153
16.2.5 Fraud Detection 153
16.3 Case Studies 154
16.3.1 BT Group: Hunting Down Nuisance Callers with Big Data 154
16.3.2 AT&T: Smart Network Management with Big Data 155
16.3.3 T-Mobile USA: Cutting Down Churn Rate with Big Data 156
16.3.4 TEOCO: Helping Service Providers Save Millions with Big Data 157
16.3.5 Freedom Mobile: Optimizing Video Quality with Big Data 158
16.3.6 Coriant: SaaS Based Analytics with Big Data 159

17 Chapter 17: Big Data in Utilities & Energy 160
17.1 Overview & Investment Potential 160
17.2 Key Applications 160
17.2.1 Customer Retention 160
17.2.2 Forecasting Energy 161
17.2.3 Billing Analytics 161
17.2.4 Predictive Maintenance 161
17.2.5 Maximizing the Potential of Drilling 161
17.2.6 Production Optimization 162
17.3 Case Studies 162
17.3.1 Royal Dutch Shell: Developing Data-Driven Oil Fields with Big Data 162
17.3.2 British Gas: Improving Customer Service with Big Data 163
17.3.3 Oncor Electric Delivery: Intelligent Power Grid Management with Big Data 164

18 Chapter 18: Future Roadmap & Value Chain 165
18.1 Future Roadmap 165
18.1.1 Pre-2020: Towards Cloud-Based Big Data Offerings for Advanced Analytics 165
18.1.2 2020 – 2025: Growing Focus on AI (Artificial Intelligence), Deep Learning & Edge Analytics 166
18.1.3 2025 – 2030: Convergence with Future IoT Applications 166
18.2 The Big Data Value Chain 167
18.2.1 Hardware Providers 167
18.2.1.1 Storage & Compute Infrastructure Providers 167
18.2.1.2 Networking Infrastructure Providers 168
18.2.2 Software Providers 168
18.2.2.1 Hadoop & Infrastructure Software Providers 169
18.2.2.2 SQL & NoSQL Providers 169
18.2.2.3 Analytic Platform & Application Software Providers 169
18.2.2.4 Cloud Platform Providers 169
18.2.3 Professional Services Providers 170
18.2.4 End-to-End Solution Providers 170
18.2.5 Vertical Enterprises 170

19 Chapter 19: Standardization & Regulatory Initiatives 171
19.1 ASF (Apache Software Foundation) 171
19.1.1 Management of Hadoop 171
19.1.2 Big Data Projects Beyond Hadoop 171
19.2 CSA (Cloud Security Alliance) 175
19.2.1 BDWG (Big Data Working Group) 175
19.3 CSCC (Cloud Standards Customer Council) 176
19.3.1 Big Data Working Group 176
19.4 DMG  (Data Mining Group) 177
19.4.1 PMML (Predictive Model Markup Language) Working Group 177
19.4.2 PFA (Portable Format for Analytics) Working Group 177
19.5 IEEE (Institute of Electrical and Electronics Engineers) 177
19.5.1 Big Data Initiative 178
19.6 INCITS (InterNational Committee for Information Technology Standards) 179
19.6.1 Big Data Technical Committee 179
19.7 ISO (International Organization for Standardization) 180
19.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange 180
19.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms 181
19.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques 181
19.7.4 ISO/IEC JTC 1/WG 9: Big Data 181
19.7.5 Collaborations with Other ISO Work Groups 182
19.8 ITU (International Telecommunication Union) 183
19.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities 183
19.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks 184
19.8.3 Other Relevant Work 184
19.9 Linux Foundation 185
19.9.1 ODPi (Open Ecosystem of Big Data) 185
19.10 NIST (National Institute of Standards and Technology) 185
19.10.1 NBD-PWG (NIST Big Data Public Working Group) 185
19.11 OASIS (Organization for the Advancement of Structured Information Standards) 186
19.11.1 Technical Committees 186
19.12 ODaF (Open Data Foundation) 187
19.12.1 Big Data Accessibility 187
19.13 ODCA (Open Data Center Alliance) 187
19.13.1 Work on Big Data 188
19.14 OGC (Open Geospatial Consortium) 188
19.14.1 Big Data DWG (Domain Working Group) 188
19.15 TM Forum 188
19.15.1 Big Data Analytics Strategic Program 189
19.16 TPC (Transaction Processing Performance Council) 189
19.16.1 TPC-BDWG (TPC Big Data Working Group) 189
19.17 W3C (World Wide Web Consortium) 189
19.17.1 Big Data Community Group 190
19.17.2 Open Government Community Group 190

20 Chapter 20: Market Sizing & Forecasts 191
20.1 Global Outlook for the Big Data Market 191
20.2 Submarket Segmentation 192
20.2.1 Storage and Compute Infrastructure 193
20.2.2 Networking Infrastructure 193
20.2.3 Hadoop & Infrastructure Software 194
20.2.4 SQL 194
20.2.5 NoSQL 195
20.2.6 Analytic Platforms & Applications 195
20.2.7 Cloud Platforms 196
20.2.8 Professional Services 196
20.3 Vertical Market Segmentation 197
20.3.1 Automotive, Aerospace & Transportation 198
20.3.2 Banking & Securities 198
20.3.3 Defense & Intelligence 199
20.3.4 Education 199
20.3.5 Healthcare & Pharmaceutical 200
20.3.6 Smart Cities & Intelligent Buildings 200
20.3.7 Insurance 201
20.3.8 Manufacturing & Natural Resources 201
20.3.9 Media & Entertainment 202
20.3.10 Public Safety & Homeland Security 202
20.3.11 Public Services 203
20.3.12 Retail, Wholesale & Hospitality 203
20.3.13 Telecommunications 204
20.3.14 Utilities & Energy 204
20.3.15 Other Sectors 205
20.4 Regional Outlook 206
20.5 Asia Pacific 207
20.5.1 Country Level Segmentation 207
20.5.2 Australia 208
20.5.3 China 208
20.5.4 India 209
20.5.5 Indonesia 209
20.5.6 Japan 210
20.5.7 Malaysia 210
20.5.8 Pakistan 211
20.5.9 Philippines 211
20.5.10 Singapore 212
20.5.11 South Korea 212
20.5.12 Taiwan 213
20.5.13 Thailand 213
20.5.14 Rest of Asia Pacific 214
20.6 Eastern Europe 215
20.6.1 Country Level Segmentation 215
20.6.2 Czech Republic 216
20.6.3 Poland 216
20.6.4 Russia 217
20.6.5 Rest of Eastern Europe 217
20.7 Latin & Central America 218
20.7.1 Country Level Segmentation 218
20.7.2 Argentina 219
20.7.3 Brazil 219
20.7.4 Mexico 220
20.7.5 Rest of Latin & Central America 220
20.8 Middle East & Africa 221
20.8.1 Country Level Segmentation 221
20.8.2 Israel 222
20.8.3 Qatar 222
20.8.4 Saudi Arabia 223
20.8.5 South Africa 223
20.8.6 UAE 224
20.8.7 Rest of the Middle East & Africa 224
20.9 North America 225
20.9.1 Country Level Segmentation 225
20.9.2 Canada 226
20.9.3 USA 226
20.10 Western Europe 227
20.10.1 Country Level Segmentation 227
20.10.2 Denmark 228
20.10.3 Finland 228
20.10.4 France 229
20.10.5 Germany 229
20.10.6 Italy 230
20.10.7 Netherlands 230
20.10.8 Norway 231
20.10.9 Spain 231
20.10.10 Sweden 232
20.10.11 UK 232
20.10.12 Rest of Western Europe 233

21 Chapter 21: Vendor Landscape 234
21.1 1010data 234
21.2 Absolutdata 235
21.3 Accenture 236
21.4 Actian Corporation/HCL Technologies 237
21.5 Adaptive Insights 239
21.6 Adobe Systems 240
21.7 Advizor Solutions 242
21.8 AeroSpike 243
21.9 AFS Technologies 244
21.10 Alation 245
21.11 Algorithmia 246
21.12 Alluxio 247
21.13 ALTEN 248
21.14 Alteryx 249
21.15 AMD (Advanced Micro Devices) 250
21.16 Anaconda 251
21.17 Apixio 252
21.18 Arcadia Data 253
21.19 ARM 254
21.20 AtScale 255
21.21 Attivio 256
21.22 Attunity 257
21.23 Automated Insights 258
21.24 AVORA 259
21.25 AWS (Amazon Web Services) 260
21.26 Axiomatics 262
21.27 Ayasdi 263
21.28 BackOffice Associates 264
21.29 Basho Technologies 265
21.30 BCG (Boston Consulting Group) 266
21.31 Bedrock Data 267
21.32 BetterWorks 268
21.33 Big Panda 269
21.34 BigML 270
21.35 Bitam 271
21.36 Blue Medora 272
21.37 BlueData Software 273
21.38 BlueTalon 274
21.39 BMC Software 275
21.40 BOARD International 276
21.41 Booz Allen Hamilton 277
21.42 Boxever 278
21.43 CACI International 279
21.44 Cambridge Semantics 280
21.45 Capgemini 281
21.46 Cazena 282
21.47 Centrifuge Systems 283
21.48 CenturyLink 284
21.49 Chartio 285
21.50 Cisco Systems 286
21.51 Civis Analytics 287
21.52 ClearStory Data 288
21.53 Cloudability 289
21.54 Cloudera 290
21.55 Cloudian 291
21.56 Clustrix 292
21.57 CognitiveScale 293
21.58 Collibra 294
21.59 Concurrent Technology/Vecima Networks 295
21.60 Confluent 296
21.61 Contexti 297
21.62 Couchbase 298
21.63 Crate.io 299
21.64 Cray 300
21.65 Databricks 301
21.66 Dataiku 302
21.67 Datalytyx 303
21.68 Datameer 304
21.69 DataRobot 305
21.70 DataStax 306
21.71 Datawatch Corporation 307
21.72 DDN (DataDirect Networks) 308
21.73 Decisyon 309
21.74 Dell Technologies 310
21.75 Deloitte 311
21.76 Demandbase 312
21.77 Denodo Technologies 313
21.78 Dianomic Systems 314
21.79 Digital Reasoning Systems 315
21.80 Dimensional Insight 316
21.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group 317
21.82 Domino Data Lab 318
21.83 Domo 319
21.84 Dremio 320
21.85 DriveScale 321
21.86 Druva 322
21.87 Dundas Data Visualization 323
21.88 DXC Technology 324
21.89 Elastic 325
21.90 Engineering Group (Engineering Ingegneria Informatica) 326
21.91 EnterpriseDB Corporation 327
21.92 eQ Technologic 328
21.93 Ericsson 329
21.94 Erwin 330
21.95 EVŌ (Big Cloud Analytics) 331
21.96 EXASOL 332
21.97 EXL (ExlService Holdings) 333
21.98 Facebook 334
21.99 FICO (Fair Isaac Corporation) 335
21.100 Figure Eight 336
21.101 FogHorn Systems 337
21.102 Fractal Analytics 338
21.103 Franz 339
21.104 Fujitsu 340
21.105 Fuzzy Logix 342
21.106 Gainsight 343
21.107 GE (General Electric) 344
21.108 Glassbeam 345
21.109 GoodData Corporation 346
21.110 Google/Alphabet 347
21.111 Grakn Labs 349
21.112 Greenwave Systems 350
21.113 GridGain Systems 351
21.114 H2O.ai 352
21.115 HarperDB 353
21.116 Hedvig 354
21.117 Hitachi Vantara 355
21.118 Hortonworks 356
21.119 HPE (Hewlett Packard Enterprise) 357
21.120 Huawei 359
21.121 HVR 360
21.122 HyperScience 361
21.123 HyTrust 362
21.124 IBM Corporation 364
21.125 iDashboards 366
21.126 IDERA 367
21.127 Ignite Technologies 368
21.128 Imanis Data 370
21.129 Impetus Technologies 371
21.130 Incorta 372
21.131 InetSoft Technology Corporation 373
21.132 InfluxData 374
21.133 Infogix 375
21.134 Infor/Birst 376
21.135 Informatica 378
21.136 Information Builders 379
21.137 Infosys 380
21.138 Infoworks 381
21.139 Insightsoftware.com 382
21.140 InsightSquared 383
21.141 Intel Corporation 384
21.142 Interana 385
21.143 InterSystems Corporation 386
21.144 Jedox 387
21.145 Jethro 388
21.146 Jinfonet Software 389
21.147 Juniper Networks 390
21.148 KALEAO 391
21.149 Keen IO 392
21.150 Keyrus 393
21.151 Kinetica 394
21.152 KNIME 395
21.153 Kognitio 396
21.154 Kyvos Insights 397
21.155 LeanXcale 398
21.156 Lexalytics 399
21.157 Lexmark International 401
21.158 Lightbend 402
21.159 Logi Analytics 403
21.160 Logical Clocks 404
21.161 Longview Solutions/Tidemark 405
21.162 Looker Data Sciences 407
21.163 LucidWorks 408
21.164 Luminoso Technologies 409
21.165 Maana 410
21.166 Manthan Software Services 411
21.167 MapD Technologies 412
21.168 MapR Technologies 413
21.169 MariaDB Corporation 414
21.170 MarkLogic Corporation 415
21.171 Mathworks 416
21.172 Melissa 417
21.173 MemSQL 418
21.174 Metric Insights 419
21.175 Microsoft Corporation 420
21.176 MicroStrategy 422
21.177 Minitab 423
21.178 MongoDB 424
21.179 Mu Sigma 425
21.180 NEC Corporation 426
21.181 Neo4j 427
21.182 NetApp 428
21.183 Nimbix 429
21.184 Nokia 430
21.185 NTT Data Corporation 431
21.186 Numerify 432
21.187 NuoDB 433
21.188 NVIDIA Corporation 434
21.189 Objectivity 435
21.190 Oblong Industries 436
21.191 OpenText Corporation 437
21.192 Opera Solutions 439
21.193 Optimal Plus 440
21.194 Oracle Corporation 441
21.195 Palantir Technologies 444
21.196 Panasonic Corporation/Arimo 446
21.197 Panorama Software 447
21.198 Paxata 448
21.199 Pepperdata 449
21.200 Phocas Software 450
21.201 Pivotal Software 451
21.202 Prognoz 453
21.203 Progress Software Corporation 454
21.204 Provalis Research 455
21.205 Pure Storage 456
21.206 PwC (PricewaterhouseCoopers International) 457
21.207 Pyramid Analytics 458
21.208 Qlik 459
21.209 Qrama/Tengu 460
21.210 Quantum Corporation 461
21.211 Qubole 462
21.212 Rackspace 463
21.213 Radius Intelligence 464
21.214 RapidMiner 465
21.215 Recorded Future 466
21.216 Red Hat 467
21.217 Redis Labs 468
21.218 RedPoint Global 469
21.219 Reltio 470
21.220 RStudio 471
21.221 Rubrik/Datos IO 472
21.222 Ryft 473
21.223 Sailthru 474
21.224 Salesforce.com 475
21.225 Salient Management Company 476
21.226 Samsung Group 477
21.227 SAP 478
21.228 SAS Institute 479
21.229 ScaleOut Software 480
21.230 Seagate Technology 481
21.231 Sinequa 482
21.232 SiSense 483
21.233 Sizmek 484
21.234 SnapLogic 485
21.235 Snowflake Computing 486
21.236 Software AG 487
21.237 Splice Machine 488
21.238 Splunk 489
21.239 Strategy Companion Corporation 491
21.240 Stratio 492
21.241 Streamlio 493
21.242 StreamSets 494
21.243 Striim 495
21.244 Sumo Logic 496
21.245 Supermicro (Super Micro Computer) 497
21.246 Syncsort 498
21.247 SynerScope 500
21.248 SYNTASA 501
21.249 Tableau Software 502
21.250 Talend 503
21.251 Tamr 504
21.252 TARGIT 505
21.253 TCS (Tata Consultancy Services) 506
21.254 Teradata Corporation 507
21.255 Thales/Guavus 509
21.256 ThoughtSpot 510
21.257 TIBCO Software 511
21.258 Toshiba Corporation 513
21.259 Transwarp 514
21.260 Trifacta 515
21.261 Unifi Software 516
21.262 Unravel Data 517
21.263 VANTIQ 518
21.264 VMware 519
21.265 VoltDB 520
21.266 WANdisco 521
21.267 Waterline Data 522
21.268 Western Digital Corporation 523
21.269 WhereScape 524
21.270 WiPro 525
21.271 Wolfram Research 526
21.272 Workday 528
21.273 Xplenty 530
21.274 Yellowfin BI 531
21.275 Yseop 532
21.276 Zendesk 533
21.277 Zoomdata 534
21.278 Zucchetti 535

22 Chapter 22: Conclusion & Strategic Recommendations 536
22.1 Why is the Market Poised to Grow? 536
22.2 Moving Towards Consolidation: Review of M&A Activity in the Vendor Arena 536
22.3 Maturation of AI (Artificial Intelligence): From  Machine Learning to Deep Learning 538
22.4 Blockchain: Impact on Big Data 539
22.5 The Emergence of Edge Analytics 539
22.6 Beyond Data Capture & Analytics 540
22.7 Transforming IT from a Cost Center to a Profit Center 540
22.8 Can Privacy Implications Hinder Success? 540
22.9 Maximizing Innovation with Careful Regulation 541
22.10 Battling Organizational & Data Silos 542
22.11 Moving Big Data to the Cloud 542
22.12 Software vs. Hardware Investments 544
22.13 Vendor Share: Who Leads the Market? 545
22.14 Big Data Driving Wider IT Industry Investments 546
22.15 Assessing the Impact of the IoT 547
22.16 Recommendations 548
22.16.1 Big Data Hardware, Software & Professional Services Providers 548
22.16.2 Enterprises 549

List of Figures

Figure 1: Hadoop Architecture 43
Figure 2: Reactive vs. Proactive Analytics 54
Figure 3: Big Data Future Roadmap: 2018 – 2030 166
Figure 4: Big Data Value Chain 168
Figure 5: Key Aspects of Big Data Standardization 179
Figure 6: Global Big Data Revenue: 2018 – 2030 ($ Million) 192
Figure 7: Global Big Data Revenue by Submarket: 2018 – 2030 ($ Million) 193
Figure 8: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2018 – 2030 ($ Million) 194
Figure 9: Global Big Data Networking Infrastructure Submarket Revenue: 2018 – 2030 ($ Million) 194
Figure 10: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2018 – 2030 ($ Million) 195
Figure 11: Global Big Data SQL Submarket Revenue: 2018 – 2030 ($ Million) 195
Figure 12: Global Big Data NoSQL Submarket Revenue: 2018 – 2030 ($ Million) 196
Figure 13: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2018 – 2030 ($ Million) 196
Figure 14: Global Big Data Cloud Platforms Submarket Revenue: 2018 – 2030 ($ Million) 197
Figure 15: Global Big Data Professional Services Submarket Revenue: 2018 – 2030 ($ Million) 197
Figure 16: Global Big Data Revenue by Vertical Market: 2018 – 2030 ($ Million) 198
Figure 17: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2018 – 2030 ($ Million) 199
Figure 18: Global Big Data Revenue in the Banking & Securities Sector: 2018 – 2030 ($ Million) 199
Figure 19: Global Big Data Revenue in the Defense & Intelligence Sector: 2018 – 2030 ($ Million) 200
Figure 20: Global Big Data Revenue in the Education Sector: 2018 – 2030 ($ Million) 200
Figure 21: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2018 – 2030 ($ Million) 201
Figure 22: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2018 – 2030 ($ Million) 201
Figure 23: Global Big Data Revenue in the Insurance Sector: 2018 – 2030 ($ Million) 202
Figure 24: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2018 – 2030 ($ Million) 202
Figure 25: Global Big Data Revenue in the Media & Entertainment Sector: 2018 – 2030 ($ Million) 203
Figure 26: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2018 – 2030 ($ Million) 203
Figure 27: Global Big Data Revenue in the Public Services Sector: 2018 – 2030 ($ Million) 204
Figure 28: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2018 – 2030 ($ Million) 204
Figure 29: Global Big Data Revenue in the Telecommunications Sector: 2018 – 2030 ($ Million) 205
Figure 30: Global Big Data Revenue in the Utilities & Energy Sector: 2018 – 2030 ($ Million) 205
Figure 31: Global Big Data Revenue in Other Vertical Sectors: 2018 – 2030 ($ Million) 206
Figure 32: Big Data Revenue by Region: 2018 – 2030 ($ Million) 207
Figure 33: Asia Pacific Big Data Revenue: 2018 – 2030 ($ Million) 208
Figure 34: Asia Pacific Big Data Revenue by Country: 2018 – 2030 ($ Million) 208
Figure 35: Australia Big Data Revenue: 2018 – 2030 ($ Million) 209
Figure 36: China Big Data Revenue: 2018 – 2030 ($ Million) 209
Figure 37: India Big Data Revenue: 2018 – 2030 ($ Million) 210
Figure 38: Indonesia Big Data Revenue: 2018 – 2030 ($ Million) 210
Figure 39: Japan Big Data Revenue: 2018 – 2030 ($ Million) 211
Figure 40: Malaysia Big Data Revenue: 2018 – 2030 ($ Million) 211
Figure 41: Pakistan Big Data Revenue: 2018 – 2030 ($ Million) 212
Figure 42: Philippines Big Data Revenue: 2018 – 2030 ($ Million) 212
Figure 43: Singapore Big Data Revenue: 2018 – 2030 ($ Million) 213
Figure 44: South Korea Big Data Revenue: 2018 – 2030 ($ Million) 213
Figure 45: Taiwan Big Data Revenue: 2018 – 2030 ($ Million) 214
Figure 46: Thailand Big Data Revenue: 2018 – 2030 ($ Million) 214
Figure 47: Big Data Revenue in the Rest of Asia Pacific: 2018 – 2030 ($ Million) 215
Figure 48: Eastern Europe Big Data Revenue: 2018 – 2030 ($ Million) 216
Figure 49: Eastern Europe Big Data Revenue by Country: 2018 – 2030 ($ Million) 216
Figure 50: Czech Republic Big Data Revenue: 2018 – 2030 ($ Million) 217
Figure 51: Poland Big Data Revenue: 2018 – 2030 ($ Million) 217
Figure 52: Russia Big Data Revenue: 2018 – 2030 ($ Million) 218
Figure 53: Big Data Revenue in the Rest of Eastern Europe: 2018 – 2030 ($ Million) 218
Figure 54: Latin & Central America Big Data Revenue: 2018 – 2030 ($ Million) 219
Figure 55: Latin & Central America Big Data Revenue by Country: 2018 – 2030 ($ Million) 219
Figure 56: Argentina Big Data Revenue: 2018 – 2030 ($ Million) 220
Figure 57: Brazil Big Data Revenue: 2018 – 2030 ($ Million) 220
Figure 58: Mexico Big Data Revenue: 2018 – 2030 ($ Million) 221
Figure 59: Big Data Revenue in the Rest of Latin & Central America: 2018 – 2030 ($ Million) 221
Figure 60: Middle East & Africa Big Data Revenue: 2018 – 2030 ($ Million) 222
Figure 61: Middle East & Africa Big Data Revenue by Country: 2018 – 2030 ($ Million) 222
Figure 62: Israel Big Data Revenue: 2018 – 2030 ($ Million) 223
Figure 63: Qatar Big Data Revenue: 2018 – 2030 ($ Million) 223
Figure 64: Saudi Arabia Big Data Revenue: 2018 – 2030 ($ Million) 224
Figure 65: South Africa Big Data Revenue: 2018 – 2030 ($ Million) 224
Figure 66: UAE Big Data Revenue: 2018 – 2030 ($ Million) 225
Figure 67: Big Data Revenue in the Rest of the Middle East & Africa: 2018 – 2030 ($ Million) 225
Figure 68: North America Big Data Revenue: 2018 – 2030 ($ Million) 226
Figure 69: North America Big Data Revenue by Country: 2018 – 2030 ($ Million) 226
Figure 70: Canada Big Data Revenue: 2018 – 2030 ($ Million) 227
Figure 71: USA Big Data Revenue: 2018 – 2030 ($ Million) 227
Figure 72: Western Europe Big Data Revenue: 2018 – 2030 ($ Million) 228
Figure 73: Western Europe Big Data Revenue by Country: 2018 – 2030 ($ Million) 228
Figure 74: Denmark Big Data Revenue: 2018 – 2030 ($ Million) 229
Figure 75: Finland Big Data Revenue: 2018 – 2030 ($ Million) 229
Figure 76: France Big Data Revenue: 2018 – 2030 ($ Million) 230
Figure 77: Germany Big Data Revenue: 2018 – 2030 ($ Million) 230
Figure 78: Italy Big Data Revenue: 2018 – 2030 ($ Million) 231
Figure 79: Netherlands Big Data Revenue: 2018 – 2030 ($ Million) 231
Figure 80: Norway Big Data Revenue: 2018 – 2030 ($ Million) 232
Figure 81: Spain Big Data Revenue: 2018 – 2030 ($ Million) 232
Figure 82: Sweden Big Data Revenue: 2018 – 2030 ($ Million) 233
Figure 83: UK Big Data Revenue: 2018 – 2030 ($ Million) 233
Figure 84: Big Data Revenue in the Rest of Western Europe: 2018 – 2030 ($ Million) 234
Figure 85: Global Big Data Workload Distribution by Environment: 2018 – 2030 (%) 544
Figure 86: Global Big Data Revenue by Hardware, Software & Professional Services: 2018 – 2030 ($ Million) 545
Figure 87: Big Data Vendor Market Share: 2017 (%) 546
Figure 88: Global IT Expenditure Driven by Big Data Investments: 2018 – 2030 ($ Million) 547
Figure 89: Global IoT Connections by Access Technology: 2018 – 2030 (Millions) 548