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Big Data Leaders: Accenture, CSC Fujitsu, HP, Informatica, Mu Sigma, Opera Solutions, Oracle, and Tata Consultancy Services

Mind Commerce Publishing
Published Date » 2014-02-10
No. Of Pages » 220
 Big Data represents a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tool.  It is also unstructured, meaning that it is not tabulated, correlated, etc. (e.g. it does not have a pre-defined data model or is not organized in a pre-defined manner).  
   
 Big Data technologies enable organizations to handle huge datasets and generate information/insights from them with minimal delay time (sometimes in real-time). 
   
 Leading companies in Big Data are the already making great strides and will surely represent the forbearers of many great solutions yet to come. 
   
  Companies evaluated in this report* include:  
     
 
 
 Accenture 
 CSC 
 Fujitsu 
 Hewlett Packard 
 Informatica...
Table of Contents:
 
1.0  BIG DATA: AN OVERVIEW 11
1.1  THE TRANSFORMATION FROM TRADITIONAL DATA TO BIG DATA 12
1.2  THE 3VS OF BIG DATA AND HOW TO DEAL WITH THEM   14
1.3  HOW TO DEALS WITH THE 3VS OF BIG DATA   15
1.4  SURVEY FINDINGS FROM BIMA SURVEY CONDUCTED BY STERIA IN EUROPE ACROSS ORGANIZATIONS   16
1.5  APPROACHES TOWARDS BIG DATA  18
1.6  THE IMPORTANCE OF BIG DATA AND ITS BENEFITS FOR THE ORGANIZATIONS 20
1.7  KEY ENABLERS OF BIG DATA   21
1.8  BIG DATA CHALLENGES FOR THE ORGANIZATIONS   21

2.0  BIG DATA: MARKET TRENDS AND FORECAST  23
2.1  HOW THE ORGANIZATIONS HAVE BENEFITTED FROM BIG DATA 29

3.0  KEY PLAYERS IN BIG DATA 32
3.1  ACCENTURE  32
3.1.1 COMPANY OVERVIEW   32
3.1.2 OFFERINGS (SOLUTIONS, APPLICATIONS, PRODUCTS, SERVICES) 33
3.1.3 STRATEGIES AND PLANS  37
3.1.4 MERGERS & ACQUISITIONS 39
3.1.5 PARTNERSHIPS & ALLIANCES 40
3.1.6 FINANCIAL & OPERATIONAL HIGHLIGHTS 42
3.1.7 KEY CONTRACT WINS 47
3.1.8 ANALYSIS & CONCLUSION   48
3.2  CSC   50
3.2.1 COMPANY OVERVIEW   50
3.2.2 OFFERINGS (SOLUTIONS, APPLICATIONS, PRODUCTS, SERVICES) 51
3.2.3 STRATEGIES AND PLANS  56
3.2.4 MERGERS & ACQUISITIONS 58
3.2.5 PARTNERSHIPS & ALLIANCES 59
3.2.6 FINANCIAL & OPERATIONAL HIGHLIGHTS 61
3.2.7 KEY CONTRACT WINS 63
3.2.8 ANALYSIS & CONCLUSION   64
3.3  FUJITSU  66
3.3.1 COMPANY OVERVIEW   66
3.3.2 OFFERINGS (SOLUTIONS, APPLICATIONS, PRODUCTS, SERVICES) 67
3.3.3 STRATEGIES AND PLANS  75
3.3.4 MERGERS & ACQUISITIONS 76
3.3.5 PARTNERSHIPS & ALLIANCES 76
3.3.6 FINANCIAL & OPERATIONAL HIGHLIGHTS 77
3.3.7 KEY CONTRACT WINS 82
3.3.8 ANALYSIS & CONCLUSION   83
3.4  HEWLETT-PACKARD (HP) 85
3.4.1 COMPANY OVERVIEW   85
3.4.2 OFFERINGS (SOLUTIONS, APPLICATIONS, PRODUCTS, SERVICES) 86
3.4.3 STRATEGIES AND PLANS  90
3.4.4 MERGERS & ACQUISITIONS 92
3.4.5 PARTNERSHIPS & ALLIANCES 92
3.4.6 FINANCIAL & OPERATIONAL HIGHLIGHTS 94
3.4.7 KEY CONTRACT WINS 97
3.4.8 ANALYSIS & CONCLUSION   98
3.5  IBM   99
3.5.1 COMPANY OVERVIEW   99
3.5.2 OFFERINGS (SOLUTIONS, APPLICATIONS, PRODUCTS, SERVICES) 101
3.5.3 STRATEGIES AND PLANS  107
3.5.4 MERGERS & ACQUISITIONS 110
3.5.5 PARTNERSHIPS & ALLIANCES 113
3.5.6 FINANCIAL & OPERATIONAL HIGHLIGHTS 115
3.5.7 KEY CONTRACT WINS 123
3.5.8 ANALYSIS & CONCLUSION   124
3.6  INFORMATICA  126
3.6.1 COMPANY OVERVIEW   126
3.6.2 OFFERINGS (SOLUTIONS, APPLICATIONS, PRODUCTS, SERVICES) 127
3.6.3 STRATEGIES AND PLANS  130
3.6.4 MERGERS & ACQUISITIONS 134
3.6.5 PARTNERSHIPS & ALLIANCES 136
3.6.6 FINANCIAL & OPERATIONAL HIGHLIGHTS 139
3.6.7 KEY CONTRACT WINS 143
3.6.8 ANALYSIS & CONCLUSION   144
3.7  MU SIGMA 145
3.7.1 COMPANY OVERVIEW   145
3.7.2 OFFERINGS (SOLUTIONS, APPLICATIONS, PRODUCTS, SERVICES) 145
3.7.3 STRATEGIES AND PLANS  151
3.7.4 PARTNERSHIPS & ALLIANCES 152
3.7.5 FINANCIAL & OPERATIONAL HIGHLIGHTS 153
3.7.6 KEY CONTRACT WINS 153
3.7.7 ANALYSIS & CONCLUSION   153
3.8  OPERA SOLUTIONS 155
3.8.1 COMPANY OVERVIEW   155
3.8.2 OFFERINGS (SOLUTIONS, APPLICATIONS, PRODUCTS, SERVICES) 155
3.8.3 STRATEGIES AND PLANS  158
3.8.4 MERGERS & ACQUISITIONS 159
3.8.5 PARTNERSHIPS & ALLIANCES 159
3.8.6 KEY CONTRACT WINS 160
3.8.7 ANALYSIS & CONCLUSION   160
3.9  ORACLE 161
3.9.1 COMPANY OVERVIEW   161
3.9.2 ORACLE: OFFERINGS (SOLUTIONS, APPLICATIONS, PRODUCTS, SERVICES) 162
3.9.3 STRATEGIES AND PLANS  173
3.9.4 MERGERS & ACQUISITIONS 176
3.9.5 PARTNERSHIPS & ALLIANCES 179
3.9.6 FINANCIAL & OPERATIONAL HIGHLIGHTS 181
3.9.7 KEY CONTRACT WINS 190
3.9.8 ANALYSIS & CONCLUSION   191
3.10   TATA CONSULTANCY SERVICES (TCS) 192
3.10.1 COMPANY OVERVIEW   192
3.10.2 OFFERINGS (SOLUTIONS, APPLICATIONS, PRODUCTS, SERVICES) 194
3.10.3 STRATEGIES AND PLANS  202
3.10.4 MERGERS & ACQUISITIONS 203
3.10.5 PARTNERSHIPS & ALLIANCES 204
3.10.6 FINANCIAL & OPERATIONAL HIGHLIGHTS 205
3.10.7 KEY CONTRACT WINS 210
3.10.8 ANALYSIS & CONCLUSION   211

4.0  BIG DATA: SWOT AND CONCLUSION  212
4.1  STRENGTHS   213
4.2  WEAKNESS 214
4.3  OPPORTUNITIES  215
4.4  THREATS 217
4.5  WHAT NEXT IN BIG DATA 219
4.6  CONCLUSION 219

List of Tables

TABLE 1: KEY FEATURES OF TRADITIONAL DATA AND BIG DATA  12
TABLE 2: ACCENTURE-KEY INFORMATION 33
TABLE 3: ACCENTURE- BIG DATA MERGERS & ACQUISITIONS  39
TABLE 4: ACCENTURE-BIG DATA PARTNERSHIPS & ALLIANCES 40
TABLE 5: ACCENTURE-OPERATIONAL HIGHLIGHTS 46
TABLE 6: ACCENTURE- MAJOR CLIENT WINS   47
TABLE 7: CSC-KEY INFORMATION 50
TABLE 8: CSC- BIG DATA MERGERS & ACQUISITIONS   58
TABLE 9: CSC-BIG DATA PARTNERSHIPS & ALLIANCES 59
TABLE 10: CSC-OPERATIONAL HIGHLIGHTS   63
TABLE 11: CSC-MAJOR CLIENT WINS   63
TABLE 12: FUJITSU-KEY INFORMATION  67
TABLE 13: FUJITSU BIG DATA PARTNERSHIPS & ALLIANCES   77
TABLE 14: FUJITSU-OPERATIONAL HIGHLIGHTS  82
TABLE 15: FUJITSU-MAJOR CLIENT WINS  82
TABLE 16: HP-KEY INFORMATION 86
TABLE 17: HP-PARTNERSHIPS & ALLIANCES  93
TABLE 18: HP-OPERATIONAL HIGHLIGHTS 97
TABLE 19: HP-MAJOR CLIENT WINS  97
TABLE 20: IBM-KEY INFORMATION   100
TABLE 21: IBM- BIG DATA MERGERS & ACQUISITIONS 110
TABLE 22: IBM- BIG DATA PARTNERSHIPS & ALLIANCES  114
TABLE 23: IBM-REVENUES FROM OPERATING SEGMENTS (IN MILLION USD)   119
TABLE 24: IBM-OPERATIONAL HIGHLIGHTS  122
TABLE 25: IBM-MAJOR CLIENT WINS  123
TABLE 26: INFORMATICA-KEY INFORMATION 127
TABLE 27: INFORMATICA-BIG DATA MERGERS & ACQUISITIONS 134
TABLE 28: INFORMATICA- BIG DATA PARTNERSHIPS & ALLIANCES 137
TABLE 29: INFORMATICA-OPERATIONAL HIGHLIGHTS 142
TABLE 30: INFORMATICA- MAJOR CLIENT WINS 143
TABLE 31: MU SIGMA-BIG DATA PARTNERSHIPS & ALLIANCES 152
TABLE 32: OPERA SOLUTIONS- BIG DATA MERGERS & ACQUISITIONS 159
TABLE 33: OPERA SOLUTIONS- BIG DATA PARTNERSHIPS & ALLIANCES 159
TABLE 34: ORACLE-KEY INFORMATION   162
TABLE 35: ORACLE- BIG DATA MERGERS & ACQUISITIONS 176
TABLE 36: ORACLE- BIG DATA PARTNERSHIPS & ALLIANCES  179
TABLE 37: ORACLE-OPERATIONAL HIGHLIGHTS   189
TABLE 38: ORACLE-KEY CLIENT WINS  190
TABLE 39- TCS-KEY INFORMATION   193
TABLE 40: TCS-OPERATIONAL HIGHLIGHTS   209
TABLE 41: TCS-NUMBER OF CLIENTS BASED ON CLIENT REVENUES 210
TABLE 42: TCS-KEY CLIENT WINS 210

List of Figures


FIGURE 1: HYPE CYCLE FOR EMERGING TECHNOLOGIES, 2013 13
FIGURE 2: BUSINESS INTELLIGENCE (BI) CHALLENGE FOR COMPANIES 16
FIGURE 3: TOTAL DATA VOLUME IN BI ENVIRONMENT   17
FIGURE 4: RELEVANCE OF BIG DATA AMONGST ORGANIZATIONS 17
FIGURE 5: USE OF BIG DATA TECHNOLOGIES  18
FIGURE 6: BIG DATA REVENUES BY SEGMENT IN 2012 ($ MILLION) 23
FIGURE 7: BIG DATA REVENUE BY LEADING VENDORS IN 2012 (IN $ MILLION) 23
FIGURE 8: BIG DATA PROFESSIONAL SERVICES REVENUE BY LEADING VENDORS IN 2012 (IN $ MILLION)  24
FIGURE 9: BIG DATA COMPUTE REVENUE BY LEADING VENDORS IN 2012 (IN $ MILLION) 24
FIGURE 10: BIG DATA STORAGE REVENUE BY LEADING VENDORS IN 2012 (IN $ MILLION)  25
FIGURE 11: BIG DATA SQL AND NOSQL DATABASE REVENUE BY VENDORS, 2012 (IN $ MILLION)  25
FIGURE 12: BIG DATA APPLICATION REVENUE BY VENDORS, 2012 (IN $ MILLION) 26
FIGURE 13: BIG DATA XAAS REVENUE BY VENDORS, 2012 (IN $ MILLION)  27
FIGURE 14: BIG DATA NETWORKING REVENUE BY VENDORS, 2012 (IN $ MILLION) 27
FIGURE 15: BIG DATA MARKET FORECAST BY COMPONENT (IN $ BILLION), 2011-2017 28
FIGURE 16: BIG DATA SQL AND NO SQL DATABASE REVENUE FORECAST (IN $ BILLION), 2011-2017 28
FIGURE 17: SERVICES OFFERINGS OF ACCENTURE 34
FIGURE 18: ANALYTICS OFFERINGS FROM ACCENTURE   35
FIGURE 19: ACCENTURE-REVENUE ACROSS GEOGRAPHIES   42
FIGURE 20: ACCENTURE-REVENUE ACROSS GEOGRAPHIES (IN MILLION USD) 43
FIGURE 21: ACCENTURE-REVENUES BY OPERATING GROUPS (IN MILLION USD) 44
FIGURE 22: ACCENTURE-REVENUES BY OPERATING GROUPS (IN MILLION USD) 45
FIGURE 23: ACCENTURE- REVENUE BY TYPE OF WORK (IN MILLION USD)  45
FIGURE 24: ACCENTURE-REVENUE BY TYPE OF WORK (IN MILLION USD) 46
FIGURE 25: CSC SOLUTIONS OFFERINGS 51
FIGURE 26: CSC BIG DATA SOLUTIONS 53
FIGURE 27: CSC- REVENUE ACROSS GEOGRAPHIES   61
FIGURE 28: CSC- REVENUE ACROSS OPERATING SEGMENTS (IN MILLION USD)   62
FIGURE 29: BUSINESS SEGMENTS OF FUJITSU 68
FIGURE 30: THE CONCEPT BEHIND THE FUJITSU BIG DATA INITIATIVE 70
FIGURE 31: OVERVIEW OF FUJITSU BIG DATA INITIATIVE  70
FIGURE 32: OVERVIEW OF THE PROFESSIONAL TEAMS FOR EACH OFFERING AT THE BIG DATA INITIATIVE CENTER 71
FIGURE 33: TEN TYPES OF OFFERINGS 71
FIGURE 34: FUJITSU BIG DATA INITIATIVE ORGANIZATION   72
FIGURE 35: FUJITSU-REVENUE ACROSS GEOGRAPHIES   78
FIGURE 36: FUJITSU- REVENUE ACROSS GEOGRAPHIES (IN MILLION ¥)   79
FIGURE 37: FUJITSU-REVENUES BY SERVICES  79
FIGURE 38: FUJITSU- REVENUES BY SERVICES (IN MILLION ¥)  80
FIGURE 39: HP- 6 MAJOR OPERATING SEGMENTS 87
FIGURE 40: HP HAVEN PLATFORM 88
FIGURE 41: HP VERTICA DATA ANALYTICS PLATFORM 89
FIGURE 42: HP-REVENUE ACROSS GEOGRAPHIES 94
FIGURE 43: HP-REVENUE ACROSS GEOGRAPHIES (IN MILLION USD) 94
FIGURE 44: HP-REVENUES ACROSS BUSINESS SEGMENTS (IN MILLION USD)   95
FIGURE 45: MAJOR OPERATING SEGMENTS OF IBM 101
FIGURE 46: IBM BIG DATA PLATFORM 102
FIGURE 47: IBM SECURITY INTELLIGENCE WITH BIG DATA 105
FIGURE 48: IBM-REVENUE ACROSS GEOGRAPHIES   116
FIGURE 49: IBM-REVENUE ACROSS GEOGRAPHIES (IN MILLION USD) 117
FIGURE 50: IBM-REVENUE ACROSS KEY SELECT COUNTRIES (IN MILLION USD) 117
FIGURE 51: IBM-REVENUES FROM OPERATING SEGMENTS (IN MILLION USD) 118
FIGURE 52: IBM-REVENUES FROM OPERATING SEGMENTS (IN MILLION USD) 118
FIGURE 53: IBM-REVENUES BY SERVICE CATEGORY  121
FIGURE 54: IBM-REVENUES BY SERVICE CATEGORY (IN MILLION USD) 122
FIGURE 55: INFORMATICA OFFERINGS   128
FIGURE 56:  INFORMATICA- REVENUE ACROSS GEOGRAPHIES 139
FIGURE 57: INFORMATICA-REVENUE ACROSS GEOGRAPHIES (IN MILLION USD) 140
FIGURE 58: INFORMATICA-REVENUES FROM BUSINESS (IN MILLION USD) 140
FIGURE 59: INFORMATICA-REVENUE FROM BUSINESS (IN MILLION USD) 141
FIGURE 60:  INFORMATICA-REVENUE FROM SERVICES BUSINESS (IN MILLION USD) 141
FIGURE 61: MU SIGMA-OFFERINGS FOR VARIOUS INDUSTRIES  146
FIGURE 62: MU SIGMA- MARKETING ANALYTICS OFFERINGS 146
FIGURE 63: MU SIGMA-RISK ANALYTICS OFFERINGS  147
FIGURE 64: MU SIGMA- SUPPLY CHAIN ANALYTICS OFFERINGS  148
FIGURE 65: MU SIGMA-OFFERINGS 149
FIGURE 66: SOLUTIONS & SERVICES OFFERINGS OF OPERA SOLUTIONS  155
FIGURE 67: SOFTWARE BUSINESS PORTFOLIO OF ORACLE 163
FIGURE 68: HARDWARE BUSINESS PORTFOLIO OF ORACLE 167
FIGURE 69: ORACLE BIG DATA OFFERINGS 169
FIGURE 70: ORACLE-REVENUE ACROSS GEOGRAPHIES 181
FIGURE 71: ORACLE-REVENUE ACROSS GEOGRAPHIES (IN MILLION USD) 182
FIGURE 72: ORACLE-REVENUE ACROSS COUNTRIES (IN MILLION USD) 182
FIGURE 73: ORACLE-REVENUE FOR NEW SOFTWARE LICENSES AND CLOUD SOFTWARE SUBSCRIPTIONS SEGMENT 183
FIGURE 74: ORACLE-REVENUE ACROSS GEOGRAPHIES (IN MILLION USD) 184
FIGURE 75: ORACLE- REVENUE FOR SOFTWARE LICENSE UPDATES AND PRODUCT SUPPORT SEGMENT   184
FIGURE 76: ORACLE- REVENUE ACROSS GEOGRAPHIES (IN MILLION USD) 185
FIGURE 77: ORACLE-REVENUE FOR HARDWARE SYSTEMS PRODUCTS SEGMENT 186
FIGURE 78: ORACLE-REVENUE ACROSS GEOGRAPHIES (IN MILLION USD) 186
FIGURE 79: ORACLE-REVENUE FOR HARDWARE SYSTEMS SUPPORT SEGMENT 187
FIGURE 80: ORACLE-REVENUE ACROSS GEOGRAPHIES (IN MILLION USD) 187
FIGURE 81: ORACLE-REVENUE ACROSS GEOGRAPHIES 188
FIGURE 82: ORACLE-REVENUE ACROSS GEOGRAPHIES (IN MILLION USD) 189
FIGURE 83 17: TCS PRODUCT OFFERINGS 194
FIGURE 84: TCS MASTERCRAFT  194
FIGURE 85: TCS SERVICE OFFERINGS  195
FIGURE 86: TCS BIG DATA OFFERINGS 199
FIGURE 87: TCS MDM METHODOLOGY   199
FIGURE 88: OVERVIEW OF TCS ANALYTICS PLATFORM 201
FIGURE 89: TCS-REVENUE ACROSS GEOGRAPHIES 205
FIGURE 90: TCS-REVENUE ACROSS GEOGRAPHIES (IN RS CRORES) 206
FIGURE 91: TCS-REVENUE BY SERVICE LINES   206
FIGURE 92: TCS-REVENUE BY SERVICE LINE (IN RS CRORES)   207
FIGURE 93: TCS-REVENUE BY INDUSTRY VERTICALS  208
FIGURE 94:  TCS-REVENUE BY INDUSTRY VERTICALS (IN RS CRORES) 209
FIGURE 95: IMPACT OF BIG DATA APPROACH V/S DATA WAREHOUSE APPROACH 213

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