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

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Feb 12, 2014, 07:20 ET

NEW YORK, Feb. 12, 2014 /PRNewswire/ -- Reportlinker.com announces that a new market research report is available in its catalogue:

Big Data Leaders: Accenture, CSC Fujitsu, HP, Informatica, Mu Sigma, Opera Solutions, Oracle, and Tata Consultancy Services
http://www.reportlinker.com/p02013773/Big-Data-Leaders-Accenture-CSC-Fujitsu-HP-Informatica-Mu-Sigma-Opera-Solutions-Oracle-and-Tata-Consultancy-Services.html#utm_source=prnewswire&utm_medium=pr&utm_campaign=Business_Consultancy_and_Audit

Overview:

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
• Mu Sigma
• Opera Solutions
• Oracle
• Tata Consultancy Services

For each company evaluated in this report we include the following:

• Company Overview
• Offering Analysis
• Strategies and Plans
• Mergers and Acquisitions
• Partnerships and Alliances
• Financial and Operational Review
• Key Contract Wins Assessment
• Analysis and Conclusions
*Note: Mind Commerce plans to evaluate additional companies in Big Data (look for similar reports).

Target Audience:

• Big Data and analytics companies
• Data as a Service (DaaS) companies
• Cloud-based service providers of all types
• Data processing and management companies
• Application Programmer Interface (API) companies
• Public investment organizations including investment banks
• Private investment including hedge funds and private equity

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 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

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