市場調査レポート

ビッグデータの主要企業:Accenture, CSC, 富士通, HP, Informatica, Mu Sigma, Opera Solutions, Oracleおよび Tata Consultancy Services

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

発行 Mind Commerce 商品コード 295719
出版日 ページ情報 英文 220 Pages
納期: 即日から翌営業日
価格
本日の銀行送金レート: 1USD=101.50円で換算しております。
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ビッグデータの主要企業:Accenture, CSC, 富士通, HP, Informatica, Mu Sigma, Opera Solutions, Oracleおよび Tata Consultancy Services Big Data Leaders: Accenture, CSC Fujitsu, HP, Informatica, Mu Sigma, Opera Solutions, Oracle, and Tata Consultancy Services
出版日: 2014年02月01日 ページ情報: 英文 220 Pages
概要

ビッグデータは非常に大きく複雑な一連のデータセットを示しており、あり合わせのデータベース管理ツールを用いた処理を難しくしています。また、ビッグデータは組織化されていないため、表にしたり、相互関係の比較が出来ません(既定データモデルを持っていないまたは既定の方法で組織化されていません)。ビッグデータ技術は企業が膨大なデータセットを処理し、また最小限の遅延時間(時にはリアルタイムで)で情報を作成/分析することを可能にします。

当レポートは、ビッグデータ市場をけん引する主要企業の詳細なプロファイルを提供しており、ビッグデータ市場の概要、動向、予測およびSWOT分析を交え、概略下記の構成でお届けいたします。

第1章 ビッグデータ:概要

  • 従来型データからビッグデータへの転換
  • ビッグデータの3VSと対応の仕方
  • ビッグデータの3VSへの対応の仕方
  • 欧州全体の組織でSTERIA が実施したBIMA SURVEYの調査結果
  • ビッグデータに向かうアプローチ
  • ビッグデータの重要性とその企業にとってのメリット
  • ビッグデータの主要イネーブラー
  • 企業にとってのビッグデータの課題

第2章 ビッグデータ:市場動向・予測

  • 企業はいかにしてビッグデータから利益を得るか?

第3章 ビッグデータの主要企業

  • Accenture
    • 企業概要
    • 提供(ソリューション、アプリケーション、製品、サービス)
    • 戦略とプラン
    • M&A
    • 提携&アライアンス
    • 金融&経営ハイライト
    • 主な契約獲得
    • 分析&結論
  • CSC
  • 富士通
  • HEWLETT-PACKARD(HP)
  • IBM
  • Informatica
  • Mu Sigma
  • Opera Solutions
  • Oracle
  • Tata Consultancy Services(TCS)

第4章 ビッグデータ:SWOT・結論

  • 強み
  • 弱み
  • 機会
  • 脅威
  • ビッグデータの次は?
  • 結論

図表リスト

目次

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
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