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The Big Data Market 2014–2020: Opportunities, Challenges, Strategies, Industry Verticals and Forecasts - Product Image

The Big Data Market 2014–2020: Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

  • Published: June 2014
  • Region: Global
  • 289 Pages
  • Signals and Systems Telecom
Big Data Investments are Set to Account for Nearly $30 Billion in 2014 Alone

FEATURED COMPANIES

  • 1010data
  • Cellwize
  • Fusion-io
  • Mahindra Satyam
  • Proctor and Gamble
  • Tableau Software
  • MORE

“Big Data” originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data to solve complex problems.

Amid the proliferation of real time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, Big Data has found a host of vertical market applications, ranging from fraud detection to R&D.

Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. It is estimated that Big Data investments will account for nearly $30 Billion in 2014 alone. These investments are further expected to grow at a CAGR of 17% over the next 6 years.

The “Big Data Market: 2014 – 2020 – Opportunities, Challenges, Strategies, Industry Verticals & Forecasts” report presents an in-depth assessment of the READ MORE >

1. Chapter 1: Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Historical Revenue & Forecast Segmentation
1.4 Key Questions Answered
1.5 Key Findings
1.6 Methodology
1.7 Target Audience
1.8 Companies & Organizations Mentioned

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

3 Chapter 3: Vertical Opportunities & Use Cases for Big Data
3.1 Automotive, Aerospace & Transportation
3.1.1 Predictive Warranty Analysis
3.1.2 Predictive Aircraft Maintenance & Fuel Optimization
3.1.3 Air Traffic Control
3.1.4 Transport Fleet Optimization
3.2 Banking & Securities
3.2.1 Customer Retention & Personalized Product Offering
3.2.2 Risk Management
3.2.3 Fraud Detection
3.2.4 Credit Scoring
3.3 Defense & Intelligence
3.3.1 Intelligence Gathering
3.3.2 Energy Saving Opportunities in the Battlefield
3.3.3 Preventing Injuries on the Battlefield
3.4 Education
3.4.1 Information Integration
3.4.2 Identifying Learning Patterns
3.4.3 Enabling Student-Directed Learning
3.5 Healthcare & Pharmaceutical
3.5.1 Managing Population Health Efficiently
3.5.2 Improving Patient Care with Medical Data Analytics
3.5.3 Improving Clinical Development & Trials
3.5.4 Improving Time to Market
3.6 Smart Cities & Intelligent Buildings
3.6.1 Energy Optimization & Fault Detection
3.6.2 Intelligent Building Analytics
3.6.3 Urban Transportation Management
3.6.4 Optimizing Energy Production
3.6.5 Water Management
3.6.6 Urban Waste Management
3.7 Insurance
3.7.1 Claims Fraud Mitigation
3.7.2 Customer Retention & Profiling
3.7.3 Risk Management
3.8 Manufacturing & Natural Resources
3.8.1 Asset Maintenance & Downtime Reduction
3.8.2 Quality & Environmental Impact Control
3.8.3 Optimized Supply Chain
3.8.4 Exploration & Identification of Wells & Mines
3.8.5 Maximizing the Potential of Drilling
3.8.6 Production Optimization
3.9 Web, Media & Entertainment
3.9.1 Audience & Advertising Optimization
3.9.2 Channel Optimization
3.9.3 Recommendation Engines
3.9.4 Optimized Search
3.9.5 Live Sports Event Analytics
3.9.6 Outsourcing Big Data Analytics to Other Verticals
3.10 Public Safety & Homeland Security
3.10.1 Cyber Crime Mitigation
3.10.2 Crime Prediction Analytics
3.10.3 Video Analytics & Situational Awareness
3.11 Public Services
3.11.1 Public Sentiment Analysis
3.11.2 Fraud Detection & Prevention
3.11.3 Economic Analysis
3.12 Retail & Hospitality
3.12.1 Customer Sentiment Analysis
3.12.2 Customer & Branch Segmentation
3.12.3 Price Optimization
3.12.4 Personalized Marketing
3.12.5 Optimized Supply Chain
3.13 Telecommunications
3.13.1 Network Performance & Coverage Optimization
3.13.2 Customer Churn Prevention
3.13.3 Personalized Marketing
3.13.4 Location Based Services
3.13.5 Fraud Detection
3.14 Utilities & Energy
3.14.1 Customer Retention
3.14.2 Forecasting Energy
3.14.3 Billing Analytics
3.14.4 Predictive Maintenance
3.14.5 Turbine Placement Optimization
3.15 Wholesale Trade
3.15.1 In-field Sales Analytics
3.15.2 Monitoring the Supply Chain

4 Chapter 4: Big Data Industry Roadmap & Value Chain
4.1 Big Data Industry Roadmap
4.1.1 2010 – 2013: Initial Hype and the Rise of Analytics
4.1.2 2014 – 2017: Emergence of SaaS Based Big Data Solutions
4.1.3 2018 – 2020 & Beyond: Large Scale Proliferation of Scalable Machine Learning
4.2 The Big Data Value Chain
4.2.1 Hardware Providers
4.2.1.1 Storage & Compute Infrastructure Providers
4.2.1.2 Networking Infrastructure Providers
4.2.2 Software Providers
4.2.2.1 Hadoop & Infrastructure Software Providers
4.2.2.2 SQL & NoSQL Providers
4.2.2.3 Analytic Platform & Application Software Providers
4.2.2.4 Cloud Platform Providers
4.2.3 Professional Services Providers
4.2.4 End-to-End Solution Providers
4.2.5 Vertical Enterprises

5 Chapter 5: Big Data Analytics
5.1 What are Big Data Analytics?
5.2 The Importance of Analytics
5.3 Reactive vs. Proactive Analytics
5.4 Customer vs. Operational Analytics
5.5 Technology & Implementation Approaches
5.5.1 Grid Computing
5.5.2 In-Database Processing
5.5.3 In-Memory Analytics
5.5.4 Machine Learning & Data Mining
5.5.5 Predictive Analytics
5.5.6 NLP (Natural Language Processing)
5.5.7 Text Analytics
5.5.8 Visual Analytics
5.5.9 Social Media, IT & Telco Network Analytics
5.6 Vertical Market Case Studies
5.6.1 Amazon – Delivering Cloud Based Big Data Analytics
5.6.2 Facebook – Using Analytics to Monetize Users with Advertising
5.6.3 WIND Mobile – Using Analytics to Monitor Video Quality
5.6.4 Coriant Analytics Services – SaaS Based Big Data Analytics for Telcos
5.6.5 Boeing – Analytics for the Battlefield
5.6.6 The Walt Disney Company – Utilizing Big Data and Analytics in Theme Parks

6 Chapter 6: Standardization & Regulatory Initiatives
6.1 CSCC (Cloud Standards Customer Council) – Big Data Working Group
6.2 NIST (National Institute of Standards and Technology) – Big Data Working Group
6.3 OASIS –Technical Committees
6.4 ODaF (Open Data Foundation)
6.5 Open Data Center Alliance
6.6 CSA (Cloud Security Alliance) – Big Data Working Group
6.7 ITU (International Telecommunications Union)
6.8 ISO (International Organization for Standardization) and Others

7 Chapter 7: Market Analysis & Forecasts
7.1 Global Outlook of the Big Data Market
7.2 Submarket Segmentation
7.2.1 Storage and Compute Infrastructure
7.2.2 Networking Infrastructure
7.2.3 Hadoop & Infrastructure Software
7.2.4 SQL
7.2.5 NoSQL
7.2.6 Analytic Platforms & Applications
7.2.7 Cloud Platforms
7.2.8 Professional Services
7.3 Vertical Market Segmentation
7.3.1 Automotive, Aerospace & Transportation
7.3.2 Banking & Securities
7.3.3 Defense & Intelligence
7.3.4 Education
7.3.5 Healthcare & Pharmaceutical
7.3.6 Smart Cities & Intelligent Buildings
7.3.7 Insurance
7.3.8 Manufacturing & Natural Resources
7.3.9 Media & Entertainment
7.3.10 Public Safety & Homeland Security
7.3.11 Public Services
7.3.12 Retail & Hospitality
7.3.13 Telecommunications
7.3.14 Utilities & Energy
7.3.15 Wholesale Trade
7.3.16 Other Sectors
7.4 Regional Outlook
7.5 Asia Pacific
7.5.1 Country Level Segmentation
7.5.2 Australia
7.5.3 China
7.5.4 India
7.5.5 Japan
7.5.6 South Korea
7.5.7 Pakistan
7.5.8 Thailand
7.5.9 Indonesia
7.5.10 Malaysia
7.5.11 Taiwan
7.5.12 Philippines
7.5.13 Singapore
7.5.14 Rest of Asia Pacific
7.6 Eastern Europe
7.6.1 Country Level Segmentation
7.6.2 Czech Republic
7.6.3 Poland
7.6.4 Russia
7.6.5 Rest of Eastern Europe
7.7 Latin & Central America
7.7.1 Country Level Segmentation
7.7.2 Argentina
7.7.3 Brazil
7.7.4 Mexico
7.7.5 Rest of Latin & Central America
7.8 Middle East & Africa
7.8.1 Country Level Segmentation
7.8.2 South Africa
7.8.3 UAE
7.8.4 Qatar
7.8.5 Saudi Arabia
7.8.6 Israel
7.8.7 Rest of the Middle East & Africa
7.9 North America
7.9.1 Country Level Segmentation
7.9.2 USA
7.9.3 Canada
7.10 Western Europe
7.10.1 Country Level Segmentation
7.10.2 Denmark
7.10.3 Finland
7.10.4 France
7.10.5 Germany
7.10.6 Italy
7.10.7 Spain
7.10.8 Sweden
7.10.9 Norway
7.10.10 UK
7.10.11 Rest of Western Europe

8 Chapter 8: Vendor Landscape
8.1 1010data
8.2 Accenture
8.3 Actian Corporation
8.4 Actuate Corporation
8.5 AeroSpike
8.6 Alpine Data Labs
8.7 Alteryx
8.8 AWS (Amazon Web Services)
8.9 Attivio
8.10 Basho
8.11 Booz Allen Hamilton
8.12 InfiniDB
8.13 Capgemini
8.14 Cellwize
8.15 CenturyLink
8.16 Cisco Systems
8.17 Cloudera
8.18 Comptel
8.19 Contexti
8.20 Couchbase
8.21 CSC (Computer Science Corporation)
8.22 Datameer
8.23 DataStax
8.24 DDN (DataDirect Network)
8.25 Dell
8.26 Deloitte
8.27 Digital Reasoning
8.28 EMC Corporation
8.29 Facebook
8.30 Fractal Analytics
8.31 Fujitsu
8.32 Fusion-io
8.33 GE (General Electric)
8.34 GoodData Corporation
8.35 Google
8.36 Guavus
8.37 HDS (Hitachi Data Systems)
8.38 Hortonworks
8.39 HP
8.40 IBM
8.41 Informatica Corporation
8.42 Information Builders
8.43 Intel
8.44 Jaspersoft
8.45 Juniper Networks
8.46 Kognitio
8.47 Lavastorm Analytics
8.48 LucidWorks
8.49 MapR
8.50 MarkLogic
8.51 Microsoft
8.52 MicroStrategy
8.53 MongoDB (formerly 10gen)
8.54 Mu Sigma
8.55 NTT Data
8.56 Neo Technology
8.57 NetApp
8.58 Opera Solutions
8.59 Oracle
8.60 Palantir Technologies
8.61 ParStream
8.62 Pentaho
8.63 Platfora
8.64 Pivotal Software
8.65 PwC
8.66 QlikTech
8.67 Quantum Corporation
8.68 Rackspace
8.69 RainStor
8.70 Revolution Analytics
8.71 Salesforce.com
8.72 Sailthru
8.73 SAP
8.74 SAS Institute
8.75 SGI
8.76 SiSense
8.77 Software AG/Terracotta
8.78 Splunk
8.79 Sqrrl
8.80 Supermicro
8.81 Tableau Software
8.82 Talend
8.83 TCS (Tata Consultancy Services)
8.84 Teradata
8.85 Think Big Analytics
8.86 TIBCO Software
8.87 Tidemark
8.88 VMware (EMC Subsidiary)
8.89 WiPro
8.90 Zettics

9 Chapter 9: Expert Opinion – Interview Transcripts
9.1 Comptel
9.2 Lavastorm Analytics
9.3 ParStream
9.4 Sailthru

10 Chapter 10: Conclusion & Strategic Recommendations
10.1 Big Data Technology: Beyond Data Capture & Analytics
10.2 Transforming IT from a Cost Center to a Profit Center
10.3 Can Privacy Implications Hinder Success?
10.4 Will Regulation have a Negative Impact on Big Data Investments?
10.5 Battling Organization & Data Silos
10.6 Software vs. Hardware Investments
10.7 Vendor Share: Who Leads the Market?
10.8 Big Data Driving Wider IT Industry Investments
10.9 Assessing the Impact of IoT & M2M
10.10 Recommendations
10.10.1 Big Data Hardware, Software & Professional Services Providers
10.10.2 Enterprises



List of Figures

Figure 1: Big Data Industry Roadmap

Figure 2: The Big Data Value Chain

Figure 3: Reactive vs. Proactive Analytics

Figure 4: Global Big Data Revenue: 2010 - 2020 ($ Million)

Figure 5: Global Big Data Revenue by Submarket: 2010 - 2020 ($ Million)

Figure 6: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2010 - 2020 ($ Million)

Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2010 - 2020 ($ Million)

Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2010 - 2020 ($ Million)

Figure 9: Global Big Data SQL Submarket Revenue: 2010 - 2020 ($ Million)

Figure 10: Global Big Data NoSQL Submarket Revenue: 2010 - 2020 ($ Million)

Figure 11: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2010 - 2020 ($ Million)

Figure 12: Global Big Data Cloud Platforms Submarket Revenue: 2010 - 2020 ($ Million)

Figure 13: Global Big Data Professional Services Submarket Revenue: 2010 - 2020 ($ Million)

Figure 14: Global Big Data Revenue by Vertical Market: 2010 - 2020 ($ Million)

Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2010 - 2020 ($ Million)

Figure 16: Global Big Data Revenue in the Banking & Securities Sector: 2010 - 2020 ($ Million)

Figure 17: Global Big Data Revenue in the Defense & Intelligence Sector: 2010 - 2020 ($ Million)

Figure 18: Global Big Data Revenue in the Education Sector: 2010 - 2020 ($ Million)

Figure 19: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2010 - 2020 ($ Million)

Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2010 - 2020 ($ Million)

Figure 21: Global Big Data Revenue in the Insurance Sector: 2010 - 2020 ($ Million)

Figure 22: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2010 - 2020 ($ Million)

Figure 23: Global Big Data Revenue in the Media & Entertainment Sector: 2010 - 2020 ($ Million)

Figure 24: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2010 - 2020 ($ Million)

Figure 25: Global Big Data Revenue in the Public Services Sector: 2010 - 2020 ($ Million)

Figure 26: Global Big Data Revenue in the Retail & Hospitality Sector: 2010 - 2020 ($ Million)

Figure 27: Global Big Data Revenue in the Telecommunications Sector: 2010 - 2020 ($ Million)

Figure 28: Global Big Data Revenue in the Utilities & Energy Sector: 2010 - 2020 ($ Million)

Figure 29: Global Big Data Revenue in the Wholesale Trade Sector: 2010 - 2020 ($ Million)

Figure 30: Global Big Data Revenue in Other Vertical Sectors: 2010 - 2020 ($ Million)

Figure 31: Big Data Revenue by Region: 2010 - 2020 ($ Million)

Figure 32: Asia Pacific Big Data Revenue: 2010 - 2020 ($ Million)

Figure 33: Asia Pacific Big Data Revenue by Country: 2010 - 2020 ($ Million)

Figure 34: Australia Big Data Revenue: 2010 - 2020 ($ Million)

Figure 35: China Big Data Revenue: 2010 - 2020 ($ Million)

Figure 36: India Big Data Revenue: 2010 - 2020 ($ Million)

Figure 37: Japan Big Data Revenue: 2010 - 2020 ($ Million)

Figure 38: South Korea Big Data Revenue: 2010 - 2020 ($ Million)

Figure 39: Pakistan Big Data Revenue: 2010 - 2020 ($ Million)

Figure 40: Thailand Big Data Revenue: 2010 - 2020 ($ Million)

Figure 41: Indonesia Big Data Revenue: 2010 - 2020 ($ Million)

Figure 42: Malaysia Big Data Revenue: 2010 - 2020 ($ Million)

Figure 43: Taiwan Big Data Revenue: 2010 - 2020 ($ Million)

Figure 44: Philippines Big Data Revenue: 2010 - 2020 ($ Million)

Figure 45: Singapore Big Data Revenue: 2010 - 2020 ($ Million)

Figure 46: Big Data Revenue in the Rest of Asia Pacific: 2010 - 2020 ($ Million)

Figure 47: Eastern Europe Big Data Revenue: 2010 - 2020 ($ Million)

Figure 48: Eastern Europe Big Data Revenue by Country: 2010 - 2020 ($ Million)

Figure 49: Czech Republic Big Data Revenue: 2010 - 2020 ($ Million)

Figure 50: Poland Big Data Revenue: 2010 - 2020 ($ Million)

Figure 51: Russia Big Data Revenue: 2010 - 2020 ($ Million)

Figure 52: Big Data Revenue in the Rest of Eastern Europe: 2010 - 2020 ($ Million)

Figure 53: Latin & Central America Big Data Revenue: 2010 - 2020 ($ Million)

Figure 54: Latin & Central America Big Data Revenue by Country: 2010 - 2020 ($ Million)

Figure 55: Argentina Big Data Revenue: 2010 - 2020 ($ Million)

Figure 56: Brazil Big Data Revenue: 2010 - 2020 ($ Million)

Figure 57: Mexico Big Data Revenue: 2010 - 2020 ($ Million)

Figure 58: Big Data Revenue in the Rest of Latin & Central America: 2010 - 2020 ($ Million)

Figure 59: Middle East & Africa Big Data Revenue: 2010 - 2020 ($ Million)

Figure 60: Middle East & Africa Big Data Revenue by Country: 2010 - 2020 ($ Million)

Figure 61: South Africa Big Data Revenue: 2010 - 2020 ($ Million)

Figure 62: UAE Big Data Revenue: 2010 - 2020 ($ Million)

Figure 63: Qatar Big Data Revenue: 2010 - 2020 ($ Million)

Figure 64: Saudi Arabia Big Data Revenue: 2010 - 2020 ($ Million)

Figure 65: Israel Big Data Revenue: 2010 - 2020 ($ Million)

Figure 66: Big Data Revenue in the Rest of the Middle East & Africa: 2010 - 2020 ($ Million)

Figure 67: North America Big Data Revenue: 2010 - 2020 ($ Million)

Figure 68: North America Big Data Revenue by Country: 2010 - 2020 ($ Million)

Figure 69: USA Big Data Revenue: 2010 - 2020 ($ Million)

Figure 70: Canada Big Data Revenue: 2010 - 2020 ($ Million)

Figure 71: Western Europe Big Data Revenue: 2010 - 2020 ($ Million)

Figure 72: Western Europe Big Data Revenue by Country: 2010 - 2020 ($ Million)

Figure 73: Denmark Big Data Revenue: 2010 - 2020 ($ Million)

Figure 74: Finland Big Data Revenue: 2010 - 2020 ($ Million)

Figure 75: France Big Data Revenue: 2010 - 2020 ($ Million)

Figure 76: Germany Big Data Revenue: 2010 - 2020 ($ Million)

Figure 77: Italy Big Data Revenue: 2010 - 2020 ($ Million)

Figure 78: Spain Big Data Revenue: 2010 - 2020 ($ Million)

Figure 79: Sweden Big Data Revenue: 2010 - 2020 ($ Million)

Figure 80: Norway Big Data Revenue: 2010 - 2020 ($ Million)

Figure 81: UK Big Data Revenue: 2010 - 2020 ($ Million)

Figure 82: Big Data Revenue in the Rest of Western Europe: 2010 - 2020 ($ Million)

Figure 83: Global Big Data Revenue by Hardware, Software & Professional Services ($ Million): 2010 - 2020

Figure 84: Big Data Vendor Market Share (%)

Figure 85: Global IT Expenditure Driven by Big Data Investments: 2010 - 2020 ($ Million)

Figure 86: Global M2M Connections by Access Technology (Millions): 2011 - 2020

- 1010data
- Accel Partners
- Accenture
- Actian Corporation
- Actuate Corporation
- adMarketplace
- Adobe
- ADP
- AeroSpike
- AlchemyDB
- Aldeasa
- Alpine Data Labs
- Alteryx
- Amazon.com
- AMD
- AnalyticsIQ
- Antic Entertainment
- AOL
- Apple
- AppNexus
- Ascendas
- AT&T
- Attivio
- AutoZone
- Avvasi
- AWS (Amazon Web Services)
- Axiata Group
- Bank of America
- Basho
- Beeline Kazakhstan
- Betfair
- BlueKai
- Bluelock
- BMC Software
- BMW
- Boeing
- Booz Allen Hamilton
- Box, Inc.
- Buffalo Studios
- BurstaBit
- CaixaTarragona
- Capgemini
- Cellwize
- CenturyLink
- Chang
China Telecom
- CIA (Central Intelligence Agency)
- Cisco Systems
- Citywire
- Cloudera
- Coca-Cola
- Comptel
- Concur
- Contexti
- Coriant
- Couchbase
- CSA (Cloud Security Alliance)
- CSC (Computer Science Corporation)
- CSCC (Cloud Standards Customer Council)
- Datameer
- DataStax
- DDN (DataDirect Network)
- Dell
- Deloitte
- Delta
- Department of Commerce
- Deutsche Bank
- Deutsche Telekom
- Digital Reasoning
- Dollar General
- Dotomi
- eBay
- El Corte Inglés
- Electronic Arts
- EMC Corporation
- Equifax
- Ericsson
- Ernst & Young
- E-Touch
- European Space Agency
- eXelate
- Experian
- Facebook
- FedEx
- Ferguson
- Ford
- Fractal Analytics
- Fujitsu
- Fusion-io
- Gamegos
- Ganz
- GE (General Electric)
- Goldman Sachs
- GoodData Corporation
- Google
- Greylock Partners
- GTRI (Georgia Tech Research Institute)
- Guavus
- Hadapt
- HDS (Hitachi Data Systems)
- Hortonworks
- HP
- Hyve Solutions
- IBM
- IEC (International Electrotechnical Commission)
- Ignition Partners
- InfiniDB
- Infobright
- Informatica Corporation
- Information Builders
- In-Q-Tel
- Intel
- Internap Network Services Corporation
- Intucell
- Inversis Banco
- ISO (International Organization for Standardization)
- ITT Corporation
- ITU (International Telecommunications Union)
- J.P. Morgan
- Jaspersoft
- Johnson & Johnson
- JP Morgan
- Juguettos
- Juniper Networks
- Kabam
- Karmasphere
- KDDI
- Kixeye
- Kobo
- Kognitio
- KPMG
- KT (Korea Telecom)
- Lavastorm Analytics
- LG CNS
- LinkedIn
- LucidWorks
- Mahindra Satyam
- MapR
- MarkLogic
- Marriott International
- Mayfield fund
- McDonnell Ventures
- McGraw Hill Education
- MediaMind
- Meritech Capital Partners
- Microsoft
- MicroStrategy
- mig33
- MongoDB
- Motorola
- Movistar
- Mu Sigma
- Myrrix
- Nami Media
- Navteq
- Neo Technology
- NetApp
- NetFlix
- Nexon
- NIST (National Institute of Standards and Technology)
- North Bridge
- NTT Data
- NTT DoCoMo
- NYSE (New York Stock Exchange)
- OASIS
- ODaF (Open Data Foundation)
- Open Data Center Alliance
- Opera Solutions
- Oracle
- Orange
- Orbitz
- Palantir Technologies
- Panorama Software
- ParAccel
- ParStream
- Pentaho
- Pervasive Software
- Pivotal Software
- Platfora
- Playtika
- Pokemon
- Proctor and Gamble
- Pronovias
- PwC
- QlikTech
- Quantum Corporation
- Quiterian
- Rackspace
- RainStor
- Relational Technology
- Renault
- ReNet Tecnologia
- Rentrak
- Revolution Analytics
- RiteAid
- Robi Axiata
- Royal Dutch Shell
- Sabre
- Sailthru
- Sain Engineering
- Salesforce.com
- Samsung
- SAP
- SAS Institute
- Savvis
- Scoreloop
- Seagate Technology
- SGI
- Shuffle Master
- Simba Technologies
- SiSense
- Skyscanner
- SmugMug
- Snapdeal
- Software AG
- Sojo Studios
- SolveDirect
- Sony
- Southern States Cooperative
- Splunk
- Spotme
- Sqrrl
- Starbucks
- Supermicro
- Tableau Software
- Talend
- Tango
- TapJoy
- TCS (Tata Consultancy Services)
- Telefónica
- Tencent
- Teradata
- Terracotta
- Terremark
- The Hut Group
- The Knot
- The Ladders
- The Trade Desk
- Think Big Analytics
- Thomson Reuters
- TIBCO Software
- Tidemark
- TubeMogul
- Tunewiki
- U.S. Air Force
- U.S. Army
- U.S. Navy
- Ubiquisys
- UBS
- Umami TV
- UN (United Nations)
- Unilever
- US Xpress
- Venture Partners
- Verizon
- Versant
- Vertica
- VIMPELCOM
- VMware (EMC Subsidiary)
- VNG
- Vodafone
- Volkswagen
- Walt Disney Company
- WIND Mobile
- WiPro
- Xclaim
- Xyratex
- Yael Software
- Zettics
- Zynga

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