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The Big Data Market: 2017 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals and Forecasts View full size

The Big Data Market: 2017 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Research estimates that Big Data investments will account for over $57 Billion in 2017 alone.

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"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 scientific R&D.

Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Research estimates that Big Data investments will account for over $57 Billion in 2017 alone. These investments are further expected to grow at a CAGR of approximately 10% over the next three years.

The “Big Data Market: 2017 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals & Forecasts” report presents an in-depth assessment of the Big Data ecosystem including key market drivers, challenges, investment potential, vertical market opportunities and use cases, future roadmap, value chain, case studies on Big Data analytics, vendor market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2017 through to 2030. The forecasts are further segmented for 8 horizontal submarkets, 14 vertical markets, 6 regions and 35 countries.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.
Topics Covered
The report covers the following topics:
 - Big Data ecosystem
 - Market drivers and barriers
 - Big Data technology, standardization and regulatory initiatives
 - Big Data industry roadmap and value chain
 - Analysis and use cases for 14 vertical markets
 - Big Data analytics technology and case studies
 - Big Data vendor market share
 - Company profiles and strategies of 240 Big Data ecosystem players
 - Strategic recommendations for Big Data hardware, software and professional services vendors, and enterprises
 - Market analysis and forecasts from 2017 till 2030

Forecast Segmentation
Market forecasts are provided for each of the following submarkets and their subcategories:

Hardware, Software & Professional Services
 - Hardware
 - Software
 - Professional Services

Horizontal Submarkets
 - Storage & Compute Infrastructure
 - Networking Infrastructure
 - Hadoop & Infrastructure Software
 - SQL
 - NoSQL
 - Analytic Platforms & Applications
 - Cloud Platforms
 - Professional Services

Vertical Submarkets
 - Automotive, Aerospace & Transportation
 - Banking & Securities
 - Defense & Intelligence
 - Education
 - Healthcare & Pharmaceutical
 - Smart Cities & Intelligent Buildings
 - Insurance
 - Manufacturing & Natural Resources
 - Web, Media & Entertainment
 - Public Safety & Homeland Security
 - Public Services
 - Retail, Wholesale & Hospitality
 - Telecommunications
 - Utilities & Energy
 - Others

Regional Markets
 - Asia Pacific
 - Eastern Europe
 - Latin & Central America
 - Middle East & Africa
 - North America
 - Western Europe

Country Markets
 - Argentina, Australia, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany,  India, Indonesia, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, UAE, UK,  USA

Key Questions Answered
The report provides answers to the following key questions:
 - How big is the Big Data ecosystem?
 - How is the ecosystem evolving by segment and region?
 - What will the market size be in 2020 and at what rate will it grow?
 - What trends, challenges and barriers are influencing its growth?
 - Who are the key Big Data software, hardware and services vendors and what are their strategies?
 - How much are vertical enterprises investing in Big Data?
 - What opportunities exist for Big Data analytics?
 - Which countries and verticals will see the highest percentage of Big Data investments?

Key Findings
The report has the following key findings:
 - In 2017, Big Data vendors will pocket over $57 Billion from hardware, software and professional services revenues. These investments are further expected to grow at a CAGR of approximately 10% over the next three years, eventually accounting for over $76 Billion by the end of 2020.
 - As part of wider plans to revitalize their economies, countries across the world are incorporating legislative initiatives to capitalize on Big Data. For example, the Japanese government is engaged in developing intellectual property protection and dispute resolution frameworks for Big Data assets, in a bid to encourage data sharing and accelerate the development of domestic industries.
 - By the end of 2017, SNS Research estimates that as much as 30% of all Big Data workloads will be processed via cloud services as enterprises seek to avoid large-scale infrastructure investments and security issues associated with on-premise implementations.
 - The vendor arena is continuing to consolidate with several prominent M&A deals such as computer hardware giant Dell's $60 Billion merger with data storage specialist EMC.

Table of Contents

1Chapter 1: Introduction
1.1Executive Summary
1.2Topics Covered
1.3Forecast Segmentation
1.4Key Questions Answered
1.5Key Findings
1.6Methodology
1.7Target Audience
1.8Companies & Organizations Mentioned
  
2Chapter 2: An Overview of Big Data
2.1What is Big Data?
2.2Key Approaches to Big Data Processing
2.2.1Hadoop
2.2.2NoSQL
2.2.3MPAD (Massively Parallel Analytic Databases)
2.2.4In-Memory Processing
2.2.5Stream Processing Technologies
2.2.6Spark
2.2.7Other Databases & Analytic Technologies
2.3Key Characteristics of Big Data
2.3.1Volume
2.3.2Velocity
2.3.3Variety
2.3.4Value
2.4Market Growth Drivers
2.4.1Awareness of Benefits
2.4.2Maturation of Big Data Platforms
2.4.3Continued Investments by Web Giants, Governments & Enterprises
2.4.4Growth of Data Volume, Velocity & Variety
2.4.5Vendor Commitments & Partnerships
2.4.6Technology Trends Lowering Entry Barriers
2.5Market Barriers
2.5.1Lack of Analytic Specialists
2.5.2Uncertain Big Data Strategies
2.5.3Organizational Resistance to Big Data Adoption
2.5.4Technical Challenges: Scalability & Maintenance
2.5.5Security & Privacy Concerns
  
3Chapter 3: Big Data Analytics
3.1What are Big Data Analytics?
3.2The Importance of Analytics
3.3Reactive vs. Proactive Analytics
3.4Customer vs. Operational Analytics
3.5Technology & Implementation Approaches
3.5.1Grid Computing
3.5.2In-Database Processing
3.5.3In-Memory Analytics
3.5.4Machine Learning & Data Mining
3.5.5Predictive Analytics
3.5.6NLP (Natural Language Processing)
3.5.7Text Analytics
3.5.8Visual Analytics
3.5.9Social Media, IT & Telco Network Analytics
  
4Chapter 4: Big Data in Automotive, Aerospace & Transportation
4.1Overview & Investment Potential
4.2Key Applications
4.2.1Autonomous Driving
4.2.2Warranty Analytics for Automotive OEMs
4.2.3Predictive Aircraft Maintenance & Fuel Optimization
4.2.4Air Traffic Control
4.2.5Transport Fleet Optimization
4.2.6UBI (Usage Based Insurance)
4.3Case Studies
4.3.1Delphi Automotive: Monetizing Connected Vehicles with Big Data
4.3.2Boeing: Making Flying More Efficient with Big Data
4.3.3BMW: Eliminating Defects in New Vehicle Models with Big Data
4.3.4Toyota Motor Corporation: Powering Smart Cars with Big Data
4.3.5Ford Motor Company: Making Efficient Transportation Decisions with Big Data
  
5Chapter 5: Big Data in Banking & Securities
5.1Overview & Investment Potential
5.2Key Applications
5.2.1Customer Retention & Personalized Product Offering
5.2.2Risk Management
5.2.3Fraud Detection
5.2.4Credit Scoring
5.3Case Studies
5.3.1HSBC Group: Avoiding Regulatory Penalties with Big Data
5.3.2JPMorgan Chase & Co.: Improving Business Processes with Big Data
5.3.3OTP Bank: Reducing Loan Defaults with Big Data
5.3.4CBA (Commonwealth Bank of Australia): Providing Personalized Services with Big Data
  
6Chapter 6: Big Data in Defense & Intelligence
6.1Overview & Investment Potential
6.2Key Applications
6.2.1Intelligence Gathering
6.2.2Battlefield Analytics
6.2.3Energy Saving Opportunities in the Battlefield
6.2.4Preventing Injuries on the Battlefield
6.3Case Studies
6.3.1U.S. Air Force: Providing Actionable Intelligence to Warfighters with Big Data
6.3.2Royal Navy: Empowering Submarine Warfare with Big Data
6.3.3NSA (National Security Agency): Capitalizing on Big Data to Detect Threats
6.3.4Ministry of State Security, China: Predictive Policing with Big Data
6.3.5French DGSE (General Directorate for External Security): Enhancing Intelligence with Big Data
  
7Chapter 7: Big Data in Education
7.1Overview & Investment Potential
7.2Key Applications
7.2.1Information Integration
7.2.2Identifying Learning Patterns
7.2.3Enabling Student-Directed Learning
7.3Case Studies
7.3.1Purdue University: Ensuring Successful Higher Education Outcomes with Big Data
7.3.2Nottingham Trent University: Successful Student Outcomes with Big Data
7.3.3Edith Cowen University: Increasing Student Retention with Big Data
  
8Chapter 8: Big Data in Healthcare & Pharma
8.1Overview & Investment Potential
8.2Key Applications
8.2.1Managing Population Health Efficiently
8.2.2Improving Patient Care with Medical Data Analytics
8.2.3Improving Clinical Development & Trials
8.2.4Drug Development: Improving Time to Market
8.3Case Studies
8.3.1Amino: Healthcare Transparency with Big Data
8.3.2Novartis: Digitizing Healthcare with Big Data
8.3.3GSK (GlaxoSmithKline): Accelerating Drug Discovering with Big Data
8.3.4Pfizer: Developing Effective and Targeted Therapies with Big Data
8.3.5Roche: Personalizing Healthcare with Big Data
8.3.6Sanofi: Proactive Diabetes Care with Big Data
  
9Chapter 9: Big Data in Smart Cities & Intelligent Buildings
9.1Overview & Investment Potential
9.2Key Applications
9.2.1Energy Optimization & Fault Detection
9.2.2Intelligent Building Analytics
9.2.3Urban Transportation Management
9.2.4Optimizing Energy Production
9.2.5Water Management
9.2.6Urban Waste Management
9.3Case Studies
9.3.1Singapore: Building a Smart Nation with Big Data
9.3.2Glasgow City Council: Promoting Smart City Efforts with Big Data
9.3.3OVG Real Estate: Powering the World’s Most Intelligent Building with Big Data
  
10Chapter 10: Big Data in Insurance
10.1Overview & Investment Potential
10.2Key Applications
10.2.1Claims Fraud Mitigation
10.2.2Customer Retention & Profiling
10.2.3Risk Management
10.3Case Studies
10.3.1Zurich Insurance Group: Enhancing Risk Management with Big Data
10.3.2RSA Group: Improving Customer Relations with Big Data
10.3.3Primerica: Improving Insurance Sales Force Productivity with Big Data
  
11Chapter 11: Big Data in Manufacturing & Natural Resources
11.1Overview & Investment Potential
11.2Key Applications
11.2.1Asset Maintenance & Downtime Reduction
11.2.2Quality & Environmental Impact Control
11.2.3Optimized Supply Chain
11.2.4Exploration & Identification of Natural Resources
11.3Case Studies
11.3.1Intel Corporation: Cutting Manufacturing Costs with Big Data
11.3.2Dow Chemical Company: Optimizing Chemical Manufacturing with Big Data
11.3.3Michelin: Improving the Efficiency of Supply Chain and Manufacturing with Big Data
11.3.4Brunei: Saving Natural Resources with Big Data
  
12Chapter 12: Big Data in Web, Media & Entertainment
12.1Overview & Investment Potential
12.2Key Applications
12.2.1Audience & Advertising Optimization
12.2.2Channel Optimization
12.2.3Recommendation Engines
12.2.4Optimized Search
12.2.5Live Sports Event Analytics
12.2.6Outsourcing Big Data Analytics to Other Verticals
12.3Case Studies
12.3.1Netflix: Improving Viewership with Big Data
12.3.2NFL (National Football League): Improving Stadium Experience with Big Data
12.3.3Walt Disney Company: Enhancing Theme Park Experience with Big Data
12.3.4Baidu: Reshaping Search Capabilities with Big Data
12.3.5Constant Contact: Effective Marketing with Big Data
  
13Chapter 13: Big Data in Public Safety & Homeland Security
13.1Overview & Investment Potential
13.2Key Applications
13.2.1Cyber Crime Mitigation
13.2.2Crime Prediction Analytics
13.2.3Video Analytics & Situational Awareness
13.3Case Studies
13.3.1DHS (U.S. Department of Homeland Security): Identifying Threats to Physical and Network Infrastructure with Big Data
13.3.2Dubai Police: Locating Wanted Vehicles More Efficiently with Big Data
13.3.3Memphis Police Department: Crime Reduction with Big Data
  
14Chapter 14: Big Data in Public Services
14.1Overview & Investment Potential
14.2Key Applications
14.2.1Public Sentiment Analysis
14.2.2Tax Collection & Fraud Detection
14.2.3Economic Analysis
14.2.4Predicting & Mitigating Disasters
14.3Case Studies
14.3.1ONS (Office for National Statistics): Exploring the UK Economy with Big Data
14.3.2New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data
14.3.3Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data
14.3.4City of Chicago: Improving Government Productivity with Big Data
14.3.5FDNY (Fire Department of the City of New York): Fighting Fires with Big Data
14.3.6Ambulance Victoria: Improving Patient Survival Rates with Big Data
  
15Chapter 15: Big Data in Retail, Wholesale & Hospitality
15.1Overview & Investment Potential
15.2Key Applications
15.2.1Customer Sentiment Analysis
15.2.2Customer & Branch Segmentation
15.2.3Price Optimization
15.2.4Personalized Marketing
15.2.5Optimizing & Monitoring the Supply Chain
15.2.6In-Field Sales Analytics
15.3Case Studies
15.3.1Walmart: Making Smarter Stocking Decision with Big Data
15.3.2Tesco: Reducing Supermarket Energy Bills with Big Data
15.3.3Marriott International: Elevating Guest Services with Big Data
15.3.4JJ Food Service: Predictive Wholesale Shopping Lists with Big Data
  
16Chapter 16: Big Data in Telecommunications
16.1Overview & Investment Potential
16.2Key Applications
16.2.1Network Performance & Coverage Optimization
16.2.2Customer Churn Prevention
16.2.3Personalized Marketing
16.2.4Tailored Location Based Services
16.2.5Fraud Detection
16.3Case Studies
16.3.1BT Group: Hunting Down Nuisance Callers with Big Data
16.3.2AT&T: Smart Network Management with Big Data
16.3.3T-Mobile USA: Cutting Down Churn Rate with Big Data
16.3.4TEOCO: Helping Service Providers Save Millions with Big Data
16.3.5Freedom Mobile: Optimizing Video Quality with Big Data
16.3.6Coriant: SaaS Based Analytics with Big Data
  
17Chapter 17: Big Data in Utilities & Energy
17.1Overview & Investment Potential
17.2Key Applications
17.2.1Customer Retention
17.2.2Forecasting Energy
17.2.3Billing Analytics
17.2.4Predictive Maintenance
17.2.5Maximizing the Potential of Drilling
17.2.6Production Optimization
17.3Case Studies
17.3.1Royal Dutch Shell: Developing Data-Driven Oil Fields with Big Data
17.3.2British Gas: Improving Customer Service with Big Data
17.3.3Oncor Electric Delivery: Intelligent Power Grid Management with Big Data
  
18Chapter 18: Big Data Industry Roadmap & Value Chain
18.1Big Data Industry Roadmap
18.1.12017 – 2020: Investments in Predictive Analytics & SaaS-Based Big Data Offerings
18.1.22020 – 2025: Growing Focus on Cognitive & Personalized Analytics
18.1.32025 – 2030: Convergence with Future IoT Applications
18.2The Big Data Value Chain
18.2.1Hardware Providers
18.2.1.1Storage & Compute Infrastructure Providers
18.2.1.2Networking Infrastructure Providers
18.2.2Software Providers
18.2.2.1Hadoop & Infrastructure Software Providers
18.2.2.2SQL & NoSQL Providers
18.2.2.3Analytic Platform & Application Software Providers
18.2.2.4Cloud Platform Providers
18.2.3Professional Services Providers
18.2.4End-to-End Solution Providers
18.2.5Vertical Enterprises
  
19Chapter 19: Standardization & Regulatory Initiatives
19.1ASF (Apache Software Foundation)
19.1.1Management of Hadoop
19.1.2Big Data Projects Beyond Hadoop
19.2CSA (Cloud Security Alliance)
19.2.1BDWG (Big Data Working Group)
19.3CSCC (Cloud Standards Customer Council)
19.3.1Big Data Working Group
19.4DMG  (Data Mining Group)
19.4.1PMML (Predictive Model Markup Language) Working Group
19.4.2PFA (Portable Format for Analytics) Working Group
19.5IEEE (Institute of Electrical and Electronics Engineers) –Big Data Initiative
19.6INCITS (InterNational Committee for Information Technology Standards)
19.6.1Big Data Technical Committee
19.7ISO (International Organization for Standardization)
19.7.1ISO/IEC JTC 1/SC 32: Data Management and Interchange
19.7.2ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms
19.7.3ISO/IEC JTC 1/SC 27: IT Security Techniques
19.7.4ISO/IEC JTC 1/WG 9: Big Data
19.7.5Collaborations with Other ISO Work Groups
19.8ITU (International Telecommunications Union)
19.8.1ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities
19.8.2Other Deliverables Through SG (Study Group) 13 on Future Networks
19.8.3Other Relevant Work
19.9Linux Foundation
19.9.1ODPi (Open Ecosystem of Big Data)
19.10NIST (National Institute of Standards and Technology)
19.10.1NBD-PWG (NIST Big Data Public Working Group)
19.11OASIS (Organization for the Advancement of Structured Information Standards)
19.11.1Technical Committees
19.12ODaF (Open Data Foundation)
19.12.1Big Data Accessibility
19.13ODCA (Open Data Center Alliance)
19.13.1Work on Big Data
19.14OGC (Open Geospatial Consortium)
19.14.1Big Data DWG (Domain Working Group)
19.15TM Forum
19.15.1Big Data Analytics Strategic Program
19.16TPC (Transaction Processing Performance Council)
19.16.1TPC-BDWG (TPC Big Data Working Group)
19.17W3C (World Wide Web Consortium)
19.17.1Big Data Community Group
19.17.2Open Government Community Group
  
20Chapter 20: Market Analysis & Forecasts
20.1Global Outlook for the Big Data Market
20.2Submarket Segmentation
20.2.1Storage and Compute Infrastructure
20.2.2Networking Infrastructure
20.2.3Hadoop & Infrastructure Software
20.2.4SQL
20.2.5NoSQL
20.2.6Analytic Platforms & Applications
20.2.7Cloud Platforms
20.2.8Professional Services
20.3Vertical Market Segmentation
20.3.1Automotive, Aerospace & Transportation
20.3.2Banking & Securities
20.3.3Defense & Intelligence
20.3.4Education
20.3.5Healthcare & Pharmaceutical
20.3.6Smart Cities & Intelligent Buildings
20.3.7Insurance
20.3.8Manufacturing & Natural Resources
20.3.9Media & Entertainment
20.3.10Public Safety & Homeland Security
20.3.11Public Services
20.3.12Retail, Wholesale & Hospitality
20.3.13Telecommunications
20.3.14Utilities & Energy
20.3.15Other Sectors
20.4Regional Outlook
20.5Asia Pacific
20.5.1Country Level Segmentation
20.5.2Australia
20.5.3China
20.5.4India
20.5.5Indonesia
20.5.6Japan
20.5.7Malaysia
20.5.8Pakistan
20.5.9Philippines
20.5.10Singapore
20.5.11South Korea
20.5.12Taiwan
20.5.13Thailand
20.5.14Rest of Asia Pacific
20.6Eastern Europe
20.6.1Country Level Segmentation
20.6.2Czech Republic
20.6.3Poland
20.6.4Russia
20.6.5Rest of Eastern Europe
20.7Latin & Central America
20.7.1Country Level Segmentation
20.7.2Argentina
20.7.3Brazil
20.7.4Mexico
20.7.5Rest of Latin & Central America
20.8Middle East & Africa
20.8.1Country Level Segmentation
20.8.2Israel
20.8.3Qatar
20.8.4Saudi Arabia
20.8.5South Africa
20.8.6UAE
20.8.7Rest of the Middle East & Africa
20.9North America
20.9.1Country Level Segmentation
20.9.2Canada
20.9.3USA
20.10Western Europe
20.10.1Country Level Segmentation
20.10.2Denmark
20.10.3Finland
20.10.4France
20.10.5Germany
20.10.6Italy
20.10.7Netherlands
20.10.8Norway
20.10.9Spain
20.10.10Sweden
20.10.11UK
20.10.12Rest of Western Europe
  
21Chapter 21: Vendor Landscape
21.11010data
21.2Absolutdata
21.3Accenture
21.4Actian Corporation
21.5Adaptive Insights
21.6Advizor Solutions
21.7AeroSpike
21.8AFS Technologies
21.9Alation
21.10Algorithmia
21.11Alluxio
21.12Alpine Data
21.13Alteryx
21.14AMD (Advanced Micro Devices)
21.15Apixio
21.16Arcadia Data
21.17Arimo
21.18ARM
21.19AtScale
21.20Attivio
21.21Attunity
21.22Automated Insights
21.23AWS (Amazon Web Services)
21.24Axiomatics
21.25Ayasdi
21.26Basho Technologies
21.27BCG (Boston Consulting Group)
21.28Bedrock Data
21.29BetterWorks
21.30Big Cloud Analytics
21.31Big Panda
21.32Birst
21.33Bitam
21.34Blue Medora
21.35BlueData Software
21.36BlueTalon
21.37BMC Software
21.38BOARD International
21.39Booz Allen Hamilton
21.40Boxever
21.41CACI International
21.42Cambridge Semantics
21.43Capgemini
21.44Cazena
21.45Centrifuge Systems
21.46CenturyLink
21.47Chartio
21.48Cisco Systems
21.49Civis Analytics
21.50ClearStory Data
21.51Cloudability
21.52Cloudera
21.53Clustrix
21.54CognitiveScale
21.55Collibra
21.56Concurrent Computer Corporation
21.57Confluent
21.58Contexti
21.59Continuum Analytics
21.60Couchbase
21.61CrowdFlower
21.62Databricks
21.63DataGravity
21.64Dataiku
21.65Datameer
21.66DataRobot
21.67DataScience
21.68DataStax
21.69DataTorrent
21.70Datawatch Corporation
21.71Datos IO
21.72DDN (DataDirect Networks)
21.73Decisyon
21.74Dell Technologies
21.75Deloitte
21.76Demandbase
21.77Denodo Technologies
21.78Digital Reasoning Systems
21.79Dimensional Insight
21.80Dolphin Enterprise Solutions Corporation
21.81Domino Data Lab
21.82Domo
21.83DriveScale
21.84Dundas Data Visualization
21.85DXC Technology
21.86Eligotech
21.87Engineering Group (Engineering Ingegneria Informatica)
21.88EnterpriseDB
21.89eQ Technologic
21.90Ericsson
21.91EXASOL
21.92Facebook
21.93FICO (Fair Isaac Corporation)
21.94Fractal Analytics
21.95Fujitsu
21.96Fuzzy Logix
21.97Gainsight
21.98GE (General Electric)
21.99Glassbeam
21.100GoodData Corporation
21.101Google
21.102Greenwave Systems
21.103GridGain Systems
21.104Guavus
21.105H2O.ai
21.106HDS (Hitachi Data Systems)
21.107Hedvig
21.108Hortonworks
21.109HPE (Hewlett Packard Enterprise)
21.110Huawei
21.111IBM Corporation
21.112iDashboards
21.113Impetus Technologies
21.114Incorta
21.115InetSoft Technology Corporation
21.116Infer
21.117Infor
21.118Informatica Corporation
21.119Information Builders
21.120Infosys
21.121Infoworks
21.122Insightsoftware.com
21.123InsightSquared
21.124Intel Corporation
21.125Interana
21.126InterSystems Corporation
21.127Jedox
21.128Jethro
21.129Jinfonet Software
21.130Juniper Networks
21.131KALEAO
21.132Keen IO
21.133Kinetica
21.134KNIME
21.135Kognitio
21.136Kyvos Insights
21.137Lavastorm
21.138Lexalytics
21.139Lexmark International
21.140Logi Analytics
21.141Longview Solutions
21.142Looker Data Sciences
21.143LucidWorks
21.144Luminoso Technologies
21.145Maana
21.146Magento Commerce
21.147Manthan Software Services
21.148MapD Technologies
21.149MapR Technologies
21.150MariaDB Corporation
21.151MarkLogic Corporation
21.152Mathworks
21.153MemSQL
21.154Metric Insights
21.155Microsoft Corporation
21.156MicroStrategy
21.157Minitab
21.158MongoDB
21.159Mu Sigma
21.160Neo Technology
21.161NetApp
21.162Nimbix
21.163Nokia
21.164NTT Data Corporation
21.165Numerify
21.166NuoDB
21.167Nutonian
21.168NVIDIA Corporation
21.169Oblong Industries
21.170OpenText Corporation
21.171Opera Solutions
21.172Optimal Plus
21.173Oracle Corporation
21.174Palantir Technologies
21.175Panorama Soft

  • Pages: 498
  • Edition: 2017
  • Published Date: April 2017
  • Geography Coverd: Global
  • Publisher: SNS Research

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