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ビッグデータ市場 2018-2030年: ビジネスチャンス、課題、戦略、垂直市場、市場予測

The Big Data Market: 2018 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals & Forecasts

 

出版社 出版年月電子版価格 ページ数図表数
SNS Telecom & IT
SNSテレコム&IT
2018年6月US$2,500
シングルユーザライセンス
549 89

サマリー

英国とドバイに拠点をおく調査会社SNSテレコム&IT (SNS Telecom & IT)の調査レポート「ビッグデータ市場 2018-2030年: ビジネスチャンス、課題、戦略、垂直市場、市場予測」は、ビッグデータの主な市場促進要因、市場課題、投資の可能性、垂直市場のビジネスチャンスと使用事例、今後のロードマップ、バリューチェーン、ビッグデータ解析のケーススタディ、ベンダの概要、市場シェア、戦略などのビッグデータのエコシステムを詳細に調査している。2018年から2030年のビッグデータのハードウェア、ソフトウェア、プロフェッショナルサービスの市場規模を、8つの水平市場毎、14の垂直市場毎、6つの地域と35ケ国に区分して予測している。

主なトピック

  • ビッグデータのエコシステム
  • ビッグデータの市場促進要因と阻害要因
  • ビッグデータの技術、標準、法規制のイニシアチブ
  • ビッグデータ解析と採用モデル
  • 人工知能(AI)、機械学習、エッジ解析、クラウドベースのビッグデータプラットフォーム、IoT(モノのインターネット)などの主要動向
  • ビッグデータ産業のロードマップとバリューチェーン
  • 14の垂直市場の主要用途と投資の可能性
  • 60以上のビッグデータと解析のケーススタディ
  • ビッグデータベンダの市場シェア
  • 今後のロードマップとバリューチェーン
  • 270社以上のビッグデータエコシステムの主要企業と新興企業の企業概要と戦略
  • ビッグデータのハードウェア、ソフトウェア、プロフェッショナルサービスベンダ、大企業への戦略的助言
  • 2018-2030年の市場分析と予測

予測セグメント

  • ハードウェア、ソフトウェア、プロフェッショナルサービス
  • 水平市場 - サブマーケット
  • ストレージ、コンピュータインフラ
  • ネットワークインフラ
  • Hadoopとインフラソフトウェア
  • SQL
  • NoSQL
  • 解析プラットフォームとアプリケーション
  • クラウドプラットフォーム
  • プロフェッショナルサービス
  • 垂直市場 - サブマーケット
  • 自動車、航空宇宙、交通運輸
  • 銀行と証券
  • 防衛と情報
  • 教育
  • 医療と医薬
  • スマートシティとインテリジェントビル
  • 保険業
  • 製造業と天然資源
  • ウェブ、メディア、エンターテインメント
  • 公共安全と国土安全保障
  • 公共サービス
  • 小売業、卸売業、観光・サービス業
  • 通信
  • 公益企業とエネルギー
  • その他

 

Synopsis

“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 Telecom & IT estimates that Big Data investments will account for over $65 Billion in 2018 alone. These investments are further expected to grow at a CAGR of approximately 14% over the next three years.

The “Big Data Market: 2018 – 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 profiles, market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2018 till 2030. The forecasts are 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.

Key Findings:

The report has the following key findings:

•In 2018, Big Data vendors will pocket over $65 Billion from hardware, software and professional services revenues. These investments are further expected to grow at a CAGR of approximately 14% over the next three years, eventually accounting for more than $96 Billion by the end of 2021.
•With ongoing advances in AI (Artificial Intelligence) technologies, Big Data analytics initiatives are beginning to leverage sophisticated deep learning systems with an autonomous sense of judgment – to enable a range of applications from chatbots and virtual assistants to self-driving vehicles and precision medicine.
•In order to analyze data closer to where it is collected, Big Data and advanced analytics technologies are increasingly being integrated into edge environments, including network nodes, numerous industrial machines and IoT (Internet of Things) devices.
•The vendor arena is continuing to consolidate with several prominent M&A deals such as Oracle's recent acquisition of enterprise data science platform provider DataScience.com – in a bid to beef up its capabilities in machine learning and Big Data for predictive analytics, and Google's acquisition of Big Data application platform provider Cask Data.
 

Topics Covered:

The report covers the following topics:

•Big Data ecosystem
•Market drivers and barriers
•Enabling technologies, standardization and regulatory initiatives
•Big Data analytics and implementation models
•Key trends – including AI (Artificial Intelligence), machine learning, edge analytics, cloud-based Big Data platforms, and the impact of the IoT (Internet of Things)

•Analysis of key applications and investment potential for 14 vertical markets
•Over 60 case studies on the use of Big Data and analytics
•Big Data vendor market share
•Future roadmap and value chain
•Profiles and strategies of over 270 leading and emerging Big Data ecosystem players
•Strategic recommendations for Big Data hardware, software and professional services vendors, and enterprises
•Market analysis and forecasts from 2018 till 2030
 

Forecast Segmentation:

Market forecasts are provided for each of the following submarkets and their categories:

•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 2021, 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?
 

List of Companies Mentioned:

The following companies and organizations have been reviewed, discussed or mentioned in the report:

1010data
Absolutdata
Accenture
Actian Corporation
Actuate Corporation
Adaptive Insights
Adobe Systems
Advizor Solutions
AeroSpike
AFS Technologies
Airbus Group
Alameda County Social Services Agency
Alation
Algorithmia
Alluxio
Alphabet
Alpine Data
ALTEN
Alteryx
Altiscale
Amazon.com
Ambulance Victoria
AMD (Advanced Micro Devices)
Amgen
Anaconda
ANSI (American National Standards Institute)
Antivia
Apixio
Arcadia Data
Arim
ARM
ASF (Apache Software Foundation)
AstraZeneca
AT&T
AtScale
Attivio
Attunity
Automated Insights
AVORA
AWS (Amazon Web Services)
Axiomatics
Ayasdi
BackOffice Associates
BAE Systems
Baidu
Bangkok Hospital Group
Basho Technologies
BCG (Boston Consulting Group)
Bedrock Data
Bet365 Group
BetterWorks
Big Panda
BigML
Bina Technologies
Biogen
Birst
Bitam
Blue Medora
BlueData Software
BlueTalon
BMC Software
BMW
BOARD International
Boeing
Booz Allen Hamilton
Boxever
British Gas
Broadcom
BT Group
CACI International
Cambridge Semantics
Capgemini
Capital One Financial Corporation
Cask Data
Cazena
CBA (Commonwealth Bank of Australia)
Centrifuge Systems
CenturyLink
Chartio
Cisco Systems
Civis Analytics
ClearStory Data
Cloudability
Cloudera
Cloudian
Clustrix
CognitiveScale
Collibra
Concurrent Technology
Confluent
Constant Contact
Contexti
Coriant
Couchbase
Crate.io
Cray
Credit Agricole Group
CSA (Cloud Security Alliance)
CSCC (Cloud Standards Customer Council)
Dash Labs
Data Clairvoyance Group
Databricks
DataGravity
Dataiku
Datalytyx
Datameer
DataRobot
DataScience.com
DataStax
Datawatch Corporation
Datos IO
DDN (DataDirect Networks)
Decisyon
Dell EMC
Dell Technologies
Deloitte
Demandbase
Denodo Technologies
Denso Corporation
DGSE (General Directorate for External Security, France)
Dianomic Systems
Digital Reasoning Systems
Dimensional Insight
DMG  (Data Mining Group)
Dolphin Enterprise Solutions Corporation
Domino Data Lab
Domo
Dow Chemical Company
Dremio
DriveScale
Druva
DT (Deutsche Telekom)
Dubai Police
Dundas Data Visualization
DXC Technology
eBay
Edith Cowen University
Elastic
Engineering Group (Engineering Ingegneria Informatica)
EnterpriseDB Corporation
eQ Technologic
Ericsson
Erwin
EVŌ (Big Cloud Analytics)
EXASOL
EXL (ExlService Holdings)
Facebook
FDNY (Fire Department of the City of New York)
FICO (Fair Isaac Corporation)
Figure Eight
FogHorn Systems
Ford Motor Company
Fractal Analytics
Franz
Fujitsu
Fuzzy Logix
Gainsight
GE (General Electric)
Glasgow City Council
Glassbeam
GoodData Corporation
Google
Grakn Labs
Greenwave Systems
GridGain Systems
Groupe Renault
Guavus
H2O.ai
Hanse Orga Group
HarperDB
HCL Technologies
Hedvig
Hitachi
Hitachi Vantara
Honda Motor Company
Hortonworks
HPE (Hewlett Packard Enterprise)
HSBC Group
Huawei
HVR
HyperScience
HyTrust
IBM Corporation
iDashboards
IDERA
IEC (International Electrotechnical Commission)
IEEE (Institute of Electrical and Electronics Engineers)
Ignite Technologies
Imanis Data
Impetus Technologies
INCITS (InterNational Committee for Information Technology Standards)
Incorta
InetSoft Technology Corporation
Infer
InfluxData
Infogix
Infor
Informatica
Information Builders
Infosys
Infoworks
Insightsoftware.com
InsightSquared
Intel Corporation
Interana
InterSystems Corporation
ISO (International Organization for Standardization)
ITU (International Telecommunication Union)
Jedox
Jethro
Jinfonet Software
JJ Food Service
JPMorgan Chase & Co.
Juniper Networks
Kaiser Permanente
KALEAO
Keen IO
Keyrus
Kinetica
KNIME
Kofax
Kognitio
Kyvos Insights
Lavastorm
Leadspace
LeanXcale
Lexalytics
Lexmark International
Lightbend
Linux Foundation
Logi Analytics
Logical Clocks
Longview Solutions
Looker Data Sciences
LucidWorks
Luminoso Technologies
Maana
Magento Commerce
Manthan Software Services
MapD Technologies
MapR Technologies
MariaDB Corporation
MarkLogic Corporation
Marriott International
Mathworks
Melissa
Memphis Police Department
MemSQL
Mercer
METI (Ministry of Economy, Trade and Industry, Japan)
Metric Insights
Michelin
Microsoft Corporation
MicroStrategy
Ministry of State Security, China
Minitab
MongoDB
Mu Sigma
NEC Corporation
Neo4j
NetApp
Netflix
Neustar
New York State Department of Taxation and Finance
NextBio
NFL (National Football League)
Nimbix
Nokia
Northwest Analytics
Nottingham Trent University
Novartis
NTT Data Corporation
NTT Group
Numerify
NuoDB
Nutonian
NVIDIA Corporation
OASIS (Organization for the Advancement of Structured Information Standards)
Objectivity
Oblong Industries
ODaF (Open Data Foundation)
ODCA (Open Data Center Alliance)
ODPi (Open Ecosystem of Big Data)
Ofcom
OGC (Open Geospatial Consortium)
Oncor Electric Delivery Company
ONS (Office for National Statistics, United Kingdom)
OpenText Corporation
Opera Solutions
Optimal Plus
Optum
OptumLabs
Oracle Corporation
OTP Bank
OVG Real Estate
Palantir Technologies
Panasonic Corporation
Panorama Software
Paxata
Pentaho
Pepperdata
Pfize
Philips
Phocas Software
Pivotal Software
Predixion Software
Primerica
Procter & Gamble
Prognoz
Progress Software Corporation
Provalis Research
Purdue University
Pure Storage
PwC (PricewaterhouseCoopers International)
Pyramid Analytics
Qlik
Qrama/Tengu
Qualcomm
Quantum Corporation
Qubole
Rackspace
Radius Intelligence
RapidMiner
Recorded Future
Red Hat
Redis Labs
RedPoint Global
Reltio
Rocket Fuel
Rosenberger
Royal Bank of Canada
Royal Dutch Shell
Royal Navy
RSA Group
RStudio
Rubrik
Ryft
Sailthru
Salesforce.com
Salient Management Company
Samsung Electronics
Samsung Group
Samsung SDS
Sanofi
SAP
SAS Institute
ScaleArc
ScaleOut Software
Scaleworks
Schneider Electric
SCIO Health Analytics
Seagate Technology
Search Technologies
Siemens
Sinequa
SiSense
Sizmek
SnapLogic
Snowflake Computing
SoftBank Group
Software AG
SpagoBI Labs
Sparkline Data
Splice Machine
Splunk
Sqrrl
Strategy Companion Corporation
Stratio
Streamlio
StreamSets
Striim
Sumo Logic
Supermicro (Super Micro Computer)
Syncsort
SynerScope
SYNTASA
Tableau Software
Talend
Tamr
TARGIT
TCS (Tata Consultancy Services)
TEOCO
Teradata Corporation
Tesco
Thales
The Walt Disney Company
The Weather Company
Thomson Reuters
ThoughtSpot
TIBCO Software
Tidemark
TM Forum
T-Mobile USA
Toshiba Corporation
TPC (Transaction Processing Performance Council)
Transwarp
Trifacta
Twitter
U.S. Air Force
U.S. Army
U.S. CBP (Customs and Border Protection)
U.S. Coast Guard
U.S. Department of Commerce
U.S. Department of Defense
U.S. DHS (Department of Homeland Security)
U.S. ICE (Immigration and Customs Enforcement)
U.S. NASA (National Aeronautics and Space Administration)
U.S. NIST (National Institute of Standards and Technology)
U.S. NSA (National Security Agency)
Unifi Software
UnitedHealth Group
Unravel Data
USCIS (U.S. Citizenship and Immigration Services)
VANTIQ
Vecima Networks
Verizon Communications
Vmware
Vodafone Group
VoltDB
W3C (World Wide Web Consortium)
WANdisco
Waterline Data
Wavefront
Western Digital Corporation
WhereScape
WiPro
Wolfram Research
Workday
Xplenty
Yellowfin BI
Yseop
Zendesk
Zoomdata
Zucchetti
Zurich Insurance Group

  • 自動車、航空宇宙、交通運輸
  • 銀行と証券
  • 防衛と情報
  • 教育
  • 医療と医薬
  • スマートシティとインテリジェントビル
  • 保険業
  • 製造業と天然資源
  • ウェブ、メディア、エンターテインメント
  • 公共安全と国土安全保障
  • 公共サービス
  • 小売業、卸売業、観光・サービス業
  • 通信
  • 公益企業とエネルギー
  • その他

垂直市場 - サブマーケット



目次

1 Chapter 1: Introduction ................................... 28
1.1 Executive Summary ....................................... 28
1.2 Topics Covered .............................................. 30
1.3 Forecast Segmentation ................................. 31
1.4 Key Questions Answered ............................... 33
1.5 Key Findings ................................... 34
1.6 Methodology ................................................. 35
1.7 Target Audience ............................................ 36
1.8 Companies & Organizations Mentioned ....................................... 37

2 Chapter 2: An Overview of Big Data ................. 41
2.1 What is Big Data? .......................................... 41
2.2 Key Approaches to Big Data Processing ........................................ 41
2.2.1 Hadoop .............................................. 42
2.2.2 NoSQL ................................................ 44
2.2.3 MPAD (Massively Parallel Analytic Databases) ................................. 44
2.2.4 In-Memory Processing ....................................... 45
2.2.5 Stream Processing Technologies ....................................... 45
2.2.6 Spark .................................................. 46
2.2.7 Other Databases & Analytic Technologies ........................ 46
2.3 Key Characteristics of Big Data ...................................... 47
2.3.1 Volume .............................................. 47
2.3.2 Velocity .............................................. 47
2.3.3 Variety ............................................... 47
2.3.4 Value .................................................. 48
2.4 Market Growth Drivers ................................. 48
2.4.1 Awareness of Benefits ....................................... 48
2.4.2 Maturation of Big Data Platforms ..................................... 48
2.4.3 Continued Investments by Web Giants, Governments & Enterprises .............. 49
2.4.4 Growth of Data Volume, Velocity & Variety ...................... 49
2.4.5 Vendor Commitments & Partnerships .............................. 49
2.4.6 Technology Trends Lowering Entry Barriers ...................... 50
2.5 Market Barriers ............................................. 50
2.5.1 Lack of Analytic Specialists ................................ 50
2.5.2 Uncertain Big Data Strategies ............................ 50
2.5.3 Organizational Resistance to Big Data Adoption ............................... 51
2.5.4 Technical Challenges: Scalability & Maintenance ............................. 51
2.5.5 Security & Privacy Concerns .............................. 51

3 Chapter 3: Big Data Analytics ........................... 52
3.1 What are Big Data Analytics? ..................................... 52
3.2 The Importance of Analytics ...................................... 52
3.3 Reactive vs. Proactive Analytics ................................. 53
3.4 Customer vs. Operational Analytics ........................... 53
3.5 Technology & Implementation Approaches .............................. 54
3.5.1 Grid Computing ................................. 54
3.5.2 In-Database Processing ..................................... 54
3.5.3 In-Memory Analytics ......................................... 55
3.5.4 Machine Learning & Data Mining ...................................... 55
3.5.5 Predictive Analytics ........................................... 56
3.5.6 NLP (Natural Language Processing) ................................... 56
3.5.7 Text Analytics .................................... 57
3.5.8 Visual Analytics .................................. 57
3.5.9 Graph Analytics ................................. 58
3.5.10 Social Media, IT & Telco Network Analytics ...................... 58

4 Chapter 4: Big Data in Automotive, Aerospace & Transportation ..... 60
4.1 Overview & Investment Potential .............................. 60
4.2 Key Applications ......................................... 60
4.2.1 Autonomous & Semi-Autonomous Driving ....................... 60
4.2.2 Streamlining Vehicle Recalls & Warranty Management ................... 61
4.2.3 Fleet Management ............................................ 62
4.2.4 Intelligent Transportation ................................. 62
4.2.5 UBI (Usage Based Insurance) ............................. 63
4.2.6 Predictive Aircraft Maintenance & Fuel Optimization ...................... 63
4.2.7 Air Traffic Control .............................. 64
4.3 Case Studies ............................................... 64
4.3.1 Boeing: Making Flying More Efficient with Big Data ......................... 64
4.3.2 BMW: Eliminating Defects in New Vehicle Models with Big Data..................... 65
4.3.3 Dash Labs: Turning Regular Cars into Data-Driven Smart Cars with Big Data ................... 67
4.3.4 Ford Motor Company: Making Efficient Transportation Decisions with Big Data ............. 68
4.3.5 Groupe Renault: Boosting Driver Safety with Big Data ..................... 70
4.3.6 Honda Motor Company: Improving F1 Performance & Fuel Efficiency with Big Data ...... 71

5 Chapter 5: Big Data in Banking & Securities ..................... 73
5.1 Overview & Investment Potential .............................. 73
5.2 Key Applications ......................................... 73
5.2.1 Customer Retention & Personalized Products .................................. 73
5.2.2 Risk Management .............................. 74
5.2.3 Fraud Detection ................................. 74
5.2.4 Credit Scoring .................................... 74
5.3 Case Studies ............................................... 75
5.3.1 HSBC Group: Avoiding Regulatory Penalties with Big Data ............... 75
5.3.2 JPMorgan Chase & Co.: Improving Business Processes with Big Data .............. 76
5.3.3 OTP Bank: Reducing Loan Defaults with Big Data ............................. 77
5.3.4 CBA (Commonwealth Bank of Australia): Providing Personalized Services with Big Data ................ 78

6 Chapter 6: Big Data in Defense & Intelligence .................. 79
6.1 Overview & Investment Potential .............................. 79
6.2 Key Applications ......................................... 79
6.2.1 Intelligence Gathering ....................................... 79
6.2.2 Battlefield Analytics ........................................... 80
6.2.3 Energy Saving Opportunities in the Battlefield ................................. 80
6.2.4 Preventing Injuries on the Battlefield................................ 81
6.3 Case Studies ............................................... 82
6.3.1 U.S. Air Force: Providing Actionable Intelligence to Warfighters with Big Data ............... 82
6.3.2 Royal Navy: Empowering Submarine Warfare with Big Data ............................ 83
6.3.3 NSA (National Security Agency): Capitalizing on Big Data to Detect Threats .................... 84
6.3.4 Ministry of State Security, China: Predictive Policing with Big Data ................. 85
6.3.5 French DGSE (General Directorate for External Security): Enhancing Intelligence with Big Data ..... 86

7 Chapter 7: Big Data in Education ...................... 87
7.1 Overview & Investment Potential .............................. 87
7.2 Key Applications ......................................... 87
7.2.1 Information Integration ..................................... 87
7.2.2 Identifying Learning Patterns ............................ 88
7.2.3 Enabling Student-Directed Learning .................................. 88
7.3 Case Studies ............................................... 89
7.3.1 Purdue University: Improving Academic Performance with Big Data ............... 89
7.3.2 Nottingham Trent University: Successful Student Outcomes with Big Data ..................... 90
7.3.3 Edith Cowen University: Increasing Student Retention with Big Data .............. 91

8 Chapter 8: Big Data in Healthcare & Pharma .................... 92
8.1 Overview & Investment Potential .............................. 92
8.2 Key Applications ......................................... 92
8.2.1 Drug Discovery, Design & Development ........................... 92
8.2.2 Clinical Development & Trials............................ 93
8.2.3 Population Health Management ....................................... 94
8.2.4 Personalized Healthcare & Targeted Treatments ............................. 95
8.2.5 Proactive & Remote Patient Monitoring ........................... 95
8.2.6 Preventive Care & Health Interventions ............................ 96
8.3 Case Studies ............................................... 96
8.3.1 AstraZeneca: Analytics-Driven Drug Development with Big Data ..................... 96
8.3.2 Bangkok Hospital Group: Transforming the Patient Experience with Big Data ................. 97
8.3.3 Novartis: Digitizing Healthcare with Big Data .................... 99
8.3.4 Pfizer: Developing Effective and Targeted Therapies with Big Data ............... 100
8.3.5 Sanofi: Proactive Diabetes Care with Big Data ................................ 101
8.3.6 UnitedHealth Group: Enhancing Patient Care & Value with Big Data ............. 103

9 Chapter 9: Big Data in Smart Cities & Intelligent Buildings ............. 105
9.1 Overview & Investment Potential ............................ 105
9.2 Key Applications ....................................... 105
9.2.1 Energy Optimization & Fault Detection ........................... 106
9.2.2 Intelligent Building Analytics ........................... 106
9.2.3 Urban Transportation Management ............................... 106
9.2.4 Optimizing Energy Production ......................... 107
9.2.5 Water Management ........................................ 107
9.2.6 Urban Waste Management ............................. 107
9.3 Case Studies ............................................. 108
9.3.1 Singapore: Building a Smart Nation with Big Data .......................... 108
9.3.2 Glasgow City Council: Promoting Smart City Efforts with Big Data ................. 109
9.3.3 OVG Real Estate: Powering the World’s Most Intelligent Building with Big Data ........... 110

10 Chapter 10: Big Data in Insurance .................. 111
10.1 Overview & Investment Potential ............................ 111
10.2 Key Applications ....................................... 111
10.2.1 Claims Fraud Mitigation .................................. 112
10.2.2 Customer Retention & Profiling ...................................... 112
10.2.3 Risk Management ............................................ 112
10.3 Case Studies ............................................. 113
10.3.1 Zurich Insurance Group: Enhancing Risk Management with Big Data ............ 113
10.3.2 RSA Group: Improving Customer Relations with Big Data .............................. 114
10.3.3 Primerica: Improving Insurance Sales Force Productivity with Big Data ......................... 115

11 Chapter 11: Big Data in Manufacturing & Natural Resources .......... 116
11.1 Overview & Investment Potential ............................ 116
11.2 Key Applications ....................................... 116
11.2.1 Asset Maintenance & Downtime Reduction ................................... 116
11.2.2 Quality & Environmental Impact Control ........................ 117
11.2.3 Optimized Supply Chain .................................. 117
11.2.4 Exploration & Identification of Natural Resources .......................... 117
11.3 Case Studies ............................................. 118
11.3.1 Intel Corporation: Cutting Manufacturing Costs with Big Data ....................... 118
11.3.2 Dow Chemical Company: Optimizing Chemical Manufacturing with Big Data ............... 119
11.3.3 Michelin: Improving the Efficiency of Supply Chain and Manufacturing with Big Data .................. 120
11.3.4 Brunei: Saving Natural Resources with Big Data ............................. 121

12 Chapter 12: Big Data in Web, Media & Entertainment ................... 122
12.1 Overview & Investment Potential ............................ 122
12.2 Key Applications ....................................... 122
12.2.1 Audience & Advertising Optimization ............................. 123
12.2.2 Channel Optimization ...................................... 123
12.2.3 Recommendation Engines ............................... 123
12.2.4 Optimized Search ............................................ 123
12.2.5 Live Sports Event Analytics .............................. 124
12.2.6 Outsourcing Big Data Analytics to Other Verticals .......................... 124
12.3 Case Studies ............................................. 124
12.3.1 Twitter: Cracking Down on Abusive Content with Big Data ............................ 124
12.3.2 Netflix: Improving Viewership with Big Data ................................... 126
12.3.3 NFL (National Football League): Improving Stadium Experience with Big Data .............. 127
12.3.4 Baidu: Reshaping Search Capabilities with Big Data ....................... 128
12.3.5 Constant Contact: Effective Marketing with Big Data ..................... 129

13 Chapter 13: Big Data in Public Safety & Homeland Security ............ 130
13.1 Overview & Investment Potential ............................ 130
13.2 Key Applications ....................................... 130
13.2.1 Cyber Crime Mitigation ................................... 131
13.2.2 Crime Prediction Analytics .............................. 131
13.2.3 Video Analytics & Situational Awareness ........................ 131
13.3 Case Studies ............................................. 132
13.3.1 DHS (Department of Homeland Security): Identifying Threats with Big Data ................. 132
13.3.2 Dubai Police: Locating Wanted Vehicles More Efficiently with Big Data ........................ 133
13.3.3 Memphis Police Department: Crime Reduction with Big Data ....................... 134

14 Chapter 14: Big Data in Public Services .......................... 135
14.1 Overview & Investment Potential ............................ 135
14.2 Key Applications ....................................... 135
14.2.1 Public Sentiment Analysis ................................ 135
14.2.2 Tax Collection & Fraud Detection .................................... 136
14.2.3 Economic Analysis ........................................... 136
14.2.4 Predicting & Mitigating Disasters .................................... 136
14.3 Case Studies ............................................. 137
14.3.1 ONS (Office for National Statistics): Exploring the UK Economy with Big Data ............... 137
14.3.2 New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data ...... 138
14.3.3 Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data .......... 139
14.3.4 City of Chicago: Improving Government Productivity with Big Data............... 140
14.3.5 FDNY (Fire Department of the City of New York): Fighting Fires with Big Data .............. 141
14.3.6 Ambulance Victoria: Improving Patient Survival Rates with Big Data ............. 142

15 Chapter 15: Big Data in Retail, Wholesale & Hospitality ................. 143
15.1 Overview & Investment Potential ............................ 143
15.2 Key Applications ....................................... 143
15.2.1 Customer Sentiment Analysis .......................... 144
15.2.2 Customer & Branch Segmentation .................................. 144
15.2.3 Price Optimization ........................................... 144
15.2.4 Personalized Marketing ................................... 144
15.2.5 Optimizing & Monitoring the Supply Chain ..................... 145
15.2.6 In-Field Sales Analytics .................................... 145
15.3 Case Studies ............................................. 146
15.3.1 Walmart: Making Smarter Stocking Decision with Big Data ........................... 146
15.3.2 Tesco: Reducing Supermarket Energy Bills with Big Data ............... 147
15.3.3 The Walt Disney Company: Theme Park Management with Big Data ............ 148
15.3.4 Marriott International: Elevating Guest Services with Big Data ...................... 150
15.3.5 JJ Food Service: Predictive Wholesale Shopping Lists with Big Data ............... 151

16 Chapter 16: Big Data in Telecommunications ................. 152
16.1 Overview & Investment Potential ............................ 152
16.2 Key Applications ....................................... 152
16.2.1 Network Performance & Coverage Optimization............................ 152
16.2.2 Customer Churn Prevention ............................ 153
16.2.3 Personalized Marketing ................................... 153
16.2.4 Tailored Location Based Services .................................... 153
16.2.5 Fraud Detection ............................... 153
16.3 Case Studies ............................................. 154
16.3.1 BT Group: Hunting Down Nuisance Callers with Big Data ............... 154
16.3.2 AT&T: Smart Network Management with Big Data ........................ 155
16.3.3 T-Mobile USA: Cutting Down Churn Rate with Big Data ................. 156
16.3.4 TEOCO: Helping Service Providers Save Millions with Big Data ...................... 157
16.3.5 Freedom Mobile: Optimizing Video Quality with Big Data ............................. 158
16.3.6 Coriant: SaaS Based Analytics with Big Data ................................... 159

17 Chapter 17: Big Data in Utilities & Energy ...................... 160
17.1 Overview & Investment Potential ............................ 160
17.2 Key Applications ....................................... 160
17.2.1 Customer Retention ........................................ 160
17.2.2 Forecasting Energy .......................................... 161
17.2.3 Billing Analytics ................................ 161
17.2.4 Predictive Maintenance .................................. 161
17.2.5 Maximizing the Potential of Drilling ................................ 161
17.2.6 Production Optimization ................................. 162
17.3 Case Studies ............................................. 162
17.3.1 Royal Dutch Shell: Developing Data-Driven Oil Fields with Big Data ............... 162
17.3.2 British Gas: Improving Customer Service with Big Data .................. 163
17.3.3 Oncor Electric Delivery: Intelligent Power Grid Management with Big Data .................. 164

18 Chapter 18: Future Roadmap & Value Chain .................. 165
18.1 Future Roadmap....................................... 165
18.1.1 Pre-2020: Towards Cloud-Based Big Data Offerings for Advanced Analytics.................. 165
18.1.2 2020 – 2025: Growing Focus on AI (Artificial Intelligence), Deep Learning & Edge Analytics ......... 166
18.1.3 2025 – 2030: Convergence with Future IoT Applications ................ 166
18.2 The Big Data Value Chain ......................................... 167
18.2.1 Hardware Providers ......................................... 167
18.2.1.1 Storage & Compute Infrastructure Providers ......................... 167
18.2.1.2 Networking Infrastructure Providers ..................................... 168
18.2.2 Software Providers .......................................... 168
18.2.2.1 Hadoop & Infrastructure Software Providers ......................... 169
18.2.2.2 SQL & NoSQL Providers .......................................... 169
18.2.2.3 Analytic Platform & Application Software Providers .............................. 169
18.2.2.4 Cloud Platform Providers ....................................... 169
18.2.3 Professional Services Providers ....................................... 170
18.2.4 End-to-End Solution Providers ........................................ 170
18.2.5 Vertical Enterprises ......................................... 170

19 Chapter 19: Standardization & Regulatory Initiatives ..................... 171
19.1 ASF (Apache Software Foundation) ......................... 171
19.1.1 Management of Hadoop ................................. 171
19.1.2 Big Data Projects Beyond Hadoop ................................... 171
19.2 CSA (Cloud Security Alliance) ................................... 175
19.2.1 BDWG (Big Data Working Group) .................................... 175
19.3 CSCC (Cloud Standards Customer Council) .............................. 176
19.3.1 Big Data Working Group .................................. 176
19.4 DMG (Data Mining Group) ...................................... 177
19.4.1 PMML (Predictive Model Markup Language) Working Group ........................ 177
19.4.2 PFA (Portable Format for Analytics) Working Group ...................... 177
19.5 IEEE (Institute of Electrical and Electronics Engineers) ............................ 177
19.5.1 Big Data Initiative ............................................ 178
19.6 INCITS (InterNational Committee for Information Technology Standards) ............. 179
19.6.1 Big Data Technical Committee ........................................ 179
19.7 ISO (International Organization for Standardization) .............................. 180
19.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange .............................. 180
19.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms ................. 181
19.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques ................................... 181
19.7.4 ISO/IEC JTC 1/WG 9: Big Data .......................... 181
19.7.5 Collaborations with Other ISO Work Groups .................................. 182
19.8 ITU (International Telecommunication Union) ........................ 183
19.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities ......... 183
19.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks .......... 184
19.8.3 Other Relevant Work ....................................... 184
19.9 Linux Foundation ...................................... 185
19.9.1 ODPi (Open Ecosystem of Big Data) ................................ 185
19.10 NIST (National Institute of Standards and Technology) ....................... 185
19.10.1 NBD-PWG (NIST Big Data Public Working Group) ....................... 185
19.11 OASIS (Organization for the Advancement of Structured Information Standards) ............. 186
19.11.1 Technical Committees ................................. 186
19.12 ODaF (Open Data Foundation) ............................ 187
19.12.1 Big Data Accessibility .................................. 187
19.13 ODCA (Open Data Center Alliance) ...................................... 187
19.13.1 Work on Big Data ........................................ 188
19.14 OGC (Open Geospatial Consortium) .................................... 188
19.14.1 Big Data DWG (Domain Working Group) ........................ 188
19.15 TM Forum ............................................ 188
19.15.1 Big Data Analytics Strategic Program .............................. 189
19.16 TPC (Transaction Processing Performance Council) ............................ 189
19.16.1 TPC-BDWG (TPC Big Data Working Group) ..................... 189
19.17 W3C (World Wide Web Consortium) ................................... 189
19.17.1 Big Data Community Group ............................. 190
19.17.2 Open Government Community Group ............................ 190

20 Chapter 20: Market Sizing & Forecasts ........................... 191
20.1 Global Outlook for the Big Data Market .................................. 191
20.2 Submarket Segmentation ........................................ 192
20.2.1 Storage and Compute Infrastructure .............................. 193
20.2.2 Networking Infrastructure ............................... 193
20.2.3 Hadoop & Infrastructure Software .................................. 194
20.2.4 SQL ................................... 194
20.2.5 NoSQL .............................................. 195
20.2.6 Analytic Platforms & Applications ................................... 195
20.2.7 Cloud Platforms ............................... 196
20.2.8 Professional Services ....................................... 196
20.3 Vertical Market Segmentation ................................. 197
20.3.1 Automotive, Aerospace & Transportation ...................... 198
20.3.2 Banking & Securities ........................................ 198
20.3.3 Defense & Intelligence .................................... 199
20.3.4 Education ......................................... 199
20.3.5 Healthcare & Pharmaceutical .......................... 200
20.3.6 Smart Cities & Intelligent Buildings ................................. 200
20.3.7 Insurance ......................................... 201
20.3.8 Manufacturing & Natural Resources ............................... 201
20.3.9 Media & Entertainment................................... 202
20.3.10 Public Safety & Homeland Security ................................. 202
20.3.11 Public Services ................................. 203
20.3.12 Retail, Wholesale & Hospitality ....................................... 203
20.3.13 Telecommunications ....................................... 204
20.3.14 Utilities & Energy ............................................. 204
20.3.15 Other Sectors ................................... 205
20.4 Regional Outlook ...................................... 206
20.5 Asia Pacific ................................................ 207
20.5.1 Country Level Segmentation ........................... 207
20.5.2 Australia .......................................... 208
20.5.3 China ................................................ 208
20.5.4 India ................................................. 209
20.5.5 Indonesia ......................................... 209
20.5.6 Japan................................................ 210
20.5.7 Malaysia .......................................... 210
20.5.8 Pakistan ........................................... 211
20.5.9 Philippines ....................................... 211
20.5.10 Singapore ......................................... 212
20.5.11 South Korea ..................................... 212
20.5.12 Taiwan ............................................. 213
20.5.13 Thailand ........................................... 213
20.5.14 Rest of Asia Pacific ........................................... 214
20.6 Eastern Europe ......................................... 215
20.6.1 Country Level Segmentation ........................... 215
20.6.2 Czech Republic ................................. 216
20.6.3 Poland .............................................. 216
20.6.4 Russia ............................................... 217
20.6.5 Rest of Eastern Europe .................................... 217
20.7 Latin & Central America ........................................... 218
20.7.1 Country Level Segmentation ........................... 218
20.7.2 Argentina ......................................... 219
20.7.3 Brazil ................................................ 219
20.7.4 Mexico ............................................. 220
20.7.5 Rest of Latin & Central America ...................................... 220
20.8 Middle East & Africa ................................. 221
20.8.1 Country Level Segmentation ........................... 221
20.8.2 Israel ................................................ 222
20.8.3 Qatar ................................................ 222
20.8.4 Saudi Arabia ..................................... 223
20.8.5 South Africa ..................................... 223
20.8.6 UAE .................................................. 224
20.8.7 Rest of the Middle East & Africa...................................... 224
20.9 North America .......................................... 225
20.9.1 Country Level Segmentation ........................... 225
20.9.2 Canada ............................................. 226
20.9.3 USA .................................................. 226
20.10 Western Europe ................................... 227
20.10.1 Country Level Segmentation ........................... 227
20.10.2 Denmark .......................................... 228
20.10.3 Finland ............................................. 228
20.10.4 France .............................................. 229
20.10.5 Germany .......................................... 229
20.10.6 Italy .................................................. 230
20.10.7 Netherlands ..................................... 230
20.10.8 Norway ............................................ 231
20.10.9 Spain ................................................ 231
20.10.10 Sweden ........................................ 232
20.10.11 UK ................................................ 232
20.10.12 Rest of Western Europe .............................. 233

21 Chapter 21: Vendor Landscape ...................... 234
21.1 1010data .................................................. 234
21.2 Absolutdata .............................................. 235
21.3 Accenture ................................................. 236
21.4 Actian Corporation/HCL Technologies ..................................... 237
21.5 Adaptive Insights ...................................... 239
21.6 Adobe Systems ......................................... 240
21.7 Advizor Solutions ...................................... 242
21.8 AeroSpike ................................................. 243
21.9 AFS Technologies ..................................... 244
21.10 Alation .................................................. 245
21.11 Algorithmia .......................................... 246
21.12 Alluxio .................................................. 247
21.13 ALTEN ................................... 248
21.14 Alteryx .................................................. 249
21.15 AMD (Advanced Micro Devices) .......................... 250
21.16 Anaconda ............................................. 251
21.17 Apixio ................................... 252
21.18 Arcadia Data......................................... 253
21.19 ARM ..................................... 254
21.20 AtScale ................................................. 255
21.21 Attivio .................................................. 256
21.22 Attunity ................................................ 257
21.23 Automated Insights .............................. 258
21.24 AVORA.................................................. 259
21.25 AWS (Amazon Web Services) ............................... 260
21.26 Axiomatics ............................................ 262
21.27 Ayasdi ................................... 263
21.28 BackOffice Associates .......................................... 264
21.29 Basho Technologies ............................................. 265
21.30 BCG (Boston Consulting Group) ........................... 266
21.31 Bedrock Data ....................................... 267
21.32 BetterWorks ......................................... 268
21.33 Big Panda ............................................. 269
21.34 BigML ................................... 270
21.35 Bitam .................................... 271
21.36 Blue Medora ........................................ 272
21.37 BlueData Software ............................... 273
21.38 BlueTalon ............................................. 274
21.39 BMC Software ...................................... 275
21.40 BOARD International ............................................ 276
21.41 Booz Allen Hamilton ............................................ 277
21.42 Boxever ................................................ 278
21.43 CACI International ................................ 279
21.44 Cambridge Semantics .......................................... 280
21.45 Capgemini ............................................ 281
21.46 Cazena .................................................. 282
21.47 Centrifuge Systems .............................. 283
21.48 CenturyLink .......................................... 284
21.49 Chartio ................................................. 285
21.50 Cisco Systems ....................................... 286
21.51 Civis Analytics....................................... 287
21.52 ClearStory Data .................................... 288
21.53 Cloudability .......................................... 289
21.54 Cloudera ............................................... 290
21.55 Cloudian ............................................... 291
21.56 Clustrix ................................................. 292
21.57 CognitiveScale ...................................... 293
21.58 Collibra ................................................. 294
21.59 Concurrent Technology/Vecima Networks .......................... 295
21.60 Confluent ............................................. 296
21.61 Contexti ................................................ 297
21.62 Couchbase ............................................ 298
21.63 Crate.io ................................................ 299
21.64 Cray ...................................... 300
21.65 Databricks ............................................ 301
21.66 Dataiku ................................................. 302
21.67 Datalytyx .............................................. 303
21.68 Datameer ............................................. 304
21.69 DataRobot ............................................ 305
21.70 DataStax ............................................... 306
21.71 Datawatch Corporation ....................................... 307
21.72 DDN (DataDirect Networks) ................................. 308
21.73 Decisyon ............................................... 309
21.74 Dell Technologies ................................. 310
21.75 Deloitte ................................................ 311
21.76 Demandbase ........................................ 312
21.77 Denodo Technologies .......................................... 313
21.78 Dianomic Systems ................................ 314
21.79 Digital Reasoning Systems ................................... 315
21.80 Dimensional Insight ............................................. 316
21.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group ............................ 317
21.82 Domino Data Lab ................................. 318
21.83 Domo ................................... 319
21.84 Dremio ................................................. 320
21.85 DriveScale ............................................ 321
21.86 Druva.................................... 322
21.87 Dundas Data Visualization ................................... 323
21.88 DXC Technology ................................... 324
21.89 Elastic ................................... 325
21.90 Engineering Group (Engineering Ingegneria Informatica) ................... 326
21.91 EnterpriseDB Corporation .................................... 327
21.92 eQ Technologic .................................... 328
21.93 Ericsson ................................................ 329
21.94 Erwin .................................... 330
21.95 EVŌ (Big Cloud Analytics)..................................... 331
21.96 EXASOL ................................................. 332
21.97 EXL (ExlService Holdings) ..................................... 333
21.98 Facebook .............................................. 334
21.99 FICO (Fair Isaac Corporation) ............................... 335
21.100 Figure Eight .......................................... 336
21.101 FogHorn Systems ................................. 337
21.102 Fractal Analytics ................................... 338
21.103 Franz .................................... 339
21.104 Fujitsu .................................................. 340
21.105 Fuzzy Logix ........................................... 342
21.106 Gainsight .............................................. 343
21.107 GE (General Electric) ............................................ 344
21.108 Glassbeam ............................................ 345
21.109 GoodData Corporation ........................................ 346
21.110 Google/Alphabet.................................. 347
21.111 Grakn Labs ........................................... 349
21.112 Greenwave Systems ............................................. 350
21.113 GridGain Systems ................................. 351
21.114 H2O.ai .................................................. 352
21.115 HarperDB ............................................. 353
21.116 Hedvig .................................................. 354
21.117 Hitachi Vantara .................................... 355
21.118 Hortonworks ........................................ 356
21.119 HPE (Hewlett Packard Enterprise) ....................................... 357
21.120 Huawei ................................................. 359
21.121 HVR ...................................... 360
21.122 HyperScience ....................................... 361
21.123 HyTrust ................................................. 362
21.124 IBM Corporation .................................. 364
21.125 iDashboards ......................................... 366
21.126 IDERA ................................... 367
21.127 Ignite Technologies .............................. 368
21.128 Imanis Data .......................................... 370
21.129 Impetus Technologies .......................................... 371
21.130 Incorta .................................................. 372
21.131 InetSoft Technology Corporation......................... 373
21.132 InfluxData ............................................. 374
21.133 Infogix .................................................. 375
21.134 Infor/Birst............................................. 376
21.135 Informatica .......................................... 378
21.136 Information Builders ............................................ 379
21.137 Infosys .................................................. 380
21.138 Infoworks ............................................. 381
21.139 Insightsoftware.com ............................................ 382
21.140 InsightSquared ..................................... 383
21.141 Intel Corporation ................................. 384
21.142 Interana ............................................... 385
21.143 InterSystems Corporation .................................... 386
21.144 Jedox .................................... 387
21.145 Jethro ................................... 388
21.146 Jinfonet Software ................................. 389
21.147 Juniper Networks ................................. 390
21.148 KALEAO ................................................ 391
21.149 Keen IO................................................. 392
21.150 Keyrus .................................................. 393
21.151 Kinetica ................................................ 394
21.152 KNIME .................................................. 395
21.153 Kognitio ................................................ 396
21.154 Kyvos Insights ....................................... 397
21.155 LeanXcale ............................................. 398
21.156 Lexalytics .............................................. 399
21.157 Lexmark International .......................................... 401
21.158 Lightbend ............................................. 402
21.159 Logi Analytics ....................................... 403
21.160 Logical Clocks ....................................... 404
21.161 Longview Solutions/Tidemark ............................. 405
21.162 Looker Data Sciences ........................................... 407
21.163 LucidWorks .......................................... 408
21.164 Luminoso Technologies ....................................... 409
21.165 Maana .................................................. 410
21.166 Manthan Software Services ................................. 411
21.167 MapD Technologies ............................................. 412
21.168 MapR Technologies .............................. 413
21.169 MariaDB Corporation ........................................... 414
21.170 MarkLogic Corporation ........................................ 415
21.171 Mathworks ........................................... 416
21.172 Melissa ................................................. 417
21.173 MemSQL .............................................. 418
21.174 Metric Insights ..................................... 419
21.175 Microsoft Corporation ......................................... 420
21.176 MicroStrategy ...................................... 422
21.177 Minitab................................................. 423
21.178 MongoDB ............................................. 424
21.179 Mu Sigma ............................................. 425
21.180 NEC Corporation .................................. 426
21.181 Neo4j .................................... 427
21.182 NetApp ................................................. 428
21.183 Nimbix .................................................. 429
21.184 Nokia .................................... 430
21.185 NTT Data Corporation .......................................... 431
21.186 Numerify .............................................. 432
21.187 NuoDB .................................................. 433
21.188 NVIDIA Corporation ............................................. 434
21.189 Objectivity ............................................ 435
21.190 Oblong Industries ................................. 436
21.191 OpenText Corporation ......................................... 437
21.192 Opera Solutions ................................... 439
21.193 Optimal Plus ......................................... 440
21.194 Oracle Corporation .............................. 441
21.195 Palantir Technologies ........................................... 444
21.196 Panasonic Corporation/Arimo ............................. 446
21.197 Panorama Software ............................................. 447
21.198 Paxata .................................................. 448
21.199 Pepperdata .......................................... 449
21.200 Phocas Software .................................. 450
21.201 Pivotal Software ................................... 451
21.202 Prognoz ................................................ 453
21.203 Progress Software Corporation ........................... 454
21.204 Provalis Research ................................. 455
21.205 Pure Storage ........................................ 456
21.206 PwC (PricewaterhouseCoopers International) .................... 457
21.207 Pyramid Analytics ................................ 458
21.208 Qlik ....................................... 459
21.209 Qrama/Tengu ....................................... 460
21.210 Quantum Corporation ......................................... 461
21.211 Qubole ................................................. 462
21.212 Rackspace ............................................ 463
21.213 Radius Intelligence ............................... 464
21.214 RapidMiner .......................................... 465
21.215 Recorded Future .................................. 466
21.216 Red Hat ................................................ 467
21.217 Redis Labs ............................................ 468
21.218 RedPoint Global ................................... 469
21.219 Reltio .................................... 470
21.220 RStudio ................................................. 471
21.221 Rubrik/Datos IO ................................... 472
21.222 Ryft ....................................... 473
21.223 Sailthru ................................................. 474
21.224 Salesforce.com ..................................... 475
21.225 Salient Management Company ........................... 476
21.226 Samsung Group .................................... 477
21.227 SAP ....................................... 478
21.228 SAS Institute ......................................... 479
21.229 ScaleOut Software ............................... 480
21.230 Seagate Technology ............................................. 481
21.231 Sinequa ................................................ 482
21.232 SiSense ................................................. 483
21.233 Sizmek .................................................. 484
21.234 SnapLogic ............................................. 485
21.235 Snowflake Computing .......................................... 486
21.236 Software AG ......................................... 487
21.237 Splice Machine ..................................... 488
21.238 Splunk .................................................. 489
21.239 Strategy Companion Corporation ........................................ 491
21.240 Stratio .................................................. 492
21.241 Streamlio .............................................. 493
21.242 StreamSets ........................................... 494
21.243 Striim .................................... 495
21.244 Sumo Logic ........................................... 496
21.245 Supermicro (Super Micro Computer) .................................. 497
21.246 Syncsort ............................................... 498
21.247 SynerScope .......................................... 500
21.248 SYNTASA .............................................. 501
21.249 Tableau Software ................................. 502
21.250 Talend .................................................. 503
21.251 Tamr ..................................... 504
21.252 TARGIT ................................................. 505
21.253 TCS (Tata Consultancy Services) .......................... 506
21.254 Teradata Corporation .......................................... 507
21.255 Thales/Guavus ..................................... 509
21.256 ThoughtSpot ........................................ 510
21.257 TIBCO Software .................................... 511
21.258 Toshiba Corporation ............................................ 513
21.259 Transwarp ............................................ 514
21.260 Trifacta ................................................. 515
21.261 Unifi Software ...................................... 516
21.262 Unravel Data ........................................ 517
21.263 VANTIQ ................................................ 518
21.264 VMware ............................................... 519
21.265 VoltDB .................................................. 520
21.266 WANdisco ............................................ 521
21.267 Waterline Data ..................................... 522
21.268 Western Digital Corporation ................................ 523
21.269 WhereScape ......................................... 524
21.270 WiPro ................................... 525
21.271 Wolfram Research ............................... 526
21.272 Workday ............................................... 528
21.273 Xplenty ................................................. 530
21.274 Yellowfin BI .......................................... 531
21.275 Yseop.................................... 532
21.276 Zendesk ................................................ 533
21.277 Zoomdata ............................................. 534
21.278 Zucchetti .............................................. 535

22 Chapter 22: Conclusion & Strategic Recommendations .................. 536
22.1 Why is the Market Poised to Grow? ........................................ 536
22.2 Moving Towards Consolidation: Review of M&A Activity in the Vendor Arena ...................... 536
22.3 Maturation of AI (Artificial Intelligence): From Machine Learning to Deep Learning ............. 538
22.4 Blockchain: Impact on Big Data ................................ 539
22.5 The Emergence of Edge Analytics ............................ 539
22.6 Beyond Data Capture & Analytics ............................ 540
22.7 Transforming IT from a Cost Center to a Profit Center ............................ 540
22.8 Can Privacy Implications Hinder Success? ................................ 540
22.9 Maximizing Innovation with Careful Regulation ...................... 541
22.10 Battling Organizational & Data Silos .................................... 542
22.11 Moving Big Data to the Cloud .............................. 542
22.12 Software vs. Hardware Investments .................................... 544
22.13 Vendor Share: Who Leads the Market? ............................... 545
22.14 Big Data Driving Wider IT Industry Investments .................................. 546
22.15 Assessing the Impact of the IoT ........................... 547
22.16 Recommendations ............................... 548
22.16.1 Big Data Hardware, Software & Professional Services Providers .................... 548
22.16.2 Enterprises ....................................... 549


List of Figures

Figure 1: Hadoop Architecture ........................................ 42
Figure 2: Reactive vs. Proactive Analytics ....................................... 53
Figure 3: Big Data Future Roadmap: 2018 – 2030 ........................................ 165
Figure 4: Big Data Value Chain ...................................... 167
Figure 5: Key Aspects of Big Data Standardization ....................................... 178
Figure 6: Global Big Data Revenue: 2018 – 2030 ($ Million) ......................... 191
Figure 7: Global Big Data Revenue by Submarket: 2018 – 2030 ($ Million).................................. 192
Figure 8: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2018 – 2030 ($ Million) ........... 193
Figure 9: Global Big Data Networking Infrastructure Submarket Revenue: 2018 – 2030 ($ Million) ........... 193
Figure 10: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2018 – 2030 ($ Million) ............. 194
Figure 11: Global Big Data SQL Submarket Revenue: 2018 – 2030 ($ Million) ............................. 194
Figure 12: Global Big Data NoSQL Submarket Revenue: 2018 – 2030 ($ Million) ......................... 195
Figure 13: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2018 – 2030 ($ Million) .............. 195
Figure 14: Global Big Data Cloud Platforms Submarket Revenue: 2018 – 2030 ($ Million) .......................... 196
Figure 15: Global Big Data Professional Services Submarket Revenue: 2018 – 2030 ($ Million) .................. 196
Figure 16: Global Big Data Revenue by Vertical Market: 2018 – 2030 ($ Million) ........................ 197
Figure 17: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2018 – 2030 ($ Million) .............. 198
Figure 18: Global Big Data Revenue in the Banking & Securities Sector: 2018 – 2030 ($ Million) ................ 198
Figure 19: Global Big Data Revenue in the Defense & Intelligence Sector: 2018 – 2030 ($ Million) ............ 199
Figure 20: Global Big Data Revenue in the Education Sector: 2018 – 2030 ($ Million) ................ 199
Figure 21: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2018 – 2030 ($ Million) .................. 200
Figure 22: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2018 – 2030 ($ Million) ......... 200
Figure 23: Global Big Data Revenue in the Insurance Sector: 2018 – 2030 ($ Million) ................. 201
Figure 24: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2018 – 2030 ($ Million) ....... 201
Figure 25: Global Big Data Revenue in the Media & Entertainment Sector: 2018 – 2030 ($ Million) .......... 202
Figure 26: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2018 – 2030 ($ Million) ......... 202
Figure 27: Global Big Data Revenue in the Public Services Sector: 2018 – 2030 ($ Million) ......................... 203
Figure 28: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2018 – 2030 ($ Million) ............... 203
Figure 29: Global Big Data Revenue in the Telecommunications Sector: 2018 – 2030 ($ Million) ............... 204
Figure 30: Global Big Data Revenue in the Utilities & Energy Sector: 2018 – 2030 ($ Million) ..................... 204
Figure 31: Global Big Data Revenue in Other Vertical Sectors: 2018 – 2030 ($ Million) ............... 205
Figure 32: Big Data Revenue by Region: 2018 – 2030 ($ Million) ................................. 206
Figure 33: Asia Pacific Big Data Revenue: 2018 – 2030 ($ Million) ............................... 207
Figure 34: Asia Pacific Big Data Revenue by Country: 2018 – 2030 ($ Million) ............................. 207
Figure 35: Australia Big Data Revenue: 2018 – 2030 ($ Million) ................................... 208
Figure 36: China Big Data Revenue: 2018 – 2030 ($ Million) ........................................ 208
Figure 37: India Big Data Revenue: 2018 – 2030 ($ Million) ......................... 209
Figure 38: Indonesia Big Data Revenue: 2018 – 2030 ($ Million) ................................. 209
Figure 39: Japan Big Data Revenue: 2018 – 2030 ($ Million) ........................................ 210
Figure 40: Malaysia Big Data Revenue: 2018 – 2030 ($ Million) ................................... 210
Figure 41: Pakistan Big Data Revenue: 2018 – 2030 ($ Million).................................... 211
Figure 42: Philippines Big Data Revenue: 2018 – 2030 ($ Million)................................ 211
Figure 43: Singapore Big Data Revenue: 2018 – 2030 ($ Million) ................................. 212
Figure 44: South Korea Big Data Revenue: 2018 – 2030 ($ Million) ............................. 212
Figure 45: Taiwan Big Data Revenue: 2018 – 2030 ($ Million) ..................................... 213
Figure 46: Thailand Big Data Revenue: 2018 – 2030 ($ Million) ................................... 213
Figure 47: Big Data Revenue in the Rest of Asia Pacific: 2018 – 2030 ($ Million) ......................... 214
Figure 48: Eastern Europe Big Data Revenue: 2018 – 2030 ($ Million) ........................ 215
Figure 49: Eastern Europe Big Data Revenue by Country: 2018 – 2030 ($ Million) ...................... 215
Figure 50: Czech Republic Big Data Revenue: 2018 – 2030 ($ Million) ......................... 216
Figure 51: Poland Big Data Revenue: 2018 – 2030 ($ Million) ...................................... 216
Figure 52: Russia Big Data Revenue: 2018 – 2030 ($ Million) ....................................... 217
Figure 53: Big Data Revenue in the Rest of Eastern Europe: 2018 – 2030 ($ Million) .................. 217
Figure 54: Latin & Central America Big Data Revenue: 2018 – 2030 ($ Million) ........................... 218
Figure 55: Latin & Central America Big Data Revenue by Country: 2018 – 2030 ($ Million) ........................ 218
Figure 56: Argentina Big Data Revenue: 2018 – 2030 ($ Million) ................................. 219
Figure 57: Brazil Big Data Revenue: 2018 – 2030 ($ Million) ........................................ 219
Figure 58: Mexico Big Data Revenue: 2018 – 2030 ($ Million) ..................................... 220
Figure 59: Big Data Revenue in the Rest of Latin & Central America: 2018 – 2030 ($ Million) ..................... 220
Figure 60: Middle East & Africa Big Data Revenue: 2018 – 2030 ($ Million) ................................ 221
Figure 61: Middle East & Africa Big Data Revenue by Country: 2018 – 2030 ($ Million) .............................. 221
Figure 62: Israel Big Data Revenue: 2018 – 2030 ($ Million) ........................................ 222
Figure 63: Qatar Big Data Revenue: 2018 – 2030 ($ Million) ........................................ 222
Figure 64: Saudi Arabia Big Data Revenue: 2018 – 2030 ($ Million) ............................. 223
Figure 65: South Africa Big Data Revenue: 2018 – 2030 ($ Million) ............................. 223
Figure 66: UAE Big Data Revenue: 2018 – 2030 ($ Million) .......................... 224
Figure 67: Big Data Revenue in the Rest of the Middle East & Africa: 2018 – 2030 ($ Million) .................... 224
Figure 68: North America Big Data Revenue: 2018 – 2030 ($ Million) ......................... 225
Figure 69: North America Big Data Revenue by Country: 2018 – 2030 ($ Million) ....................... 225
Figure 70: Canada Big Data Revenue: 2018 – 2030 ($ Million) ..................................... 226
Figure 71: USA Big Data Revenue: 2018 – 2030 ($ Million) .......................... 226
Figure 72: Western Europe Big Data Revenue: 2018 – 2030 ($ Million) ....................... 227
Figure 73: Western Europe Big Data Revenue by Country: 2018 – 2030 ($ Million) .................... 227
Figure 74: Denmark Big Data Revenue: 2018 – 2030 ($ Million) .................................. 228
Figure 75: Finland Big Data Revenue: 2018 – 2030 ($ Million) ..................................... 228
Figure 76: France Big Data Revenue: 2018 – 2030 ($ Million) ...................................... 229
Figure 77: Germany Big Data Revenue: 2018 – 2030 ($ Million) .................................. 229
Figure 78: Italy Big Data Revenue: 2018 – 2030 ($ Million) .......................... 230
Figure 79: Netherlands Big Data Revenue: 2018 – 2030 ($ Million) ............................. 230
Figure 80: Norway Big Data Revenue: 2018 – 2030 ($ Million) .................................... 231
Figure 81: Spain Big Data Revenue: 2018 – 2030 ($ Million) ........................................ 231
Figure 82: Sweden Big Data Revenue: 2018 – 2030 ($ Million) .................................... 232
Figure 83: UK Big Data Revenue: 2018 – 2030 ($ Million) ............................ 232
Figure 84: Big Data Revenue in the Rest of Western Europe: 2018 – 2030 ($ Million) ................. 233
Figure 85: Global Big Data Workload Distribution by Environment: 2018 – 2030 (%) .................. 543
Figure 86: Global Big Data Revenue by Hardware, Software & Professional Services: 2018 – 2030 ($ Million) ........... 544
Figure 87: Big Data Vendor Market Share: 2017 (%) .................................... 545
Figure 88: Global IT Expenditure Driven by Big Data Investments: 2018 – 2030 ($ Million) ........................ 546
Figure 89: Global IoT Connections by Access Technology: 2018 – 2030 (Millions) ....................... 547
 

 

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プレスリリース

[プレスリリース原文]

Big Data a $65 Billion market in 2018, says SNS Telecom & IT


2018/06/12

SNS Telecom & IT's latest research report indicates that global spending on Big Data technology is expected to surpass $65 Billion by the end of 2018.

Originally used as a term to describe datasets whose size is beyond the ability of traditional databases, the scope of Big Data 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 Telecom & IT estimates that Big Data investments will account for over $65 Billion in 2018 alone. These investments are further expected to grow at a CAGR of approximately 14% over the next three years.

The “Big Data Market: 2018 – 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 profiles, market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2018 till 2030. The forecasts are segmented for 8 horizontal submarkets, 14 vertical markets, 6 regions and 35 countries.
 

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