保険産業のビッグデータ 2018-2030年:ビジネスチャンス、課題、戦略、市場予測 / SNS Telecom & IT

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保険産業のビッグデータ 2018-2030年:ビジネスチャンス、課題、戦略、市場予測

Big Data in the Insurance Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts

 

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SNS Telecom & IT
SNSテレコム&IT
2018年8月US$2,500
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500 98

サマリー

英国とドバイに拠点をおく調査会社シグナルズアンドシステムズテレコム/SNSリサーチ (Signals and Systems Telecom/SNS Research)の調査レポートの調査レポート「保険産業のビッグデータ 2018-2030年:ビジネスチャンス、課題、戦略、市場予測」は、保険業界でのビッグデータの主要な市場促進要因や課題、投資の可能性、用途分野、利用事例、今後のロードマップ、バリューチェーン、ケーススタディ、ベンダの概要と戦略などを詳細に分析している。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つの水平サブ市場、8つの用途分野、9つの利用ケース、6つの地域と35ケ国に区分して予測している。

目次(抜粋)

  • ビッグデータの市場概観
  • ビッグデータの解析
  • ビッグデータの保険業界でのビジネスケースと用途
  • 保険産業のケーススタディ
  • 今後のロードマップとバリューチェーン
  • 法規制・標準化のイニシアチブ
  • 市場規模と予測
  • ベンダの概要

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 and historical data from sources such as connected devices, web, social media, sensors, log files and transactional applications, Big Data is rapidly gaining traction from a diverse range of vertical sectors. The insurance industry is no exception to this trend, where Big Data has found a host of applications ranging from targeted marketing and personalized products to usage-based insurance, efficient claims processing, proactive fraud detection and beyond.

SNS Telecom & IT estimates that Big Data investments in the insurance industry will account for more than $2.4 Billion in 2018 alone. Led by a plethora of business opportunities for insurers, reinsurers, insurance brokers, InsurTech specialists and other stakeholders, these investments are further expected to grow at a CAGR of approximately 14% over the next three years.

The “Big Data in the Insurance Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of Big Data in the insurance industry including key market drivers, challenges, investment potential, application areas, use cases, future roadmap, value chain, case studies, vendor profiles and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services investments from 2018 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 8 application areas, 9 use cases, 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 more than $2.4 Billion from hardware, software and professional services revenues in the insurance industry. These investments are further expected to grow at a CAGR of approximately 14% over the next three years, eventually accounting for nearly $3.6 Billion by the end of 2021.

•Through the use of Big Data technologies, insurers and other stakeholders are beginning to exploit their data assets in a number of innovative ways ranging from targeted marketing and personalized products to usage-based insurance, efficient claims processing, proactive fraud detection and beyond.

•The growing adoption of Big Data technologies has brought about an array of benefits for insurers and other stakeholders. Based on feedback from insurers worldwide, these include but are not limited to an increase in access to insurance services by more than 30%, a reduction in policy administration workload by up to 50%, prediction of large loss claims with an accuracy of nearly 80%, cost savings in claims processing and management by 40-70%, accelerated processing of non-emergency insurance claims by a staggering 90%; and improvements in fraud detection rates by as much as 60%.

•In addition, Big Data technologies are playing a pivotal role in facilitating the adoption of on-demand insurance models – particularly in auto, life and health insurance, as well as the insurance of new and underinsured risks such as cyber crime.

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
•Business case, application areas and use cases in the insurance industry
•20 case studies of Big Data investments by insurers, reinsurers, InsurTech specialists and other stakeholders in the insurance industry
•Future roadmap and value chain
•Profiles and strategies of over 270 leading and emerging Big Data ecosystem players
•Strategic recommendations for Big Data vendors and insurance industry stakeholders
•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

•Application Areas

◦Auto Insurance
◦Property & Casualty Insurance
◦Life Insurance
◦Health Insurance
◦Multi-Line Insurance
◦Other Forms of Insurance
◦Reinsurance
◦Insurance Broking

•Use Cases

◦Personalized & Targeted Marketing
◦Customer Service & Experience
◦Product Innovation & Development
◦Risk Awareness & Control
◦Policy Administration, Pricing & Underwriting
◦Claims Processing & Management
◦Fraud Detection & Prevention
◦Usage & Analytics-Based Insurance
◦Other Use Cases

•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 opportunity in the insurance industry?
•How is the market 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 insurers, reinsurers, InsurTech specialists and other stakeholders investing in Big Data?
•What opportunities exist for Big Data analytics in the insurance industry?
•Which countries, application areas and use cases will see the highest percentage of Big Data investments in the insurance industry?

List of Companies Mentioned:

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

1010data

Absolutdata

Accenture

Actian Corporation

Adaptive Insights

Adobe Systems

Advizor Solutions

Aegon

AeroSpike

Aetna

AFS Technologies

Alation

Algorithmia

Allianz Group

Allstate Corporation

Alluxio

Alphabet

ALTEN

Alteryx

AMD (Advanced Micro Devices)

Anaconda

Apixio

Arcadia Data

Arimo

Arity

ARM

ASF (Apache Software Foundation)

Atidot

AtScale

Attivio

Attunity

Automated Insights

AVORA

AWS (Amazon Web Services)

AXA

Axiomatics

Ayasdi

BackOffice Associates

Basho Technologies

BCG (Boston Consulting Group)

Bedrock Data

BetterWorks

Big Panda

BigML

Birst

Bitam

Blue Medora

BlueData Software

BlueTalon

BMC Software

BOARD International

Booz Allen Hamilton

Boxever

CACI International

Cambridge Semantics

Cape Analytics

Capgemini

Cazena

Centrifuge Systems

CenturyLink

Chartio

China Life Insurance Company

Cigna

Cisco Systems

Civis Analytics

ClearStory Data

Cloudability

Cloudera

Cloudian

Clustrix

CognitiveScale

Collibra

Concirrus

Concurrent Technology

Confluent

Contexti

Couchbase

Crate.io

Cray

CSA (Cloud Security Alliance)

CSCC (Cloud Standards Customer Council)

Dai-ichi Life Holdings

Databricks

Dataiku

Datalytyx

Datameer

DataRobot

DataStax

Datawatch Corporation

Datos IO

DDN (DataDirect Networks)

Decisyon

Dell Technologies

Deloitte

Demandbase

Denodo Technologies

Dianomic Systems

Digital Reasoning Systems

Dimensional Insight

DMG  (Data Mining Group)

Dolphin Enterprise Solutions Corporation

Domino Data Lab

Domo

Dremio

DriveScale

Druva

Dundas Data Visualization

DXC Technology

Elastic

Engineering Group (Engineering Ingegneria Informatica)

EnterpriseDB Corporation

eQ Technologic

ERGO Group

Ericsson

Erwin

EVŌ (Big Cloud Analytics)

EXASOL

EXL (ExlService Holdings)

Facebook

FICO (Fair Isaac Corporation)

Figure Eight

FogHorn Systems

Fractal Analytics

Franz

Fujitsu

Fuzzy Logix

Gainsight

GE (General Electric)

Generali Group

Glassbeam

GNS Healthcare

GoodData Corporation

Google

Grakn Labs

Greenwave Systems

GridGain Systems

Guavus

H2O.ai

Hanse Orga Group

HarperDB

HCL Technologies

Hedvig

Hitachi Vantara

Hortonworks

HPE (Hewlett Packard Enterprise)

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

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

JMDC Corporation

Juniper Networks

KALEAO

Keen IO

Kenko-Nenrei Shogaku Tanki Hoken

Keyrus

Kinetica

KNIME

Kognitio

Kyvos Insights

LeanXcale

Lexalytics

Lexmark International

Lightbend

Linux Foundation

Logi Analytics

Logical Clocks

Longview Solutions

Looker Data Sciences

LucidWorks

Luminoso Technologies

Maana

Manthan Software Services

MapD Technologies

MapR Technologies

MariaDB Corporation

MarkLogic Corporation

Mathworks

MEAG (Munich Ergo Asset Management)

Melissa

MemSQL

Metric Insights

MetroMile

Microsoft Corporation

MicroStrategy

Minitab

MongoDB

Mu Sigma

Munich Re

NEC Corporation

Neo First Life Insurance Company

Neo4j

NetApp

Nimbix

Nokia

Noritsu Koki

NTT Data Corporation

Numerify

NuoDB

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)

OGC (Open Geospatial Consortium)

OpenText Corporation

Opera Solutions

Optimal Plus

Optum

OptumLabs

Oracle Corporation

Oscar Health

Palantir Technologies

Panasonic Corporation

Panorama Software

Paxata

Pepperdata

Phocas Software

Pivotal Software

Prognoz

Progress Software Corporation

Progressive Corporation

Provalis Research

Pure Storage

PwC (PricewaterhouseCoopers International)

Pyramid Analytics

Qlik

Qrama/Tengu

Quantum Corporation

Qubole

Rackspace

Radius Intelligence

RapidMiner

Recorded Future

Red Hat

Redis Labs

RedPoint Global

Reltio

RStudio

Rubrik

Ryft

Sailthru

Salesforce.com

Salient Management Company

Samsung Fire & Marine Insurance

Samsung Group

SAP

SAS Institute

ScaleOut Software

Seagate Technology

Sinequa

SiSense

Sizmek

SnapLogic

Snowflake Computing

Software AG

Splice Machine

Splunk

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)

Teradata Corporation

Thales

ThoughtSpot

TIBCO Software

Tidemark

TM Forum

Toshiba Corporation

TPC (Transaction Processing Performance Council)

Transwarp

Trifacta

U.S. NIST (National Institute of Standards and Technology)

Unifi Software

UnitedHealth Group

Unravel Data

VANTIQ

Vecima Networks

VMware

VoltDB

W3C (World Wide Web Consortium)

WANdisco

Waterline Data

Western Digital Corporation

WhereScape

WiPro

Wolfram Research

Workday

Xplenty

Yellowfin BI

Yseop

Zendesk

Zoomdata

Zucchetti

Zurich Insurance Group



目次

1 Chapter 1: Introduction ................................... 23
1.1 Executive Summary ....................................................... 23
1.2 Topics Covered .............................................................. 25
1.3 Forecast Segmentation ......................................................... 26
1.4 Key Questions Answered ....................................................... 28
1.5 Key Findings ........................................................... 29
1.6 Methodology ......................................................... 30
1.7 Target Audience ............................................................ 31
1.8 Companies & Organizations Mentioned .................................... 32

2 Chapter 2: An Overview of Big Data ......................... 35
2.1 What is Big Data? .......................................................... 35
2.2 Key Approaches to Big Data Processing .................................... 35
2.2.1 Hadoop .............................................................. 36
2.2.2 NoSQL ................................................................ 38
2.2.3 MPAD (Massively Parallel Analytic Databases) ............................ 38
2.2.4 In-Memory Processing ....................................................... 39
2.2.5 Stream Processing Technologies ................................................ 39
2.2.6 Spark .................................................................. 40
2.2.7 Other Databases & Analytic Technologies ................................ 40
2.3 Key Characteristics of Big Data .............................................. 41
2.3.1 Volume ............................................................... 41
2.3.2 Velocity .............................................................. 41
2.3.3 Variety................................................................ 41
2.3.4 Value .................................................................. 42
2.4 Market Growth Drivers ......................................................... 42
2.4.1 Awareness of Benefits ....................................................... 42
2.4.2 Maturation of Big Data Platforms .............................................. 42
2.4.3 Continued Investments by Web Giants, Governments & Enterprises ................ 43
2.4.4 Growth of Data Volume, Velocity & Variety .............................................. 43
2.4.5 Vendor Commitments & Partnerships ............................................... 43
2.4.6 Technology Trends Lowering Entry Barriers .............................................. 44
2.5 Market Barriers ............................................................. 44
2.5.1 Lack of Analytic Specialists ......................................................... 44
2.5.2 Uncertain Big Data Strategies .................................................... 44
2.5.3 Organizational Resistance to Big Data Adoption ....................................... 45
2.5.4 Technical Challenges: Scalability & Maintenance ...................................... 45
2.5.5 Security & Privacy Concerns ...................................................... 45

3 Chapter 3: Big Data Analytics ................................... 46
3.1 What are Big Data Analytics? ............................................. 46
3.2 The Importance of Analytics .............................................. 46
3.3 Reactive vs. Proactive Analytics ................................................. 47
3.4 Customer vs. Operational Analytics ........................................... 47
3.5 Technology & Implementation Approaches .................................. 48
3.5.1 Grid Computing .......................................................... 48
3.5.2 In-Database Processing ...................................................... 48
3.5.3 In-Memory Analytics .......................................................... 49
3.5.4 Machine Learning & Data Mining .............................................. 49
3.5.5 Predictive Analytics ............................................................ 50
3.5.6 NLP (Natural Language Processing) ............................... 50
3.5.7 Text Analytics ............................................................. 51
3.5.8 Visual Analytics .......................................................... 51
3.5.9 Graph Analytics .......................................................... 52
3.5.10 Social Media, IT & Telco Network Analytics ....................... 52

4 Chapter 4: Business Case & Applications in the Insurance Industry .......... 54
4.1 Overview & Investment Potential .............................................. 54
4.2 Industry Specific Market Growth Drivers ........................................... 55
4.3 Industry Specific Market Barriers ............................................... 57
4.4 Key Application Areas ........................................................ 58
4.4.1 Auto Insurance ........................................................... 58
4.4.2 Property & Casualty Insurance .................................................. 59
4.4.3 Life Insurance ............................................................. 60
4.4.4 Health Insurance ........................................................ 60
4.4.5 Multi-Line Insurance .......................................................... 61
4.4.6 Other Forms of Insurance .................................................. 61
4.4.7 Reinsurance ............................................................... 62
4.4.8 Insurance Broking .............................................................. 62
4.5 Use Cases ........................................................... 63
4.5.1 Personalized & Targeted Marketing .................................................. 63
4.5.2 Customer Service & Experience ................................................. 64
4.5.3 Product Innovation & Development .................................................. 65
4.5.4 Risk Awareness & Control .................................................. 66
4.5.5 Policy Administration, Pricing & Underwriting .......................................... 66
4.5.6 Claims Processing & Management............................................. 67
4.5.7 Fraud Detection & Prevention ................................................... 68
4.5.8 Usage & Analytics-Based Insurance ................................................... 69
4.5.9 Other Use Cases ......................................................... 69

5 Chapter 5: Insurance Industry Case Studies ...................... 71
5.1 Insurers .............................................................. 71
5.1.1 Aegon: Driving Customer Engagement & Sales with Big Data ................................... 71
5.1.2 Aetna: Predicting & Improving Health with Big Data ......................................... 74
5.1.3 Allianz Group: Uncovering Insurance Fraud with Big Data ................................ 76
5.1.4 Allstate Corporation & Arity: Making Transportation Safer & Smarter with Big Data ........... 78
5.1.5 AXA: Simplifying Customer Interaction with Big Data........................................ 80
5.1.6 China Life Insurance Company: Elevating Risk Awareness with Big Data .......................... 83
5.1.7 Cigna: Streamlining Health Insurance Claims with Big Data .............................. 85
5.1.8 Dai-ichi Life Holdings: Unlocking & Opening Doors to Life Insurance with Big Data ............ 87
5.1.9 Generali Group: Digitizing the Insurance Value Chain with Big Data ......................... 89
5.1.10 Progressive Corporation: Rewarding Safe Drivers & Improving Traffic Safety with Big Data ............ 92
5.1.11 Samsung Fire & Marine Insurance: Transforming Insurance Underwriting with Big Data ......... 95
5.1.12 UnitedHealth Group: Enhancing Patient Care & Value with Big Data ......................... 97
5.1.13 Zurich Insurance Group: Improving Risk Management with Big Data .......................... 99
5.2 Reinsurers, InsurTech Specialists & Other Stakeholders.................................. 101
5.2.1 Atidot: Empowering Life Insurance with Big Data ................................... 101
5.2.2 Cape Analytics: Delivering Instant Property Intelligence with Big Data ........................... 103
5.2.3 Concirrus: Enabling Smarter Marine & Auto Insurance with Big Data ............................. 105
5.2.4 JMDC Corporation: Optimizing Health Insurance Premiums with Big Data ..................... 107
5.2.5 MetroMile: Revolutionizing Auto Insurance with Big Data .............................. 109
5.2.6 Munich Re: Pioneering Cyber Insurance with Big Data .................................... 111
5.2.7 Oscar Health: Humanizing Health Insurance with Big Data ............................... 114

6 Chapter 6: Future Roadmap & Value Chain .................... 116
6.1 Future Roadmap....................................................... 116
6.1.1 Pre-2020: Investments in Advanced Analytics & AI (Artificial Intelligence) .............. 116
6.1.2 2020 – 2025: Large-Scale Adoption of Usage & Analytics-Based Insurance ............ 117
6.1.3 2025 – 2030: Towards the Digitization of Insurance Services .......................... 118
6.2 The Big Data Value Chain ................................................. 119
6.2.1 Hardware Providers ......................................................... 119
6.2.1.1 Storage & Compute Infrastructure Providers ..................................... 119
6.2.1.2 Networking Infrastructure Providers .......................................... 120
6.2.2 Software Providers .......................................................... 120
6.2.2.1 Hadoop & Infrastructure Software Providers ..................................... 121
6.2.2.2 SQL & NoSQL Providers ....................................................... 121
6.2.2.3 Analytic Platform & Application Software Providers .................. 121
6.2.2.4 Cloud Platform Providers .................................................... 121
6.2.3 Professional Services Providers ............................................... 122
6.2.4 End-to-End Solution Providers ................................................. 122
6.2.5 Insurance Industry ........................................................... 122

7 Chapter 7: Standardization & Regulatory Initiatives ............... 123
7.1 ASF (Apache Software Foundation) ......................................... 123
7.1.1 Management of Hadoop .................................................. 123
7.1.2 Big Data Projects Beyond Hadoop .......................................... 123
7.2 CSA (Cloud Security Alliance) ................................................... 127
7.2.1 BDWG (Big Data Working Group) ........................................... 127
7.3 CSCC (Cloud Standards Customer Council) ................................. 128
7.3.1 Big Data Working Group .................................................. 128
7.4 DMG (Data Mining Group) .............................................. 129
7.4.1 PMML (Predictive Model Markup Language) Working Group ......................... 129
7.4.2 PFA (Portable Format for Analytics) Working Group .................................... 129
7.5 IEEE (Institute of Electrical and Electronics Engineers) ................................... 129
7.5.1 Big Data Initiative ............................................................. 130
7.6 INCITS (InterNational Committee for Information Technology Standards) ......... 131
7.6.1 Big Data Technical Committee ................................................. 131
7.7 ISO (International Organization for Standardization) .............................. 132
7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange ............................... 132
7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms .................... 133
7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques ............................................ 133
7.7.4 ISO/IEC JTC 1/WG 9: Big Data .................................................. 133
7.7.5 Collaborations with Other ISO Work Groups ........................................... 134
7.8 ITU (International Telecommunication Union) ........................................ 135
7.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities.......... 135
7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks ......................... 136
7.8.3 Other Relevant Work ....................................................... 136
7.9 Linux Foundation ...................................................... 137
7.9.1 ODPi (Open Ecosystem of Big Data) ................................................. 137
7.10 NIST (National Institute of Standards and Technology) ................................... 137
7.10.1 NBD-PWG (NIST Big Data Public Working Group) .................................... 137
7.11 OASIS (Organization for the Advancement of Structured Information Standards) ........... 138
7.11.1 Technical Committees...................................................... 138
7.12 ODaF (Open Data Foundation) ................................................. 139
7.12.1 Big Data Accessibility ....................................................... 139
7.13 ODCA (Open Data Center Alliance) .......................................... 139
7.13.1 Work on Big Data ............................................................. 140
7.14 OGC (Open Geospatial Consortium) ........................................ 140
7.14.1 Big Data DWG (Domain Working Group) ......................................... 140
7.15 TM Forum ......................................................... 140
7.15.1 Big Data Analytics Strategic Program ............................................... 141
7.16 TPC (Transaction Processing Performance Council) ................................. 141
7.16.1 TPC-BDWG (TPC Big Data Working Group) ......................................... 141
7.17 W3C (World Wide Web Consortium) ............................................. 141
7.17.1 Big Data Community Group ..................................................... 142
7.17.2 Open Government Community Group......................................... 142

8 Chapter 8: Market Sizing & Forecasts ............................. 143
8.1 Global Outlook for the Big Data in the Insurance Industry ............................ 143
8.2 Hardware, Software & Professional Services Segmentation .......................... 144
8.3 Horizontal Submarket Segmentation .......................................... 145
8.4 Hardware Submarkets ......................................................... 146
8.4.1 Storage and Compute Infrastructure ............................................... 146
8.4.2 Networking Infrastructure ....................................................... 146
8.5 Software Submarkets .................................................. 147
8.5.1 Hadoop & Infrastructure Software .................................................. 147
8.5.2 SQL ................................................................... 147
8.5.3 NoSQL .............................................................. 148
8.5.4 Analytic Platforms & Applications.................................................... 148
8.5.5 Cloud Platforms ....................................................... 149
8.6 Professional Services Submarket................................................. 149
8.6.1 Professional Services ....................................................... 149
8.7 Application Area Segmentation .................................................. 150
8.7.1 Auto Insurance ......................................................... 151
8.7.2 Property & Casualty Insurance ................................................ 151
8.7.3 Life Insurance ........................................................... 152
8.7.4 Health Insurance .............................................................. 152
8.7.5 Multi-Line Insurance ........................................................ 153
8.7.6 Other Forms of Insurance ........................................................ 153
8.7.7 Reinsurance ............................................................. 154
8.7.8 Insurance Broking ............................................................ 154
8.8 Use Case Segmentation ....................................................... 155
8.8.1 Personalized & Targeted Marketing ................................................ 156
8.8.2 Customer Service & Experience ............................................... 156
8.8.3 Product Innovation & Development ................................................ 157
8.8.4 Risk Awareness & Control ........................................................ 157
8.8.5 Policy Administration, Pricing & Underwriting ........................................ 158
8.8.6 Claims Processing & Management................................................... 158
8.8.7 Fraud Detection & Prevention ................................................. 159
8.8.8 Usage & Analytics-Based Insurance ................................................. 159
8.8.9 Other Use Cases ....................................................... 160
8.9 Regional Outlook ......................................................... 161
8.10 Asia Pacific ........................................................ 162
8.10.1 Country Level Segmentation .................................................... 162
8.10.2 Australia ................................................................... 163
8.10.3 China ................................................................ 163
8.10.4 India ................................................................. 164
8.10.5 Indonesia ................................................................. 164
8.10.6 Japan ................................................................ 165
8.10.7 Malaysia ................................................................... 165
8.10.8 Pakistan............................................................ 166
8.10.9 Philippines................................................................ 166
8.10.10 Singapore ................................................................. 167
8.10.11 South Korea ............................................................. 167
8.10.12 Taiwan ............................................................. 168
8.10.13 Thailand ................................................................... 168
8.10.14 Rest of Asia Pacific ........................................................... 169
8.11 Eastern Europe ......................................................... 170
8.11.1 Country Level Segmentation .................................................... 170
8.11.2 Czech Republic ......................................................... 171
8.11.3 Poland .............................................................. 171
8.11.4 Russia ............................................................... 172
8.11.5 Rest of Eastern Europe .................................................... 172
8.12 Latin & Central America ................................................... 173
8.12.1 Country Level Segmentation .................................................... 173
8.12.2 Argentina ................................................................. 174
8.12.3 Brazil ................................................................ 174
8.12.4 Mexico ............................................................. 175
8.12.5 Rest of Latin & Central America ............................................... 175
8.13 Middle East & Africa ......................................................... 176
8.13.1 Country Level Segmentation .................................................... 176
8.13.2 Israel ................................................................ 177
8.13.3 Qatar ................................................................ 177
8.13.4 Saudi Arabia ............................................................. 178
8.13.5 South Africa ............................................................. 178
8.13.6 UAE .................................................................. 179
8.13.7 Rest of the Middle East & Africa .............................................. 179
8.14 North America .......................................................... 180
8.14.1 Country Level Segmentation .................................................... 180
8.14.2 Canada ............................................................. 181
8.14.3 USA .................................................................. 181
8.15 Western Europe ....................................................... 182
8.15.1 Country Level Segmentation .................................................... 182
8.15.2 Denmark .................................................................. 183
8.15.3 Finland ............................................................. 183
8.15.4 France .............................................................. 184
8.15.5 Germany .................................................................. 184
8.15.6 Italy .................................................................. 185
8.15.7 Netherlands ............................................................. 185
8.15.8 Norway ............................................................ 186
8.15.9 Spain ................................................................ 186
8.15.10 Sweden ............................................................ 187
8.15.11 UK .................................................................... 187
8.15.12 Rest of Western Europe ................................................... 188

9 Chapter 9: Vendor Landscape ................................ 189
9.1 1010data ............................................................. 189
9.2 Absolutdata ......................................................... 190
9.3 Accenture ............................................................ 191
9.4 Actian Corporation/HCL Technologies ........................................ 192
9.5 Adaptive Insights ......................................................... 194
9.6 Adobe Systems ............................................................ 195
9.7 Advizor Solutions ......................................................... 197
9.8 AeroSpike ............................................................ 198
9.9 AFS Technologies ......................................................... 199
9.10 Alation .............................................................. 200
9.11 Algorithmia ....................................................... 201
9.12 Alluxio ............................................................... 202
9.13 ALTEN ............................................................... 203
9.14 Alteryx .............................................................. 204
9.15 AMD (Advanced Micro Devices) ............................... 205
9.16 Anaconda ......................................................... 206
9.17 Apixio ............................................................... 207
9.18 Arcadia Data ............................................................. 208
9.19 ARM .................................................................. 209
9.20 AtScale .............................................................. 210
9.21 Attivio ............................................................... 211
9.22 Attunity ............................................................ 212
9.23 Automated Insights .................................................. 213
9.24 AVORA .............................................................. 214
9.25 AWS (Amazon Web Services) ...................................... 215
9.26 Axiomatics ........................................................ 217
9.27 Ayasdi ............................................................... 218
9.28 BackOffice Associates ....................................................... 219
9.29 Basho Technologies .................................................. 220
9.30 BCG (Boston Consulting Group) ............................................... 221
9.31 Bedrock Data ............................................................ 222
9.32 BetterWorks ............................................................. 223
9.33 Big Panda .......................................................... 224
9.34 BigML ............................................................... 225
9.35 Bitam ................................................................ 226
9.36 Blue Medora ............................................................. 227
9.37 BlueData Software ................................................... 228
9.38 BlueTalon ......................................................... 229
9.39 BMC Software .......................................................... 230
9.40 BOARD International ........................................................ 231
9.41 Booz Allen Hamilton ......................................................... 232
9.42 Boxever ............................................................ 233
9.43 CACI International .................................................... 234
9.44 Cambridge Semantics ................................................. 235
9.45 Capgemini ........................................................ 236
9.46 Cazena .............................................................. 237
9.47 Centrifuge Systems .................................................. 238
9.48 CenturyLink .............................................................. 239
9.49 Chartio .............................................................. 240
9.50 Cisco Systems ........................................................... 241
9.51 Civis Analytics ........................................................... 242
9.52 ClearStory Data ........................................................ 243
9.53 Cloudability .............................................................. 244
9.54 Cloudera ........................................................... 245
9.55 Cloudian ........................................................... 246
9.56 Clustrix ............................................................. 247
9.57 CognitiveScale .......................................................... 248
9.58 Collibra ............................................................. 249
9.59 Concurrent Technology/Vecima Networks ................... 250
9.60 Confluent .......................................................... 251
9.61 Contexti ............................................................ 252
9.62 Couchbase ........................................................ 253
9.63 Crate.io ............................................................. 254
9.64 Cray .................................................................. 255
9.65 Databricks ........................................................ 256
9.66 Dataiku ............................................................. 257
9.67 Datalytyx .......................................................... 258
9.68 Datameer ......................................................... 259
9.69 DataRobot ........................................................ 260
9.70 DataStax ........................................................... 261
9.71 Datawatch Corporation .................................................... 262
9.72 DDN (DataDirect Networks) ............................................. 263
9.73 Decisyon ........................................................... 264
9.74 Dell Technologies ..................................................... 265
9.75 Deloitte............................................................. 266
9.76 Demandbase ............................................................ 267
9.77 Denodo Technologies ....................................................... 268
9.78 Dianomic Systems .................................................... 269
9.79 Digital Reasoning Systems ................................................ 270
9.80 Dimensional Insight .................................................. 271
9.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group ................................ 272
9.82 Domino Data Lab ...................................................... 273
9.83 Domo ................................................................ 274
9.84 Dremio.............................................................. 275
9.85 DriveScale ......................................................... 276
9.86 Druva ................................................................ 277
9.87 Dundas Data Visualization ................................................ 278
9.88 DXC Technology ....................................................... 279
9.89 Elastic ............................................................... 280
9.90 Engineering Group (Engineering Ingegneria Informatica) .................. 281
9.91 EnterpriseDB Corporation ................................................ 282
9.92 eQ Technologic ......................................................... 283
9.93 Ericsson ............................................................ 284
9.94 Erwin ................................................................ 285
9.95 EVŌ (Big Cloud Analytics) ................................................. 286
9.96 EXASOL ............................................................. 287
9.97 EXL (ExlService Holdings).................................................. 288
9.98 Facebook .......................................................... 289
9.99 FICO (Fair Isaac Corporation) ................................................... 290
9.100 Figure Eight .......................................................... 291
9.101 FogHorn Systems ......................................................... 292
9.102 Fractal Analytics ................................................... 293
9.103 Franz ............................................................ 294
9.104 Fujitsu .......................................................... 295
9.105 Fuzzy Logix ........................................................... 297
9.106 Gainsight .............................................................. 298
9.107 GE (General Electric) .................................................... 299
9.108 Glassbeam ............................................................ 300
9.109 GoodData Corporation ................................................ 301
9.110 Google/Alphabet.................................................. 302
9.111 Grakn Labs ........................................................... 304
9.112 Greenwave Systems ..................................................... 305
9.113 GridGain Systems ......................................................... 306
9.114 H2O.ai .......................................................... 307
9.115 HarperDB ............................................................. 308
9.116 Hedvig .......................................................... 309
9.117 Hitachi Vantara .................................................... 310
9.118 Hortonworks ........................................................ 311
9.119 HPE (Hewlett Packard Enterprise) ............................. 312
9.120 Huawei ......................................................... 314
9.121 HVR .............................................................. 315
9.122 HyperScience ....................................................... 316
9.123 HyTrust ......................................................... 317
9.124 IBM Corporation .................................................. 319
9.125 iDashboards ......................................................... 321
9.126 IDERA ........................................................... 322
9.127 Ignite Technologies ...................................................... 323
9.128 Imanis Data .......................................................... 325
9.129 Impetus Technologies .................................................. 326
9.130 Incorta .......................................................... 327
9.131 InetSoft Technology Corporation......................................... 328
9.132 InfluxData ............................................................. 329
9.133 Infogix .......................................................... 330
9.134 Infor/Birst............................................................. 331
9.135 Informatica .......................................................... 333
9.136 Information Builders .................................................... 334
9.137 Infosys .......................................................... 335
9.138 Infoworks ............................................................. 336
9.139 Insightsoftware.com .................................................... 337
9.140 InsightSquared ..................................................... 338
9.141 Intel Corporation ......................................................... 339
9.142 Interana ....................................................... 340
9.143 InterSystems Corporation .................................................... 341
9.144 Jedox ............................................................ 342
9.145 Jethro ........................................................... 343
9.146 Jinfonet Software ......................................................... 344
9.147 Juniper Networks ......................................................... 345
9.148 KALEAO ........................................................ 346
9.149 Keen IO......................................................... 347
9.150 Keyrus .......................................................... 348
9.151 Kinetica ........................................................ 349
9.152 KNIME .......................................................... 350
9.153 Kognitio ........................................................ 351
9.154 Kyvos Insights ....................................................... 352
9.155 LeanXcale ............................................................. 353
9.156 Lexalytics .............................................................. 354
9.157 Lexmark International .................................................. 356
9.158 Lightbend ............................................................. 357
9.159 Logi Analytics ....................................................... 358
9.160 Logical Clocks ....................................................... 359
9.161 Longview Solutions/Tidemark ............................................. 360
9.162 Looker Data Sciences ................................................... 362
9.163 LucidWorks .......................................................... 363
9.164 Luminoso Technologies ............................................... 364
9.165 Maana .......................................................... 365
9.166 Manthan Software Services ................................................. 366
9.167 MapD Technologies ..................................................... 367
9.168 MapR Technologies ...................................................... 368
9.169 MariaDB Corporation ................................................... 369
9.170 MarkLogic Corporation ................................................ 370
9.171 Mathworks ........................................................... 371
9.172 Melissa ......................................................... 372
9.173 MemSQL .............................................................. 373
9.174 Metric Insights ..................................................... 374
9.175 Microsoft Corporation ................................................. 375
9.176 MicroStrategy ...................................................... 377
9.177 Minitab......................................................... 378
9.178 MongoDB ............................................................. 379
9.179 Mu Sigma ............................................................. 380
9.180 NEC Corporation .................................................. 381
9.181 Neo4j ............................................................ 382
9.182 NetApp ......................................................... 383
9.183 Nimbix .......................................................... 384
9.184 Nokia ............................................................ 385
9.185 NTT Data Corporation .................................................. 386
9.186 Numerify .............................................................. 387
9.187 NuoDB .......................................................... 388
9.188 NVIDIA Corporation ..................................................... 389
9.189 Objectivity ............................................................ 390
9.190 Oblong Industries ......................................................... 391
9.191 OpenText Corporation ................................................. 392
9.192 Opera Solutions ................................................... 394
9.193 Optimal Plus ......................................................... 395
9.194 Oracle Corporation ...................................................... 396
9.195 Palantir Technologies ................................................... 399
9.196 Panasonic Corporation/Arimo ............................................. 401
9.197 Panorama Software ..................................................... 402
9.198 Paxata .......................................................... 403
9.199 Pepperdata .......................................................... 404
9.200 Phocas Software .................................................. 405
9.201 Pivotal Software ................................................... 406
9.202 Prognoz ........................................................ 408
9.203 Progress Software Corporation ........................................... 409
9.204 Provalis Research ......................................................... 410
9.205 Pure Storage ........................................................ 411
9.206 PwC (PricewaterhouseCoopers International) .................................... 412
9.207 Pyramid Analytics ........................................................ 413
9.208 Qlik ............................................................... 414
9.209 Qrama/Tengu ....................................................... 415
9.210 Quantum Corporation ................................................. 416
9.211 Qubole ......................................................... 417
9.212 Rackspace ............................................................ 418
9.213 Radius Intelligence ....................................................... 419
9.214 RapidMiner .......................................................... 420
9.215 Recorded Future .................................................. 421
9.216 Red Hat ........................................................ 422
9.217 Redis Labs ............................................................ 423
9.218 RedPoint Global ................................................... 424
9.219 Reltio ............................................................ 425
9.220 RStudio ......................................................... 426
9.221 Rubrik/Datos IO ................................................... 427
9.222 Ryft ............................................................... 428
9.223 Sailthru ......................................................... 429
9.224 Salesforce.com ..................................................... 430
9.225 Salient Management Company ........................................... 431
9.226 Samsung Group .................................................... 432
9.227 SAP ............................................................... 433
9.228 SAS Institute ......................................................... 434
9.229 ScaleOut Software ....................................................... 435
9.230 Seagate Technology ..................................................... 436
9.231 Sinequa ........................................................ 437
9.232 SiSense ......................................................... 438
9.233 Sizmek .......................................................... 439
9.234 SnapLogic ............................................................. 440
9.235 Snowflake Computing .................................................. 441
9.236 Software AG ......................................................... 442
9.237 Splice Machine ..................................................... 443
9.238 Splunk .......................................................... 444
9.239 Strategy Companion Corporation ........................................ 446
9.240 Stratio .......................................................... 447
9.241 Streamlio .............................................................. 448
9.242 StreamSets ........................................................... 449
9.243 Striim ............................................................ 450
9.244 Sumo Logic ........................................................... 451
9.245 Supermicro (Super Micro Computer) .......................................... 452
9.246 Syncsort ....................................................... 453
9.247 SynerScope .......................................................... 455
9.248 SYNTASA .............................................................. 456
9.249 Tableau Software ......................................................... 457
9.250 Talend .......................................................... 458
9.251 Tamr ............................................................. 459
9.252 TARGIT ......................................................... 460
9.253 TCS (Tata Consultancy Services) .......................................... 461
9.254 Teradata Corporation .................................................. 462
9.255 Thales/Guavus ..................................................... 464
9.256 ThoughtSpot ........................................................ 465
9.257 TIBCO Software .................................................... 466
9.258 Toshiba Corporation .................................................... 468
9.259 Transwarp ............................................................ 469
9.260 Trifacta ......................................................... 470
9.261 Unifi Software ...................................................... 471
9.262 Unravel Data ........................................................ 472
9.263 VANTIQ ........................................................ 473
9.264 VMware ....................................................... 474
9.265 VoltDB .......................................................... 475
9.266 WANdisco ............................................................ 476
9.267 Waterline Data ..................................................... 477
9.268 Western Digital Corporation ................................................ 478
9.269 WhereScape ......................................................... 479
9.270 WiPro ........................................................... 480
9.271 Wolfram Research ....................................................... 481
9.272 Workday ....................................................... 483
9.273 Xplenty ......................................................... 485
9.274 Yellowfin BI .......................................................... 486
9.275 Yseop............................................................ 487
9.276 Zendesk ........................................................ 488
9.277 Zoomdata ............................................................. 489
9.278 Zucchetti .............................................................. 490

10 Chapter 10: Conclusion & Strategic Recommendations .................. 491
10.1 Why is the Market Poised to Grow? ........................................ 491
10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential? ................. 492
10.3 Big Data is for Everyone ................................................... 492
10.4 Evaluating the Business Value of Big Data for Insurers .................................... 493
10.5 Transforming Risk Management .............................................. 493
10.6 Tackling Cyber Crime & Under-Insured Risks ................................... 494
10.7 Accelerating the Transition Towards Usage & Analytics-Based Insurance ...................... 494
10.8 Addressing Customer Expectations with Data-Driven Services ............................... 495
10.9 The Importance of AI (Artificial Intelligence) & Machine Learning .......................... 495
10.10 Impact of Blockchain on Big Data Processing ...................................... 496
10.11 Adoption of Cloud Platforms to Address On-Premise System Limitations .................. 496
10.12 Data Security & Privacy Concerns ........................................ 497
10.13 Recommendations ....................................................... 498
10.13.1 Big Data Hardware, Software & Professional Services Providers............................. 498
10.13.2 Insurance Industry Stakeholders.............................................. 499


List of Figures

Figure 1: Hadoop Architecture ........................................................................ 36
Figure 2: Reactive vs. Proactive Analytics ................................................................ 47
Figure 3: Distribution of Big Data Investments in the Insurance Industry, by Use Case: 2018 (%) ...... 54
Figure 4: Aegon's Use of Big Data & Advanced Analytics Across the Insurance Value Chain ......... 72
Figure 5: Key Elements of Generali's ASC (Analytics Solutions Center) ........................... 90
Figure 6: Progressive Corporation's Use of Big Data for Auto Insurance .......................... 93
Figure 7: Atidot's Big Data Platform for Life Insurers .................................................. 101
Figure 8: Cape Analytics' Property Intelligence Database ............................................ 103
Figure 9: Applications of Quest Marine Across the Insurance Value Chain ...................... 106
Figure 10: JMDC's Services for Insurance Companies ................................................ 107
Figure 11: Metromile's Pay-Per-Mile Auto Insurance Program ............................... 109
Figure 12: Munich Re's Data Management Infrastructure ................................. 112
Figure 13: Big Data Roadmap in the Insurance Industry: 2018 – 2030 .......................... 116
Figure 14: Big Data Value Chain in the Insurance Industry .......................................... 119
Figure 15: Key Aspects of Big Data Standardization ................................................. 130
Figure 16: Global Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............ 143
Figure 17: Global Big Data Revenue in the Insurance Industry, by Hardware, Software & Professional Services: 2018 – 2030 ($ Million) ........... 144
Figure 18: Global Big Data Revenue in the Insurance Industry, by Submarket: 2018 – 2030 ($ Million) ......... 145
Figure 19: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Insurance Industry: 2018 – 2030 ($ Million) .......... 146
Figure 20: Global Big Data Networking Infrastructure Submarket Revenue in the Insurance Industry: 2018 – 2030 ($ Million) .......... 146
Figure 21: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ..... 147
Figure 22: Global Big Data SQL Submarket Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ................. 147
Figure 23: Global Big Data NoSQL Submarket Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ........ 148
Figure 24: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ....... 148
Figure 25: Global Big Data Cloud Platforms Submarket Revenue in the Insurance Industry: 2018 – 2030 ($ Million) .................. 149
Figure 26: Global Big Data Professional Services Submarket Revenue in the Insurance Industry: 2018 – 2030 ($ Million)................... 149
Figure 27: Global Big Data Revenue in the Insurance Industry, by Application Area: 2018 – 2030 ($ Million) ......... 150
Figure 28: Global Big Data Revenue in Auto Insurance: 2018 – 2030 ($ Million) ...................... 151
Figure 29: Global Big Data Revenue in Property & Casualty Insurance: 2018 – 2030 ($ Million).......... 151
Figure 30: Global Big Data Revenue in Life Insurance: 2018 – 2030 ($ Million) ...................... 152
Figure 31: Global Big Data Revenue in Health Insurance: 2018 – 2030 ($ Million) ............... 152
Figure 32: Global Big Data Revenue in Multi-line Insurance: 2018 – 2030 ($ Million) ............ 153
Figure 33: Global Big Data Revenue in Other Forms of Insurance: 2018 – 2030 ($ Million) ......... 153
Figure 34: Global Big Data Revenue in Reinsurance: 2018 – 2030 ($ Million) ................... 154
Figure 35: Global Big Data Revenue in Insurance Broking: 2018 – 2030 ($ Million) ............ 154
Figure 36: Global Big Data Revenue in the Insurance Industry, by Use Case: 2018 – 2030 ($ Million) ........ 155
Figure 37: Global Big Data Revenue in Personalized & Targeted Marketing for Insurance Services: 2018 – 2030 ($ Million) ............... 156
Figure 38: Global Big Data Revenue in Customer Service & Experience for Insurance Services: 2018 – 2030 ($ Million) ........... 156
Figure 39: Global Big Data Revenue in Product Innovation & Development for Insurance Services: 2018 – 2030 ($ Million) .............. 157
Figure 40: Global Big Data Revenue in Risk Awareness & Control for Insurance Services: 2018 – 2030 ($ Million) .............. 157
Figure 41: Global Big Data Revenue in Policy Administration, Pricing & Underwriting: 2018 – 2030 ($ Million) ........ 158
Figure 42: Global Big Data Revenue in Claims Processing & Management: 2018 – 2030 ($ Million) ............. 158
Figure 43: Global Big Data Revenue in Fraud Detection & Prevention for Insurance Services: 2018 – 2030 ($ Million) ............... 159
Figure 44: Global Big Data Revenue in Usage & Analytics-Based Insurance: 2018 – 2030 ($ Million) ......... 159
Figure 45: Global Big Data Revenue in Other Use Cases for Insurance Services: 2018 – 2030 ($ Million) ............ 160
Figure 46: Big Data Revenue in the Insurance Industry, by Region: 2018 – 2030 ($ Million) ............ 161
Figure 47: Asia Pacific Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............ 162
Figure 48: Asia Pacific Big Data Revenue in the Insurance Industry, by Country: 2018 – 2030 ($ Million) ...... 162
Figure 49: Australia Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............... 163
Figure 50: China Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ....... 163
Figure 51: India Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ........ 164
Figure 52: Indonesia Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ....... 164
Figure 53: Japan Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............. 165
Figure 54: Malaysia Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............ 165
Figure 55: Pakistan Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ................ 166
Figure 56: Philippines Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............. 166
Figure 57: Singapore Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) .................. 167
Figure 58: South Korea Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............ 167
Figure 59: Taiwan Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) .................... 168
Figure 60: Thailand Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) .................. 168
Figure 61: Rest of Asia Pacific Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) .............. 169
Figure 62: Eastern Europe Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ........... 170
Figure 63: Eastern Europe Big Data Revenue in the Insurance Industry, by Country: 2018 – 2030 ($ Million) ......... 170
Figure 64: Czech Republic Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ........ 171
Figure 65: Poland Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ....... 171
Figure 66: Russia Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ...... 172
Figure 67: Rest of Eastern Europe Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) .............. 172
Figure 68: Latin & Central America Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ........... 173
Figure 69: Latin & Central America Big Data Revenue in the Insurance Industry, by Country: 2018 – 2030 ($ Million) ................ 173
Figure 70: Argentina Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............ 174
Figure 71: Brazil Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ...... 174
Figure 72: Mexico Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ........ 175
Figure 73: Rest of Latin & Central America Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) .......... 175
Figure 74: Middle East & Africa Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) .... 176
Figure 75: Middle East & Africa Big Data Revenue in the Insurance Industry, by Country: 2018 – 2030 ($ Million)...................... 176
Figure 76: Israel Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............ 177
Figure 77: Qatar Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............. 177
Figure 78: Saudi Arabia Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ...... 178
Figure 79: South Africa Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ....... 178
Figure 80: UAE Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............... 179
Figure 81: Rest of the Middle East & Africa Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ........... 179
Figure 82: North America Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............ 180
Figure 83: North America Big Data Revenue in the Insurance Industry, by Country: 2018 – 2030 ($ Million) .......... 180
Figure 84: Canada Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ....... 181
Figure 85: USA Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............. 181
Figure 86: Western Europe Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ........ 182
Figure 87: Western Europe Big Data Revenue in the Insurance Industry, by Country: 2018 – 2030 ($ Million) .................... 182
Figure 88: Denmark Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ......... 183
Figure 89: Finland Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ......... 183
Figure 90: France Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million)............ 184
Figure 91: Germany Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ......... 184
Figure 92: Italy Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ............ 185
Figure 93: Netherlands Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ........ 185
Figure 94: Norway Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ......... 186
Figure 95: Spain Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ......... 186
Figure 96: Sweden Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ......... 187
Figure 97: UK Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ........... 187
Figure 98: Rest of Western Europe Big Data Revenue in the Insurance Industry: 2018 – 2030 ($ Million) ......... 188

 

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Big Data a $2.4 Billion opportunity in the insurance industry, says SNS Telecom & IT

Text: SNS Telecom & IT's latest research report indicates that Big Data investments in the insurance industry are expected to account for more than $2.4 Billion by the end of 2018.

2018/08/03

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 and historical data from sources such as connected devices, web, social media, sensors, log files and transactional applications, Big Data is rapidly gaining traction from a diverse range of vertical sectors. The insurance industry is no exception to this trend, where Big Data has found a host of applications ranging from targeted marketing and personalized products to usage-based insurance, efficient claims processing, proactive fraud detection and beyond.

SNS Telecom & IT estimates that Big Data investments in the insurance industry will account for more than $2.4 Billion in 2018 alone. Led by a plethora of business opportunities for insurers, reinsurers, insurance brokers, InsurTech specialists and other stakeholders, these investments are further expected to grow at a CAGR of approximately 14% over the next three years.

The “Big Data in the Insurance Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of Big Data in the insurance industry including key market drivers, challenges, investment potential, application areas, use cases, future roadmap, value chain, case studies, vendor profiles and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services investments from 2018 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 8 application areas, 9 use cases, 6 regions and 35 countries.

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