보험산업의 빅데이터: 2018-2023 기회 한계 전략 전망

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보험산업의 빅데이터: 2018-2023 – 기회, 한계, 전략 & 전망

발행사: SNS Telecom & IT / 발행일: 2018-08-03 / 페이지: 500 / 가격: Single User PDF; $2,500

개요

“빅데이터”는 기존 데이터베이스의 캡처, 저장, 관리 및 분석 능력 및 크기를 뛰어 넘는 데이터

세트를 나타내는 용어로 등장했다. 하지만 빅데이터 단어는 여러 해에 걸쳐 의미가 확장되었다.

빅데이터는 데이터 그 자체뿐만 아니라 복잡한 문제를 해결하기 위해 크고 다양한 데이터 컬렉션

을 캡처, 저장, 관리 및 분석하는 일련의 기술을 의미한다.

빅데이터는 커넥티드 기기, 웹, 소셜 미디어, 센서, 로그 파일 및 트랜잭션 애플리케이션과 같은

소스로부터 실시간 및 과거 데이터가 급증 됨에 따라 다양한 수직 부문에서 빠르게 주목 받고 있

다. 빅데이터가 표적 마케팅 및 맞춤형 제품에서부터 보험, 효율적인 클레임 처리, 사기 사전 예

방에 이르기까지 빅데이터의 응용분야는 다양하며 보험 업계에서도 적용된다.

SNS Telecom & IT에 분석에 따르면 2018년에만 보험업계의 빅데이터에 대한 투자가 24억 달러

이상을 차지할 것으로 예측된다. 보험사, 재보험사, 보험 중개인, InsurTech 전문가 및 기타 이해

관계자를 위한 수많은 비즈니스 기회로 인해 향후 3년간 연평균 성장률 약14%로 성장 할 것으로

예상된다.

본 보고서는 보험업계의 빅데이터 시장의 주요 성장 요인, 도전 과제, 투자 잠재력, 응용분야, 사

용 사례, 미래 로드맵, 가치사슬, 사례연구, 공급업체 프로필 및 전략 등을 포함하여 심층적인 분

석을 제공한다. 본 보고서는 또한 2018년부터 2030년까지 빅데이터의 하드웨어, 소프트웨어 및

전문 서비스 투자에 대한 시장 규모 예측을 제시한다. 예측에는 8개의 수평 하위시장, 8개의 응용

분야, 9개의 사용 사례, 6개 지역 및 35개 국가로 구분된다.

http://www.sbdi.co.kr/cart/shop/item.php?it_id=1538641768


주요 발견:

본 보고서에는 아래와 같은 주요 발견들이 포함되어 있다:

2018년에 보험업계의 빅데이터 제공업체들은 하드웨어, 소프트웨어 및 전문 서비스 수익

을 통해 24억 달러에 이르렀다. 이러한 투자는 향후 3년간 약 14%의 연평균 성장률을

보일 것으로 예상되며, 2021년 말에는 36억 달러에 이를 것이다.

빅데이터 기술을 통해 보험사 및 기타 이해관계자는 표적 마케팅 및 맞춤형 제품, 보험,

효율적인 청구 처리, 사기 사전 예방 탐지 등 다양한 방법으로 데이터 자산을 활용하기

시작했다.

빅데이터 기술의 채택이 증가함에 따라 보험사 및 기타 이해관계자에게 다양한 혜택이 제

공된다. 전 세계 보험사의 의결을 토대로 보험 서비스 이용이 30% 증가하고, 정책 정책관

리자의 일이 50% 줄었고, 큰 손실에 대한 청구가 80%의 정확성을 자랑하며, 청구 과정

및 관리의 비용이 40-70%정도 줄었고, 비응급 보험 청구의 처리 속도를 90% 증가시켰으

며; 사기 탐지를 60% 향상됐다.

또한 빅데이터 기술은 사용자 요구에 따른 보험 (on-demand) 모델을 가능하게 하여; 자

동차, 생명 및 헬스케어 보험 및 사이버 범죄 (새로운 분야이며 부분적으로 보험을 들 수

있다)와 같은 분야에서의 보험 채택률을 촉진 시킨다.

포함된 주제:

본 보고서에는 아래와 같은 주제들이 포함되어 있다:

빅데이터 생태계

시장 성장 요인 및 제한 요인

기술, 표준화 및 규정

빅데이터 분석 및 구현 모델

비즈니스 사례, 응용분야 및 보험업계에서의 사용 사례

보험회사, 재보험사, InsurTech 전문가 및 보험업계의 다른 이해관계자들의 빅데이터 투자

사례연구 20개

미래 로드맵 및 가치 체인

빅데이터 생태계의 선두 업체들의 프로필 및 전략

빅데이터 제공업체 및 업계 이해관계자를 위한 전략적 추천

시장 분석 및 2018년부터 2023년까지의 전망

전망 세그먼트: Forecast Segmentation:

본 시장은 아래와 같이 세분화 되어있다:

하드웨어, 소프트웨어 & 전문 서비스

하드웨어


소프트웨어

전문 서비스

수평적 하위시장

저장 & 사회 기반시설

네트워킹 인프라

Hadoop & 인프라 소프트웨어

SQL

NoSQL

분석 플랫폼 & 응용분야

클라우드 플랫폼

전문 서비스

응용분야

자동차 보험

재산 & 상해 보험

생명 보험

건강 보험

여러 개의 보험

기타 형식에 보험

재보험

보험 설계사

사용 사례

개인 & 타겟 마케팅

고객 서비스 & 경험

제품 혁신 & 개발

위험 인식 & 제어

정책 관리, 가격 책정 & 보증

클레임 처리 & 관리

사기 탐지 & 예방

사용 & 분석 기반 보험

기타 사용 사례

지역별 시장

아시아 태평양

동유럽

라틴 & 중앙 아메리카


중동 & 아프리카

북미

서유럽

국가별 시장

아르헨티나, 호주, 브라질, 캐나다, 중국, 체코, 덴마크, 핀란드, 프랑스, 독일, 인도, 인

도네시아, 이스라엘, 이탈리아, 일본, 말레이시아, 멕시코, 네덜란드, 노르웨이, 파키스

탄, 필리핀, 폴란드, 카타르, 러시아, 사우디 아라비아, 싱가포르, 남아프리카 공화국,

한국, 스페인, 스웨덴, 대만, 태국, UAE, 영국, 미국

주요 질문 답변:

보고서에는 아래와 같은 질문의 답변이 포함되어 있다:

보험업계에서 빅데이터의 기회는 얼마나 큰가?

세그먼트 및 지역별로 시장은 어떻게 변화하고 있나?

2021년의 시장 규모를 어떻게 되며, 어느 정도로 성장할 것인가?

성장에 영향을 미치는 추세, 도전과제 및 한계점은 무엇인가?

빅데이터 주요 소프트웨어, 하드웨어 및 서비스 공급업체는 누구이며 이들의 전략은 무

엇인가?

보험사, 재보험사, InsurTech 전문가 및 기타 이해관계자들은 빅데이터에 얼마나 투자하고

있나?

보험업계에서 빅데이터 분석에는 어떤 기회가 있나?

보험업계에서 빅데이터에 대한 투자는 어느 나라, 응용분야 및 사용 사례에서 가장 높은

퍼센트를 보일 것인가?

보고서에 언급된 회사들:

아래는 보고서에서 검토, 논의 또는 언급된 회사들이다:

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


목차

1 도입

1.1 종합 개요

1.2 다룬 주제

1.3 전망 세분화

1.4 주요 질문 답변

1.5 주요 발견

1.6 방법론

1.7 목표로 하는 독자

1.8 언급된 회사 & 기관

2 빅데이터 개요

2.1 빅데이터란 무엇인가?

2.2 빅데이터 처리 주요 과정

2.3 빅데이터 주요 특징

2.4 시장 성장 동인

2.5 시장 한계점

3 빅데이터 분석

3.1 빅데이터 분석이란?

3.2 분석의 중요성

3.3 반작용 vs. 능동적 분석

3.4 소비자 vs. 경영 분석

3.5 기술 & 실행 접근

4 경영 사례 & 금융 서비스 산업내 응용 프로그램

4.1 개요 & 투자 가능성

4.2 산업 특정 시장 성장 동인

4.3 산업 특정 시장 한계점

4.4 주요 응용 프로그램 분야

4.5 사용 사례

5 보험업계 사용 사례

5.1 보험사

5.2 재보험사, InsurTech 전문가 & 기타 이해관계자


6 미래 로드맵 & 가치 체인

6.1 미래 로드맵

6.2 빅데이터 가치 체인

7 표준화 & 규제 계획

7.1 ASF (Apache Software Foundation)

7.2 CSA (Cloud Security Alliance)

7.3 CSCC (Cloud Standards Customer Council)

7.4 DMG (Data Mining Group)

7.5 IEEE (Institute of Electrical and Electronics Engineers)

7.6 INCITS (InterNational Committee for Information Technology Standards)

7.7 ISO (International Organization for Standardization)

7.8 ITU (International Telecommunication Union)

7.9 Linux Foundation

7.10 NIST (National Institute of Standards and Technology)

7.11 OASIS (Organization for the Advancement of Structured Information Standards)

7.12 ODaF (Open Data Foundation)

7.13 ODCA (Open Data Center Alliance)

7.14 OGC (Open Geospatial Consortium)

7.15 TM Forum

7.16 TPC (Transaction Processing Performance Council)

7.17 W3C (World Wide Web Consortium)

8 시장 규모 & 전망

8.1 보험업계의 빅데이터 글로벌 전망

8.2 하드웨어, 소프트웨어 & 전문 서비스 세그먼트

8.3 수평 하부시장 세그먼트

8.4 하드웨어 하부시장

8.5 소프트웨어 하부시장

8.6 전문 서비스 하부시장

8.7 응용 프로그램 분야 하부시장

8.8 사용 사례 세그먼트

8.9 지역 전망

8.10 아시아 태평양

8.11 서유럽

8.12 라틴 & 중앙 아메리카

8.13 중동 & 아프리카

8.14 북미


8.15 서유럽

9 공급업체 전망

9.1 1010data

9.2 Absolutdata

9.3 Accenture

9.4 Actian Corporation/HCL Technologies

9.5 Adaptive Insights

9.6 Adobe Systems

9.7 Advizor Solutions

9.8 AeroSpike

9.9 AFS Technologies

9.10 Alation

9.11 Algorithmia

9.12 Alluxio

9.13 ALTEN

9.14 Alteryx

9.15 AMD (Advanced Micro Devices)

9.16 Anaconda

9.17 Apixio

9.18 Arcadia Data

9.19 Arimo

9.20 Arity

9.21 ARM


9.23 AtScale

9.24 Attivio

9.25 Attunity

9.26 Audi

9.27 Automated Insights

9.28 Automobili Lamborghini

9.29 automotiveMastermind

9.30 AVORA

9.31 AWS (Amazon Web Services)


9.32 Axiomatics

9.33 Ayasdi

9.34 BackOffice Associates

9.35 Basho Technologies

9.36 BCG (Boston Consulting Group)

9.37 Bedrock Data

9.38 BetterWorks

9.39 Big Panda

9.40 BigML

9.41 Birst

9.42 Bitam

9.43 Blue Medora

9.44 BlueData Software

9.45 BlueTalon

9.46 BMC Software

9.47 BMW

9.48 BOARD International

9.49 Booz Allen Hamilton

9.50 Bosch

9.51 Boxever

9.52 CACI International

9.53 Cambridge Semantics

9.54 Capgemini

9.55 Cazena

9.56 Centrifuge Systems

9.57 CenturyLink

9.58 Chartio

9.59 Cisco Systems

9.60 Citro?n

9.61 Civis Analytics

9.62 ClearStory Data

9.63 Cloudability

9.64 Cloudera

9.65 Cloudian


9.66 Clustrix

9.67 CognitiveScale

9.68 Collibra

9.69 Concurrent Technology

9.70 Confluent

9.71 Contexti

9.72 Continental

9.73 Couchbase

9.74 Cox Automotive

9.75 Cox Enterprises

9.76 Crate.io

9.77 Cray



9.80 Daimler

9.81 Dash Labs

9.82 Databricks

9.83 Dataiku

9.84 Datalytyx

9.85 Datameer

9.86 DataRobot

9.87 DataStax

9.88 Datawatch Corporation

9.89 Datos IO

9.90 DDN (DataDirect Networks)

9.91 Decisyon

9.92 Dell Technologies

9.93 Deloitte

9.94 Delphi Automotive

9.95 Demandbase

9.96 Denodo Technologies

9.97 Denso Corporation

9.98 Dianomic Systems

9.99 Digital Reasoning Systems


9.100 Dimensional Insight


9.102 Dolphin Enterprise Solutions Corporation

9.103 Domino Data Lab

9.104 Domo

9.105 Dongfeng Motor Corporation

9.106 Dremio

9.107 DriveScale

9.108 Druva

9.109 DS Automobiles

9.110 Ducati

9.111 Dundas Data Visualization

9.112 DXC Technology

9.113 Elastic

9.114 Engineering Group (Engineering Ingegneria Informatica)

9.115 EnterpriseDB Corporation

9.116 eQ Technologic

9.117 Ericsson

9.118 Erwin

9.119 EV? (Big Cloud Analytics)

9.120 EXASOL

9.121 EXL (ExlService Holdings)

9.122 Facebook

9.123 FCA (Fiat Chrysler Automobiles)

9.124 FICO (Fair Isaac Corporation)

9.125 Figure Eight

9.126 FogHorn Systems

9.127 Ford Motor Company

9.128 Fractal Analytics

9.129 Franz

9.130 Fujitsu

9.131 Fuzzy Logix

9.132 Gainsight

9.133 GE (General Electric)


9.134 Geely (Zhejiang Geely Holding Group)

9.135 Glassbeam

9.136 GM (General Motors Company)

9.137 GoodData Corporation

9.138 Google

9.139 Grakn Labs

9.140 Greenwave Systems

9.141 GridGain Systems

9.142 Groupe PSA

9.143 Groupe Renault

9.144 Guavus

9.145 H2O.ai

9.146 Hanse Orga Group

9.147 HarperDB

9.148 HCL Technologies

9.149 Hedvig

9.150 HERE

9.151 Hitachi Vantara

9.152 Honda Motor Company

9.153 Hortonworks

9.154 HPE (Hewlett Packard Enterprise)

9.155 Huawei

9.156 HVR

9.157 HyperScience

9.158 HyTrust

9.159 Hyundai Motor Company

9.160 IBM Corporation

9.161 iDashboards

9.162 IDERA

9.163 IEC (International Electrotechnical Commission)


9.165 Ignite Technologies

9.166 Imanis Data

9.167 Impetus Technologies



9.169 Incorta

9.170 InetSoft Technology Corporation

9.171 InfluxData

9.172 Infogix

9.173 Infor

9.174 Informatica

9.175 Information Builders

9.176 Infosys

9.177 Infoworks

9.178 Insightsoftware.com

9.179 InsightSquared

9.180 Intel Corporation

9.181 Interana

9.182 InterSystems Corporation



9.185 Jaguar Land Rover

9.186 Jedox

9.187 Jethro

9.188 Jinfonet Software

9.189 Juniper Networks

9.190 KALEAO

9.191 KDDI Corporation

9.192 Keen IO

9.193 Keyrus

9.194 Kinetica

9.195 KNIME

9.196 Kognitio

9.197 Kyvos Insights

9.198 LeanXcale

9.199 Lexalytics

9.200 Lexmark International

9.201 Lightbend



9.203 Logi Analytics

9.204 Logical Clocks

9.205 Longview Solutions

9.206 Looker Data Sciences

9.207 LucidWorks

9.208 Luminoso Technologies

9.209 Lytx

9.210 Maana

9.211 Manthan Software Services

9.212 MapD Technologies

9.213 MapR Technologies

9.214 MariaDB Corporation

9.215 MarkLogic Corporation

9.216 Mathworks

9.217 Mazda Motor Corporation

9.218 Melissa

9.219 MemSQL

9.220 Mercedes-Benz

9.221 METI (Ministry of Economy, Trade and Industry, Japan)

9.222 Metric Insights

9.223 Michelin

9.224 Microsoft Corporation

9.225 MicroStrategy

9.226 Minitab

9.227 Mobileye

9.228 MongoDB

9.229 Mu Sigma

9.230 NEC Corporation

9.231 Neo4j

9.232 NetApp

9.233 Nimbix

9.234 Nissan Motor Company

9.235 Nokia


9.236 NTT Data Corporation

9.237 NTT DoCoMo

9.238 Numerify

9.239 NuoDB

9.240 NVIDIA Corporation


9.242 Objectivity

9.243 Oblong Industries




9.247 OpenText Corporation

9.248 Opera Solutions

9.249 Optimal Plus

9.250 Oracle Corporation

9.251 Otonomo

9.252 Palantir Technologies

9.253 Panasonic Corporation

9.254 Panorama Software

9.255 Paxata

9.256 Pepperdata

9.257 Peugeot

9.258 Phocas Software

9.259 Pivotal Software

9.260 Prognoz

9.261 Progress Software Corporation

9.262 Progressive Corporation

9.263 Provalis Research

9.264 Pure Storage

9.265 PwC (PricewaterhouseCoopers International)

9.266 Pyramid Analytics

9.267 Qlik

9.268 Qrama/Tengu

9.269 Quantum Corporation


9.270 Qubole

9.271 Rackspace

9.272 Radius Intelligence

9.273 RapidMiner

9.274 Recorded Future

9.275 Red Hat

9.276 Redis Labs

9.277 RedPoint Global

9.278 Reltio

9.279 RStudio

9.280 Rubrik

9.281 Ryft

9.282 SAIC Motor Corporation

9.283 Sailthru

9.284 Salesforce.com

9.285 Salient Management Company

9.286 Samsung Group

9.287 SAP

9.288 SAS Institute

9.289 ScaleOut Software

9.290 Seagate Technology

9.291 Sinequa

9.292 SiSense

9.293 Sizmek

9.294 SnapLogic

9.295 Snowflake Computing

9.296 Software AG

9.297 Splice Machine

9.298 Splunk

9.299 Strategy Companion Corporation

9.300 Stratio

9.301 Streamlio

9.302 StreamSets

9.303 Striim


9.304 Subaru

9.305 Sumo Logic

9.306 Supermicro (Super Micro Computer)

9.307 Suzuki Motor Corporation

9.308 Syncsort

9.309 SynerScope

9.310 SYNTASA

9.311 Tableau Software

9.312 Talend

9.313 Tamr

9.314 TARGIT

9.315 Tata Motors

9.316 TCS (Tata Consultancy Services)

9.317 Teradata Corporation

9.318 Tesla

9.319 Thales

9.320 ThoughtSpot

9.321 THTA (Tokyo Hire-Taxi Association)

9.322 TIBCO Software

9.323 Tidemark

9.324 TM Forum

9.325 Toshiba Corporation

9.326 Toyota Motor Corporation


9.328 Transwarp

9.329 Trifacta

9.330 U.S. FTC (Federal Trade Commission)

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

9.332 U.S. Xpress

9.333 Uber Technologies

9.334 Unifi Software

9.335 Unravel Data

9.336 Valens

9.337 VANTIQ


9.338 Vecima Networks

9.339 VMware

9.340 Volkswagen Group

9.341 VoltDB

9.342 Volvo Cars


9.344 WANdisco

9.345 Waterline Data

9.346 Western Digital Corporation

9.347 WhereScape

9.348 WiPro

9.349 Wolfram Research

9.350 Workday

9.351 Xevo

9.352 Xplenty

9.353 Yellowfin BI

9.354 Yseop

9.355 Zendesk

9.356 Zoomdata

9.357 Zucchetti

10 결론 & 전략적 추천

10.1 시장이 성장하는 이유는 무엇인가?

10.2 지역 전망: 어느 나라가 가장 성장 가능성이 있는가?

10.3 빅데이터는 모두를 위한 것이다

10.4 보험사를 위한 빅데이터의 비즈니스 가치 분석

10.5 위험관리 변화

10.6 사이버 범죄 & 일부 보험의 위험

10.7 사용 & 분석 기반 보험으로 전환 가속화

10.8 데이터 기반 서비스로 고객 기대치 충족

10.9 AI 및 기계 학습의 중요성

10.10 빅데이터 처리에 대한 블록 체인의 영향

10.11 사내 시스템 제한사항을 해결하기 위한 클라우드 플랫폼 채택

10.12 데이터 보안 및 개인 정보 보호 관련


10.13 권장 사항

언급된 회사들

1010data

Absolutdata

Accenture

ACEA (European Automobile Manufacturers’ Association)

Actian Corporation

Adaptive Insights

Adobe Systems

Advizor Solutions

AeroSpike

AFS Technologies

Alation

Algorithmia


Alluxio

Alphabet

ALTEN

Alteryx


Anaconda

1010data

Absolutdata

Accenture


Actian Corporation

Adaptive Insights

Adobe Systems

Advizor Solutions

AeroSpike

AFS Technologies

Alation

Algorithmia



Alluxio

Alphabet

ALTEN

Alteryx


Anaconda

Apixio

Arcadia Data

Arimo

Arity

ARM


AtScale

Attivio

Attunity

Audi

Automated Insights

Automobili Lamborghini

automotiveMastermind

AVORA


Axiomatics

Ayasdi


Basho Technologies


Bedrock Data

BetterWorks

Big Panda

BigML

Birst

Bitam

Blue Medora


BlueData Software

BlueTalon

BMC Software

BMW

BOARD International

Booz Allen Hamilton

Bosch

Boxever

CACI International

Cambridge Semantics

Capgemini

Cazena

Centrifuge Systems

CenturyLink

Chartio

Cisco Systems

Citro?n

Civis Analytics

ClearStory Data

Cloudability

Cloudera

Cloudian

Clustrix

CognitiveScale

Collibra


Confluent

Contexti

Continental

Couchbase

Cox Automotive

Cox Enterprises

Crate.io

Cray




Daimler

Dash Labs

Databricks

Dataiku

Datalytyx

Datameer

DataRobot

DataStax


Datos IO


Decisyon

Dell Technologies

Deloitte

Delphi Automotive

Demandbase

Denodo Technologies

Denso Corporation

Dianomic Systems


Dimensional Insight



Domino Data Lab

Domo

Dongfeng Motor Corporation

Dremio

DriveScale

Druva

DS Automobiles

Ducati



DXC Technology

Elastic



eQ Technologic

Ericsson

Erwin

EV? (Big Cloud Analytics)

EXASOL


Facebook

FCA (Fiat Chrysler Automobiles)


Figure Eight

FogHorn Systems

Ford Motor Company

Fractal Analytics

Franz

Fujitsu

Fuzzy Logix

Gainsight


Geely (Zhejiang Geely Holding Group)

Glassbeam

GM (General Motors Company)


Google

Grakn Labs

Greenwave Systems

GridGain Systems

Groupe PSA

Groupe Renault

Guavus

H2O.ai


Hanse Orga Group

HarperDB

HCL Technologies

Hedvig

HERE

Hitachi Vantara

Honda Motor Company

Hortonworks


Huawei

HVR

HyperScience

HyTrust

Hyundai Motor Company

IBM Corporation

iDashboards

IDERA



Ignite Technologies

Imanis Data



Incorta


InfluxData

Infogix

Infor

Informatica


Infosys

Infoworks

Insightsoftware.com

InsightSquared


Intel Corporation

Interana




Jaguar Land Rover

Jedox

Jethro

Jinfonet Software

Juniper Networks

KALEAO

KDDI Corporation

Keen IO

Keyrus

Kinetica

KNIME

Kognitio

Kyvos Insights

LeanXcale

Lexalytics


Lightbend

Linux Foundation

Logi Analytics

Logical Clocks

Longview Solutions


LucidWorks


Lytx

Maana


MapD Technologies

MapR Technologies


MariaDB Corporation


Mathworks

Mazda Motor Corporation

Melissa

MemSQL

Mercedes-Benz

METI (Ministry of Economy, Trade and Industry, Japan)

Metric Insights

Michelin


MicroStrategy

Minitab

Mobileye

MongoDB

Mu Sigma

NEC Corporation

Neo4j

NetApp

Nimbix

Nissan Motor Company

Nokia


NTT DoCoMo

Numerify

NuoDB

NVIDIA Corporation


Objectivity

Oblong Industries






Opera Solutions

Optimal Plus

Oracle Corporation

Otonomo



Panorama Software

Paxata

Pepperdata

Peugeot

Phocas Software

Pivotal Software

Prognoz



Provalis Research

Pure Storage


Pyramid Analytics

Qlik

Qrama/Tengu

Quantum Corporation

Qubole

Rackspace

Radius Intelligence

RapidMiner

Recorded Future

Red Hat

Redis Labs

RedPoint Global

Reltio

RStudio

Rubrik

Ryft


SAIC Motor Corporation

Sailthru

Salesforce.com


Samsung Group

SAP

SAS Institute

ScaleOut Software

Seagate Technology

Sinequa

SiSense

Sizmek

SnapLogic

Snowflake Computing

Software AG

Splice Machine

Splunk


Stratio

Streamlio

StreamSets

Striim

Subaru

Sumo Logic


Suzuki Motor Corporation

Syncsort

SynerScope

SYNTASA

Tableau Software

Talend

Tamr

TARGIT

Tata Motors




Tesla

Thales

ThoughtSpot

THTA (Tokyo Hire-Taxi Association)

TIBCO Software

Tidemark

TM Forum

Toshiba Corporation

Toyota Motor Corporation


Transwarp

Trifacta

U.S. FTC (Federal Trade Commission)


U.S. Xpress

Uber Technologies

Unifi Software

Unravel Data

Valens

VANTIQ

Vecima Networks

VMware

Volkswagen Group

VoltDB

Volvo Cars


WANdisco

Waterline Data


WhereScape

WiPro

Wolfram Research


Workday

Xevo

Xplenty

Yellowfin BI

Yseop

Zendesk

Zoomdata

Zucchetti

보고서 문의


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

Strategies & Forecasts

Publisher: SNS Telecom & IT / Date: 2018-08-03 / Page: 500 / Price: Single User PDF; $2,500

Summary

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.




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

o Hardware


o Software

o Professional Services

Horizontal Submarkets

o Storage & Compute Infrastructure

o Networking Infrastructure

o Hadoop & Infrastructure Software

o SQL

o NoSQL

o Analytic Platforms & Applications

o Cloud Platforms

o Professional Services

Application Areas

o Auto Insurance

o Property & Casualty Insurance

o Life Insurance

o Health Insurance

o Multi-Line Insurance

o Other Forms of Insurance

o Reinsurance

o Insurance Broking

Use Cases

o Personalized & Targeted Marketing

o Customer Service & Experience

o Product Innovation & Development

o Risk Awareness & Control

o Policy Administration, Pricing & Underwriting

o Claims Processing & Management

o Fraud Detection & Prevention

o Usage & Analytics-Based Insurance

o Other Use Cases

Regional Markets

o Asia Pacific

o Eastern Europe

o Latin & Central America

o Middle East & Africa

o North America

o Western Europe

Country Markets

o 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


Alluxio

Alphabet


ALTEN

Alteryx


Anaconda

Apixio

Arcadia Data

Arimo

Arity

ARM


Atidot

AtScale

Attivio

Attunity

Automated Insights

AVORA


AXA

Axiomatics

Ayasdi


Basho Technologies


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


Confluent

Contexti

Couchbase

Crate.io

Cray



Dai-ichi Life Holdings

Databricks

Dataiku

Datalytyx

Datameer

DataRobot

DataStax


Datos IO


Decisyon

Dell Technologies

Deloitte

Demandbase

Denodo Technologies

Dianomic Systems



Dimensional Insight



Domino Data Lab

Domo

Dremio

DriveScale

Druva


DXC Technology

Elastic



eQ Technologic

ERGO Group

Ericsson

Erwin

EVŌ (Big Cloud Analytics)

EXASOL


Facebook


Figure Eight

FogHorn Systems

Fractal Analytics

Franz

Fujitsu

Fuzzy Logix

Gainsight


Generali Group

Glassbeam

GNS Healthcare


Google

Grakn Labs

Greenwave Systems

GridGain Systems

Guavus


H2O.ai

Hanse Orga Group

HarperDB

HCL Technologies

Hedvig

Hitachi Vantara

Hortonworks


Huawei

HVR

HyperScience

HyTrust

IBM Corporation

iDashboards

IDERA



Ignite Technologies

Imanis Data



Incorta


InfluxData

Infogix

Infor

Informatica


Infosys

Infoworks

Insightsoftware.com

InsightSquared

Intel Corporation

Interana




Jedox

Jethro

Jinfonet Software


JMDC Corporation

Juniper Networks

KALEAO

Keen IO

Kenko-Nenrei Shogaku Tanki Hoken

Keyrus

Kinetica

KNIME

Kognitio

Kyvos Insights

LeanXcale

Lexalytics


Lightbend

Linux Foundation

Logi Analytics

Logical Clocks

Longview Solutions


LucidWorks


Maana


MapD Technologies

MapR Technologies

MariaDB Corporation


Mathworks

MEAG (Munich Ergo Asset Management)

Melissa

MemSQL

Metric Insights

MetroMile


MicroStrategy

Minitab

MongoDB

Mu Sigma

Munich Re

NEC Corporation


Neo First Life Insurance Company

Neo4j

NetApp

Nimbix

Nokia

Noritsu Koki


Numerify

NuoDB

NVIDIA Corporation


Objectivity

Oblong Industries



ODPi (Open Ecosystem of Big Data)



Opera Solutions

Optimal Plus

Optum

OptumLabs

Oracle Corporation

Oscar Health



Panorama Software

Paxata

Pepperdata

Phocas Software

Pivotal Software

Prognoz



Provalis Research

Pure Storage


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


Samsung Fire & Marine Insurance

Samsung Group

SAP

SAS Institute

ScaleOut Software

Seagate Technology

Sinequa

SiSense

Sizmek

SnapLogic

Snowflake Computing

Software AG

Splice Machine

Splunk


Stratio

Streamlio

StreamSets

Striim

Sumo Logic


Syncsort

SynerScope

SYNTASA


Tableau Software

Talend

Tamr

TARGIT



Thales

ThoughtSpot

TIBCO Software

Tidemark

TM Forum

Toshiba Corporation


Transwarp

Trifacta


Unifi Software

UnitedHealth Group

Unravel Data

VANTIQ

Vecima Networks

VMware

VoltDB


WANdisco

Waterline Data


WhereScape

WiPro

Wolfram Research

Workday

Xplenty

Yellowfin BI

Yseop

Zendesk

Zoomdata

Zucchetti

Zurich Insurance Group


Table of Contents

Table of Contents

1 Chapter 1: Introduction

1.1 Executive Summary

1.2 Topics Covered

1.3 Forecast Segmentation

1.4 Key Questions Answered

1.5 Key Findings

1.6 Methodology

1.7 Target Audience

1.8 Companies & Organizations Mentioned

2 Chapter 2: An Overview of Big Data

2.1 What is Big Data?

2.2 Key Approaches to Big Data Processing

2.2.1 Hadoop

2.2.2 NoSQL

2.2.3 MPAD (Massively Parallel Analytic Databases)

2.2.4 In-Memory Processing

2.2.5 Stream Processing Technologies

2.2.6 Spark

2.2.7 Other Databases & Analytic Technologies

2.3 Key Characteristics of Big Data

2.3.1 Volume

2.3.2 Velocity

2.3.3 Variety

2.3.4 Value

2.4 Market Growth Drivers

2.4.1 Awareness of Benefits

2.4.2 Maturation of Big Data Platforms

2.4.3 Continued Investments by Web Giants, Governments & Enterprises

2.4.4 Growth of Data Volume, Velocity & Variety

2.4.5 Vendor Commitments & Partnerships

2.4.6 Technology Trends Lowering Entry Barriers

2.5 Market Barriers

2.5.1 Lack of Analytic Specialists

2.5.2 Uncertain Big Data Strategies

2.5.3 Organizational Resistance to Big Data Adoption

2.5.4 Technical Challenges: Scalability & Maintenance


2.5.5 Security & Privacy Concerns

3 Chapter 3: Big Data Analytics

3.1 What are Big Data Analytics?

3.2 The Importance of Analytics

3.3 Reactive vs. Proactive Analytics

3.4 Customer vs. Operational Analytics

3.5 Technology & Implementation Approaches

3.5.1 Grid Computing

3.5.2 In-Database Processing

3.5.3 In-Memory Analytics

3.5.4 Machine Learning & Data Mining

3.5.5 Predictive Analytics

3.5.6 NLP (Natural Language Processing)

3.5.7 Text Analytics

3.5.8 Visual Analytics

3.5.9 Graph Analytics

3.5.10 Social Media, IT & Telco Network Analytics

4 Chapter 4: Business Case & Applications in the Insurance Industry

4.1 Overview & Investment Potential

4.2 Industry Specific Market Growth Drivers

4.3 Industry Specific Market Barriers

4.4 Key Application Areas

4.4.1 Auto Insurance

4.4.2 Property & Casualty Insurance

4.4.3 Life Insurance

4.4.4 Health Insurance

4.4.5 Multi-Line Insurance

4.4.6 Other Forms of Insurance

4.4.7 Reinsurance

4.4.8 Insurance Broking

4.5 Use Cases

4.5.1 Personalized & Targeted Marketing

4.5.2 Customer Service & Experience

4.5.3 Product Innovation & Development

4.5.4 Risk Awareness & Control

4.5.5 Policy Administration, Pricing & Underwriting

4.5.6 Claims Processing & Management

4.5.7 Fraud Detection & Prevention


4.5.8 Usage & Analytics-Based Insurance

4.5.9 Other Use Cases

5 Chapter 5: Insurance Industry Case Studies

5.1 Insurers

5.1.1 Aegon: Driving Customer Engagement & Sales with Big Data

5.1.2 Aetna: Predicting & Improving Health with Big Data

5.1.3 Allianz Group: Uncovering Insurance Fraud with Big Data

5.1.4 Allstate Corporation & Arity: Making Transportation Safer & Smarter with Big Data

5.1.5 AXA: Simplifying Customer Interaction with Big Data

5.1.6 China Life Insurance Company: Elevating Risk Awareness with Big Data

5.1.7 Cigna: Streamlining Health Insurance Claims with Big Data

5.1.8 Dai-ichi Life Holdings: Unlocking & Opening Doors to Life Insurance with Big Data

5.1.9 Generali Group: Digitizing the Insurance Value Chain with Big Data

5.1.10 Progressive Corporation: Rewarding Safe Drivers & Improving Traffic Safety with Big Data

5.1.11 Samsung Fire & Marine Insurance: Transforming Insurance Underwriting with Big Data

5.1.12 UnitedHealth Group: Enhancing Patient Care & Value with Big Data

5.1.13 Zurich Insurance Group: Improving Risk Management with Big Data

5.2 Reinsurers, InsurTech Specialists & Other Stakeholders

5.2.1 Atidot: Empowering Life Insurance with Big Data

5.2.2 Cape Analytics: Delivering Instant Property Intelligence with Big Data

5.2.3 Concirrus: Enabling Smarter Marine & Auto Insurance with Big Data

5.2.4 JMDC Corporation: Optimizing Health Insurance Premiums with Big Data

5.2.5 MetroMile: Revolutionizing Auto Insurance with Big Data

5.2.6 Munich Re: Pioneering Cyber Insurance with Big Data

5.2.7 Oscar Health: Humanizing Health Insurance with Big Data

6 Chapter 6: Future Roadmap & Value Chain

6.1 Future Roadmap

6.1.1 Pre-2020: Investments in Advanced Analytics & AI (Artificial Intelligence)

6.1.2 2020 ? 2025: Large-Scale Adoption of Usage & Analytics-Based Insurance

6.1.3 2025 ? 2030: Towards the Digitization of Insurance Services

6.2 The Big Data Value Chain

6.2.1 Hardware Providers

6.2.1.1 Storage & Compute Infrastructure Providers

6.2.1.2 Networking Infrastructure Providers

6.2.2 Software Providers

6.2.2.1 Hadoop & Infrastructure Software Providers

6.2.2.2 SQL & NoSQL Providers

6.2.2.3 Analytic Platform & Application Software Providers


6.2.2.4 Cloud Platform Providers

6.2.3 Professional Services Providers

6.2.4 End-to-End Solution Providers

6.2.5 Insurance Industry

7 Chapter 7: Standardization & Regulatory Initiatives


7.1.1 Management of Hadoop

7.1.2 Big Data Projects Beyond Hadoop


7.2.1 BDWG (Big Data Working Group)


7.3.1 Big Data Working Group


7.4.1 PMML (Predictive Model Markup Language) Working Group

7.4.2 PFA (Portable Format for Analytics) Working Group


7.5.1 Big Data Initiative


7.6.1 Big Data Technical Committee


7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange

7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms

7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques

7.7.4 ISO/IEC JTC 1/WG 9: Big Data

7.7.5 Collaborations with Other ISO Work Groups


7.8.1 ITU-T Y.3600: Big Data ? Cloud Computing Based Requirements and Capabilities

7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks

7.8.3 Other Relevant Work


7.9.1 ODPi (Open Ecosystem of Big Data)

7.10 NIST (National Institute of Standards and Technology)

7.10.1 NBD-PWG (NIST Big Data Public Working Group)


7.11.1 Technical Committees


7.12.1 Big Data Accessibility


7.13.1 Work on Big Data



7.14.1 Big Data DWG (Domain Working Group)

7.15 TM Forum

7.15.1 Big Data Analytics Strategic Program


7.16.1 TPC-BDWG (TPC Big Data Working Group)


7.17.1 Big Data Community Group

7.17.2 Open Government Community Group

8 Chapter 8: Market Sizing & Forecasts

8.1 Global Outlook for the Big Data in the Insurance Industry

8.2 Hardware, Software & Professional Services Segmentation

8.3 Horizontal Submarket Segmentation

8.4 Hardware Submarkets

8.4.1 Storage and Compute Infrastructure

8.4.2 Networking Infrastructure

8.5 Software Submarkets

8.5.1 Hadoop & Infrastructure Software

8.5.2 SQL

8.5.3 NoSQL

8.5.4 Analytic Platforms & Applications

8.5.5 Cloud Platforms

8.6 Professional Services Submarket

8.6.1 Professional Services

8.7 Application Area Segmentation

8.7.1 Auto Insurance

8.7.2 Property & Casualty Insurance

8.7.3 Life Insurance

8.7.4 Health Insurance

8.7.5 Multi-Line Insurance

8.7.6 Other Forms of Insurance

8.7.7 Reinsurance

8.7.8 Insurance Broking

8.8 Use Case Segmentation

8.8.1 Personalized & Targeted Marketing

8.8.2 Customer Service & Experience

8.8.3 Product Innovation & Development

8.8.4 Risk Awareness & Control

8.8.5 Policy Administration, Pricing & Underwriting


8.8.6 Claims Processing & Management

8.8.7 Fraud Detection & Prevention

8.8.8 Usage & Analytics-Based Insurance

8.8.9 Other Use Cases

8.9 Regional Outlook

8.10 Asia Pacific

8.10.1 Country Level Segmentation

8.10.2 Australia

8.10.3 China

8.10.4 India

8.10.5 Indonesia

8.10.6 Japan

8.10.7 Malaysia

8.10.8 Pakistan

8.10.9 Philippines

8.10.10 Singapore

8.10.11 South Korea

8.10.12 Taiwan

8.10.13 Thailand

8.10.14 Rest of Asia Pacific

8.11 Eastern Europe


8.11.2 Czech Republic

8.11.3 Poland

8.11.4 Russia

8.11.5 Rest of Eastern Europe

8.12 Latin & Central America


8.12.2 Argentina

8.12.3 Brazil

8.12.4 Mexico

8.12.5 Rest of Latin & Central America

8.13 Middle East & Africa


8.13.2 Israel

8.13.3 Qatar

8.13.4 Saudi Arabia

8.13.5 South Africa

8.13.6 UAE

8.13.7 Rest of the Middle East & Africa


8.14 North America


8.14.2 Canada

8.14.3 USA

8.15 Western Europe


8.15.2 Denmark

8.15.3 Finland

8.15.4 France

8.15.5 Germany

8.15.6 Italy

8.15.7 Netherlands

8.15.8 Norway

8.15.9 Spain

8.15.10 Sweden

8.15.11 UK

8.15.12 Rest of Western Europe

9 Chapter 9: Vendor Landscape

9.1 1010data

9.2 Absolutdata

9.3 Accenture

9.4 Actian Corporation/HCL Technologies

9.5 Adaptive Insights

9.6 Adobe Systems

9.7 Advizor Solutions

9.8 AeroSpike

9.9 AFS Technologies

9.10 Alation

9.11 Algorithmia

9.12 Alluxio

9.13 ALTEN

9.14 Alteryx

9.15 AMD (Advanced Micro Devices)

9.16 Anaconda

9.17 Apixio

9.18 Arcadia Data

9.19 ARM

9.20 AtScale

9.21 Attivio


9.22 Attunity

9.23 Automated Insights

9.24 AVORA

9.25 AWS (Amazon Web Services)

9.26 Axiomatics

9.27 Ayasdi

9.28 BackOffice Associates

9.29 Basho Technologies

9.30 BCG (Boston Consulting Group)

9.31 Bedrock Data

9.32 BetterWorks

9.33 Big Panda

9.34 BigML

9.35 Bitam

9.36 Blue Medora

9.37 BlueData Software

9.38 BlueTalon

9.39 BMC Software

9.40 BOARD International

9.41 Booz Allen Hamilton

9.42 Boxever

9.43 CACI International

9.44 Cambridge Semantics

9.45 Capgemini

9.46 Cazena

9.47 Centrifuge Systems

9.48 CenturyLink

9.49 Chartio

9.50 Cisco Systems

9.51 Civis Analytics

9.52 ClearStory Data

9.53 Cloudability

9.54 Cloudera

9.55 Cloudian

9.56 Clustrix

9.57 CognitiveScale

9.58 Collibra

9.59 Concurrent Technology/Vecima Networks

9.60 Confluent

9.61 Contexti


9.62 Couchbase

9.63 Crate.io

9.64 Cray

9.65 Databricks

9.66 Dataiku

9.67 Datalytyx

9.68 Datameer

9.69 DataRobot

9.70 DataStax

9.71 Datawatch Corporation

9.72 DDN (DataDirect Networks)

9.73 Decisyon

9.74 Dell Technologies

9.75 Deloitte

9.76 Demandbase

9.77 Denodo Technologies

9.78 Dianomic Systems

9.79 Digital Reasoning Systems

9.80 Dimensional Insight

9.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group

9.82 Domino Data Lab

9.83 Domo

9.84 Dremio

9.85 DriveScale

9.86 Druva

9.87 Dundas Data Visualization

9.88 DXC Technology

9.89 Elastic

9.90 Engineering Group (Engineering Ingegneria Informatica)

9.91 EnterpriseDB Corporation

9.92 eQ Technologic

9.93 Ericsson

9.94 Erwin

9.95 EV? (Big Cloud Analytics)

9.96 EXASOL

9.97 EXL (ExlService Holdings)

9.98 Facebook

9.99 FICO (Fair Isaac Corporation)

9.100 Figure Eight

9.101 FogHorn Systems


9.102 Fractal Analytics

9.103 Franz

9.104 Fujitsu

9.105 Fuzzy Logix

9.106 Gainsight

9.107 GE (General Electric)

9.108 Glassbeam

9.109 GoodData Corporation

9.110 Google/Alphabet

9.111 Grakn Labs

9.112 Greenwave Systems

9.113 GridGain Systems

9.114 H2O.ai

9.115 HarperDB

9.116 Hedvig

9.117 Hitachi Vantara

9.118 Hortonworks

9.119 HPE (Hewlett Packard Enterprise)

9.120 Huawei

9.121 HVR

9.122 HyperScience

9.123 HyTrust

9.124 IBM Corporation

9.125 iDashboards

9.126 IDERA

9.127 Ignite Technologies

9.128 Imanis Data

9.129 Impetus Technologies

9.130 Incorta

9.131 InetSoft Technology Corporation

9.132 InfluxData

9.133 Infogix

9.134 Infor/Birst

9.135 Informatica

9.136 Information Builders

9.137 Infosys

9.138 Infoworks

9.139 Insightsoftware.com

9.140 InsightSquared

9.141 Intel Corporation


9.142 Interana

9.143 InterSystems Corporation

9.144 Jedox

9.145 Jethro

9.146 Jinfonet Software

9.147 Juniper Networks

9.148 KALEAO

9.149 Keen IO

9.150 Keyrus

9.151 Kinetica

9.152 KNIME

9.153 Kognitio

9.154 Kyvos Insights

9.155 LeanXcale

9.156 Lexalytics

9.157 Lexmark International

9.158 Lightbend

9.159 Logi Analytics

9.160 Logical Clocks

9.161 Longview Solutions/Tidemark

9.162 Looker Data Sciences

9.163 LucidWorks

9.164 Luminoso Technologies

9.165 Maana

9.166 Manthan Software Services

9.167 MapD Technologies

9.168 MapR Technologies

9.169 MariaDB Corporation

9.170 MarkLogic Corporation

9.171 Mathworks

9.172 Melissa

9.173 MemSQL

9.174 Metric Insights

9.175 Microsoft Corporation

9.176 MicroStrategy

9.177 Minitab

9.178 MongoDB

9.179 Mu Sigma

9.180 NEC Corporation

9.181 Neo4j


9.182 NetApp

9.183 Nimbix

9.184 Nokia

9.185 NTT Data Corporation

9.186 Numerify

9.187 NuoDB

9.188 NVIDIA Corporation

9.189 Objectivity

9.190 Oblong Industries

9.191 OpenText Corporation

9.192 Opera Solutions

9.193 Optimal Plus

9.194 Oracle Corporation

9.195 Palantir Technologies

9.196 Panasonic Corporation/Arimo

9.197 Panorama Software

9.198 Paxata

9.199 Pepperdata

9.200 Phocas Software

9.201 Pivotal Software

9.202 Prognoz

9.203 Progress Software Corporation

9.204 Provalis Research

9.205 Pure Storage

9.206 PwC (PricewaterhouseCoopers International)

9.207 Pyramid Analytics

9.208 Qlik

9.209 Qrama/Tengu

9.210 Quantum Corporation

9.211 Qubole

9.212 Rackspace

9.213 Radius Intelligence

9.214 RapidMiner

9.215 Recorded Future

9.216 Red Hat

9.217 Redis Labs

9.218 RedPoint Global

9.219 Reltio

9.220 RStudio

9.221 Rubrik/Datos IO


9.222 Ryft

9.223 Sailthru

9.224 Salesforce.com

9.225 Salient Management Company

9.226 Samsung Group

9.227 SAP

9.228 SAS Institute

9.229 ScaleOut Software

9.230 Seagate Technology

9.231 Sinequa

9.232 SiSense

9.233 Sizmek

9.234 SnapLogic

9.235 Snowflake Computing

9.236 Software AG

9.237 Splice Machine

9.238 Splunk

9.239 Strategy Companion Corporation

9.240 Stratio

9.241 Streamlio

9.242 StreamSets

9.243 Striim

9.244 Sumo Logic

9.245 Supermicro (Super Micro Computer)

9.246 Syncsort

9.247 SynerScope

9.248 SYNTASA

9.249 Tableau Software

9.250 Talend

9.251 Tamr

9.252 TARGIT

9.253 TCS (Tata Consultancy Services)

9.254 Teradata Corporation

9.255 Thales/Guavus

9.256 ThoughtSpot

9.257 TIBCO Software

9.258 Toshiba Corporation

9.259 Transwarp

9.260 Trifacta

9.261 Unifi Software


9.262 Unravel Data

9.263 VANTIQ

9.264 VMware

9.265 VoltDB

9.266 WANdisco

9.267 Waterline Data

9.268 Western Digital Corporation

9.269 WhereScape

9.270 WiPro

9.271 Wolfram Research

9.272 Workday

9.273 Xplenty

9.274 Yellowfin BI

9.275 Yseop

9.276 Zendesk

9.277 Zoomdata

9.278 Zucchetti

10 Chapter 10: Conclusion & Strategic Recommendations

10.1 Why is the Market Poised to Grow?

10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential?

10.3 Big Data is for Everyone

10.4 Evaluating the Business Value of Big Data for Insurers

10.5 Transforming Risk Management

10.6 Tackling Cyber Crime & Under-Insured Risks

10.7 Accelerating the Transition Towards Usage & Analytics-Based Insurance

10.8 Addressing Customer Expectations with Data-Driven Services

10.9 The Importance of AI (Artificial Intelligence) & Machine Learning

10.10 Impact of Blockchain on Big Data Processing

10.11 Adoption of Cloud Platforms to Address On-Premise System Limitations

10.12 Data Security & Privacy Concerns

10.13 Recommendations

10.13.1 Big Data Hardware, Software & Professional Services Providers

10.13.2 Insurance Industry Stakeholders

List of Figures

Figure 1: Hadoop Architecture

Figure 2: Reactive vs. Proactive Analytics

Figure 3: Distribution of Big Data Investments in the Insurance Industry, by Use Case: 2018 (%)

Figure 4: Aegon's Use of Big Data & Advanced Analytics Across the Insurance Value Chain


Figure 5: Key Elements of Generali's ASC (Analytics Solutions Center)

Figure 6: Progressive Corporation's Use of Big Data for Auto Insurance

Figure 7: Atidot's Big Data Platform for Life Insurers

Figure 8: Cape Analytics' Property Intelligence Database

Figure 9: Applications of Quest Marine Across the Insurance Value Chain

Figure 10: JMDC's Services for Insurance Companies

Figure 11: Metromile's Pay-Per-Mile Auto Insurance Program

Figure 12: Munich Re's Data Management Infrastructure

Figure 13: Big Data Roadmap in the Insurance Industry: 2018 ? 2030

Figure 14: Big Data Value Chain in the Insurance Industry

Figure 15: Key Aspects of Big Data Standardization

Figure 16: Global Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 17: Global Big Data Revenue in the Insurance Industry, by Hardware, Software &

Professional Services: 2018 ? 2030 ($ Million)

Figure 18: Global Big Data Revenue in the Insurance Industry, by Submarket: 2018 ? 2030

($ Million)

Figure 19: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the

Insurance Industry: 2018 ? 2030 ($ Million)

Figure 20: Global Big Data Networking Infrastructure Submarket Revenue in the Insurance

Industry: 2018 ? 2030 ($ Million)

Figure 21: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the

Insurance Industry: 2018 ? 2030 ($ Million)

Figure 22: Global Big Data SQL Submarket Revenue in the Insurance Industry: 2018 ? 2030

($ Million)

Figure 23: Global Big Data NoSQL Submarket Revenue in the Insurance Industry: 2018 ? 2030

($ Million)

Figure 24: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Insurance

Industry: 2018 ? 2030 ($ Million)

Figure 25: Global Big Data Cloud Platforms Submarket Revenue in the Insurance Industry: 2018 ?

2030 ($ Million)

Figure 26: Global Big Data Professional Services Submarket Revenue in the Insurance Industry:

2018 ? 2030 ($ Million)

Figure 27: Global Big Data Revenue in the Insurance Industry, by Application Area: 2018 ? 2030

($ Million)

Figure 28: Global Big Data Revenue in Auto Insurance: 2018 ? 2030 ($ Million)

Figure 29: Global Big Data Revenue in Property & Casualty Insurance: 2018 ? 2030 ($ Million)

Figure 30: Global Big Data Revenue in Life Insurance: 2018 ? 2030 ($ Million)

Figure 31: Global Big Data Revenue in Health Insurance: 2018 ? 2030 ($ Million)

Figure 32: Global Big Data Revenue in Multi-line Insurance: 2018 ? 2030 ($ Million)

Figure 33: Global Big Data Revenue in Other Forms of Insurance: 2018 ? 2030 ($ Million)


Figure 34: Global Big Data Revenue in Reinsurance: 2018 ? 2030 ($ Million)

Figure 35: Global Big Data Revenue in Insurance Broking: 2018 ? 2030 ($ Million)

Figure 36: Global Big Data Revenue in the Insurance Industry, by Use Case: 2018 ? 2030

($ Million)

Figure 37: Global Big Data Revenue in Personalized & Targeted Marketing for Insurance Services:

2018 ? 2030 ($ Million)

Figure 38: Global Big Data Revenue in Customer Service & Experience for Insurance Services:

2018 ? 2030 ($ Million)

Figure 39: Global Big Data Revenue in Product Innovation & Development for Insurance Services:

2018 ? 2030 ($ Million)

Figure 40: Global Big Data Revenue in Risk Awareness & Control for Insurance Services: 2018 ?

2030 ($ Million)

Figure 41: Global Big Data Revenue in Policy Administration, Pricing & Underwriting: 2018 ? 2030

($ Million)

Figure 42: Global Big Data Revenue in Claims Processing & Management: 2018 ? 2030 ($ Million)

Figure 43: Global Big Data Revenue in Fraud Detection & Prevention for Insurance Services:

2018 ? 2030 ($ Million)

Figure 44: Global Big Data Revenue in Usage & Analytics-Based Insurance: 2018 ? 2030

($ Million)

Figure 45: Global Big Data Revenue in Other Use Cases for Insurance Services: 2018 ? 2030

($ Million)

Figure 46: Big Data Revenue in the Insurance Industry, by Region: 2018 ? 2030 ($ Million)

Figure 47: Asia Pacific Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 48: Asia Pacific Big Data Revenue in the Insurance Industry, by Country: 2018 ? 2030

($ Million)

Figure 49: Australia Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 50: China Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 51: India Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 52: Indonesia Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 53: Japan Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 54: Malaysia Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 55: Pakistan Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 56: Philippines Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 57: Singapore Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 58: South Korea Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 59: Taiwan Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 60: Thailand Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 61: Rest of Asia Pacific Big Data Revenue in the Insurance Industry: 2018 ? 2030

($ Million)

Figure 62: Eastern Europe Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)


Figure 63: Eastern Europe Big Data Revenue in the Insurance Industry, by Country: 2018 ? 2030

($ Million)

Figure 64: Czech Republic Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 65: Poland Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 66: Russia Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 67: Rest of Eastern Europe Big Data Revenue in the Insurance Industry: 2018 ? 2030

($ Million)

Figure 68: Latin & Central America Big Data Revenue in the Insurance Industry: 2018 ? 2030

($ Million)

Figure 69: Latin & Central America Big Data Revenue in the Insurance Industry, by Country:

2018 ? 2030 ($ Million)

Figure 70: Argentina Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 71: Brazil Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 72: Mexico Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 73: Rest of Latin & Central America Big Data Revenue in the Insurance Industry: 2018 ?

2030 ($ Million)

Figure 74: Middle East & Africa Big Data Revenue in the Insurance Industry: 2018 ? 2030

($ Million)

Figure 75: Middle East & Africa Big Data Revenue in the Insurance Industry, by Country: 2018 ?

2030 ($ Million)

Figure 76: Israel Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 77: Qatar Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 78: Saudi Arabia Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 79: South Africa Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 80: UAE Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 81: Rest of the Middle East & Africa Big Data Revenue in the Insurance Industry: 2018 ?

2030 ($ Million)

Figure 82: North America Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 83: North America Big Data Revenue in the Insurance Industry, by Country: 2018 ? 2030

($ Million)

Figure 84: Canada Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 85: USA Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 86: Western Europe Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 87: Western Europe Big Data Revenue in the Insurance Industry, by Country: 2018 ? 2030

($ Million)

Figure 88: Denmark Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 89: Finland Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 90: France Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 91: Germany Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 92: Italy Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)


Figure 93: Netherlands Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 94: Norway Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 95: Spain Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 96: Sweden Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 97: UK Big Data Revenue in the Insurance Industry: 2018 ? 2030 ($ Million)

Figure 98: Rest of Western Europe Big Data Revenue in the Insurance Industry: 2018 ? 2030

($ Million)

List of Companies Mentioned

1010data

Absolutdata

Accenture


Actian Corporation

Adaptive Insights

Adobe Systems

Advizor Solutions

AeroSpike

AFS Technologies

Alation

Algorithmia


Alluxio

Alphabet

ALTEN

Alteryx


Anaconda

Apixio

Arcadia Data

Arimo

Arity

ARM


AtScale

Attivio

Attunity

Audi

Automated Insights

Automobili Lamborghini


automotiveMastermind

AVORA


Axiomatics

Ayasdi


Basho Technologies


Bedrock Data

BetterWorks

Big Panda

BigML

Birst

Bitam

Blue Medora

BlueData Software

BlueTalon

BMC Software

BMW

BOARD International

Booz Allen Hamilton

Bosch

Boxever

CACI International

Cambridge Semantics

Capgemini

Cazena

Centrifuge Systems

CenturyLink

Chartio

Cisco Systems

Citro?n

Civis Analytics

ClearStory Data

Cloudability

Cloudera

Cloudian

Clustrix

CognitiveScale

Collibra



Confluent

Contexti

Continental

Couchbase

Cox Automotive

Cox Enterprises

Crate.io

Cray



Daimler

Dash Labs

Databricks

Dataiku

Datalytyx

Datameer

DataRobot

DataStax


Datos IO


Decisyon

Dell Technologies

Deloitte

Delphi Automotive

Demandbase

Denodo Technologies

Denso Corporation

Dianomic Systems


Dimensional Insight



Domino Data Lab

Domo

Dongfeng Motor Corporation

Dremio

DriveScale

Druva


DS Automobiles

Ducati


DXC Technology

Elastic



eQ Technologic

Ericsson

Erwin

EV? (Big Cloud Analytics)

EXASOL


Facebook

FCA (Fiat Chrysler Automobiles)


Figure Eight

FogHorn Systems

Ford Motor Company

Fractal Analytics

Franz

Fujitsu

Fuzzy Logix

Gainsight


Geely (Zhejiang Geely Holding Group)

Glassbeam

GM (General Motors Company)


Google

Grakn Labs

Greenwave Systems

GridGain Systems

Groupe PSA

Groupe Renault

Guavus

H2O.ai

Hanse Orga Group

HarperDB

HCL Technologies


Hedvig

HERE

Hitachi Vantara

Honda Motor Company

Hortonworks


Huawei

HVR

HyperScience

HyTrust

Hyundai Motor Company

IBM Corporation

iDashboards

IDERA



Ignite Technologies

Imanis Data



Incorta


InfluxData

Infogix

Infor

Informatica


Infosys

Infoworks

Insightsoftware.com

InsightSquared

Intel Corporation

Interana




Jaguar Land Rover

Jedox

Jethro

Jinfonet Software


Juniper Networks

KALEAO

KDDI Corporation

Keen IO

Keyrus

Kinetica

KNIME

Kognitio

Kyvos Insights

LeanXcale

Lexalytics


Lightbend

Linux Foundation

Logi Analytics

Logical Clocks

Longview Solutions


LucidWorks


Lytx

Maana


MapD Technologies

MapR Technologies

MariaDB Corporation


Mathworks

Mazda Motor Corporation

Melissa

MemSQL

Mercedes-Benz

METI (Ministry of Economy, Trade and Industry, Japan)

Metric Insights

Michelin


MicroStrategy

Minitab

Mobileye

MongoDB


Mu Sigma

NEC Corporation

Neo4j

NetApp

Nimbix

Nissan Motor Company

Nokia


NTT DoCoMo

Numerify

NuoDB

NVIDIA Corporation


Objectivity

Oblong Industries





Opera Solutions

Optimal Plus

Oracle Corporation

Otonomo



Panorama Software

Paxata

Pepperdata

Peugeot

Phocas Software

Pivotal Software

Prognoz



Provalis Research

Pure Storage


Pyramid Analytics

Qlik

Qrama/Tengu


Quantum Corporation

Qubole

Rackspace

Radius Intelligence

RapidMiner

Recorded Future

Red Hat

Redis Labs

RedPoint Global

Reltio

RStudio

Rubrik

Ryft

SAIC Motor Corporation

Sailthru

Salesforce.com


Samsung Group

SAP

SAS Institute

ScaleOut Software

Seagate Technology

Sinequa

SiSense

Sizmek

SnapLogic

Snowflake Computing

Software AG

Splice Machine

Splunk


Stratio

Streamlio

StreamSets

Striim

Subaru

Sumo Logic


Suzuki Motor Corporation

Syncsort


SynerScope

SYNTASA

Tableau Software

Talend

Tamr

TARGIT

Tata Motors



Tesla

Thales

ThoughtSpot

THTA (Tokyo Hire-Taxi Association)

TIBCO Software

Tidemark

TM Forum

Toshiba Corporation

Toyota Motor Corporation


Transwarp

Trifacta

U.S. FTC (Federal Trade Commission)


U.S. Xpress

Uber Technologies

Unifi Software

Unravel Data

Valens

VANTIQ

Vecima Networks

VMware

Volkswagen Group

VoltDB

Volvo Cars


WANdisco

Waterline Data


WhereScape

WiPro


Wolfram Research

Workday

Xevo

Xplenty

Yellowfin BI

Yseop

Zendesk

Zoomdata

Zucchetti

보고서 문의

보험산업의 빅데이터: 2018-2023 기회 한계 전략 전망

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