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

Published: Aug, 2018 | Pages: 500 | Publisher: SNS Research
Industry: ICT | Report Format: Electronic (PDF)

“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.

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 subcategories:

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

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

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?

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.

List of Companies Mentioned

•	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
 Table of Contents

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
		
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

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
		
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	65
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
		
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
		
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
		
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
		
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
		
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
		
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	37
	Figure 2: Reactive vs. Proactive Analytics	48
	Figure 3: Distribution of Big Data Investments in the Insurance Industry, by Use Case: 2018 (%)	55
	Figure 4: Aegon's Use of Big Data & Advanced Analytics Across the Insurance Value Chain	73
	Figure 5: Key Elements of Generali's ASC (Analytics Solutions Center)	91
	Figure 6: Progressive Corporation's Use of Big Data for Auto Insurance	94
	Figure 7: Atidot's Big Data Platform for Life Insurers	102
	Figure 8: Cape Analytics' Property Intelligence Database	104
	Figure 9: Applications of Quest Marine Across the Insurance Value Chain	107
	Figure 10: JMDC's Services for Insurance Companies	108
	Figure 11: Metromile's Pay-Per-Mile Auto Insurance Program	110
	Figure 12: Munich Re's Data Management Infrastructure	113
	Figure 13: Big Data Roadmap in the Insurance Industry: 2018 - 2030	117
	Figure 14: Big Data Value Chain in the Insurance Industry	120
	Figure 15: Key Aspects of Big Data Standardization	131
	Figure 16: Global Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	144
	Figure 17: Global Big Data Revenue in the Insurance Industry, by Hardware, Software & Professional Services: 2018 - 2030 ($ Million)	145
	Figure 18: Global Big Data Revenue in the Insurance Industry, by Submarket: 2018 - 2030 ($ Million)	146
	Figure 19: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	147
	Figure 20: Global Big Data Networking Infrastructure Submarket Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	147
	Figure 21: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	148
	Figure 22: Global Big Data SQL Submarket Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	148
	Figure 23: Global Big Data NoSQL Submarket Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	149
	Figure 24: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	149
	Figure 25: Global Big Data Cloud Platforms Submarket Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	150
	Figure 26: Global Big Data Professional Services Submarket Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	150
	Figure 27: Global Big Data Revenue in the Insurance Industry, by Application Area: 2018 - 2030 ($ Million)	151
	Figure 28: Global Big Data Revenue in Auto Insurance: 2018 - 2030 ($ Million)	152
	Figure 29: Global Big Data Revenue in Property & Casualty Insurance: 2018 - 2030 ($ Million)	152
	Figure 30: Global Big Data Revenue in Life Insurance: 2018 - 2030 ($ Million)	153
	Figure 31: Global Big Data Revenue in Health Insurance: 2018 - 2030 ($ Million)	153
	Figure 32: Global Big Data Revenue in Multi-line Insurance: 2018 - 2030 ($ Million)	154
	Figure 33: Global Big Data Revenue in Other Forms of Insurance: 2018 - 2030 ($ Million)	154
	Figure 34: Global Big Data Revenue in Reinsurance: 2018 - 2030 ($ Million)	155
	Figure 35: Global Big Data Revenue in Insurance Broking: 2018 - 2030 ($ Million)	155
	Figure 36: Global Big Data Revenue in the Insurance Industry, by Use Case: 2018 - 2030 ($ Million)	156
	Figure 37: Global Big Data Revenue in Personalized & Targeted Marketing for Insurance Services: 2018 - 2030 ($ Million)	157
	Figure 38: Global Big Data Revenue in Customer Service & Experience for Insurance Services: 2018 - 2030 ($ Million)	157
	Figure 39: Global Big Data Revenue in Product Innovation & Development for Insurance Services: 2018 - 2030 ($ Million)	158
	Figure 40: Global Big Data Revenue in Risk Awareness & Control for Insurance Services: 2018 - 2030 ($ Million)	158
	Figure 41: Global Big Data Revenue in Policy Administration, Pricing & Underwriting: 2018 - 2030 ($ Million)	159
	Figure 42: Global Big Data Revenue in Claims Processing & Management: 2018 - 2030 ($ Million)	159
	Figure 43: Global Big Data Revenue in Fraud Detection & Prevention for Insurance Services: 2018 - 2030 ($ Million)	160
	Figure 44: Global Big Data Revenue in Usage & Analytics-Based Insurance: 2018 - 2030 ($ Million)	160
	Figure 45: Global Big Data Revenue in Other Use Cases for Insurance Services: 2018 - 2030 ($ Million)	161
	Figure 46: Big Data Revenue in the Insurance Industry, by Region: 2018 - 2030 ($ Million)	162
	Figure 47: Asia Pacific Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	163
	Figure 48: Asia Pacific Big Data Revenue in the Insurance Industry, by Country: 2018 - 2030 ($ Million)	163
	Figure 49: Australia Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	164
	Figure 50: China Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	164
	Figure 51: India Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	165
	Figure 52: Indonesia Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	165
	Figure 53: Japan Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	166
	Figure 54: Malaysia Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	166
	Figure 55: Pakistan Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	167
	Figure 56: Philippines Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	167
	Figure 57: Singapore Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	168
	Figure 58: South Korea Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	168
	Figure 59: Taiwan Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	169
	Figure 60: Thailand Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	169
	Figure 61: Rest of Asia Pacific Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	170
	Figure 62: Eastern Europe Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	171
	Figure 63: Eastern Europe Big Data Revenue in the Insurance Industry, by Country: 2018 - 2030 ($ Million)	171
	Figure 64: Czech Republic Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	172
	Figure 65: Poland Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	172
	Figure 66: Russia Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	173
	Figure 67: Rest of Eastern Europe Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	173
	Figure 68: Latin & Central America Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	174
	Figure 69: Latin & Central America Big Data Revenue in the Insurance Industry, by Country: 2018 - 2030 ($ Million)	174
	Figure 70: Argentina Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	175
	Figure 71: Brazil Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	175
	Figure 72: Mexico Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	176
	Figure 73: Rest of Latin & Central America Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	176
	Figure 74: Middle East & Africa Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	177
	Figure 75: Middle East & Africa Big Data Revenue in the Insurance Industry, by Country: 2018 - 2030 ($ Million)	177
	Figure 76: Israel Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	178
	Figure 77: Qatar Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	178
	Figure 78: Saudi Arabia Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	179
	Figure 79: South Africa Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	179
	Figure 80: UAE Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	180
	Figure 81: Rest of the Middle East & Africa Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	180
	Figure 82: North America Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	181
	Figure 83: North America Big Data Revenue in the Insurance Industry, by Country: 2018 - 2030 ($ Million)	181
	Figure 84: Canada Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	182
	Figure 85: USA Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	182
	Figure 86: Western Europe Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	183
	Figure 87: Western Europe Big Data Revenue in the Insurance Industry, by Country: 2018 - 2030 ($ Million)	183
	Figure 88: Denmark Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	184
	Figure 89: Finland Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	184
	Figure 90: France Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	185
	Figure 91: Germany Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	185
	Figure 92: Italy Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	186
	Figure 93: Netherlands Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	186
	Figure 94: Norway Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	187
	Figure 95: Spain Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	187
	Figure 96: Sweden Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	188
	Figure 97: UK Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	188
	Figure 98: Rest of Western Europe Big Data Revenue in the Insurance Industry: 2018 - 2030 ($ Million)	189 



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