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

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Date: 02-Aug-2018
No. of pages: 500
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“Big Data” originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data, to solve complex problems.

Amid the proliferation of real-time 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

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

Table of Contents

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

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

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

4 Chapter 4: Business Case & Applications in the Insurance Industry 54
4.1 Overview & Investment Potential 54
4.2 Industry Specific Market Growth Drivers 55
4.3 Industry Specific Market Barriers 57
4.4 Key Application Areas 58
4.4.1 Auto Insurance 58
4.4.2 Property & Casualty Insurance 59
4.4.3 Life Insurance 60
4.4.4 Health Insurance 60
4.4.5 Multi-Line Insurance 61
4.4.6 Other Forms of Insurance 61
4.4.7 Reinsurance 62
4.4.8 Insurance Broking 62
4.5 Use Cases 63
4.5.1 Personalized & Targeted Marketing 63
4.5.2 Customer Service & Experience 64
4.5.3 Product Innovation & Development 65
4.5.4 Risk Awareness & Control 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

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

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

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

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

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

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

List of Figures

Figure 1: Hadoop Architecture 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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