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

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

Report Code: KNJ00008 | No. of Pages: 500 | Category: Services
Publisher: SnS Telecom | Date of Publish: Aug-2018 
“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
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 ASF (Apache Software Foundation)
7.1.1 Management of Hadoop
7.1.2 Big Data Projects Beyond Hadoop
7.2 CSA (Cloud Security Alliance)
7.2.1 BDWG (Big Data Working Group)
7.3 CSCC (Cloud Standards Customer Council)
7.3.1 Big Data Working Group
7.4 DMG  (Data Mining Group)
7.4.1 PMML (Predictive Model Markup Language) Working Group
7.4.2 PFA (Portable Format for Analytics) Working Group
7.5 IEEE (Institute of Electrical and Electronics Engineers)
7.5.1 Big Data Initiative
7.6 INCITS (InterNational Committee for Information Technology Standards)
7.6.1 Big Data Technical Committee
7.7 ISO (International Organization for Standardization)
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 ITU (International Telecommunication Union)
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 Linux Foundation
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 OASIS (Organization for the Advancement of Structured Information Standards)
7.11.1 Technical Committees
7.12 ODaF (Open Data Foundation)
7.12.1 Big Data Accessibility
7.13 ODCA (Open Data Center Alliance)
7.13.1 Work on Big Data
7.14 OGC (Open Geospatial Consortium)
7.14.1 Big Data DWG (Domain Working Group)
7.15 TM Forum
7.15.1 Big Data Analytics Strategic Program
7.16 TPC (Transaction Processing Performance Council)
7.16.1 TPC-BDWG (TPC Big Data Working Group)
7.17 W3C (World Wide Web Consortium)
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
8.11.1 Country Level Segmentation
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
8.12.1 Country Level Segmentation
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
8.13.1 Country Level Segmentation
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
8.14.1 Country Level Segmentation
8.14.2 Canada
8.14.3 USA
8.15 Western Europe
8.15.1 Country Level Segmentation
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 EVŌ (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

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