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Competitive Analysis of Big Data In The Insurance Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts

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.

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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

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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.

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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
  • EV? (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

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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

Continue….

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