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Big Data in the Healthcare & Pharmaceutical Industry: 2017 - 2030 - Opportunities, Challenges, Strategies & Forecasts

Published: August 2017

No. of Pages: 499

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Report Description
Table of Contents

“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 healthcare and pharmaceutical industry is no exception to this trend, where Big Data has found a host of applications ranging from drug discovery and precision medicine to clinical decision support and population health management.

SNS Research estimates that Big Data investments in the healthcare and pharmaceutical industry will account for nearly $4 Billion in 2017 alone. Led by a plethora of business opportunities for healthcare providers, insurers, payers, government agencies, pharmaceutical companies and other stakeholders, these investments are further expected to grow at a CAGR of more than 15% over the next three years.

The “Big Data in the Healthcare & Pharmaceutical Industry: 2017 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of Big Data in the healthcare and pharmaceutical 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 2017 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 5 application areas, 36 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.

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 healthcare and pharmaceutical industry
34 case studies of Big Data investments by healthcare providers, insurers, payers, pharmaceutical companies and other stakeholders
Future roadmap and value chain
Company profiles and strategies of over 240 Big Data vendors
Strategic recommendations for Big Data vendors, and healthcare and pharmaceutical industry stakeholders
Market analysis and forecasts from 2017 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

Pharmaceutical & Medical Products
Core Healthcare Operations
Healthcare Support, Awareness & Disease Prevention
Health Insurance & Payer Services
Marketing, Sales & Other Applications

Use Cases

Drug Discovery, Design & Development
Medical Product Design & Development
Clinical Development & Trials
Precision Medicine & Genomics
Manufacturing & Supply Chain Management
Post-Market Surveillance & Pharmacovigilance
Medical Product Fault Monitoring
Clinical Decision Support
Care Coordination & Delivery Management
CER (Comparative Effectiveness Research) & Observational Evidence
Personalized Healthcare & Targeted Treatments
Data-Driven Preventive Care & Health Interventions
Surgical Practice & Complex Medical Procedures
Pathology, Medical Imaging & Other Medical Tests
Proactive & Remote Patient Monitoring
Predictive Maintenance of Medical Equipment
Pharmacy Services
Self-Care & Lifestyle Support
Medication Adherence & Management
Vaccine Development & Promotion
Population Health Management
Connected Health Communities & Medical Knowledge Dissemination
Epidemiology & Disease Surveillance
Health Policy Decision Making
Controlling Substance Abuse & Addiction
Increasing Awareness & Accessible Healthcare
Health Insurance Claims Processing & Management
Fraud & Abuse Prevention
Proactive Patient Engagement
Accountable & Value-Based Care
Data-Driven Health Insurance Premiums
Marketing & Sales
Administrative & Customer Services
Finance & Risk Management
Healthcare Data Monetization
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 healthcare and pharmaceutical industry?
How is the market evolving by segment and region?
What will the market size be in 2020 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 healthcare providers, insurers, payers, pharmaceutical companies and other stakeholders investing in Big Data?
What opportunities exist for Big Data analytics in the healthcare and pharmaceutical industry?
Which countries, application areas and use cases will see the highest percentage of Big Data investments in the healthcare and pharmaceutical industry?

Key Findings

The report has the following key findings:

In 2017, Big Data vendors will pocket nearly $4 Billion from hardware, software and professional services revenues in the healthcare and pharmaceutical industry. These investments are further expected to grow at a CAGR of more than 15% over the next three years, eventually accounting for over $5.8 Billion by the end of 2020.
Through the use of Big Data technologies, hospitals and other healthcare facilities have been able to achieve cost reductions of more than 10%, improvements in outcomes by as much as 20% for certain conditions, growth in revenue by 30%, and increase in patient access to services by more than 35%.
Big Data technologies are playing a pivotal role in accelerating the transition towards accountable and value-based care models, by enabling the continuous collection, consolidation and analysis of clinical and operational data from healthcare facilities and other available data sources.
Addressing privacy and security concerns is necessary in order to fully leverage the benefits of Big Data in the healthcare and pharmaceutical industry. Therefore, it is essential for key stakeholders to make significant investments in data encryption and cybersecurity, in addition to adopting defensible de-identification techniques and implementing strict restrictions on data use.

List of Companies Mentioned

1010data
Absolutdata
Accenture
ACR (American College of Radiology)
Actian Corporation
Adaptive Insights
Advizor Solutions
AeroSpike
Aetna
AFS Technologies
Alation
Algorithmia
Alluxio
Alphabet
Alpine Data
Alteryx
Ambient Clinical Analytics
AMD (Advanced Micro Devices)
Amino
Apixio
Arcadia Data
Arimo
ARM
ASF (Apache Software Foundation)
ASTM (American Society for Testing and Materials)
AstraZeneca
AtScale
Attivio
Attunity
Australian Digital Health Agency
Automated Insights
AWS (Amazon Web Services)
Axiomatics
Ayasdi
Bangkok Hospital Group
Basho Technologies
Bayer
BCG (Boston Consulting Group)
Bedrock Data
BetterWorks
Big Cloud Analytics
Big Panda
BigML
Birst
Bitam
Blue Medora
BlueData Software
BlueTalon
BMC Software
BOARD International
Booz Allen Hamilton
Boxever
CACI International
Cambridge Semantics
Capgemini
Cazena
CDC (Centers for Disease Control & Prevention)
Centerstone
Centrifuge Systems
CenturyLink
Chartio
Cincinnati Children’s Hospital Medical Center
Cisco Systems
Civis Analytics
ClearStory Data
Cloudability
Cloudera
Clustrix
CMS (U.S. Centers for Medicare & Medicaid Services)
CNIL (Data Protection Regulatory Authority, France)
CognitiveScale
Collibra
Concurrent Computer Corporation
Confluent
Contexti
Continuum Analytics
CosmosID
Couchbase
CrowdFlower
CSA (Cloud Security Alliance)
CSCC (Cloud Standards Customer Council)
CSIRO (Commonwealth Scientific and Industrial Research Organization)
Databricks
DataGravity
Dataiku
Datameer
DataRobot
DataScience
DataStax
DataTorrent
Datawatch Corporation
Datos IO
DDN (DataDirect Networks)
Decisyon
Dell Technologies
Deloitte
Demandbase
Denodo Technologies
Digital Reasoning Systems
Dimensional Insight
DMG (Data Mining Group)
Dolphin Enterprise Solutions Corporation
Domino Data Lab
Domo
DriveScale
Dundas Data Visualization
DXC Technology
Eligotech
Engineering Group (Engineering Ingegneria Informatica)
EnterpriseDB
eQ Technologic
Ericsson
EXASOL
Express Scripts
Exscientia
Facebook
Faros Healthcare
FDA (U.S. Food and Drug Administration)
FICO (Fair Isaac Corporation)
Fractal Analytics
Fujitsu
Fuzzy Logix
Gainsight
GE (General Electric)
Genomics England
Ginger.io
Glassbeam
GNS Healthcare
Gold Coast Health
GoodData Corporation
Google
Greenwave Systems
GridGain Systems
GSK (GlaxoSmithKline)
Guavus
H2O.ai
HDS (Hitachi Data Systems)
Hedvig
HHS (U.S. Department of Health & Human Services)
HL7 (Health Level Seven)
HLI (Human Longevity Inc.)
Hortonworks
HPE (Hewlett Packard Enterprise)
Huawei
IBM Corporation
iDashboards
IEEE (Institute of Electrical and Electronics Engineers)
IHE (Integrating the Healthcare Enterprise)
Illumina
IMI (Innovative Medicines Initiative)
Impetus Technologies
INCITS (InterNational Committee for Information Technology Standards)
Incorta
INDS (National Institute of Health Data, France)
InetSoft Technology Corporation
Infer
Infor
Informatica Corporation
Information Builders
Infosys
Infoworks
Insightsoftware.com
InsightSquared
Intel Corporation
Interana
InterSystems Corporation
ISO (International Organization for Standardization)
ITU (International Telecommunications Union)
IU Health (Indiana University Health)
IURTC (Indiana University Research & Technology Corporation)
Jedox
Jethro
Jinfonet Software
Johnson & Johnson
Juniper Networks
KALEAO
KBV/NASHIP (National Association of Statutory Health Insurance Physicians, Germany)
Keen IO
Kinetica
KNIME
Kognitio
Kyvos Insights
Lavastorm
Lexalytics
Lexmark International
Linux Foundation
Logi Analytics
Longview Solutions
Looker Data Sciences
LucidWorks
Luminoso Technologies
Maana
Magento Commerce
Manthan Software Services
MapD Technologies
MapR Technologies
MariaDB Corporation
MarkLogic Corporation
Mathworks
Mayo Clinic
Medtronic
MemSQL
Merck & Co.
Merck KGaA
Metric Insights
Microsoft Corporation
MicroStrategy
Ministry of Health, Labor and Welfare, Japan
Minitab
MolecularMatch
MongoDB
MSQC (Michigan Surgical Quality Collaborative)
Mu Sigma
NCCS (National Cancer Centre Singapore)
NCPDP (National Council for Prescription Drug Programs)
NEC Corporation
NEMA (National Electrical Manufacturers Association)
Neo Technology
NetApp
NHS (National Health Service, United Kingdom)
NHS England
NHS Scotland
Nimbix
NIST (U.S. National Institute of Standards and Technology)
Nokia
Novartis
NTT Data Corporation
Numerify
NuoDB
Nutonian
NVIDIA Corporation
OASIS (Organization for the Advancement of Structured Information Standards)
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
Palantir Technologies
Panorama Software
Paxata
Pentaho Corporation
Pepperdata
Pfizer
Phocas Software
Pivotal Software
Prognoz
Progress Software Corporation
Proteus Digital Health
PwC (PricewaterhouseCoopers International)
Pyramid Analytics
Qlik
Quantum Corporation
Qubole
Rackspace
Radius Intelligence
RapidMiner
Recorded Future
Red Hat
Redis Labs
RedPoint Global
Reltio
Roche
Rocket Fuel
Royal Philips
RStudio
Ryft Systems
Sailthru
Salesforce.com
Salient Management Company
Samsung Group
Sanofi
SAP
SAS Institute
ScaleDB
ScaleOut Software
SCIO Health Analytics
Seagate Technology
Seattle Children's Hospital
Sickweather
Sinequa
SingHealth (Singapore Health Services)
SiSense
SnapLogic
Snowflake Computing
Software AG
Splice Machine
Splunk
Sproxil
Sqrrl
Strategy Companion Corporation
StreamSets
Striim
Sumo Logic
Supermicro (Super Micro Computer)
Syncsort
SynerScope
Tableau Software
Talena
Talend
Tamr
TARGIT
TCS (Tata Consultancy Services)
Teradata Corporation
The Weather Company
ThoughtSpot
TIBCO Software
Tidemark
TM Forum
Toshiba Corporation
TPC (Transaction Processing Performance Council)
Trifacta
U.S. Department of Energy
U.S. Department of Veterans Affairs
UN (United Nations)
UnitedHealth Group
University of Michigan
University of Utah Health Care
Unravel Data
VHA (U.S. Veterans Health Administration)
VMware
VoltDB
W3C (World Wide Web Consortium)
Waterline Data
Western Digital Corporation
WiPro
Workday
X12
Xplenty
Yellowfin International
Yseop
Zendesk
Zoomdata
Zucchetti

Table of Contents

1 Chapter 1: Introduction 24
1.1 Executive Summary 24
1.2 Topics Covered 26
1.3 Forecast Segmentation 27
1.4 Key Questions Answered 30
1.5 Key Findings 31
1.6 Methodology 32
1.7 Target Audience 33
1.8 Companies & Organizations Mentioned 34

2 Chapter 2: An Overview of Big Data 38
2.1 What is Big Data? 38
2.2 Key Approaches to Big Data Processing 38
2.2.1 Hadoop 39
2.2.2 NoSQL 41
2.2.3 MPAD (Massively Parallel Analytic Databases) 41
2.2.4 In-Memory Processing 42
2.2.5 Stream Processing Technologies 42
2.2.6 Spark 43
2.2.7 Other Databases & Analytic Technologies 43
2.3 Key Characteristics of Big Data 44
2.3.1 Volume 44
2.3.2 Velocity 44
2.3.3 Variety 44
2.3.4 Value 45
2.4 Market Growth Drivers 46
2.4.1 Awareness of Benefits 46
2.4.2 Maturation of Big Data Platforms 46
2.4.3 Continued Investments by Web Giants, Governments & Enterprises 47
2.4.4 Growth of Data Volume, Velocity & Variety 47
2.4.5 Vendor Commitments & Partnerships 47
2.4.6 Technology Trends Lowering Entry Barriers 48
2.5 Market Barriers 48
2.5.1 Lack of Analytic Specialists 48
2.5.2 Uncertain Big Data Strategies 48
2.5.3 Organizational Resistance to Big Data Adoption 49
2.5.4 Technical Challenges: Scalability & Maintenance 49
2.5.5 Security & Privacy Concerns 49

3 Chapter 3: Big Data Analytics 51
3.1 What are Big Data Analytics? 51
3.2 The Importance of Analytics 51
3.3 Reactive vs. Proactive Analytics 52
3.4 Customer vs. Operational Analytics 53
3.5 Technology & Implementation Approaches 53
3.5.1 Grid Computing 53
3.5.2 In-Database Processing 54
3.5.3 In-Memory Analytics 54
3.5.4 Machine Learning & Data Mining 54
3.5.5 Predictive Analytics 55
3.5.6 NLP (Natural Language Processing) 55
3.5.7 Text Analytics 56
3.5.8 Visual Analytics 57
3.5.9 Graph Analytics 57
3.5.10 Social Media, IT & Telco Network Analytics 58

4 Chapter 4: Business Case & Applications in the Healthcare & Pharmaceutical Industry 59
4.1 Overview & Investment Potential 59
4.2 Industry Specific Market Growth Drivers 60
4.3 Industry Specific Market Barriers 61
4.4 Key Applications 63
4.4.1 Pharmaceutical & Medical Products 63
4.4.1.1 Drug Discovery, Design & Development 63
4.4.1.2 Medical Product Design & Development 64
4.4.1.3 Clinical Development & Trials 64
4.4.1.4 Precision Medicine & Genomics 65
4.4.1.5 Manufacturing & Supply Chain Management 66
4.4.1.6 Post-Market Surveillance & Pharmacovigilance 68
4.4.1.7 Medical Product Fault Monitoring 68
4.4.2 Core Healthcare Operations 69
4.4.2.1 Clinical Decision Support 69
4.4.2.2 Care Coordination & Delivery Management 70
4.4.2.3 CER (Comparative Effectiveness Research) & Observational Evidence 71
4.4.2.4 Personalized Healthcare & Targeted Treatments 71
4.4.2.5 Data-Driven Preventive Care & Health Interventions 72
4.4.2.6 Surgical Practice & Complex Medical Procedures 72
4.4.2.7 Pathology, Medical Imaging & Other Medical Tests 73
4.4.2.8 Proactive & Remote Patient Monitoring 73
4.4.2.9 Predictive Maintenance of Medical Equipment 74
4.4.2.10 Pharmacy Services 74
4.4.3 Healthcare Support, Awareness & Disease Prevention 75
4.4.3.1 Self-Care & Lifestyle Support 75
4.4.3.2 Medication Adherence & Management 76
4.4.3.3 Vaccine Development & Promotion 77
4.4.3.4 Population Health Management 77
4.4.3.5 Connected Health Communities & Medical Knowledge Dissemination 78
4.4.3.6 Epidemiology & Disease Surveillance 79
4.4.3.7 Health Policy Decision Making 79
4.4.3.8 Controlling Substance Abuse & Addiction 80
4.4.3.9 Increasing Awareness & Accessible Healthcare 81
4.4.4 Health Insurance & Payer Services 81
4.4.4.1 Health Insurance Claims Processing & Management 81
4.4.4.2 Fraud & Abuse Prevention 82
4.4.4.3 Proactive Patient Engagement 83
4.4.4.4 Accountable & Value-Based Care 83
4.4.4.5 Data-Driven Health Insurance Premiums 84
4.4.5 Marketing, Sales & Other Applications 84
4.4.5.1 Marketing & Sales 84
4.4.5.2 Administrative & Customer Services 85
4.4.5.3 Finance & Risk Management 86
4.4.5.4 Healthcare Data Monetization 87
4.4.5.5 Other Applications 88

5 Chapter 5: Healthcare & Pharmaceutical Industry Case Studies 89
5.1 Pharmaceutical & Medical Device Companies 89
5.1.1 AstraZeneca: Analytics-Driven Drug Development with Big Data 89
5.1.2 Bayer: Accelerating Clinical Trials with Big Data 91
5.1.3 GSK (GlaxoSmithKline): Increasing Success Rates in Drug Discovery with Big Data 93
5.1.4 Johnson & Johnson: Intelligent Pharmaceutical Marketing with Big Data 95
5.1.5 Medtronic: Facilitating Predictive Care with Big Data 96
5.1.6 Merck & Co.: Optimizing Vaccine Manufacturing with Big Data 97
5.1.7 Merck KGaA: Discovering Drugs Faster with Big Data 98
5.1.8 Novartis: Digitizing Healthcare with Big Data 99
5.1.9 Pfizer: Developing Effective and Targeted Therapies with Big Data 101
5.1.10 Roche: Personalizing Healthcare with Big Data 103
5.1.11 Sanofi: Proactive Diabetes Care with Big Data 104
5.2 Healthcare Providers, Insurers & Payers 106
5.2.1 Aetna: Predicting & Improving Health with Big Data 106
5.2.2 Bangkok Hospital Group: Transforming the Patient Experience with Big Data 108
5.2.3 Gold Coast Health: Reducing Hospital Waiting Times with Big Data 110
5.2.4 IU Health (Indiana University Health): Preventing Hospital-Acquired Infections with Big Data 111
5.2.5 MSQC (Michigan Surgical Quality Collaborative): Surgical Quality Improvement with Big Data 112
5.2.6 NCCS (National Cancer Centre Singapore): Advancing Cancer Treatment with Big Data 113
5.2.7 NHS Scotland: Improving Outcomes with Big Data 115
5.2.8 Seattle Children's Hospital: Enabling Faster & Accurate Diagnosis with Big Data 116
5.2.9 UnitedHealth Group: Enhancing Patient Care & Value with Big Data 117
5.2.10 VHA (Veterans Health Administration): Streamlining Healthcare Delivery with Big Data 119
5.3 Other Stakeholders 121
5.3.1 Amino: Healthcare Transparency with Big Data 121
5.3.2 CosmosID: Advancing Microbial Genomics with Big Data 122
5.3.3 Express Scripts: Improving Medication Adherence with Big Data 123
5.3.4 Faros Healthcare: Enhancing Clinical Decision Making with Big Data 125
5.3.5 Genomics England: Developing the World's First Genomics Medicine Service with Big Data 126
5.3.6 Ginger.io: Improving Mental Wellbeing with Big Data 128
5.3.7 Illumina: Enabling Precision Medicine with Big Data 129
5.3.8 INDS (National Institute of Health Data, France): Population Health Management with Big Data 130
5.3.9 MolecularMatch: Advancing the Clinical Utility of Genomics with Big Data 131
5.3.10 Proteus Digital Health: Pioneering Digital Medicine with Big Data 133
5.3.11 Royal Philips: Enhancing Workflows in ICUs (Intensive Care Units) with Big Data 135
5.3.12 Sickweather: Sickness Forecasting & Mapping with Big Data 136
5.3.13 Sproxil: Fighting Counterfeit Drugs with Big Data 138

6 Chapter 6: Future Roadmap & Value Chain 140
6.1 Future Roadmap 140
6.1.1 2017 – 2020: Growing Investments in Real-Time & Predictive Health Analytics 140
6.1.2 2020 – 2025: Large-Scale Adoption of Precision Medicine 141
6.1.3 2025 – 2030: Moving Beyond National-Level Population Health Management 142
6.2 Value Chain 142
6.2.1 Hardware Providers 143
6.2.1.1 Storage & Compute Infrastructure Providers 143
6.2.1.2 Networking Infrastructure Providers 144
6.2.2 Software Providers 144
6.2.2.1 Hadoop & Infrastructure Software Providers 144
6.2.2.2 SQL & NoSQL Providers 145
6.2.2.3 Analytic Platform & Application Software Providers 145
6.2.2.4 Cloud Platform Providers 145
6.2.3 Professional Services Providers 145
6.2.4 End-to-End Solution Providers 146
6.2.5 Healthcare & Pharmaceutical Industry 146

7 Chapter 7: Standardization & Regulatory Initiatives 147
7.1 ASF (Apache Software Foundation) 147
7.1.1 Management of Hadoop 147
7.1.2 Big Data Projects Beyond Hadoop 147
7.2 CSA (Cloud Security Alliance) 150
7.2.1 BDWG (Big Data Working Group) 151
7.3 CSCC (Cloud Standards Customer Council) 151
7.3.1 Big Data Working Group 151
7.4 DMG (Data Mining Group) 152
7.4.1 PMML (Predictive Model Markup Language) Working Group 152
7.4.2 PFA (Portable Format for Analytics) Working Group 152
7.5 IEEE (Institute of Electrical and Electronics Engineers) 153
7.5.1 Big Data Initiative 153
7.6 INCITS (InterNational Committee for Information Technology Standards) 154
7.6.1 Big Data Technical Committee 154
7.7 ISO (International Organization for Standardization) 155
7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange 155
7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms 156
7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques 156
7.7.4 ISO/IEC JTC 1/WG 9: Big Data 156
7.7.5 Collaborations with Other ISO Work Groups 158
7.8 ITU (International Telecommunications Union) 158
7.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities 158
7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks 159
7.8.3 Other Relevant Work 160
7.9 Linux Foundation 160
7.9.1 ODPi (Open Ecosystem of Big Data) 160
7.10 NIST (National Institute of Standards and Technology) 161
7.10.1 NBD-PWG (NIST Big Data Public Working Group) 161
7.11 OASIS (Organization for the Advancement of Structured Information Standards) 162
7.11.1 Technical Committees 162
7.12 ODaF (Open Data Foundation) 163
7.12.1 Big Data Accessibility 163
7.13 ODCA (Open Data Center Alliance) 163
7.13.1 Work on Big Data 163
7.14 OGC (Open Geospatial Consortium) 164
7.14.1 Big Data DWG (Domain Working Group) 164
7.15 TM Forum 164
7.15.1 Big Data Analytics Strategic Program 164
7.16 TPC (Transaction Processing Performance Council) 165
7.16.1 TPC-BDWG (TPC Big Data Working Group) 165
7.17 W3C (World Wide Web Consortium) 165
7.17.1 Big Data Community Group 165
7.17.2 Open Government Community Group 166
7.18 Other Initiatives Relevant to the Healthcare & Pharmaceutical Industry 166
7.18.1 HIPAA (Health Insurance Portability and Accountability Act of 1996) 166
7.18.2 HITECH (Health Information Technology for Economic and Clinical Health) Act 167
7.18.3 European Union's GDPR (General Data Protection Regulation) 168
7.18.4 Australian Digital Health Agency 168
7.18.5 United Kingdom's ITK (Interoperability Toolkit) 169
7.18.6 Japan's SS-MIX (Standard Structured Medical Information eXchange) 169
7.18.7 Germany's xDT 169
7.18.8 France's DMP (Dossier Médical Personnel) 170
7.18.9 HL7 (Health Level Seven) Specifications 170
7.18.10 IHE (Integrating the Healthcare Enterprise) 171
7.18.11 NCPDP (National Council for Prescription Drug Programs) 172
7.18.12 DICOM (Digital Imaging and Communications in Medicine) 173
7.18.13 eHealth Exchange 173
7.18.14 EDIFACT (Electronic Data Interchange For Administration, Commerce, and Transport) 173
7.18.15 X12 & Others 173

8 Chapter 8: Market Analysis & Forecasts 175
8.1 Global Outlook for Big Data in the Healthcare & Pharmaceutical Industry 175
8.2 Hardware, Software & Professional Services Segmentation 176
8.3 Horizontal Submarket Segmentation 177
8.4 Hardware Submarkets 177
8.4.1 Storage and Compute Infrastructure 177
8.4.2 Networking Infrastructure 178
8.5 Software Submarkets 178
8.5.1 Hadoop & Infrastructure Software 178
8.5.2 SQL 179
8.5.3 NoSQL 179
8.5.4 Analytic Platforms & Applications 180
8.5.5 Cloud Platforms 180
8.6 Professional Services Submarket 181
8.6.1 Professional Services 181
8.7 Application Area Segmentation 182
8.7.1 Pharmaceutical & Medical Products 182
8.7.2 Core Healthcare Operations 183
8.7.3 Healthcare Support, Awareness & Disease Prevention 183
8.7.4 Health Insurance & Payer Services 184
8.7.5 Marketing, Sales & Other Applications 184
8.8 Use Case Segmentation 185
8.9 Pharmaceutical & Medical Products 187
8.9.1 Drug Discovery, Design & Development 187
8.9.2 Medical Product Design & Development 187
8.9.3 Clinical Development & Trials 188
8.9.4 Precision Medicine & Genomics 188
8.9.5 Manufacturing & Supply Chain Management 189
8.9.6 Post-Market Surveillance & Pharmacovigilance 189
8.9.7 Medical Product Fault Monitoring 190
8.10 Core Healthcare Operations 190
8.10.1 Clinical Decision Support 190
8.10.2 Care Coordination & Delivery Management 191
8.10.3 CER (Comparative Effectiveness Research) & Observational Evidence 191
8.10.4 Personalized Healthcare & Targeted Treatments 192
8.10.5 Data-Driven Preventive Care & Health Interventions 192
8.10.6 Surgical Practice & Complex Medical Procedures 193
8.10.7 Pathology, Medical Imaging & Other Medical Tests 193
8.10.8 Proactive & Remote Patient Monitoring 194
8.10.9 Predictive Maintenance of Medical Equipment 194
8.10.10 Pharmacy Services 195
8.11 Healthcare Support, Awareness & Disease Prevention 195
8.11.1 Self-Care & Lifestyle Support 195
8.11.2 Medication Adherence & Management 196
8.11.3 Vaccine Development & Promotion 196
8.11.4 Population Health Management 197
8.11.5 Connected Health Communities & Medical Knowledge Dissemination 197
8.11.6 Epidemiology & Disease Surveillance 198
8.11.7 Health Policy Decision Making 198
8.11.8 Controlling Substance Abuse & Addiction 199
8.11.9 Increasing Awareness & Accessible Healthcare 199
8.12 Health Insurance & Payer Services 200
8.12.1 Health Insurance Claims Processing & Management 200
8.12.2 Fraud & Abuse Prevention 200
8.12.3 Proactive Patient Engagement 201
8.12.4 Accountable & Value-Based Care 201
8.12.5 Data-Driven Health Insurance Premiums 202
8.13 Marketing, Sales & Other Application Use Cases 202
8.13.1 Marketing & Sales 202
8.13.2 Administrative & Customer Services 203
8.13.3 Finance & Risk Management 203
8.13.4 Healthcare Data Monetization 204
8.13.5 Other Use Cases 204
8.14 Regional Outlook 205
8.15 Asia Pacific 205
8.15.1 Country Level Segmentation 206
8.15.2 Australia 206
8.15.3 China 207
8.15.4 India 207
8.15.5 Indonesia 208
8.15.6 Japan 208
8.15.7 Malaysia 209
8.15.8 Pakistan 209
8.15.9 Philippines 210
8.15.10 Singapore 210
8.15.11 South Korea 211
8.15.12 Taiwan 211
8.15.13 Thailand 212
8.15.14 Rest of Asia Pacific 212
8.16 Eastern Europe 213
8.16.1 Country Level Segmentation 213
8.16.2 Czech Republic 214
8.16.3 Poland 214
8.16.4 Russia 215
8.16.5 Rest of Eastern Europe 215
8.17 Latin & Central America 216
8.17.1 Country Level Segmentation 216
8.17.2 Argentina 217
8.17.3 Brazil 217
8.17.4 Mexico 218
8.17.5 Rest of Latin & Central America 218
8.18 Middle East & Africa 219
8.18.1 Country Level Segmentation 219
8.18.2 Israel 220
8.18.3 Qatar 220
8.18.4 Saudi Arabia 221
8.18.5 South Africa 221
8.18.6 UAE 222
8.18.7 Rest of the Middle East & Africa 222
8.19 North America 223
8.19.1 Country Level Segmentation 223
8.19.2 Canada 224
8.19.3 USA 224
8.20 Western Europe 225
8.20.1 Country Level Segmentation 225
8.20.2 Denmark 226
8.20.3 Finland 226
8.20.4 France 227
8.20.5 Germany 227
8.20.6 Italy 228
8.20.7 Netherlands 228
8.20.8 Norway 229
8.20.9 Spain 229
8.20.10 Sweden 230
8.20.11 UK 230
8.20.12 Rest of Western Europe 231

9 Chapter 9: Vendor Landscape 232
9.1 1010data 232
9.2 Absolutdata 233
9.3 Accenture 234
9.4 Actian Corporation 235
9.5 Adaptive Insights 236
9.6 Advizor Solutions 237
9.7 AeroSpike 238
9.8 AFS Technologies 239
9.9 Alation 240
9.10 Algorithmia 241
9.11 Alluxio 242
9.12 Alpine Data 243
9.13 Alteryx 244
9.14 AMD (Advanced Micro Devices) 245
9.15 Apixio 246
9.16 Arcadia Data 247
9.17 Arimo 248
9.18 ARM 249
9.19 AtScale 250
9.20 Attivio 251
9.21 Attunity 252
9.22 Automated Insights 253
9.23 AWS (Amazon Web Services) 254
9.24 Axiomatics 255
9.25 Ayasdi 256
9.26 Basho Technologies 257
9.27 BCG (Boston Consulting Group) 258
9.28 Bedrock Data 259
9.29 BetterWorks 260
9.30 Big Cloud Analytics 261
9.31 BigML 262
9.32 Big Panda 263
9.33 Birst 264
9.34 Bitam 265
9.35 Blue Medora 266
9.36 BlueData Software 267
9.37 BlueTalon 268
9.38 BMC Software 269
9.39 BOARD International 270
9.40 Booz Allen Hamilton 271
9.41 Boxever 272
9.42 CACI International 273
9.43 Cambridge Semantics 274
9.44 Capgemini 275
9.45 Cazena 276
9.46 Centrifuge Systems 277
9.47 CenturyLink 278
9.48 Chartio 279
9.49 Cisco Systems 280
9.50 Civis Analytics 281
9.51 ClearStory Data 282
9.52 Cloudability 283
9.53 Cloudera 284
9.54 Clustrix 285
9.55 CognitiveScale 286
9.56 Collibra 287
9.57 Concurrent Computer Corporation 288
9.58 Confluent 289
9.59 Contexti 290
9.60 Continuum Analytics 291
9.61 Couchbase 292
9.62 CrowdFlower 293
9.63 Databricks 294
9.64 DataGravity 295
9.65 Dataiku 296
9.66 Datameer 297
9.67 DataRobot 298
9.68 DataScience 299
9.69 DataStax 300
9.70 DataTorrent 301
9.71 Datawatch Corporation 302
9.72 Datos IO 303
9.73 DDN (DataDirect Networks) 304
9.74 Decisyon 305
9.75 Dell Technologies 306
9.76 Deloitte 307
9.77 Demandbase 308
9.78 Denodo Technologies 309
9.79 Digital Reasoning Systems 310
9.80 Dimensional Insight 311
9.81 Dolphin Enterprise Solutions Corporation 312
9.82 Domino Data Lab 313
9.83 Domo 314
9.84 DriveScale 315
9.85 Dundas Data Visualization 316
9.86 DXC Technology 317
9.87 Eligotech 318
9.88 Engineering Group (Engineering Ingegneria Informatica) 319
9.89 EnterpriseDB 320
9.90 eQ Technologic 321
9.91 Ericsson 322
9.92 EXASOL 323
9.93 Facebook 324
9.94 FICO (Fair Isaac Corporation) 325
9.95 Fractal Analytics 326
9.96 Fujitsu 327
9.97 Fuzzy Logix 329
9.98 Gainsight 330
9.99 GE (General Electric) 331
9.100 Glassbeam 332
9.101 GoodData Corporation 333
9.102 Google 334
9.103 Greenwave Systems 335
9.104 GridGain Systems 336
9.105 Guavus 337
9.106 H2O.ai 338
9.107 HDS (Hitachi Data Systems) 339
9.108 Hedvig 340
9.109 Hortonworks 341
9.110 HPE (Hewlett Packard Enterprise) 342
9.111 Huawei 344
9.112 IBM Corporation 345
9.113 iDashboards 347
9.114 Impetus Technologies 348
9.115 Incorta 349
9.116 InetSoft Technology Corporation 350
9.117 Infer 351
9.118 Infor 352
9.119 Informatica Corporation 353
9.120 Information Builders 354
9.121 Infosys 355
9.122 Infoworks 356
9.123 Insightsoftware.com 357
9.124 InsightSquared 358
9.125 Intel Corporation 359
9.126 Interana 360
9.127 InterSystems Corporation 361
9.128 Jedox 362
9.129 Jethro 363
9.130 Jinfonet Software 364
9.131 Juniper Networks 365
9.132 KALEAO 366
9.133 Keen IO 367
9.134 Kinetica 368
9.135 KNIME 369
9.136 Kognitio 370
9.137 Kyvos Insights 371
9.138 Lavastorm 372
9.139 Lexalytics 373
9.140 Lexmark International 374
9.141 Logi Analytics 375
9.142 Longview Solutions 376
9.143 Looker Data Sciences 377
9.144 LucidWorks 378
9.145 Luminoso Technologies 379
9.146 Maana 380
9.147 Magento Commerce 381
9.148 Manthan Software Services 382
9.149 MapD Technologies 383
9.150 MapR Technologies 384
9.151 MariaDB Corporation 385
9.152 MarkLogic Corporation 386
9.153 Mathworks 387
9.154 MemSQL 388
9.155 Metric Insights 389
9.156 Microsoft Corporation 390
9.157 MicroStrategy 391
9.158 Minitab 392
9.159 MongoDB 393
9.160 Mu Sigma 394
9.161 NEC Corporation 395
9.162 Neo Technology 396
9.163 NetApp 397
9.164 Nimbix 398
9.165 Nokia 399
9.166 NTT Data Corporation 400
9.167 Numerify 401
9.168 NuoDB 402
9.169 Nutonian 403
9.170 NVIDIA Corporation 404
9.171 Oblong Industries 405
9.172 OpenText Corporation 406
9.173 Opera Solutions 408
9.174 Optimal Plus 409
9.175 Oracle Corporation 410
9.176 Palantir Technologies 412
9.177 Panorama Software 413
9.178 Paxata 414
9.179 Pentaho Corporation 415
9.180 Pepperdata 416
9.181 Phocas Software 417
9.182 Pivotal Software 418
9.183 Prognoz 420
9.184 Progress Software Corporation 421
9.185 PwC (PricewaterhouseCoopers International) 422
9.186 Pyramid Analytics 423
9.187 Qlik 424
9.188 Quantum Corporation 425
9.189 Qubole 426
9.190 Rackspace 427
9.191 Radius Intelligence 428
9.192 RapidMiner 429
9.193 Recorded Future 430
9.194 Red Hat 431
9.195 Redis Labs 432
9.196 RedPoint Global 433
9.197 Reltio 434
9.198 Rocket Fuel 435
9.199 RStudio 436
9.200 Ryft Systems 437
9.201 Sailthru 438
9.202 Salesforce.com 439
9.203 Salient Management Company 440
9.204 Samsung Group 441
9.205 SAP 442
9.206 SAS Institute 443
9.207 ScaleDB 444
9.208 ScaleOut Software 445
9.209 SCIO Health Analytics 446
9.210 Seagate Technology 447
9.211 Sinequa 448
9.212 SiSense 449
9.213 SnapLogic 450
9.214 Snowflake Computing 451
9.215 Software AG 452
9.216 Splice Machine 453
9.217 Splunk 454
9.218 Sqrrl 455
9.219 Strategy Companion Corporation 456
9.220 StreamSets 457
9.221 Striim 458
9.222 Sumo Logic 459
9.223 Supermicro (Super Micro Computer) 460
9.224 Syncsort 461
9.225 SynerScope 462
9.226 Tableau Software 463
9.227 Talena 464
9.228 Talend 465
9.229 Tamr 466
9.230 TARGIT 467
9.231 TCS (Tata Consultancy Services) 468
9.232 Teradata Corporation 469
9.233 ThoughtSpot 471
9.234 TIBCO Software 472
9.235 Tidemark 473
9.236 Toshiba Corporation 474
9.237 Trifacta 475
9.238 Unravel Data 476
9.239 VMware 477
9.240 VoltDB 478
9.241 Waterline Data 479
9.242 Western Digital Corporation 480
9.243 WiPro 481
9.244 Workday 482
9.245 Xplenty 483
9.246 Yellowfin International 484
9.247 Yseop 485
9.248 Zendesk 486
9.249 Zoomdata 487
9.250 Zucchetti 488

10 Chapter 10: Conclusion & Strategic Recommendations 489
10.1 Why is the Market Poised to Grow? 489
10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential? 489
10.3 Partnerships & M&A Activity: Highlighting the Importance of Big Data 490
10.4 Improving Outcomes, Achieving Operational Efficiency and Reducing Costs 491
10.5 Assessing the Impact of Connected Health Solutions 491
10.6 Accelerating the Transition Towards Value-Based Care 492
10.7 The Value of Big Data in Precision Medicine 493
10.8 Addressing Privacy & Security Concerns 494
10.9 The Role of Data Protection Legislation 494
10.10 Blockchain: Enabling Secure, Efficient and Interoperable Data Sharing 495
10.11 Recommendations 496
10.11.1 Big Data Hardware, Software & Professional Services Providers 496
10.11.2 Healthcare & Pharmaceutical Industry Stakeholders 497

List of Figures

Figure 1: Hadoop Architecture 42
Figure 2: Reactive vs. Proactive Analytics 55
Figure 3: Distribution of Big Data Investments in the Healthcare & Pharmaceutical Industry, by Application Area: 2016 (%) 62
Figure 4: Key Characteristics of Genomics and Three Major Sources of Big Data 69
Figure 5: Bayer's Vision of Big Data in Medicine 95
Figure 6: Sickweather's Sickness Forecasting & Mapping Service 139
Figure 7: Counterfeit Drug Identification with Big Data & Mobile Technology 141
Figure 8: Big Data Roadmap in the Healthcare & Pharmaceutical Industry 143
Figure 9: Big Data Value Chain in the Healthcare & Pharmaceutical Industry 146
Figure 10: Key Aspects of Big Data Standardization 156
Figure 11: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 178
Figure 12: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Hardware, Software & Professional Services: 2017 - 2030 ($ Million) 179
Figure 13: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Submarket: 2017 - 2030 ($ Million) 180
Figure 14: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 180
Figure 15: Global Big Data Networking Infrastructure Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 181
Figure 16: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 181
Figure 17: Global Big Data SQL Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 182
Figure 18: Global Big Data NoSQL Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 182
Figure 19: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 183
Figure 20: Global Big Data Cloud Platforms Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 183
Figure 21: Global Big Data Professional Services Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 184
Figure 22: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Application Area: 2017 - 2030 ($ Million) 185
Figure 23: Global Big Data Revenue in Pharmaceutical & Medical Products: 2017 - 2030 ($ Million) 185
Figure 24: Global Big Data Revenue in Core Healthcare Operations: 2017 - 2030 ($ Million) 186
Figure 25: Global Big Data Revenue in Healthcare Support, Awareness & Disease Prevention: 2017 - 2030 ($ Million) 186
Figure 26: Global Big Data Revenue in Health Insurance & Payer Services: 2017 - 2030 ($ Million) 187
Figure 27: Global Big Data Revenue in Healthcare/Pharmaceutical Marketing, Sales & Other Applications: 2017 - 2030 ($ Million) 187
Figure 28: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Use Case: 2017 - 2030 ($ Million) 189
Figure 29: Global Big Data Revenue in Drug Discovery, Design & Development: 2017 - 2030 ($ Million) 190
Figure 30: Global Big Data Revenue in Medical Product Design & Development: 2017 - 2030 ($ Million) 190
Figure 31: Global Big Data Revenue in Clinical Development & Trials: 2017 - 2030 ($ Million) 191
Figure 32: Global Big Data Revenue in Precision Medicine & Genomics: 2017 - 2030 ($ Million) 191
Figure 33: Global Big Data Revenue in Pharmaceutical/Medical Manufacturing & Supply Chain Management: 2017 - 2030 ($ Million) 192
Figure 34: Global Big Data Revenue in Post-Market Surveillance & Pharmacovigilance: 2017 - 2030 ($ Million) 192
Figure 35: Global Big Data Revenue in Medical Product Fault Monitoring: 2017 - 2030 ($ Million) 193
Figure 36: Global Big Data Revenue in Clinical Decision Support: 2017 - 2030 ($ Million) 193
Figure 37: Global Big Data Revenue in Care Coordination & Delivery Management: 2017 - 2030 ($ Million) 194
Figure 38: Global Big Data Revenue in CER (Comparative Effectiveness Research) & Observational Evidence: 2017 - 2030 ($ Million) 194
Figure 39: Global Big Data Revenue in Personalized Healthcare & Targeted Treatments: 2017 - 2030 ($ Million) 195
Figure 40: Global Big Data Revenue in Data-Driven Preventive Care & Health Interventions: 2017 - 2030 ($ Million) 195
Figure 41: Global Big Data Revenue in Surgical Practice & Complex Medical Procedures: 2017 - 2030 ($ Million) 196
Figure 42: Global Big Data Revenue in Pathology, Medical Imaging & Other Medical Tests: 2017 - 2030 ($ Million) 196
Figure 43: Global Big Data Revenue in Proactive & Remote Patient Monitoring: 2017 - 2030 ($ Million) 197
Figure 44: Global Big Data Revenue in Predictive Maintenance of Medical Equipment: 2017 - 2030 ($ Million) 197
Figure 45: Global Big Data Revenue in Pharmacy Services: 2017 - 2030 ($ Million) 198
Figure 46: Global Big Data Revenue in Self-Care & Lifestyle Support: 2017 - 2030 ($ Million) 198
Figure 47: Global Big Data Revenue in Medication Adherence & Management: 2017 - 2030 ($ Million) 199
Figure 48: Global Big Data Revenue in Vaccine Development & Promotion: 2017 - 2030 ($ Million) 199
Figure 49: Global Big Data Revenue in Population Health Management: 2017 - 2030 ($ Million) 200
Figure 50: Global Big Data Revenue in Connected Health Communities & Medical Knowledge Dissemination: 2017 - 2030 ($ Million) 200
Figure 51: Global Big Data Revenue in Epidemiology & Disease Surveillance: 2017 - 2030 ($ Million) 201
Figure 52: Global Big Data Revenue in Health Policy Decision Making: 2017 - 2030 ($ Million) 201
Figure 53: Global Big Data Revenue in Controlling Substance Abuse & Addiction: 2017 - 2030 ($ Million) 202
Figure 54: Global Big Data Revenue in Increasing Awareness & Accessible Healthcare: 2017 - 2030 ($ Million) 202
Figure 55: Global Big Data Revenue in Health Insurance Claims Processing & Management: 2017 - 2030 ($ Million) 203
Figure 56: Global Big Data Revenue in Fraud & Abuse Prevention: 2017 - 2030 ($ Million) 203
Figure 57: Global Big Data Revenue in Proactive Patient Engagement: 2017 - 2030 ($ Million) 204
Figure 58: Global Big Data Revenue in Accountable & Value-Based Care: 2017 - 2030 ($ Million) 204
Figure 59: Global Big Data Revenue in Data-Driven Health Insurance Premiums: 2017 - 2030 ($ Million) 205
Figure 60: Global Big Data Revenue in Healthcare/Pharmaceutical Marketing & Sales: 2017 - 2030 ($ Million) 205
Figure 61: Global Big Data Revenue in Healthcare/Pharmaceutical Administrative & Customer Services: 2017 - 2030 ($ Million) 206
Figure 62: Global Big Data Revenue in Healthcare/Pharmaceutical Finance & Risk Management: 2017 - 2030 ($ Million) 206
Figure 63: Global Big Data Revenue in Healthcare Data Monetization: 2017 - 2030 ($ Million) 207
Figure 64: Global Big Data Revenue in Other Healthcare & Pharmaceutical Industry Use Cases: 2017 - 2030 ($ Million) 207
Figure 65: Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Region: 2017 - 2030 ($ Million) 208
Figure 66: Asia Pacific Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 208
Figure 67: Asia Pacific Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2017 - 2030 ($ Million) 209
Figure 68: Australia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 209
Figure 69: China Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 210
Figure 70: India Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 210
Figure 71: Indonesia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 211
Figure 72: Japan Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 211
Figure 73: Malaysia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 212
Figure 74: Pakistan Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 212
Figure 75: Philippines Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 213
Figure 76: Singapore Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 213
Figure 77: South Korea Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 214
Figure 78: Taiwan Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 214
Figure 79: Thailand Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 215
Figure 80: Rest of Asia Pacific Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 215
Figure 81: Eastern Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 216
Figure 82: Eastern Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2017 - 2030 ($ Million) 216
Figure 83: Czech Republic Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 217
Figure 84: Poland Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 217
Figure 85: Russia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 218
Figure 86: Rest of Eastern Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 218
Figure 87: Latin & Central America Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 219
Figure 88: Latin & Central America Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2017 - 2030 ($ Million) 219
Figure 89: Argentina Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 220
Figure 90: Brazil Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 220
Figure 91: Mexico Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 221
Figure 92: Rest of Latin & Central America Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 221
Figure 93: Middle East & Africa Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 222
Figure 94: Middle East & Africa Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2017 - 2030 ($ Million) 222
Figure 95: Israel Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 223
Figure 96: Qatar Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 223
Figure 97: Saudi Arabia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 224
Figure 98: South Africa Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 224
Figure 99: UAE Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 225
Figure 100: Rest of the Middle East & Africa Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 225
Figure 101: North America Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 226
Figure 102: North America Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2017 - 2030 ($ Million) 226
Figure 103: Canada Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 227
Figure 104: USA Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 227
Figure 105: Western Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 228
Figure 106: Western Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2017 - 2030 ($ Million) 228
Figure 107: Denmark Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 229
Figure 108: Finland Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 229
Figure 109: France Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 230
Figure 110: Germany Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 230
Figure 111: Italy Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 231
Figure 112: Netherlands Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 231
Figure 113: Norway Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 232
Figure 114: Spain Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 232
Figure 115: Sweden Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 233
Figure 116: UK Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 233
Figure 117: Rest of Western Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) 234

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Published By: SNS Telecom & IT

Product Code: Signals and Systems Telecom12

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