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

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Date: 24-Jul-2018
No. of pages: 561
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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 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 Telecom & IT estimates that Big Data investments in the healthcare and pharmaceutical industry will account for nearly $4.7 Billion in 2018 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 approximately 12% over the next three years.

The “Big Data in the Healthcare & Pharmaceutical Industry: 2018 – 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 2018 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 5 application areas, 37 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 healthcare and pharmaceutical industry

  • Over 40 case studies of Big Data investments by healthcare providers, insurers, payers, pharmaceutical companies and other stakeholders

  • 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 healthcare and pharmaceutical 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


  • 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

  • Digital Therapeutics

  • 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 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 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 2018, Big Data vendors will pocket nearly $4.7 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 approximately 12% over the next three years, eventually accounting for more than $7 Billion by the end of 2021.

  • Big Data and advanced analytics are driving a paradigm shift in the healthcare and pharmaceutical industry with multiple innovations ranging from precision medicine and digital therapeutics to the adoption of accountable and value-based care models.

  • Drug developers are making substantial investments in Big Data and artificial intelligence-driven drug discovery platforms to shorten the process of successfully discovering promising compounds. In addition, Big Data technologies are increasingly being utilized to streamline clinical trials, enabling biopharmaceutical companies to significantly lower costs and accelerate productive trials.

  • The growing adoption of Big Data technologies has also brought about an array of benefits for hospitals and other healthcare facilities. Based on feedback from healthcare providers worldwide, these include but are not limited to cost savings in the range of 20-30%, an increase in patient access to services by more than 35%, growth in revenue by up to 30%, a reduction in emergency room visits by 10%, a drop in patient wait times by 30-60%, improvements in outcomes by as much as 20%, a 10-50% decline in mortality rates for conditions such as heart failure, and a reduction in the occurrence of hospital acquired and surgical site infections by nearly 60%.


List of Companies Mentioned


  • 1010 data

  • AbbVie

  • Absolutdata

  • Accenture

  • ACR (American College of Radiology)

  • Actian Corporation

  • Adaptive Insights

  • Adobe Systems

  • Advizor Solutions

  • AeroSpike

  • Aetna

  • AFS Technologies

  • Alation

  • Algorithmia

  • Alluxio

  • Alphabet

  • ALTEN

  • Alteryx

  • Ambient Clinical Analytics

  • Ambulance Victoria

  • AMD (Advanced Micro Devices)

  • Amino

  • Anaconda

  • Apixio

  • Arcadia Data

  • Arimo

  • ARM

  • ASF (Apache Software Foundation)

  • ASTM (American Society for Testing and Materials)

  • AstraZeneca

  • Atomwise

  • AtScale

  • Attivio

  • Attunity

  • Australian Digital Health Agency

  • Automated Insights

  • AVORA

  • AWS (Amazon Web Services)

  • Axiomatics

  • Ayasdi

  • BackOffice Associates

  • Bangkok Hospital Group

  • Basho Technologies

  • Bayer

  • BCG (Boston Consulting Group)

  • Bedrock Data

  • BetterWorks

  • Big Panda

  • BigML

  • Birst

  • Bitam

  • Blue Medora

  • BlueData Software

  • BlueTalon

  • BMC Software

  • BMS (Bristol-Myers Squibb)

  • BOARD International

  • Booz Allen Hamilton

  • Boxever

  • CACI International

  • Cambridge Semantics

  • Capgemini

  • Cazena

  • Centerstone

  • Centrifuge Systems

  • CenturyLink

  • Chartio

  • Cigna

  • Cincinnati Children’s Hospital Medical Center

  • Cisco Systems

  • Civis Analytics

  • ClearStory Data

  • Cloudability

  • Cloudera

  • Cloudian

  • Clustrix

  • CNIL (Data Protection Regulatory Authority, France)

  • CognitiveScale

  • Collibra

  • Concurrent Technology

  • Confluent

  • Contexti

  • CosmosID

  • Couchbase

  • Crate.io

  • Cray

  • CSA (Cloud Security Alliance)

  • CSCC (Cloud Standards Customer Council)

  • CSIRO (Commonwealth Scientific and Industrial Research Organization)

  • Databricks

  • Dataiku

  • Datalytyx

  • Datameer

  • DataRobot

  • DataStax

  • Datawatch Corporation

  • Datos IO

  • DCRI (Duke Clinical Research Institute)

  • DDN (DataDirect Networks)

  • Decisyon

  • Deep Genomics

  • DeepMind Technologies

  • Dell Technologies

  • Deloitte

  • Demandbase

  • Denodo Technologies

  • Desktop Genetics

  • Dianomic Systems

  • Digital Reasoning Systems

  • Dimensional Insight

  • DMG  (Data Mining Group)

  • Dolphin Enterprise Solutions Corporation

  • Domino Data Lab

  • Domo

  • Dremio

  • DriveScale

  • Druva

  • DTA (Digital Therapeutics Alliance)

  • Dundas Data Visualization

  • DXC Technology

  • Elastic

  • Engineering Group (Engineering Ingegneria Informatica)

  • EnterpriseDB Corporation

  • eQ Technologic

  • Ericsson

  • Erwin

  • EVŌ (Big Cloud Analytics)

  • EXASOL

  • EXL (ExlService Holdings)

  • Express Scripts

  • Exscientia

  • Facebook

  • Faros Healthcare

  • FICO (Fair Isaac Corporation)

  • Figure Eight

  • FogHorn Systems

  • Fractal Analytics

  • Franz

  • Fujitsu

  • Fuzzy Logix

  • Gainsight

  • GE (General Electric)

  • Genomics England

  • Ginger.io

  • Glassbeam

  • GNS Healthcare

  • Gold Coast Health

  • GoodData Corporation

  • Google

  • Grakn Labs

  • Greenwave Systems

  • GridGain Systems

  • GSK (GlaxoSmithKline)

  • Guavus

  • H2O.ai

  • Hanse Orga Group

  • HarperDB

  • HCL Technologies

  • Hedvig

  • Hitachi Vantara

  • HITRUST Alliance

  • HL7 (Health Level Seven)

  • HLI (Human Longevity Inc.)

  • 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

  • IHE (Integrating the Healthcare Enterprise)

  • Illumina

  • Imanis Data

  • Impetus Technologies

  • INCITS (InterNational Committee for Information Technology Standards)

  • Incorta

  • INDS (National Institute of Health Data, France)

  • 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)

  • 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

  • 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

  • Massachusetts General Hospital

  • Mathworks

  • Mayo Clinic

  • Medtronic

  • Melissa

  • MemSQL

  • Merck & Co.

  • Merck KGaA

  • Metric Insights

  • Microsoft Corporation

  • MicroStrategy

  • Ministry of Health, Labor and Welfare, Japan

  • Minitab

  • MolecularMatch

  • MongoDB

  • Moorfields Eye Hospital

  • 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)

  • Neo4j

  • NetApp

  • NextBio

  • NHS (National Health Service, United Kingdom)

  • NHS England

  • NHS Scotland

  • Nimbix

  • Nokia

  • Novartis

  • 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

  • Oxford Nanopore Technologies

  • Pacific Biosciences

  • Palantir Technologies

  • Panasonic Corporation

  • Panorama Software

  • PatientsLikeMe

  • Paxata

  • Pepperdata

  • Pfizer

  • Phocas Software

  • Pivotal Software

  • Prognoz

  • Progress Software Corporation

  • Proteus Digital Health

  • 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

  • Roche

  • Royal Philips

  • RStudio

  • Rubrik

  • Ryft

  • Sailthru

  • Salesforce.com

  • Salient Management Company

  • Samsung Group

  • Sanofi

  • SAP

  • SAS Institute

  • ScaleOut Software

  • Seagate Technology

  • Seattle Children's Hospital

  • Sickweather

  • Sinequa

  • SingHealth (Singapore Health Services)

  • SiSense

  • Sizmek

  • SnapLogic

  • Snowflake Computing

  • Software AG

  • Splice Machine

  • Splunk

  • Sproxil

  • Strategy Companion Corporation

  • Stratio

  • Streamlio

  • StreamSets

  • Striim

  • Sumo Logic

  • Supermicro (Super Micro Computer)

  • Syncsort

  • SynerScope

  • SYNTASA

  • Tableau Software

  • Takeda Pharmaceutical Company

  • Talend

  • Tamr

  • TARGIT

  • TCS (Tata Consultancy Services)

  • Teradata Corporation

  • Thales

  • Thermo Fisher Scientific

  • ThoughtSpot

  • TIBCO Software

  • Tidemark

  • TM Forum

  • Toshiba Corporation

  • TPC (Transaction Processing Performance Council)

  • Transwarp

  • Trifacta

  • Twitter

  • U.S. CDC (Centers for Disease Control & Prevention)

  • U.S. CMS (Centers for Medicare & Medicaid Services)

  • U.S. Department of Veterans Affairs

  • U.S. FDA (Food and Drug Administration)

  • U.S. HHS (Department of Health & Human Services)

  • U.S. NIST (National Institute of Standards and Technology)

  • U.S. VHA (Veterans Health Administration)

  • UCL (University College London) Institute of Ophthalmology

  • UN (United Nations)

  • Unifi Software

  • UnitedHealth Group

  • University of Illinois at Urbana-Champaign

  • University of Michigan

  • University of Pittsburgh

  • University of Utah Health

  • Unravel Data

  • VANTIQ

  • Vecima Networks

  • VMware

  • VoltDB

  • W3C (World Wide Web Consortium)

  • WANdisco

  • Waterline Data

  • WellDoc

  • Western Digital Corporation

  • WhereScape

  • WiPro

  • Wolfram Research

  • Workday

  • X12

  • Xplenty

  • Yellowfin BI

  • Yseop

  • Zendesk

  • Zoomdata

  • Zucchetti

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

Table of Contents

1 Chapter 1: Introduction 26
1.1 Executive Summary 26
1.2 Topics Covered 28
1.3 Forecast Segmentation 29
1.4 Key Questions Answered 33
1.5 Key Findings 34
1.6 Methodology 35
1.7 Target Audience 36
1.8 Companies & Organizations Mentioned 37

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 45
2.4.1 Awareness of Benefits 45
2.4.2 Maturation of Big Data Platforms 45
2.4.3 Continued Investments by Web Giants, Governments & Enterprises 46
2.4.4 Growth of Data Volume, Velocity & Variety 46
2.4.5 Vendor Commitments & Partnerships 46
2.4.6 Technology Trends Lowering Entry Barriers 47
2.5 Market Barriers 47
2.5.1 Lack of Analytic Specialists 47
2.5.2 Uncertain Big Data Strategies 47
2.5.3 Organizational Resistance to Big Data Adoption 48
2.5.4 Technical Challenges: Scalability & Maintenance 48
2.5.5 Security & Privacy Concerns 48

3 Chapter 3: Big Data Analytics 49
3.1 What are Big Data Analytics? 49
3.2 The Importance of Analytics 49
3.3 Reactive vs. Proactive Analytics 50
3.4 Customer vs. Operational Analytics 50
3.5 Technology & Implementation Approaches 51
3.5.1 Grid Computing 51
3.5.2 In-Database Processing 51
3.5.3 In-Memory Analytics 52
3.5.4 Machine Learning & Data Mining 52
3.5.5 Predictive Analytics 53
3.5.6 NLP (Natural Language Processing) 53
3.5.7 Text Analytics 54
3.5.8 Visual Analytics 54
3.5.9 Graph Analytics 55
3.5.10 Social Media, IT & Telco Network Analytics 55

4 Chapter 4: Business Case & Applications in the Healthcare & Pharmaceutical Industry 57
4.1 Overview & Investment Potential 57
4.2 Industry Specific Market Growth Drivers 58
4.3 Industry Specific Market Barriers 59
4.4 Key Applications 61
4.4.1 Pharmaceutical & Medical Products 61
4.4.1.1 Drug Discovery, Design & Development 61
4.4.1.2 Medical Product Design & Development 62
4.4.1.3 Clinical Development & Trials 62
4.4.1.4 Precision Medicine & Genomics 63
4.4.1.5 Manufacturing & Supply Chain Management 64
4.4.1.6 Post-Market Surveillance & Pharmacovigilance 66
4.4.1.7 Medical Product Fault Monitoring 66
4.4.2 Core Healthcare Operations 67
4.4.2.1 Clinical Decision Support 67
4.4.2.2 Care Coordination & Delivery Management 68
4.4.2.3 CER (Comparative Effectiveness Research) & Observational Evidence 69
4.4.2.4 Personalized Healthcare & Targeted Treatments 69
4.4.2.5 Data-Driven Preventive Care & Health Interventions 70
4.4.2.6 Surgical Practice & Complex Medical Procedures 70
4.4.2.7 Pathology, Medical Imaging & Other Medical Tests 71
4.4.2.8 Proactive & Remote Patient Monitoring 71
4.4.2.9 Predictive Maintenance of Medical Equipment 72
4.4.2.10 Pharmacy Services 72
4.4.3 Healthcare Support, Awareness & Disease Prevention 73
4.4.3.1 Self-Care & Lifestyle Support 73
4.4.3.2 Digital Therapeutics 74
4.4.3.3 Medication Adherence & Management 74
4.4.3.4 Vaccine Development & Promotion 75
4.4.3.5 Population Health Management 76
4.4.3.6 Connected Health Communities & Medical Knowledge Dissemination 77
4.4.3.7 Epidemiology & Disease Surveillance 77
4.4.3.8 Health Policy Decision Making 78
4.4.3.9 Controlling Substance Abuse & Addiction 79
4.4.3.10 Increasing Awareness & Accessible Healthcare 79
4.4.4 Health Insurance & Payer Services 80
4.4.4.1 Health Insurance Claims Processing & Management 80
4.4.4.2 Fraud & Abuse Prevention 81
4.4.4.3 Proactive Patient Engagement 81
4.4.4.4 Accountable & Value-Based Care 82
4.4.4.5 Data-Driven Health Insurance Premiums 82
4.4.5 Marketing, Sales & Other Applications 83
4.4.5.1 Marketing & Sales 83
4.4.5.2 Administrative & Customer Services 84
4.4.5.3 Finance & Risk Management 85
4.4.5.4 Healthcare Data Monetization 85
4.4.5.5 Other Applications 86

5 Chapter 5: Healthcare & Pharmaceutical Industry Case Studies 87
5.1 Pharmaceutical & Medical Device Companies 87
5.1.1 AbbVie: Designing & Implementing Clinical Trials with Big Data 87
5.1.2 AstraZeneca: Analytics-Driven Drug Development with Big Data 89
5.1.3 Bayer: Accelerating Clinical Trials with Big Data 90
5.1.4 BMS (Bristol-Myers Squibb): Driving Clinical Discovery with Big Data 92
5.1.5 GSK (GlaxoSmithKline): Increasing Success Rates in Drug Discovery with Big Data 94
5.1.6 Johnson & Johnson: Intelligent Pharmaceutical Marketing with Big Data 96
5.1.7 Medtronic: Facilitating Predictive Care with Big Data 97
5.1.8 Merck & Co.: Optimizing Vaccine Manufacturing with Big Data 98
5.1.9 Merck KGaA: Discovering Drugs Faster with Big Data 99
5.1.10 Novartis: Digitizing Healthcare with Big Data 100
5.1.11 Pfizer: Developing Effective and Targeted Therapies with Big Data 102
5.1.12 Roche: Personalizing Healthcare with Big Data 104
5.1.13 Sanofi: Proactive Diabetes Care with Big Data 105
5.2 Healthcare Providers, Insurers & Payers 107
5.2.1 Aetna: Predicting & Improving Health with Big Data 107
5.2.2 Ambulance Victoria: Improving Patient Survival Rates with Big Data 109
5.2.3 Bangkok Hospital Group: Transforming the Patient Experience with Big Data 110
5.2.4 Cigna: Streamlining Health Insurance Claims with Big Data 112
5.2.5 Gold Coast Health: Reducing Hospital Waiting Times with Big Data 114
5.2.6 IU Health (Indiana University Health): Preventing Hospital-Acquired Infections with Big Data 115
5.2.7 Moorfields Eye Hospital: Diagnosing Eye Diseases with Big Data 116
5.2.8 MSQC (Michigan Surgical Quality Collaborative): Surgical Quality Improvement with Big Data 118
5.2.9 NCCS (National Cancer Centre Singapore): Advancing Cancer Treatment with Big Data 119
5.2.10 NHS Scotland: Improving Outcomes with Big Data 121
5.2.11 Seattle Children's Hospital: Enabling Faster & Accurate Diagnosis with Big Data 122
5.2.12 UnitedHealth Group: Enhancing Patient Care & Value with Big Data 123
5.2.13 VHA (Veterans Health Administration): Streamlining Healthcare Delivery with Big Data 125
5.3 Other Stakeholders 127
5.3.1 Amino: Healthcare Transparency with Big Data 127
5.3.2 Atomwise: Improving Drug Discovery with Big Data 128
5.3.3 CosmosID: Advancing Microbial Genomics with Big Data 129
5.3.4 Deep Genomics: Discovering Novel Oligonucleotide Therapies with Big Data 130
5.3.5 Desktop Genetics: Facilitating Genome Editing with Big Data 131
5.3.6 Express Scripts: Improving Medication Adherence with Big Data 133
5.3.7 Faros Healthcare: Enhancing Clinical Decision Making with Big Data 135
5.3.8 Genomics England: Developing the World's First Genomics Medicine Service with Big Data 136
5.3.9 Ginger.io: Improving Mental Wellbeing with Big Data 138
5.3.10 Illumina: Enabling Precision Medicine with Big Data 139
5.3.11 INDS (National Institute of Health Data, France): Population Health Management with Big Data 140
5.3.12 MolecularMatch: Advancing the Clinical Utility of Genomics with Big Data 141
5.3.13 Proteus Digital Health: Pioneering Digital Medicine with Big Data 143
5.3.14 Royal Philips: Enhancing Workflows in ICUs (Intensive Care Units) with Big Data 145
5.3.15 Sickweather: Sickness Forecasting & Mapping with Big Data 146
5.3.16 Sproxil: Fighting Counterfeit Drugs with Big Data 148

6 Chapter 6: Future Roadmap & Value Chain 150
6.1 Future Roadmap 150
6.1.1 Pre-2020: Growing Investments in Real-Time & Predictive Health Analytics 150
6.1.2 2020 – 2025: Data-Driven Advances in Drug Discovery & Precision Medicine 151
6.1.3 2025 – 2030: Moving Beyond National-Level Population Health Management 152
6.2 The Big Data Value Chain 153
6.2.1 Hardware Providers 153
6.2.1.1 Storage & Compute Infrastructure Providers 154
6.2.1.2 Networking Infrastructure Providers 154
6.2.2 Software Providers 155
6.2.2.1 Hadoop & Infrastructure Software Providers 155
6.2.2.2 SQL & NoSQL Providers 155
6.2.2.3 Analytic Platform & Application Software Providers 155
6.2.2.4 Cloud Platform Providers 156
6.2.3 Professional Services Providers 156
6.2.4 End-to-End Solution Providers 156
6.2.5 Healthcare & Pharmaceutical Industry 156

7 Chapter 7: Standardization & Regulatory Initiatives 157
7.1 ASF (Apache Software Foundation) 157
7.1.1 Management of Hadoop 157
7.1.2 Big Data Projects Beyond Hadoop 157
7.2 CSA (Cloud Security Alliance) 161
7.2.1 BDWG (Big Data Working Group) 161
7.3 CSCC (Cloud Standards Customer Council) 162
7.3.1 Big Data Working Group 162
7.4 DMG (Data Mining Group) 163
7.4.1 PMML (Predictive Model Markup Language) Working Group 163
7.4.2 PFA (Portable Format for Analytics) Working Group 163
7.5 IEEE (Institute of Electrical and Electronics Engineers) 163
7.5.1 Big Data Initiative 164
7.6 INCITS (InterNational Committee for Information Technology Standards) 165
7.6.1 Big Data Technical Committee 165
7.7 ISO (International Organization for Standardization) 166
7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange 166
7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms 167
7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques 167
7.7.4 ISO/IEC JTC 1/WG 9: Big Data 167
7.7.5 Collaborations with Other ISO Work Groups 168
7.8 ITU (International Telecommunication Union) 169
7.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities 169
7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks 170
7.8.3 Other Relevant Work 170
7.9 Linux Foundation 171
7.9.1 ODPi (Open Ecosystem of Big Data) 171
7.10 NIST (National Institute of Standards and Technology) 171
7.10.1 NBD-PWG (NIST Big Data Public Working Group) 171
7.11 OASIS (Organization for the Advancement of Structured Information Standards) 172
7.11.1 Technical Committees 172
7.12 ODaF (Open Data Foundation) 173
7.12.1 Big Data Accessibility 173
7.13 ODCA (Open Data Center Alliance) 173
7.13.1 Work on Big Data 174
7.14 OGC (Open Geospatial Consortium) 174
7.14.1 Big Data DWG (Domain Working Group) 174
7.15 TM Forum 174
7.15.1 Big Data Analytics Strategic Program 175
7.16 TPC (Transaction Processing Performance Council) 175
7.16.1 TPC-BDWG (TPC Big Data Working Group) 175
7.17 W3C (World Wide Web Consortium) 175
7.17.1 Big Data Community Group 176
7.17.2 Open Government Community Group 176
7.18 Other Initiatives Relevant to the Healthcare & Pharmaceutical Industry 177
7.18.1 HIPAA (Health Insurance Portability and Accountability Act of 1996) 177
7.18.2 HITECH (Health Information Technology for Economic and Clinical Health) Act 178
7.18.3 European Union's GDPR (General Data Protection Regulation) 178
7.18.4 Australian Digital Health Agency 179
7.18.5 United Kingdom's ITK (Interoperability Toolkit) 180
7.18.6 Japan's SS-MIX (Standard Structured Medical Information eXchange) 180
7.18.7 Germany's xDT 180
7.18.8 France's DMP (Dossier Médical Personnel) 180
7.18.9 HL7 (Health Level Seven) Specifications 181
7.18.10 IHE (Integrating the Healthcare Enterprise) 182
7.18.11 NCPDP (National Council for Prescription Drug Programs) 183
7.18.12 DICOM (Digital Imaging and Communications in Medicine) 183
7.18.13 eHealth Exchange 184
7.18.14 EDIFACT (Electronic Data Interchange For Administration, Commerce, and Transport) 184
7.18.15 HITRUST CSF (Common Security Framework) 184
7.18.16 DTA (Digital Therapeutics Alliance) 185
7.18.17 X12 & Others 185

8 Chapter 8: Market Sizing & Forecasts 187
8.1 Global Outlook for Big Data in the Healthcare & Pharmaceutical Industry 187
8.2 Hardware, Software & Professional Services Segmentation 188
8.3 Horizontal Submarket Segmentation 189
8.4 Hardware Submarkets 189
8.4.1 Storage and Compute Infrastructure 189
8.4.2 Networking Infrastructure 190
8.5 Software Submarkets 190
8.5.1 Hadoop & Infrastructure Software 190
8.5.2 SQL 191
8.5.3 NoSQL 191
8.5.4 Analytic Platforms & Applications 192
8.5.5 Cloud Platforms 192
8.6 Professional Services Submarket 193
8.6.1 Professional Services 193
8.7 Application Area Segmentation 194
8.7.1 Pharmaceutical & Medical Products 194
8.7.2 Core Healthcare Operations 195
8.7.3 Healthcare Support, Awareness & Disease Prevention 195
8.7.4 Health Insurance & Payer Services 196
8.7.5 Marketing, Sales & Other Applications 196
8.8 Use Case Segmentation 197
8.9 Pharmaceutical & Medical Products 199
8.9.1 Drug Discovery, Design & Development 199
8.9.2 Medical Product Design & Development 199
8.9.3 Clinical Development & Trials 200
8.9.4 Precision Medicine & Genomics 200
8.9.5 Manufacturing & Supply Chain Management 201
8.9.6 Post-Market Surveillance & Pharmacovigilance 201
8.9.7 Medical Product Fault Monitoring 202
8.10 Core Healthcare Operations 202
8.10.1 Clinical Decision Support 202
8.10.2 Care Coordination & Delivery Management 203
8.10.3 CER (Comparative Effectiveness Research) & Observational Evidence 203
8.10.4 Personalized Healthcare & Targeted Treatments 204
8.10.5 Data-Driven Preventive Care & Health Interventions 204
8.10.6 Surgical Practice & Complex Medical Procedures 205
8.10.7 Pathology, Medical Imaging & Other Medical Tests 205
8.10.8 Proactive & Remote Patient Monitoring 206
8.10.9 Predictive Maintenance of Medical Equipment 206
8.10.10 Pharmacy Services 207
8.11 Healthcare Support, Awareness & Disease Prevention 207
8.11.1 Self-Care & Lifestyle Support 207
8.11.2 Digital Therapeutics 208
8.11.3 Medication Adherence & Management 208
8.11.4 Vaccine Development & Promotion 209
8.11.5 Population Health Management 209
8.11.6 Connected Health Communities & Medical Knowledge Dissemination 210
8.11.7 Epidemiology & Disease Surveillance 210
8.11.8 Health Policy Decision Making 211
8.11.9 Controlling Substance Abuse & Addiction 211
8.11.10 Increasing Awareness & Accessible Healthcare 212
8.12 Health Insurance & Payer Services 213
8.12.1 Health Insurance Claims Processing & Management 213
8.12.2 Fraud & Abuse Prevention 213
8.12.3 Proactive Patient Engagement 214
8.12.4 Accountable & Value-Based Care 214
8.12.5 Data-Driven Health Insurance Premiums 215
8.13 Marketing, Sales & Other Application Use Cases 215
8.13.1 Marketing & Sales 215
8.13.2 Administrative & Customer Services 216
8.13.3 Finance & Risk Management 216
8.13.4 Healthcare Data Monetization 217
8.13.5 Other Use Cases 217
8.14 Regional Outlook 218
8.15 Asia Pacific 218
8.15.1 Country Level Segmentation 219
8.15.2 Australia 219
8.15.3 China 220
8.15.4 India 220
8.15.5 Indonesia 221
8.15.6 Japan 221
8.15.7 Malaysia 222
8.15.8 Pakistan 222
8.15.9 Philippines 223
8.15.10 Singapore 223
8.15.11 South Korea 224
8.15.12 Taiwan 224
8.15.13 Thailand 225
8.15.14 Rest of Asia Pacific 225
8.16 Eastern Europe 226
8.16.1 Country Level Segmentation 226
8.16.2 Czech Republic 227
8.16.3 Poland 227
8.16.4 Russia 228
8.16.5 Rest of Eastern Europe 228
8.17 Latin & Central America 229
8.17.1 Country Level Segmentation 229
8.17.2 Argentina 230
8.17.3 Brazil 230
8.17.4 Mexico 231
8.17.5 Rest of Latin & Central America 231
8.18 Middle East & Africa 232
8.18.1 Country Level Segmentation 232
8.18.2 Israel 233
8.18.3 Qatar 233
8.18.4 Saudi Arabia 234
8.18.5 South Africa 234
8.18.6 UAE 235
8.18.7 Rest of the Middle East & Africa 235
8.19 North America 236
8.19.1 Country Level Segmentation 236
8.19.2 Canada 237
8.19.3 USA 237
8.20 Western Europe 238
8.20.1 Country Level Segmentation 238
8.20.2 Denmark 239
8.20.3 Finland 239
8.20.4 France 240
8.20.5 Germany 240
8.20.6 Italy 241
8.20.7 Netherlands 241
8.20.8 Norway 242
8.20.9 Spain 242
8.20.10 Sweden 243
8.20.11 UK 243
8.20.12 Rest of Western Europe 244

9 Chapter 9: Vendor Landscape 245
9.1 1010data 245
9.2 Absolutdata 246
9.3 Accenture 247
9.4 Actian Corporation/HCL Technologies 248
9.5 Adaptive Insights 250
9.6 Adobe Systems 251
9.7 Advizor Solutions 253
9.8 AeroSpike 254
9.9 AFS Technologies 255
9.10 Alation 256
9.11 Algorithmia 257
9.12 Alluxio 258
9.13 ALTEN 259
9.14 Alteryx 260
9.15 AMD (Advanced Micro Devices) 261
9.16 Anaconda 262
9.17 Apixio 263
9.18 Arcadia Data 264
9.19 ARM 265
9.20 AtScale 266
9.21 Attivio 267
9.22 Attunity 268
9.23 Automated Insights 269
9.24 AVORA 270
9.25 AWS (Amazon Web Services) 271
9.26 Axiomatics 273
9.27 Ayasdi 274
9.28 BackOffice Associates 275
9.29 Basho Technologies 276
9.30 BCG (Boston Consulting Group) 277
9.31 Bedrock Data 278
9.32 BetterWorks 279
9.33 Big Panda 280
9.34 BigML 281
9.35 Bitam 282
9.36 Blue Medora 283
9.37 BlueData Software 284
9.38 BlueTalon 285
9.39 BMC Software 286
9.40 BOARD International 287
9.41 Booz Allen Hamilton 288
9.42 Boxever 289
9.43 CACI International 290
9.44 Cambridge Semantics 291
9.45 Capgemini 292
9.46 Cazena 293
9.47 Centrifuge Systems 294
9.48 CenturyLink 295
9.49 Chartio 296
9.50 Cisco Systems 297
9.51 Civis Analytics 298
9.52 ClearStory Data 299
9.53 Cloudability 300
9.54 Cloudera 301
9.55 Cloudian 302
9.56 Clustrix 303
9.57 CognitiveScale 304
9.58 Collibra 305
9.59 Concurrent Technology/Vecima Networks 306
9.60 Confluent 307
9.61 Contexti 308
9.62 Couchbase 309
9.63 Crate.io 310
9.64 Cray 311
9.65 Databricks 312
9.66 Dataiku 313
9.67 Datalytyx 314
9.68 Datameer 315
9.69 DataRobot 316
9.70 DataStax 317
9.71 Datawatch Corporation 318
9.72 DDN (DataDirect Networks) 319
9.73 Decisyon 320
9.74 Dell Technologies 321
9.75 Deloitte 322
9.76 Demandbase 323
9.77 Denodo Technologies 324
9.78 Dianomic Systems 325
9.79 Digital Reasoning Systems 326
9.80 Dimensional Insight 327
9.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group 328
9.82 Domino Data Lab 329
9.83 Domo 330
9.84 Dremio 331
9.85 DriveScale 332
9.86 Druva 333
9.87 Dundas Data Visualization 334
9.88 DXC Technology 335
9.89 Elastic 336
9.90 Engineering Group (Engineering Ingegneria Informatica) 337
9.91 EnterpriseDB Corporation 338
9.92 eQ Technologic 339
9.93 Ericsson 340
9.94 Erwin 341
9.95 EVŌ (Big Cloud Analytics) 342
9.96 EXASOL 343
9.97 EXL (ExlService Holdings) 344
9.98 Facebook 345
9.99 FICO (Fair Isaac Corporation) 346
9.100 Figure Eight 347
9.101 FogHorn Systems 348
9.102 Fractal Analytics 349
9.103 Franz 350
9.104 Fujitsu 351
9.105 Fuzzy Logix 353
9.106 Gainsight 354
9.107 GE (General Electric) 355
9.108 Glassbeam 356
9.109 GoodData Corporation 357
9.110 Google/Alphabet 358
9.111 Grakn Labs 360
9.112 Greenwave Systems 361
9.113 GridGain Systems 362
9.114 H2O.ai 363
9.115 HarperDB 364
9.116 Hedvig 365
9.117 Hitachi Vantara 366
9.118 Hortonworks 367
9.119 HPE (Hewlett Packard Enterprise) 368
9.120 Huawei 370
9.121 HVR 371
9.122 HyperScience 372
9.123 HyTrust 373
9.124 IBM Corporation 375
9.125 iDashboards 377
9.126 IDERA 378
9.127 Ignite Technologies 379
9.128 Imanis Data 381
9.129 Impetus Technologies 382
9.130 Incorta 383
9.131 InetSoft Technology Corporation 384
9.132 InfluxData 385
9.133 Infogix 386
9.134 Infor/Birst 387
9.135 Informatica 389
9.136 Information Builders 390
9.137 Infosys 391
9.138 Infoworks 392
9.139 Insightsoftware.com 393
9.140 InsightSquared 394
9.141 Intel Corporation 395
9.142 Interana 396
9.143 InterSystems Corporation 397
9.144 Jedox 398
9.145 Jethro 399
9.146 Jinfonet Software 400
9.147 Juniper Networks 401
9.148 KALEAO 402
9.149 Keen IO 403
9.150 Keyrus 404
9.151 Kinetica 405
9.152 KNIME 406
9.153 Kognitio 407
9.154 Kyvos Insights 408
9.155 LeanXcale 409
9.156 Lexalytics 410
9.157 Lexmark International 412
9.158 Lightbend 413
9.159 Logi Analytics 414
9.160 Logical Clocks 415
9.161 Longview Solutions/Tidemark 416
9.162 Looker Data Sciences 418
9.163 LucidWorks 419
9.164 Luminoso Technologies 420
9.165 Maana 421
9.166 Manthan Software Services 422
9.167 MapD Technologies 423
9.168 MapR Technologies 424
9.169 MariaDB Corporation 425
9.170 MarkLogic Corporation 426
9.171 Mathworks 427
9.172 Melissa 428
9.173 MemSQL 429
9.174 Metric Insights 430
9.175 Microsoft Corporation 431
9.176 MicroStrategy 433
9.177 Minitab 434
9.178 MongoDB 435
9.179 Mu Sigma 436
9.180 NEC Corporation 437
9.181 Neo4j 438
9.182 NetApp 439
9.183 Nimbix 440
9.184 Nokia 441
9.185 NTT Data Corporation 442
9.186 Numerify 443
9.187 NuoDB 444
9.188 NVIDIA Corporation 445
9.189 Objectivity 446
9.190 Oblong Industries 447
9.191 OpenText Corporation 448
9.192 Opera Solutions 450
9.193 Optimal Plus 451
9.194 Oracle Corporation 452
9.195 Palantir Technologies 455
9.196 Panasonic Corporation/Arimo 457
9.197 Panorama Software 458
9.198 Paxata 459
9.199 Pepperdata 460
9.200 Phocas Software 461
9.201 Pivotal Software 462
9.202 Prognoz 464
9.203 Progress Software Corporation 465
9.204 Provalis Research 466
9.205 Pure Storage 467
9.206 PwC (PricewaterhouseCoopers International) 468
9.207 Pyramid Analytics 469
9.208 Qlik 470
9.209 Qrama/Tengu 471
9.210 Quantum Corporation 472
9.211 Qubole 473
9.212 Rackspace 474
9.213 Radius Intelligence 475
9.214 RapidMiner 476
9.215 Recorded Future 477
9.216 Red Hat 478
9.217 Redis Labs 479
9.218 RedPoint Global 480
9.219 Reltio 481
9.220 RStudio 482
9.221 Rubrik/Datos IO 483
9.222 Ryft 484
9.223 Sailthru 485
9.224 Salesforce.com 486
9.225 Salient Management Company 487
9.226 Samsung Group 488
9.227 SAP 489
9.228 SAS Institute 490
9.229 ScaleOut Software 491
9.230 Seagate Technology 492
9.231 Sinequa 493
9.232 SiSense 494
9.233 Sizmek 495
9.234 SnapLogic 496
9.235 Snowflake Computing 497
9.236 Software AG 498
9.237 Splice Machine 499
9.238 Splunk 500
9.239 Strategy Companion Corporation 502
9.240 Stratio 503
9.241 Streamlio 504
9.242 StreamSets 505
9.243 Striim 506
9.244 Sumo Logic 507
9.245 Supermicro (Super Micro Computer) 508
9.246 Syncsort 509
9.247 SynerScope 511
9.248 SYNTASA 512
9.249 Tableau Software 513
9.250 Talend 514
9.251 Tamr 515
9.252 TARGIT 516
9.253 TCS (Tata Consultancy Services) 517
9.254 Teradata Corporation 518
9.255 Thales/Guavus 520
9.256 ThoughtSpot 521
9.257 TIBCO Software 522
9.258 Toshiba Corporation 524
9.259 Transwarp 525
9.260 Trifacta 526
9.261 Unifi Software 527
9.262 Unravel Data 528
9.263 VANTIQ 529
9.264 VMware 530
9.265 VoltDB 531
9.266 WANdisco 532
9.267 Waterline Data 533
9.268 Western Digital Corporation 534
9.269 WhereScape 535
9.270 WiPro 536
9.271 Wolfram Research 537
9.272 Workday 539
9.273 Xplenty 541
9.274 Yellowfin BI 542
9.275 Yseop 543
9.276 Zendesk 544
9.277 Zoomdata 545
9.278 Zucchetti 546

10 Chapter 10: Conclusion & Strategic Recommendations 547
10.1 Why is the Market Poised to Grow? 547
10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential? 547
10.3 Partnerships & M&A Activity: Highlighting the Importance of Big Data 548
10.4 Driving the Development of Digital Therapeutics 549
10.5 Improving Outcomes, Achieving Operational Efficiency and Reducing Costs 550
10.6 Assessing the Impact of Connected Health Solutions 550
10.7 Accelerating the Transition Towards Value-Based Care 551
10.8 The Emergence of Advanced AI (Artificial Intelligence) & Machine Learning Techniques 553
10.9 The Value of Big Data in Precision Medicine 553
10.10 Addressing Privacy & Security Concerns 554
10.11 The Role of Data Protection Legislation 554
10.12 Blockchain: Enabling Secure, Efficient and Interoperable Data Sharing 555
10.13 Recommendations 556
10.13.1 Big Data Hardware, Software & Professional Services Providers 556
10.13.2 Healthcare & Pharmaceutical Industry Stakeholders 557

List of Figures

Figure 1: Hadoop Architecture 46
Figure 2: Reactive vs. Proactive Analytics 57
Figure 3: Distribution of Big Data Investments in the Healthcare & Pharmaceutical Industry, by Application Area: 2018 (%) 64
Figure 4: Key Characteristics of Genomics and Three Major Sources of Big Data 71
Figure 5: Bayer's Vision of Big Data in Medicine 98
Figure 6: Sickweather's Sickness Forecasting & Mapping Service 153
Figure 7: Counterfeit Drug Identification with Big Data & Mobile Technology 155
Figure 8: Big Data Roadmap in the Healthcare & Pharmaceutical Industry: 2018 – 2030 157
Figure 9: Big Data Value Chain in the Healthcare & Pharmaceutical Industry 160
Figure 10: Key Aspects of Big Data Standardization 171
Figure 11: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 194
Figure 12: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Hardware, Software & Professional Services: 2018 – 2030 ($ Million) 195
Figure 13: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Submarket: 2018 – 2030 ($ Million) 196
Figure 14: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 196
Figure 15: Global Big Data Networking Infrastructure Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 197
Figure 16: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 197
Figure 17: Global Big Data SQL Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 198
Figure 18: Global Big Data NoSQL Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 198
Figure 19: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 199
Figure 20: Global Big Data Cloud Platforms Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 199
Figure 21: Global Big Data Professional Services Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 200
Figure 22: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Application Area: 2018 – 2030 ($ Million) 201
Figure 23: Global Big Data Revenue in Pharmaceutical & Medical Products: 2018 – 2030 ($ Million) 201
Figure 24: Global Big Data Revenue in Core Healthcare Operations: 2018 – 2030 ($ Million) 202
Figure 25: Global Big Data Revenue in Healthcare Support, Awareness & Disease Prevention: 2018 – 2030 ($ Million) 202
Figure 26: Global Big Data Revenue in Health Insurance & Payer Services: 2018 – 2030 ($ Million) 203
Figure 27: Global Big Data Revenue in Healthcare/Pharmaceutical Marketing, Sales & Other Applications: 2018 – 2030 ($ Million) 203
Figure 28: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Use Case: 2018 – 2030 ($ Million) 205
Figure 29: Global Big Data Revenue in Drug Discovery, Design & Development: 2018 – 2030 ($ Million) 206
Figure 30: Global Big Data Revenue in Medical Product Design & Development: 2018 – 2030 ($ Million) 206
Figure 31: Global Big Data Revenue in Clinical Development & Trials: 2018 – 2030 ($ Million) 207
Figure 32: Global Big Data Revenue in Precision Medicine & Genomics: 2018 – 2030 ($ Million) 207
Figure 33: Global Big Data Revenue in Pharmaceutical/Medical Manufacturing & Supply Chain Management: 2018 – 2030 ($ Million) 208
Figure 34: Global Big Data Revenue in Post-Market Surveillance & Pharmacovigilance: 2018 – 2030 ($ Million) 208
Figure 35: Global Big Data Revenue in Medical Product Fault Monitoring: 2018 – 2030 ($ Million) 209
Figure 36: Global Big Data Revenue in Clinical Decision Support: 2018 – 2030 ($ Million) 209
Figure 37: Global Big Data Revenue in Care Coordination & Delivery Management: 2018 – 2030 ($ Million) 210
Figure 38: Global Big Data Revenue in CER (Comparative Effectiveness Research) & Observational Evidence: 2018 – 2030 ($ Million) 210
Figure 39: Global Big Data Revenue in Personalized Healthcare & Targeted Treatments: 2018 – 2030 ($ Million) 211
Figure 40: Global Big Data Revenue in Data-Driven Preventive Care & Health Interventions: 2018 – 2030 ($ Million) 211
Figure 41: Global Big Data Revenue in Surgical Practice & Complex Medical Procedures: 2018 – 2030 ($ Million) 212
Figure 42: Global Big Data Revenue in Pathology, Medical Imaging & Other Medical Tests: 2018 – 2030 ($ Million) 212
Figure 43: Global Big Data Revenue in Proactive & Remote Patient Monitoring: 2018 – 2030 ($ Million) 213
Figure 44: Global Big Data Revenue in Predictive Maintenance of Medical Equipment: 2018 – 2030 ($ Million) 213
Figure 45: Global Big Data Revenue in Pharmacy Services: 2018 – 2030 ($ Million) 214
Figure 46: Global Big Data Revenue in Self-Care & Lifestyle Support: 2018 – 2030 ($ Million) 214
Figure 47: Global Big Data Revenue in Digital Therapeutics: 2018 – 2030 ($ Million) 215
Figure 48: Global Big Data Revenue in Medication Adherence & Management: 2018 – 2030 ($ Million) 215
Figure 49: Global Big Data Revenue in Vaccine Development & Promotion: 2018 – 2030 ($ Million) 216
Figure 50: Global Big Data Revenue in Population Health Management: 2018 – 2030 ($ Million) 216
Figure 51: Global Big Data Revenue in Connected Health Communities & Medical Knowledge Dissemination: 2018 – 2030 ($ Million) 217
Figure 52: Global Big Data Revenue in Epidemiology & Disease Surveillance: 2018 – 2030 ($ Million) 217
Figure 53: Global Big Data Revenue in Health Policy Decision Making: 2018 – 2030 ($ Million) 218
Figure 54: Global Big Data Revenue in Controlling Substance Abuse & Addiction: 2018 – 2030 ($ Million) 218
Figure 55: Global Big Data Revenue in Increasing Awareness & Accessible Healthcare: 2018 – 2030 ($ Million) 219
Figure 56: Global Big Data Revenue in Health Insurance Claims Processing & Management: 2018 – 2030 ($ Million) 220
Figure 57: Global Big Data Revenue in Fraud & Abuse Prevention: 2018 – 2030 ($ Million) 220
Figure 58: Global Big Data Revenue in Proactive Patient Engagement: 2018 – 2030 ($ Million) 221
Figure 59: Global Big Data Revenue in Accountable & Value-Based Care: 2018 – 2030 ($ Million) 221
Figure 60: Global Big Data Revenue in Data-Driven Health Insurance Premiums: 2018 – 2030 ($ Million) 222
Figure 61: Global Big Data Revenue in Healthcare/Pharmaceutical Marketing & Sales: 2018 – 2030 ($ Million) 222
Figure 62: Global Big Data Revenue in Healthcare/Pharmaceutical Administrative & Customer Services: 2018 – 2030 ($ Million) 223
Figure 63: Global Big Data Revenue in Healthcare/Pharmaceutical Finance & Risk Management: 2018 – 2030 ($ Million) 223
Figure 64: Global Big Data Revenue in Healthcare Data Monetization: 2018 – 2030 ($ Million) 224
Figure 65: Global Big Data Revenue in Other Healthcare & Pharmaceutical Industry Use Cases: 2018 – 2030 ($ Million) 224
Figure 66: Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Region: 2018 – 2030 ($ Million) 225
Figure 67: Asia Pacific Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 225
Figure 68: Asia Pacific Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2018 – 2030 ($ Million) 226
Figure 69: Australia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 226
Figure 70: China Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 227
Figure 71: India Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 227
Figure 72: Indonesia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 228
Figure 73: Japan Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 228
Figure 74: Malaysia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 229
Figure 75: Pakistan Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 229
Figure 76: Philippines Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 230
Figure 77: Singapore Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 230
Figure 78: South Korea Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 231
Figure 79: Taiwan Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 231
Figure 80: Thailand Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 232
Figure 81: Rest of Asia Pacific Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 232
Figure 82: Eastern Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 233
Figure 83: Eastern Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2018 – 2030 ($ Million) 233
Figure 84: Czech Republic Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 234
Figure 85: Poland Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 234
Figure 86: Russia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 235
Figure 87: Rest of Eastern Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 235
Figure 88: Latin & Central America Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 236
Figure 89: Latin & Central America Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2018 – 2030 ($ Million) 236
Figure 90: Argentina Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 237
Figure 91: Brazil Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 237
Figure 92: Mexico Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 238
Figure 93: Rest of Latin & Central America Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 238
Figure 94: Middle East & Africa Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 239
Figure 95: Middle East & Africa Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2018 – 2030 ($ Million) 239
Figure 96: Israel Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 240
Figure 97: Qatar Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 240
Figure 98: Saudi Arabia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 241
Figure 99: South Africa Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 241
Figure 100: UAE Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 242
Figure 101: Rest of the Middle East & Africa Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 242
Figure 102: North America Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 243
Figure 103: North America Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2018 – 2030 ($ Million) 243
Figure 104: Canada Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 244
Figure 105: USA Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 244
Figure 106: Western Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 245
Figure 107: Western Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2018 – 2030 ($ Million) 245
Figure 108: Denmark Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 246
Figure 109: Finland Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 246
Figure 110: France Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 247
Figure 111: Germany Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 247
Figure 112: Italy Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 248
Figure 113: Netherlands Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 248
Figure 114: Norway Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 249
Figure 115: Spain Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 249
Figure 116: Sweden Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 250
Figure 117: UK Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 250
Figure 118: Rest of Western Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2018 – 2030 ($ Million) 251

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  • Global Hyperscale Data Center Market 2018 by Manufacturers, Countries, Type and Application, Forecast to 2023
    Published: 22-Oct-2018        Price: US 3480 Onwards        Pages: 130
    A data center is a centralized facility used for data computing, processing, and storage. A data center consists of networking equipment; high-performance servers; storage arrays; and supporting services, such as powering and cooling solutions. A hyperscale data center is more like a customized data center that has wider racks and requires more space compared with a traditional data center. Hyperscale data centers are designed based on the storage requirement. With the evolution of cloud computi......
  • Global Virtual Data Center Market Size, Status and Forecast 2018-2025
    Published: 18-Oct-2018        Price: US 3900 Onwards        Pages: 97
    In 2017, the global Virtual Data Center market size was million US$ and it is expected to reach million US$ by the end of 2025, with a CAGR of during 2018-2025. This report focuses on the global Virtual Data Center status, future forecast, growth opportunity, key market and key players. The study objectives are to present the Virtual Data Center development in United States, Europe and China. The key players covered in this study - VMware - Microsoft......
  • Global Green Data Center Market 2018 by Manufacturers, Countries, Type and Application, Forecast to 2023
    Published: 17-Oct-2018        Price: US 3480 Onwards        Pages: 134
    Green data center refers to an enterprise class computing facility that is entirely built, managed, and operated on green computing principles. Scope of the Report: This report studies the Green Data Center market status and outlook of Global and major regions, from angles of players, countries, product types and end industries; this report analyzes the top players in global market, and splits the Green Data Center market by product type and applications/end industr......
  • 2018-2023 Global Data Broker Market Report (Status and Outlook)
    Published: 15-Oct-2018        Price: US 4660 Onwards        Pages: 154
    In this report, LP Information studies the present scenario (with the base year being 2017) and the growth prospects of global Data Broker market for 2018-2023. Data Broker is a business that aggregates information from a variety of sources; processes it to enrich, cleanse or analyze it; and licenses it to other organizations. Data brokers can also license another company's data directly, or process another organization's data to provide them with enhanced results. ......
  • Global Big Data Enabled Market 2018 by Manufacturers, Countries, Type and Application, Forecast to 2023
    Published: 15-Oct-2018        Price: US 3480 Onwards        Pages: 119
    Big data is more than just a buzzword. In fact, the huge amounts of data that we're gathering could well change all areas of our life, from improving healthcare outcomes to helping to manage traffic levels in metropolitan areas and, of course, making our marketing campaigns far more powerful. Scope of the Report: This report studies the Big Data Enabled market status and outlook of Global and major regions, from angles of players, countries, product types and end in......
  • Global and United States Modular Data Centers Market Research by Company, Type & Application 2013-2025
    Published: 14-Oct-2018        Price: US 2000 Onwards        Pages: 113
    Summary A modular data center system is a portable method of deploying data center capacity. A modular data center can be placed anywhere data capacity is needed.Modular data center systems consist of purpose-engineered modules and components to offer scalable data center capacity with multiple power and cooling options. Modules can be shipped to be added, integrated or retrofitted into an existing data center or combined into a system of modules. Modular data centers typically co......
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