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医薬産業のビッグデータ 2017-2030年:ビジネスチャンス、課題、戦略、市場予測

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

 

出版社 出版年月電子媒体価格ページ数図表数
Signals and Systems Telecom
シグナルズアンドシステムズテレコム
2017年8月US$2,500
シングルユーザライセンス (PDF+Excel)
499 117

サマリー

英国とドバイに拠点をおく調査会社シグナルズアンドシステムズテレコム/SNSリサーチ (Signals and Systems Telecom/SNS Research)の調査レポートの調査レポート「医薬産業のビッグデータ 2017-2030年:ビジネスチャンス、課題、戦略、市場予測」は、医療と医薬品産業でのビッグデータの主要な市場促進要因や課題、投資の可能性、用途分野、利用事例、今後のロードマップ、バリューチェーン、ケーススタディ、ベンダの概要と戦略などを詳細に分析している。2017年から2030年のビッグデータのハードウェア、ソフトウェア、プロフェッショナルサービスの市場規模を、8つの水平サブ市場毎、5つの用途分野毎、36の利用ケース毎、6つの地域と35ケ国毎に区分して予測している。

目次(抜粋)

  • ビッグデータの市場概観
  • ビッグデータの解析
  • ビッグデータの利用事例
  • ビッグデータのケーススタディ
  • ビッグデータの今後のロードマップとバリューチェーン
  • ビッグデータの法規制・標準化のイニシアチブ
  • ビッグデータの市場分析と予測
  • ビッグデータのベンダの概要

Synopsis:

“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.

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.

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
•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 categories:

•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?

List of Companies Mentioned:

The following companies and organizations have been reviewed, discussed or mentioned in the report:

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



目次

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 ..................................... 31
1.5 Key Findings ....................................... 32
1.6 Methodology ..................................... 33
1.7 Target Audience ......................................... 34
1.8 Companies & Organizations Mentioned ............................... 35

2 Chapter 2: An Overview of Big Data ....................... 39
2.1 What is Big Data? ....................................... 39
2.2 Key Approaches to Big Data Processing ................................ 39
2.2.1 Hadoop ........................................... 40
2.2.2 NoSQL .................................... 42
2.2.3 MPAD (Massively Parallel Analytic Databases) ........................... 42
2.2.4 In-Memory Processing ...................................... 43
2.2.5 Stream Processing Technologies ............................... 43
2.2.6 Spark ...................................... 44
2.2.7 Other Databases & Analytic Technologies ......................... 44
2.3 Key Characteristics of Big Data ..................................... 45
2.3.1 Volume ........................................... 45
2.3.2 Velocity ........................................... 45
2.3.3 Variety ............................................ 45
2.3.4 Value ...................................... 46
2.4 Market Growth Drivers ....................................... 47
2.4.1 Awareness of Benefits ...................................... 47
2.4.2 Maturation of Big Data Platforms ............................. 47
2.4.3 Continued Investments by Web Giants, Governments & Enterprises ................... 48
2.4.4 Growth of Data Volume, Velocity & Variety ....................... 48
2.4.5 Vendor Commitments & Partnerships ............................... 48
2.4.6 Technology Trends Lowering Entry Barriers ....................... 49
2.5 Market Barriers .......................................... 49
2.5.1 Lack of Analytic Specialists ............................... 49
2.5.2 Uncertain Big Data Strategies ........................... 49
2.5.3 Organizational Resistance to Big Data Adoption ......................... 50
2.5.4 Technical Challenges: Scalability & Maintenance ....................... 50
2.5.5 Security & Privacy Concerns ............................. 50

3 Chapter 3: Big Data Analytics ................................ 52
3.1 What are Big Data Analytics? .................................... 52
3.2 The Importance of Analytics ..................................... 52
3.3 Reactive vs. Proactive Analytics ................................ 53
3.4 Customer vs. Operational Analytics ................................... 54
3.5 Technology & Implementation Approaches ............................... 54
3.5.1 Grid Computing ....................................... 54
3.5.2 In-Database Processing .................................... 55
3.5.3 In-Memory Analytics ............................... 55
3.5.4 Machine Learning & Data Mining .............................. 55
3.5.5 Predictive Analytics ................................. 56
3.5.6 NLP (Natural Language Processing) ........................... 56
3.5.7 Text Analytics ................................. 57
3.5.8 Visual Analytics ........................................ 58
3.5.9 Graph Analytics ....................................... 58
3.5.10 Social Media, IT & Telco Network Analytics ....................... 59

4 Chapter 4: Business Case & Applications in the Healthcare & Pharmaceutical Industry .......... 60
4.1 Overview & Investment Potential ............................. 60
4.2 Industry Specific Market Growth Drivers ........................... 61
4.3 Industry Specific Market Barriers .............................. 62
4.4 Key Applications ...................................... 64
4.4.1 Pharmaceutical & Medical Products ......................... 64
4.4.1.1 Drug Discovery, Design & Development ............................... 64
4.4.1.2 Medical Product Design & Development .............................. 65
4.4.1.3 Clinical Development & Trials ...................................... 65
4.4.1.4 Precision Medicine & Genomics .................................. 66
4.4.1.5 Manufacturing & Supply Chain Management ................................ 67
4.4.1.6 Post-Market Surveillance & Pharmacovigilance ............................ 69
4.4.1.7 Medical Product Fault Monitoring ............................... 69
4.4.2 Core Healthcare Operations ............................. 70
4.4.2.1 Clinical Decision Support ..................................... 70
4.4.2.2 Care Coordination & Delivery Management ......................... 71
4.4.2.3 CER (Comparative Effectiveness Research) & Observational Evidence..................... 72
4.4.2.4 Personalized Healthcare & Targeted Treatments .......................... 72
4.4.2.5 Data-Driven Preventive Care & Health Interventions ............................. 73
4.4.2.6 Surgical Practice & Complex Medical Procedures .......................... 73
4.4.2.7 Pathology, Medical Imaging & Other Medical Tests ...................... 74
4.4.2.8 Proactive & Remote Patient Monitoring ............................... 74
4.4.2.9 Predictive Maintenance of Medical Equipment ............................. 75
4.4.2.10 Pharmacy Services ................................. 75
4.4.3 Healthcare Support, Awareness & Disease Prevention ........................ 76
4.4.3.1 Self-Care & Lifestyle Support .............................. 76
4.4.3.2 Medication Adherence & Management................................ 77
4.4.3.3 Vaccine Development & Promotion............................. 78
4.4.3.4 Population Health Management .................................. 78
4.4.3.5 Connected Health Communities & Medical Knowledge Dissemination.................... 79
4.4.3.6 Epidemiology & Disease Surveillance........................... 80
4.4.3.7 Health Policy Decision Making ..................................... 80
4.4.3.8 Controlling Substance Abuse & Addiction ............................ 81
4.4.3.9 Increasing Awareness & Accessible Healthcare ............................. 82
4.4.4 Health Insurance & Payer Services ............................ 82
4.4.4.1 Health Insurance Claims Processing & Management ..................... 82
4.4.4.2 Fraud & Abuse Prevention .................................. 83
4.4.4.3 Proactive Patient Engagement ..................................... 84
4.4.4.4 Accountable & Value-Based Care................................. 84
4.4.4.5 Data-Driven Health Insurance Premiums .............................. 85
4.4.5 Marketing, Sales & Other Applications .............................. 85
4.4.5.1 Marketing & Sales ...................................... 85
4.4.5.2 Administrative & Customer Services ............................ 86
4.4.5.3 Finance & Risk Management .............................. 87
4.4.5.4 Healthcare Data Monetization ..................................... 88
4.4.5.5 Other Applications ..................................... 89

5 Chapter 5: Healthcare & Pharmaceutical Industry Case Studies .................. 90
5.1 Pharmaceutical & Medical Device Companies ............................ 90
5.1.1 AstraZeneca: Analytics-Driven Drug Development with Big Data ................. 90
5.1.2 Bayer: Accelerating Clinical Trials with Big Data ......................... 92
5.1.3 GSK (GlaxoSmithKline): Increasing Success Rates in Drug Discovery with Big Data ........ 94
5.1.4 Johnson & Johnson: Intelligent Pharmaceutical Marketing with Big Data ............. 96
5.1.5 Medtronic: Facilitating Predictive Care with Big Data .......................... 97
5.1.6 Merck & Co.: Optimizing Vaccine Manufacturing with Big Data ................... 98
5.1.7 Merck KGaA: Discovering Drugs Faster with Big Data .......................... 99
5.1.8 Novartis: Digitizing Healthcare with Big Data ............................ 100
5.1.9 Pfizer: Developing Effective and Targeted Therapies with Big Data .................... 102
5.1.10 Roche: Personalizing Healthcare with Big Data ......................... 104
5.1.11 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 Bangkok Hospital Group: Transforming the Patient Experience with Big Data ............. 109
5.2.3 Gold Coast Health: Reducing Hospital Waiting Times with Big Data.................... 111
5.2.4 IU Health (Indiana University Health): Preventing Hospital-Acquired Infections with Big Data ..... 112
5.2.5 MSQC (Michigan Surgical Quality Collaborative): Surgical Quality Improvement with Big Data .... 113
5.2.6 NCCS (National Cancer Centre Singapore): Advancing Cancer Treatment with Big Data ..... 114
5.2.7 NHS Scotland: Improving Outcomes with Big Data ................... 116
5.2.8 Seattle Children's Hospital: Enabling Faster & Accurate Diagnosis with Big Data ........ 117
5.2.9 UnitedHealth Group: Enhancing Patient Care & Value with Big Data .................. 118
5.2.10 VHA (Veterans Health Administration): Streamlining Healthcare Delivery with Big Data ..... 120
5.3 Other Stakeholders ........................................ 122
5.3.1 Amino: Healthcare Transparency with Big Data ........................ 122
5.3.2 CosmosID: Advancing Microbial Genomics with Big Data ................. 123
5.3.3 Express Scripts: Improving Medication Adherence with Big Data .............. 124
5.3.4 Faros Healthcare: Enhancing Clinical Decision Making with Big Data .................. 126
5.3.5 Genomics England: Developing the World's First Genomics Medicine Service with Big Data ........ 127
5.3.6 Ginger.io: Improving Mental Wellbeing with Big Data ....................... 129
5.3.7 Illumina: Enabling Precision Medicine with Big Data ......................... 130
5.3.8 INDS (National Institute of Health Data, France): Population Health Management with Big Data 131
5.3.9 MolecularMatch: Advancing the Clinical Utility of Genomics with Big Data ................. 132
5.3.10 Proteus Digital Health: Pioneering Digital Medicine with Big Data ............. 134
5.3.11 Royal Philips: Enhancing Workflows in ICUs (Intensive Care Units) with Big Data ........ 136
5.3.12 Sickweather: Sickness Forecasting & Mapping with Big Data ..................... 137
5.3.13 Sproxil: Fighting Counterfeit Drugs with Big Data ..................... 139

6 Chapter 6: Future Roadmap & Value Chain ................... 141
6.1 Future Roadmap.................................... 141
6.1.1 2017 – 2020: Growing Investments in Real-Time & Predictive Health Analytics .......... 141
6.1.2 2020 – 2025: Large-Scale Adoption of Precision Medicine ................ 142
6.1.3 2025 – 2030: Moving Beyond National-Level Population Health Management ........... 143
6.2 Value Chain ........................................... 143
6.2.1 Hardware Providers ............................... 144
6.2.1.1 Storage & Compute Infrastructure Providers .............................. 144
6.2.1.2 Networking Infrastructure Providers .................................. 145
6.2.2 Software Providers ................................ 145
6.2.2.1 Hadoop & Infrastructure Software Providers .............................. 145
6.2.2.2 SQL & NoSQL Providers ..................................... 146
6.2.2.3 Analytic Platform & Application Software Providers ............................ 146
6.2.2.4 Cloud Platform Providers .................................. 146
6.2.3 Professional Services Providers ............................... 146
6.2.4 End-to-End Solution Providers ................................ 147
6.2.5 Healthcare & Pharmaceutical Industry ............................ 147

7 Chapter 7: Standardization & Regulatory Initiatives ............... 148
7.1 ASF (Apache Software Foundation) ................................. 148
7.1.1 Management of Hadoop ................................ 148
7.1.2 Big Data Projects Beyond Hadoop ........................... 148
7.2 CSA (Cloud Security Alliance) .................................. 151
7.2.1 BDWG (Big Data Working Group) ............................ 152
7.3 CSCC (Cloud Standards Customer Council) ............................... 152
7.3.1 Big Data Working Group ................................. 152
7.4 DMG (Data Mining Group) ..................................... 153
7.4.1 PMML (Predictive Model Markup Language) Working Group .................... 153
7.4.2 PFA (Portable Format for Analytics) Working Group ......................... 153
7.5 IEEE (Institute of Electrical and Electronics Engineers) ...................... 154
7.5.1 Big Data Initiative .................................. 154
7.6 INCITS (InterNational Committee for Information Technology Standards) .................. 155
7.6.1 Big Data Technical Committee ................................ 155
7.7 ISO (International Organization for Standardization) ........................ 156
7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange ................. 156
7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms ............. 157
7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques ............................. 157
7.7.4 ISO/IEC JTC 1/WG 9: Big Data .................................. 157
7.7.5 Collaborations with Other ISO Work Groups ............................ 159
7.8 ITU (International Telecommunications Union) ........................ 159
7.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities ....... 159
7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks ............... 160
7.8.3 Other Relevant Work ...................................... 161
7.9 Linux Foundation ................................... 161
7.9.1 ODPi (Open Ecosystem of Big Data) ........................ 161
7.10 NIST (National Institute of Standards and Technology) ..................... 162
7.10.1 NBD-PWG (NIST Big Data Public Working Group) ..................... 162
7.11 OASIS (Organization for the Advancement of Structured Information Standards) ............... 163
7.11.1 Technical Committees .................................... 163
7.12 ODaF (Open Data Foundation) ................................ 164
7.12.1 Big Data Accessibility ...................................... 164
7.13 ODCA (Open Data Center Alliance) .................................. 164
7.13.1 Work on Big Data ................................... 164
7.14 OGC (Open Geospatial Consortium) ................................ 165
7.14.1 Big Data DWG (Domain Working Group) ......................... 165
7.15 TM Forum ..................................... 165
7.15.1 Big Data Analytics Strategic Program ............................... 165
7.16 TPC (Transaction Processing Performance Council) ........................... 166
7.16.1 TPC-BDWG (TPC Big Data Working Group) ...................... 166
7.17 W3C (World Wide Web Consortium) ............................... 166
7.17.1 Big Data Community Group ............................ 166
7.17.2 Open Government Community Group ............................. 167
7.18 Other Initiatives Relevant to the Healthcare & Pharmaceutical Industry ............ 167
7.18.1 HIPAA (Health Insurance Portability and Accountability Act of 1996) ................. 167
7.18.2 HITECH (Health Information Technology for Economic and Clinical Health) Act .......... 168
7.18.3 European Union's GDPR (General Data Protection Regulation) .................. 169
7.18.4 Australian Digital Health Agency ............................. 169
7.18.5 United Kingdom's ITK (Interoperability Toolkit) ........................ 170
7.18.6 Japan's SS-MIX (Standard Structured Medical Information eXchange) ............... 170
7.18.7 Germany's xDT....................................... 170
7.18.8 France's DMP (Dossier Médical Personnel) ...................... 171
7.18.9 HL7 (Health Level Seven) Specifications ........................... 171
7.18.10 IHE (Integrating the Healthcare Enterprise) ..................... 172
7.18.11 NCPDP (National Council for Prescription Drug Programs) ................ 173
7.18.12 DICOM (Digital Imaging and Communications in Medicine) ....................... 174
7.18.13 eHealth Exchange .................................. 174
7.18.14 EDIFACT (Electronic Data Interchange For Administration, Commerce, and Transport) ....... 174
7.18.15 X12 & Others ......................................... 174

8 Chapter 8: Market Analysis & Forecasts ........................ 176
8.1 Global Outlook for Big Data in the Healthcare & Pharmaceutical Industry ............ 176
8.2 Hardware, Software & Professional Services Segmentation .......................... 177
8.3 Horizontal Submarket Segmentation .................................. 178
8.4 Hardware Submarkets ....................................... 178
8.4.1 Storage and Compute Infrastructure ............................... 178
8.4.2 Networking Infrastructure .............................. 179
8.5 Software Submarkets ........................................ 179
8.5.1 Hadoop & Infrastructure Software .......................... 179
8.5.2 SQL ....................................... 180
8.5.3 NoSQL ........................................... 180
8.5.4 Analytic Platforms & Applications ........................... 181
8.5.5 Cloud Platforms ..................................... 181
8.6 Professional Services Submarket................................ 182
8.6.1 Professional Services ...................................... 182
8.7 Application Area Segmentation ................................. 183
8.7.1 Pharmaceutical & Medical Products ................................ 183
8.7.2 Core Healthcare Operations ........................... 184
8.7.3 Healthcare Support, Awareness & Disease Prevention ...................... 184
8.7.4 Health Insurance & Payer Services .......................... 185
8.7.5 Marketing, Sales & Other Applications ............................ 185
8.8 Use Case Segmentation ..................................... 186
8.9 Pharmaceutical & Medical Products ................................... 188
8.9.1 Drug Discovery, Design & Development .......................... 188
8.9.2 Medical Product Design & Development ......................... 188
8.9.3 Clinical Development & Trials.................................. 189
8.9.4 Precision Medicine & Genomics .............................. 189
8.9.5 Manufacturing & Supply Chain Management ........................... 190
8.9.6 Post-Market Surveillance & Pharmacovigilance ........................ 190
8.9.7 Medical Product Fault Monitoring .......................... 191
8.10 Core Healthcare Operations .................................... 191
8.10.1 Clinical Decision Support ................................ 191
8.10.2 Care Coordination & Delivery Management ............................. 192
8.10.3 CER (Comparative Effectiveness Research) & Observational Evidence................ 192
8.10.4 Personalized Healthcare & Targeted Treatments ..................... 193
8.10.5 Data-Driven Preventive Care & Health Interventions ........................ 193
8.10.6 Surgical Practice & Complex Medical Procedures ..................... 194
8.10.7 Pathology, Medical Imaging & Other Medical Tests .......................... 194
8.10.8 Proactive & Remote Patient Monitoring .......................... 195
8.10.9 Predictive Maintenance of Medical Equipment ........................ 195
8.10.10 Pharmacy Services ................................. 196
8.11 Healthcare Support, Awareness & Disease Prevention ..................... 196
8.11.1 Self-Care & Lifestyle Support ................................... 196
8.11.2 Medication Adherence & Management ........................... 197
8.11.3 Vaccine Development & Promotion ........................ 197
8.11.4 Population Health Management ............................. 198
8.11.5 Connected Health Communities & Medical Knowledge Dissemination............... 198
8.11.6 Epidemiology & Disease Surveillance ............................... 199
8.11.7 Health Policy Decision Making ................................ 199
8.11.8 Controlling Substance Abuse & Addiction ........................ 200
8.11.9 Increasing Awareness & Accessible Healthcare ........................ 200
8.12 Health Insurance & Payer Services ................................... 201
8.12.1 Health Insurance Claims Processing & Management ......................... 201
8.12.2 Fraud & Abuse Prevention ............................. 201
8.12.3 Proactive Patient Engagement ................................ 202
8.12.4 Accountable & Value-Based Care ............................ 202
8.12.5 Data-Driven Health Insurance Premiums ......................... 203
8.13 Marketing, Sales & Other Application Use Cases ...................... 203
8.13.1 Marketing & Sales ................................. 203
8.13.2 Administrative & Customer Services ................................ 204
8.13.3 Finance & Risk Management ................................... 204
8.13.4 Healthcare Data Monetization ................................ 205
8.13.5 Other Use Cases .................................... 205
8.14 Regional Outlook ................................... 206
8.15 Asia Pacific .................................... 206
8.15.1 Country Level Segmentation ................................... 207
8.15.2 Australia ....................................... 207
8.15.3 China .................................... 208
8.15.4 India ..................................... 208
8.15.5 Indonesia ...................................... 209
8.15.6 Japan.................................... 209
8.15.7 Malaysia ....................................... 210
8.15.8 Pakistan ........................................ 210
8.15.9 Philippines .................................... 211
8.15.10 Singapore ...................................... 211
8.15.11 South Korea .................................. 212
8.15.12 Taiwan .......................................... 212
8.15.13 Thailand ........................................ 213
8.15.14 Rest of Asia Pacific ................................. 213
8.16 Eastern Europe ...................................... 214
8.16.1 Country Level Segmentation ................................... 214
8.16.2 Czech Republic ....................................... 215
8.16.3 Poland ........................................... 215
8.16.4 Russia ............................................ 216
8.16.5 Rest of Eastern Europe ................................... 216
8.17 Latin & Central America ................................. 217
8.17.1 Country Level Segmentation ................................... 217
8.17.2 Argentina ...................................... 218
8.17.3 Brazil .................................... 218
8.17.4 Mexico .......................................... 219
8.17.5 Rest of Latin & Central America .............................. 219
8.18 Middle East & Africa ....................................... 220
8.18.1 Country Level Segmentation ................................... 220
8.18.2 Israel .................................... 221
8.18.3 Qatar .................................... 221
8.18.4 Saudi Arabia .................................. 222
8.18.5 South Africa .................................. 222
8.18.6 UAE ...................................... 223
8.18.7 Rest of the Middle East & Africa.............................. 223
8.19 North America ....................................... 224
8.19.1 Country Level Segmentation ................................... 224
8.19.2 Canada .......................................... 225
8.19.3 USA ...................................... 225
8.20 Western Europe .................................... 226
8.20.1 Country Level Segmentation ................................... 226
8.20.2 Denmark ....................................... 227
8.20.3 Finland .......................................... 227
8.20.4 France ........................................... 228
8.20.5 Germany ....................................... 228
8.20.6 Italy ...................................... 229
8.20.7 Netherlands .................................. 229
8.20.8 Norway ......................................... 230
8.20.9 Spain .................................... 230
8.20.10 Sweden ......................................... 231
8.20.11 UK ........................................ 231
8.20.12 Rest of Western Europe ................................. 232

9 Chapter 9: Vendor Landscape .............................. 233
9.1 1010data ......................................... 233
9.2 Absolutdata ..................................... 234
9.3 Accenture ........................................ 235
9.4 Actian Corporation ................................... 236
9.5 Adaptive Insights ...................................... 237
9.6 Advizor Solutions ...................................... 238
9.7 AeroSpike ........................................ 239
9.8 AFS Technologies ...................................... 240
9.9 Alation ............................................. 241
9.10 Algorithmia ............................................ 242
9.11 Alluxio ........................................... 243
9.12 Alpine Data ............................................ 244
9.13 Alteryx .......................................... 245
9.14 AMD (Advanced Micro Devices) .............................. 246
9.15 Apixio ........................................... 247
9.16 Arcadia Data .......................................... 248
9.17 Arimo ............................................ 249
9.18 ARM .............................................. 250
9.19 AtScale .......................................... 251
9.20 Attivio ........................................... 252
9.21 Attunity ........................................ 253
9.22 Automated Insights ........................................ 254
9.23 AWS (Amazon Web Services) .................................. 255
9.24 Axiomatics .................................... 256
9.25 Ayasdi ........................................... 257
9.26 Basho Technologies ........................................ 258
9.27 BCG (Boston Consulting Group) .............................. 259
9.28 Bedrock Data ......................................... 260
9.29 BetterWorks .......................................... 261
9.30 Big Cloud Analytics ......................................... 262
9.31 BigML ........................................... 263
9.32 Big Panda ...................................... 264
9.33 Birst .............................................. 265
9.34 Bitam ............................................ 266
9.35 Blue Medora .......................................... 267
9.36 BlueData Software ......................................... 268
9.37 BlueTalon ..................................... 269
9.38 BMC Software ....................................... 270
9.39 BOARD International ...................................... 271
9.40 Booz Allen Hamilton ....................................... 272
9.41 Boxever ........................................ 273
9.42 CACI International ................................. 274
9.43 Cambridge Semantics ..................................... 275
9.44 Capgemini .................................... 276
9.45 Cazena .......................................... 277
9.46 Centrifuge Systems ........................................ 278
9.47 CenturyLink ........................................... 279
9.48 Chartio .......................................... 280
9.49 Cisco Systems ........................................ 281
9.50 Civis Analytics ........................................ 282
9.51 ClearStory Data ..................................... 283
9.52 Cloudability ........................................... 284
9.53 Cloudera ....................................... 285
9.54 Clustrix ......................................... 286
9.55 CognitiveScale ....................................... 287
9.56 Collibra ......................................... 288
9.57 Concurrent Computer Corporation .................................. 289
9.58 Confluent ...................................... 290
9.59 Contexti ........................................ 291
9.60 Continuum Analytics ...................................... 292
9.61 Couchbase .................................... 293
9.62 CrowdFlower ......................................... 294
9.63 Databricks .................................... 295
9.64 DataGravity ........................................... 296
9.65 Dataiku ......................................... 297
9.66 Datameer ..................................... 298
9.67 DataRobot .................................... 299
9.68 DataScience ........................................... 300
9.69 DataStax ....................................... 301
9.70 DataTorrent ........................................... 302
9.71 Datawatch Corporation .................................. 303
9.72 Datos IO ........................................ 304
9.73 DDN (DataDirect Networks) .................................... 305
9.74 Decisyon ....................................... 306
9.75 Dell Technologies .................................. 307
9.76 Deloitte......................................... 308
9.77 Demandbase ......................................... 309
9.78 Denodo Technologies ..................................... 310
9.79 Digital Reasoning Systems .............................. 311
9.80 Dimensional Insight ........................................ 312
9.81 Dolphin Enterprise Solutions Corporation ................................ 313
9.82 Domino Data Lab ................................... 314
9.83 Domo ............................................ 315
9.84 DriveScale ..................................... 316
9.85 Dundas Data Visualization .............................. 317
9.86 DXC Technology .................................... 318
9.87 Eligotech ....................................... 319
9.88 Engineering Group (Engineering Ingegneria Informatica) .......................... 320
9.89 EnterpriseDB ......................................... 321
9.90 eQ Technologic ...................................... 322
9.91 Ericsson ........................................ 323
9.92 EXASOL ......................................... 324
9.93 Facebook ...................................... 325
9.94 FICO (Fair Isaac Corporation) .................................. 326
9.95 Fractal Analytics .................................... 327
9.96 Fujitsu ........................................... 328
9.97 Fuzzy Logix ............................................ 330
9.98 Gainsight ...................................... 331
9.99 GE (General Electric) ...................................... 332
9.100 Glassbeam ......................................... 333
9.101 GoodData Corporation .............................. 334
9.102 Google ...................................... 335
9.103 Greenwave Systems ................................... 336
9.104 GridGain Systems ....................................... 337
9.105 Guavus ..................................... 338
9.106 H2O.ai ...................................... 339
9.107 HDS (Hitachi Data Systems) ................................ 340
9.108 Hedvig ...................................... 341
9.109 Hortonworks ..................................... 342
9.110 HPE (Hewlett Packard Enterprise) ............................... 343
9.111 Huawei ..................................... 345
9.112 IBM Corporation ........................................ 346
9.113 iDashboards ...................................... 348
9.114 Impetus Technologies ................................ 349
9.115 Incorta ...................................... 350
9.116 InetSoft Technology Corporation................................. 351
9.117 Infer ......................................... 352
9.118 Infor ......................................... 353
9.119 Informatica Corporation ..................................... 354
9.120 Information Builders .................................. 355
9.121 Infosys ...................................... 356
9.122 Infoworks .......................................... 357
9.123 Insightsoftware.com .................................. 358
9.124 InsightSquared .................................. 359
9.125 Intel Corporation ....................................... 360
9.126 Interana ............................................ 361
9.127 InterSystems Corporation ................................... 362
9.128 Jedox ........................................ 363
9.129 Jethro ....................................... 364
9.130 Jinfonet Software ....................................... 365
9.131 Juniper Networks ....................................... 366
9.132 KALEAO .................................... 367
9.133 Keen IO..................................... 368
9.134 Kinetica .................................... 369
9.135 KNIME ...................................... 370
9.136 Kognitio .................................... 371
9.137 Kyvos Insights .................................... 372
9.138 Lavastorm ......................................... 373
9.139 Lexalytics ........................................... 374
9.140 Lexmark International ................................ 375
9.141 Logi Analytics .................................... 376
9.142 Longview Solutions .................................... 377
9.143 Looker Data Sciences ................................. 378
9.144 LucidWorks ....................................... 379
9.145 Luminoso Technologies ...................................... 380
9.146 Maana ...................................... 381
9.147 Magento Commerce .................................. 382
9.148 Manthan Software Services ................................ 383
9.149 MapD Technologies ................................... 384
9.150 MapR Technologies .................................... 385
9.151 MariaDB Corporation ................................. 386
9.152 MarkLogic Corporation .............................. 387
9.153 Mathworks ........................................ 388
9.154 MemSQL ........................................... 389
9.155 Metric Insights .................................. 390
9.156 Microsoft Corporation ............................... 391
9.157 MicroStrategy ................................... 392
9.158 Minitab..................................... 393
9.159 MongoDB .......................................... 394
9.160 Mu Sigma .......................................... 395
9.161 NEC Corporation ........................................ 396
9.162 Neo Technology ......................................... 397
9.163 NetApp ..................................... 398
9.164 Nimbix ...................................... 399
9.165 Nokia ........................................ 400
9.166 NTT Data Corporation ................................ 401
9.167 Numerify ........................................... 402
9.168 NuoDB ...................................... 403
9.169 Nutonian ........................................... 404
9.170 NVIDIA Corporation ................................... 405
9.171 Oblong Industries ....................................... 406
9.172 OpenText Corporation ............................... 407
9.173 Opera Solutions ......................................... 409
9.174 Optimal Plus ...................................... 410
9.175 Oracle Corporation .................................... 411
9.176 Palantir Technologies ................................. 413
9.177 Panorama Software ................................... 414
9.178 Paxata ...................................... 415
9.179 Pentaho Corporation ................................. 416
9.180 Pepperdata ....................................... 417
9.181 Phocas Software ........................................ 418
9.182 Pivotal Software ......................................... 419
9.183 Prognoz .................................... 421
9.184 Progress Software Corporation ................................... 422
9.185 PwC (PricewaterhouseCoopers International) ..................... 423
9.186 Pyramid Analytics ...................................... 424
9.187 Qlik ........................................... 425
9.188 Quantum Corporation ............................... 426
9.189 Qubole ..................................... 427
9.190 Rackspace ......................................... 428
9.191 Radius Intelligence ..................................... 429
9.192 RapidMiner ....................................... 430
9.193 Recorded Future ........................................ 431
9.194 Red Hat .................................... 432
9.195 Redis Labs ......................................... 433
9.196 RedPoint Global ......................................... 434
9.197 Reltio ........................................ 435
9.198 Rocket Fuel ....................................... 436
9.199 RStudio ..................................... 437
9.200 Ryft Systems ...................................... 438
9.201 Sailthru ..................................... 439
9.202 Salesforce.com .................................. 440
9.203 Salient Management Company ................................... 441
9.204 Samsung Group ................................. 442
9.205 SAP ........................................... 443
9.206 SAS Institute ...................................... 444
9.207 ScaleDB .................................... 445
9.208 ScaleOut Software ..................................... 446
9.209 SCIO Health Analytics ................................. 447
9.210 Seagate Technology ................................... 448
9.211 Sinequa .................................... 449
9.212 SiSense ..................................... 450
9.213 SnapLogic .......................................... 451
9.214 Snowflake Computing ................................ 452
9.215 Software AG ...................................... 453
9.216 Splice Machine .................................. 454
9.217 Splunk ...................................... 455
9.218 Sqrrl .......................................... 456
9.219 Strategy Companion Corporation ................................ 457
9.220 StreamSets ........................................ 458
9.221 Striim ........................................ 459
9.222 Sumo Logic ........................................ 460
9.223 Supermicro (Super Micro Computer) .......................... 461
9.224 Syncsort ............................................ 462
9.225 SynerScope ....................................... 463
9.226 Tableau Software ....................................... 464
9.227 Talena ...................................... 465
9.228 Talend ...................................... 466
9.229 Tamr ......................................... 467
9.230 TARGIT ..................................... 468
9.231 TCS (Tata Consultancy Services) .................................. 469
9.232 Teradata Corporation ................................ 470
9.233 ThoughtSpot ..................................... 472
9.234 TIBCO Software ................................. 473
9.235 Tidemark ........................................... 474
9.236 Toshiba Corporation .................................. 475
9.237 Trifacta ..................................... 476
9.238 Unravel Data ..................................... 477
9.239 VMware ............................................ 478
9.240 VoltDB ...................................... 479
9.241 Waterline Data .................................. 480
9.242 Western Digital Corporation ............................... 481
9.243 WiPro ....................................... 482
9.244 Workday ............................................ 483
9.245 Xplenty ..................................... 484
9.246 Yellowfin International .............................. 485
9.247 Yseop........................................ 486
9.248 Zendesk .................................... 487
9.249 Zoomdata .......................................... 488
9.250 Zucchetti ........................................... 489

10 Chapter 10: Conclusion & Strategic Recommendations ................... 490
10.1 Why is the Market Poised to Grow? ................................ 490
10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential? .............. 490
10.3 Partnerships & M&A Activity: Highlighting the Importance of Big Data .............. 491
10.4 Improving Outcomes, Achieving Operational Efficiency and Reducing Costs ............... 492
10.5 Assessing the Impact of Connected Health Solutions ........................ 492
10.6 Accelerating the Transition Towards Value-Based Care .................... 493
10.7 The Value of Big Data in Precision Medicine ............................. 494
10.8 Addressing Privacy & Security Concerns .......................... 495
10.9 The Role of Data Protection Legislation ........................... 495
10.10 Blockchain: Enabling Secure, Efficient and Interoperable Data Sharing ................... 496
10.11 Recommendations ..................................... 497
10.11.1 Big Data Hardware, Software & Professional Services Providers ................ 497
10.11.2 Healthcare & Pharmaceutical Industry Stakeholders ......................... 498


List of Figures

Figure 1: Hadoop Architecture ...................................................... 40
Figure 2: Reactive vs. Proactive Analytics ........................................................ 53
Figure 3: Distribution of Big Data Investments in the Healthcare & Pharmaceutical Industry, by Application Area: 2016 (%) .................. 60
Figure 4: Key Characteristics of Genomics and Three Major Sources of Big Data ......................................... 67
Figure 5: Bayer's Vision of Big Data in Medicine ............................................. 93
Figure 6: Sickweather's Sickness Forecasting & Mapping Service ............................................ 137
Figure 7: Counterfeit Drug Identification with Big Data & Mobile Technology .................................. 139
Figure 8: Big Data Roadmap in the Healthcare & Pharmaceutical Industry ....................................... 141
Figure 9: Big Data Value Chain in the Healthcare & Pharmaceutical Industry ................................... 144
Figure 10: Key Aspects of Big Data Standardization ............................................... 154
Figure 11: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million).......................... 176
Figure 12: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Hardware, Software & Professional Services: 2017 - 2030 ($ Million) .... 177
Figure 13: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Submarket: 2017 - 2030 ($ Million) .................... 178
Figure 14: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ... 178
Figure 15: Global Big Data Networking Infrastructure Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) .......... 179
Figure 16: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ...... 179
Figure 17: Global Big Data SQL Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) .................. 180
Figure 18: Global Big Data NoSQL Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ....................... 180
Figure 19: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ........ 181
Figure 20: Global Big Data Cloud Platforms Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ................. 181
Figure 21: Global Big Data Professional Services Submarket Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million).......... 182
Figure 22: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Application Area: 2017 - 2030 ($ Million) .................... 183
Figure 23: Global Big Data Revenue in Pharmaceutical & Medical Products: 2017 - 2030 ($ Million) .......................... 183
Figure 24: Global Big Data Revenue in Core Healthcare Operations: 2017 - 2030 ($ Million) .............................. 184
Figure 25: Global Big Data Revenue in Healthcare Support, Awareness & Disease Prevention: 2017 - 2030 ($ Million) ......................... 184
Figure 26: Global Big Data Revenue in Health Insurance & Payer Services: 2017 - 2030 ($ Million) ............................. 185
Figure 27: Global Big Data Revenue in Healthcare/Pharmaceutical Marketing, Sales & Other Applications: 2017 - 2030 ($ Million) .............. 185
Figure 28: Global Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Use Case: 2017 - 2030 ($ Million) ....................... 187
Figure 29: Global Big Data Revenue in Drug Discovery, Design & Development: 2017 - 2030 ($ Million) .............................. 188
Figure 30: Global Big Data Revenue in Medical Product Design & Development: 2017 - 2030 ($ Million) ............................ 188
Figure 31: Global Big Data Revenue in Clinical Development & Trials: 2017 - 2030 ($ Million) ........................... 189
Figure 32: Global Big Data Revenue in Precision Medicine & Genomics: 2017 - 2030 ($ Million) ................................. 189
Figure 33: Global Big Data Revenue in Pharmaceutical/Medical Manufacturing & Supply Chain Management: 2017 - 2030 ($ Million) .................. 190
Figure 34: Global Big Data Revenue in Post-Market Surveillance & Pharmacovigilance: 2017 - 2030 ($ Million) ........................... 190
Figure 35: Global Big Data Revenue in Medical Product Fault Monitoring: 2017 - 2030 ($ Million) ............................. 191
Figure 36: Global Big Data Revenue in Clinical Decision Support: 2017 - 2030 ($ Million) ................................... 191
Figure 37: Global Big Data Revenue in Care Coordination & Delivery Management: 2017 - 2030 ($ Million) ....................... 192
Figure 38: Global Big Data Revenue in CER (Comparative Effectiveness Research) & Observational Evidence: 2017 - 2030 ($ Million) .................... 192
Figure 39: Global Big Data Revenue in Personalized Healthcare & Targeted Treatments: 2017 - 2030 ($ Million) ......................... 193
Figure 40: Global Big Data Revenue in Data-Driven Preventive Care & Health Interventions: 2017 - 2030 ($ Million) ................... 193
Figure 41: Global Big Data Revenue in Surgical Practice & Complex Medical Procedures: 2017 - 2030 ($ Million) ........................ 194
Figure 42: Global Big Data Revenue in Pathology, Medical Imaging & Other Medical Tests: 2017 - 2030 ($ Million) ..................... 194
Figure 43: Global Big Data Revenue in Proactive & Remote Patient Monitoring: 2017 - 2030 ($ Million) ............................. 195
Figure 44: Global Big Data Revenue in Predictive Maintenance of Medical Equipment: 2017 - 2030 ($ Million) ........................... 195
Figure 45: Global Big Data Revenue in Pharmacy Services: 2017 - 2030 ($ Million) ................................... 196
Figure 46: Global Big Data Revenue in Self-Care & Lifestyle Support: 2017 - 2030 ($ Million) ............................ 196
Figure 47: Global Big Data Revenue in Medication Adherence & Management: 2017 - 2030 ($ Million) .............................. 197
Figure 48: Global Big Data Revenue in Vaccine Development & Promotion: 2017 - 2030 ($ Million) ........................... 197
Figure 49: Global Big Data Revenue in Population Health Management: 2017 - 2030 ($ Million) ................................ 198
Figure 50: Global Big Data Revenue in Connected Health Communities & Medical Knowledge Dissemination: 2017 - 2030 ($ Million) ................... 198
Figure 51: Global Big Data Revenue in Epidemiology & Disease Surveillance: 2017 - 2030 ($ Million) ......................... 199
Figure 52: Global Big Data Revenue in Health Policy Decision Making: 2017 - 2030 ($ Million) ................................... 199
Figure 53: Global Big Data Revenue in Controlling Substance Abuse & Addiction: 2017 - 2030 ($ Million) ........................... 200
Figure 54: Global Big Data Revenue in Increasing Awareness & Accessible Healthcare: 2017 - 2030 ($ Million) ............................ 200
Figure 55: Global Big Data Revenue in Health Insurance Claims Processing & Management: 2017 - 2030 ($ Million) ................... 201
Figure 56: Global Big Data Revenue in Fraud & Abuse Prevention: 2017 - 2030 ($ Million) ................................ 201
Figure 57: Global Big Data Revenue in Proactive Patient Engagement: 2017 - 2030 ($ Million) ................................... 202
Figure 58: Global Big Data Revenue in Accountable & Value-Based Care: 2017 - 2030 ($ Million) ............................... 202
Figure 59: Global Big Data Revenue in Data-Driven Health Insurance Premiums: 2017 - 2030 ($ Million) ............................ 203
Figure 60: Global Big Data Revenue in Healthcare/Pharmaceutical Marketing & Sales: 2017 - 2030 ($ Million) ............................ 203
Figure 61: Global Big Data Revenue in Healthcare/Pharmaceutical Administrative & Customer Services: 2017 - 2030 ($ Million) .................. 204
Figure 62: Global Big Data Revenue in Healthcare/Pharmaceutical Finance & Risk Management: 2017 - 2030 ($ Million)..................... 204
Figure 63: Global Big Data Revenue in Healthcare Data Monetization: 2017 - 2030 ($ Million) ................................... 205
Figure 64: Global Big Data Revenue in Other Healthcare & Pharmaceutical Industry Use Cases: 2017 - 2030 ($ Million) ....................... 205
Figure 65: Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Region: 2017 - 2030 ($ Million) ............................ 206
Figure 66: Asia Pacific Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ........................... 206
Figure 67: Asia Pacific Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2017 - 2030 ($ Million) ................. 207
Figure 68: Australia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ...................... 207
Figure 69: China Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ........................... 208
Figure 70: India Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ............................ 208
Figure 71: Indonesia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ............................. 209
Figure 72: Japan Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million)........................... 209
Figure 73: Malaysia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ...................... 210
Figure 74: Pakistan Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ...................... 210
Figure 75: Philippines Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ........................... 211
Figure 76: Singapore Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ............................. 211
Figure 77: South Korea Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ......................... 212
Figure 78: Taiwan Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ........................ 212
Figure 79: Thailand Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ...................... 213
Figure 80: Rest of Asia Pacific Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ........................ 213
Figure 81: Eastern Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) .................... 214
Figure 82: Eastern Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2017 - 2030 ($ Million) ................... 214
Figure 83: Czech Republic Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ..................... 215
Figure 84: Poland Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ......................... 215
Figure 85: Russia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) .......................... 216
Figure 86: Rest of Eastern Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) .................. 216
Figure 87: Latin & Central America Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ................ 217
Figure 88: Latin & Central America Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2017 - 2030 ($ Million) ............... 217
Figure 89: Argentina Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ............................. 218
Figure 90: Brazil Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ........................... 218
Figure 91: Mexico Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ........................ 219
Figure 92: Rest of Latin & Central America Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ............. 219
Figure 93: Middle East & Africa Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ..................... 220
Figure 94: Middle East & Africa Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2017 - 2030 ($ Million) .................... 220
Figure 95: Israel Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ........................... 221
Figure 96: Qatar Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ........................... 221
Figure 97: Saudi Arabia Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ......................... 222
Figure 98: South Africa Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ......................... 222
Figure 99: UAE Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ............................. 223
Figure 100: Rest of the Middle East & Africa Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ................... 223
Figure 101: North America Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million).................... 224
Figure 102: North America Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2017 - 2030 ($ Million) .................. 224
Figure 103: Canada Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ...................... 225
Figure 104: USA Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ........................... 225
Figure 105: Western Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) .......................... 226
Figure 106: Western Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry, by Country: 2017 - 2030 ($ Million) ................ 226
Figure 107: Denmark Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ............................ 227
Figure 108: Finland Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ...................... 227
Figure 109: France Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ....................... 228
Figure 110: Germany Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ............................ 228
Figure 111: Italy Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ........................... 229
Figure 112: Netherlands Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ....................... 229
Figure 113: Norway Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ..................... 230
Figure 114: Spain Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ......................... 230
Figure 115: Sweden Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ..................... 231
Figure 116: UK Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ............................. 231
Figure 117: Rest of Western Europe Big Data Revenue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) ....................... 232
 

 

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Big Data a $4 Billion opportunity in the healthcare & pharmaceutical industry, says SNS Research report

Text: SNS Research's latest report indicates that Big Data investments in the healthcare and pharmaceutical industry are expected to reach nearly $4 Billion by the end of 2017.

2017/08/08

Originally used as a term to describe datasets whose size is beyond the ability of traditional databases, the scope of Big Data 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.

 

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