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

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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.

The report 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.

 

Chapter 1: Introduction
Executive Summary
Topics Covered
Forecast Segmentation
Key Questions Answered
Key Findings
Methodology
Target Audience
Companies & Organizations Mentioned

Chapter 2: An Overview of Big Data
What is Big Data?
Key Approaches to Big Data Processing
Hadoop
NoSQL
MPAD (Massively Parallel Analytic Databases)
In-Memory Processing
Stream Processing Technologies
Spark
Other Databases & Analytic Technologies
Key Characteristics of Big Data
Volume
Velocity
Variety
Value
Market Growth Drivers
Awareness of Benefits
Maturation of Big Data Platforms
Continued Investments by Web Giants, Governments & Enterprises
Growth of Data Volume, Velocity & Variety
Vendor Commitments & Partnerships
Technology Trends Lowering Entry Barriers
Market Barriers
Lack of Analytic Specialists
Uncertain Big Data Strategies
Organizational Resistance to Big Data Adoption
Technical Challenges: Scalability & Maintenance
Security & Privacy Concerns

Chapter 3: Big Data Analytics
What are Big Data Analytics?
The Importance of Analytics
Reactive vs. Proactive Analytics
Customer vs. Operational Analytics
Technology & Implementation Approaches
Grid Computing
In-Database Processing
In-Memory Analytics
Machine Learning & Data Mining
Predictive Analytics
NLP (Natural Language Processing)
Text Analytics
Visual Analytics
Graph Analytics
Social Media, IT & Telco Network Analytics

Chapter 4: Business Case & Applications in the Healthcare & Pharmaceutical Industry
Overview & Investment Potential
Industry Specific Market Growth Drivers
Industry Specific Market Barriers
Key Applications
Pharmaceutical & Medical Products
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
Core Healthcare Operations
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
Healthcare Support, Awareness & Disease Prevention
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 & Payer Services
Health Insurance Claims Processing & Management
Fraud & Abuse Prevention
Proactive Patient Engagement
Accountable & Value-Based Care
Data-Driven Health Insurance Premiums
Marketing, Sales & Other Applications
Marketing & Sales
Administrative & Customer Services
Finance & Risk Management
Healthcare Data Monetization
Other Applications

Chapter 5: Healthcare & Pharmaceutical Industry Case Studies
Pharmaceutical & Medical Device Companies
AbbVie: Designing & Implementing Clinical Trials with Big Data
AstraZeneca: Analytics-Driven Drug Development with Big Data
Bayer: Accelerating Clinical Trials with Big Data
BMS (Bristol-Myers Squibb): Driving Clinical Discovery with Big Data
GSK (GlaxoSmithKline): Increasing Success Rates in Drug Discovery with Big Data
Johnson & Johnson: Intelligent Pharmaceutical Marketing with Big Data
Medtronic: Facilitating Predictive Care with Big Data
Merck & Co.: Optimizing Vaccine Manufacturing with Big Data
Merck KGaA: Discovering Drugs Faster with Big Data
Novartis: Digitizing Healthcare with Big Data
Pfizer: Developing Effective and Targeted Therapies with Big Data
Roche: Personalizing Healthcare with Big Data
Sanofi: Proactive Diabetes Care with Big Data
Healthcare Providers, Insurers & Payers
Aetna: Predicting & Improving Health with Big Data
Ambulance Victoria: Improving Patient Survival Rates with Big Data
Bangkok Hospital Group: Transforming the Patient Experience with Big Data
Cigna: Streamlining Health Insurance Claims with Big Data
Gold Coast Health: Reducing Hospital Waiting Times with Big Data
IU Health (Indiana University Health): Preventing Hospital-Acquired Infections with Big Data
Moorfields Eye Hospital: Diagnosing Eye Diseases with Big Data
MSQC (Michigan Surgical Quality Collaborative): Surgical Quality Improvement with Big Data
NCCS (National Cancer Centre Singapore): Advancing Cancer Treatment with Big Data
NHS Scotland: Improving Outcomes with Big Data
Seattle Childrens Hospital: Enabling Faster & Accurate Diagnosis with Big Data
UnitedHealth Group: Enhancing Patient Care & Value with Big Data
VHA (Veterans Health Administration): Streamlining Healthcare Delivery with Big Data
Other Stakeholders
Amino: Healthcare Transparency with Big Data
Atomwise: Improving Drug Discovery with Big Data
CosmosID: Advancing Microbial Genomics with Big Data
Deep Genomics: Discovering Novel Oligonucleotide Therapies with Big Data
Desktop Genetics: Facilitating Genome Editing with Big Data
Express Scripts: Improving Medication Adherence with Big Data
Faros Healthcare: Enhancing Clinical Decision Making with Big Data
Genomics England: Developing the Worlds First Genomics Medicine Service with Big Data
Ginger.io: Improving Mental Wellbeing with Big Data
Illumina: Enabling Precision Medicine with Big Data
INDS (National Institute of Health Data, France): Population Health Management with Big Data
MolecularMatch: Advancing the Clinical Utility of Genomics with Big Data
Proteus Digital Health: Pioneering Digital Medicine with Big Data
Royal Philips: Enhancing Workflows in ICUs (Intensive Care Units) with Big Data
Sickweather: Sickness Forecasting & Mapping with Big Data
Sproxil: Fighting Counterfeit Drugs with Big Data

Chapter 6: Future Roadmap & Value Chain
Future Roadmap
Pre-2020: Growing Investments in Real-Time & Predictive Health Analytics
2020 – 2025: Data-Driven Advances in Drug Discovery & Precision Medicine
2025 – 2030: Moving Beyond National-Level Population Health Management
The Big Data Value Chain
Hardware Providers
Storage & Compute Infrastructure Providers
Networking Infrastructure Providers
Software Providers
Hadoop & Infrastructure Software Providers
SQL & NoSQL Providers
Analytic Platform & Application Software Providers
Cloud Platform Providers
Professional Services Providers
End-to-End Solution Providers
Healthcare & Pharmaceutical Industry

Chapter 7: Standardization & Regulatory Initiatives
ASF (Apache Software Foundation)
Management of Hadoop
Big Data Projects Beyond Hadoop
CSA (Cloud Security Alliance)
BDWG (Big Data Working Group)
CSCC (Cloud Standards Customer Council)
Big Data Working Group
DMG (Data Mining Group)
PMML (Predictive Model Markup Language) Working Group
PFA (Portable Format for Analytics) Working Group
IEEE (Institute of Electrical and Electronics Engineers)
Big Data Initiative
INCITS (InterNational Committee for Information Technology Standards)
Big Data Technical Committee
ISO (International Organization for Standardization)
ISO/IEC JTC 1/SC 32: Data Management and Interchange
ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms
ISO/IEC JTC 1/SC 27: IT Security Techniques
ISO/IEC JTC 1/WG 9: Big Data
Collaborations with Other ISO Work Groups
ITU (International Telecommunication Union)
ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities
Other Deliverables Through SG (Study Group) 13 on Future Networks
Other Relevant Work
Linux Foundation
ODPi (Open Ecosystem of Big Data)
NIST (National Institute of Standards and Technology)
NBD-PWG (NIST Big Data Public Working Group)
OASIS (Organization for the Advancement of Structured Information Standards)
Technical Committees
ODaF (Open Data Foundation)
Big Data Accessibility
ODCA (Open Data Center Alliance)
Work on Big Data
OGC (Open Geospatial Consortium)
Big Data DWG (Domain Working Group)
TM Forum
Big Data Analytics Strategic Program
TPC (Transaction Processing Performance Council)
TPC-BDWG (TPC Big Data Working Group)
W3C (World Wide Web Consortium)
Big Data Community Group
Open Government Community Group
Other Initiatives Relevant to the Healthcare & Pharmaceutical Industry
HIPAA (Health Insurance Portability and Accountability Act of 1996)
HITECH (Health Information Technology for Economic and Clinical Health) Act
European Unions GDPR (General Data Protection Regulation)
Australian Digital Health Agency
United Kingdoms ITK (Interoperability Toolkit)
Japans SS-MIX (Standard Structured Medical Information eXchange)
Germanys xDT
Frances DMP (Dossier Médical Personnel)
HL7 (Health Level Seven) Specifications
IHE (Integrating the Healthcare Enterprise)
NCPDP (National Council for Prescription Drug Programs)
DICOM (Digital Imaging and Communications in Medicine)
eHealth Exchange
EDIFACT (Electronic Data Interchange For Administration, Commerce, and Transport)
HITRUST CSF (Common Security Framework)
DTA (Digital Therapeutics Alliance)
X12 & Others

Chapter 8: Market Sizing & Forecasts
Global Outlook for Big Data in the Healthcare & Pharmaceutical Industry
Hardware, Software & Professional Services Segmentation
Horizontal Submarket Segmentation
Hardware Submarkets
Storage and Compute Infrastructure
Networking Infrastructure
Software Submarkets
Hadoop & Infrastructure Software
SQL
NoSQL
Analytic Platforms & Applications
Cloud Platforms
Professional Services Submarket
Professional Services
Application Area Segmentation
Pharmaceutical & Medical Products
Core Healthcare Operations
Healthcare Support, Awareness & Disease Prevention
Health Insurance & Payer Services
Marketing, Sales & Other Applications
Use Case Segmentation
Pharmaceutical & Medical Products
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
Core Healthcare Operations
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
Healthcare Support, Awareness & Disease Prevention
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 & Payer Services
Health Insurance Claims Processing & Management
Fraud & Abuse Prevention
Proactive Patient Engagement
Accountable & Value-Based Care
Data-Driven Health Insurance Premiums
Marketing, Sales & Other Application Use Cases
Marketing & Sales
Administrative & Customer Services
Finance & Risk Management
Healthcare Data Monetization
Other Use Cases
Regional Outlook
Asia Pacific
Country Level Segmentation
Australia
China
India
Indonesia
Japan
Malaysia
Pakistan
Philippines
Singapore
South Korea
Taiwan
Thailand
Rest of Asia Pacific
Eastern Europe
Country Level Segmentation
Czech Republic
Poland
Russia
Rest of Eastern Europe
Latin & Central America
Country Level Segmentation
Argentina
Brazil
Mexico
Rest of Latin & Central America
Middle East & Africa
Country Level Segmentation
Israel
Qatar
Saudi Arabia
South Africa
UAE
Rest of the Middle East & Africa
North America
Country Level Segmentation
Canada
USA
Western Europe
Country Level Segmentation
Denmark
Finland
France
Germany
Italy
Netherlands
Norway
Spain
Sweden
UK
Rest of Western Europe

Chapter 9: Vendor Landscape
1010data
Absolutdata
Accenture
Actian Corporation/HCL Technologies
Adaptive Insights
Adobe Systems
Advizor Solutions
AeroSpike
AFS Technologies
Alation
Algorithmia
Alluxio
ALTEN
Alteryx
AMD (Advanced Micro Devices)
Anaconda
Apixio
Arcadia Data
ARM
AtScale
Attivio
Attunity
Automated Insights
AVORA
AWS (Amazon Web Services)
Axiomatics
Ayasdi
BackOffice Associates
Basho Technologies
BCG (Boston Consulting Group)
Bedrock Data
BetterWorks
Big Panda
BigML
Bitam
Blue Medora
BlueData Software
BlueTalon
BMC Software
BOARD International
Booz Allen Hamilton
Boxever
CACI International
Cambridge Semantics
Capgemini
Cazena
Centrifuge Systems
CenturyLink
Chartio
Cisco Systems
Civis Analytics
ClearStory Data
Cloudability
Cloudera
Cloudian
Clustrix
CognitiveScale
Collibra
Concurrent Technology/Vecima Networks
Confluent
Contexti
Couchbase
Crate.io
Cray
Databricks
Dataiku
Datalytyx
Datameer
DataRobot
DataStax
Datawatch Corporation
DDN (DataDirect Networks)
Decisyon
Dell Technologies
Deloitte
Demandbase
Denodo Technologies
Dianomic Systems
Digital Reasoning Systems
Dimensional Insight
Dolphin Enterprise Solutions Corporation/Hanse Orga Group
Domino Data Lab
Domo
Dremio
DriveScale
Druva
Dundas Data Visualization
DXC Technology
Elastic
Engineering Group (Engineering Ingegneria Informatica)
EnterpriseDB Corporation
eQ Technologic
Ericsson
Erwin
EV (Big Cloud Analytics)
EXASOL
EXL (ExlService Holdings)
Facebook
FICO (Fair Isaac Corporation)
Figure Eight
FogHorn Systems
Fractal Analytics
Franz
Fujitsu
Fuzzy Logix
Gainsight
GE (General Electric)
Glassbeam
GoodData Corporation
Google/Alphabet
Grakn Labs
Greenwave Systems
GridGain Systems
H2O.ai
HarperDB
Hedvig
Hitachi Vantara
Hortonworks
HPE (Hewlett Packard Enterprise)
Huawei
HVR
HyperScience
HyTrust
IBM Corporation
iDashboards
IDERA
Ignite Technologies
Imanis Data
Impetus Technologies
Incorta
InetSoft Technology Corporation
InfluxData
Infogix
Infor/Birst
Informatica
Information Builders
Infosys
Infoworks
Insightsoftware.com
InsightSquared
Intel Corporation
Interana
InterSystems Corporation
Jedox
Jethro
Jinfonet Software
Juniper Networks
KALEAO
Keen IO
Keyrus
Kinetica
KNIME
Kognitio
Kyvos Insights
LeanXcale
Lexalytics
Lexmark International
Lightbend
Logi Analytics
Logical Clocks
Longview Solutions/Tidemark
Looker Data Sciences
LucidWorks
Luminoso Technologies
Maana
Manthan Software Services
MapD Technologies
MapR Technologies
MariaDB Corporation
MarkLogic Corporation
Mathworks
Melissa
MemSQL
Metric Insights
Microsoft Corporation
MicroStrategy
Minitab
MongoDB
Mu Sigma
NEC Corporation
Neo4j
NetApp
Nimbix
Nokia
NTT Data Corporation
Numerify
NuoDB
NVIDIA Corporation
Objectivity
Oblong Industries
OpenText Corporation
Opera Solutions
Optimal Plus
Oracle Corporation
Palantir Technologies
Panasonic Corporation/Arimo
Panorama Software
Paxata
Pepperdata
Phocas Software
Pivotal Software
Prognoz
Progress Software Corporation
Provalis Research
Pure Storage
PwC (PricewaterhouseCoopers International)
Pyramid Analytics
Qlik
Qrama/Tengu
Quantum Corporation
Qubole
Rackspace
Radius Intelligence
RapidMiner
Recorded Future
Red Hat
Redis Labs
RedPoint Global
Reltio
RStudio
Rubrik/Datos IO
Ryft
Sailthru
Salesforce.com
Salient Management Company
Samsung Group
SAP
SAS Institute
ScaleOut Software
Seagate Technology
Sinequa
SiSense
Sizmek
SnapLogic
Snowflake Computing
Software AG
Splice Machine
Splunk
Strategy Companion Corporation
Stratio
Streamlio
StreamSets
Striim
Sumo Logic
Supermicro (Super Micro Computer)
Syncsort
SynerScope
SYNTASA
Tableau Software
Talend
Tamr
TARGIT
TCS (Tata Consultancy Services)
Teradata Corporation
Thales/Guavus
ThoughtSpot
TIBCO Software
Toshiba Corporation
Transwarp
Trifacta
Unifi Software
Unravel Data
VANTIQ
VMware
VoltDB
WANdisco
Waterline Data
Western Digital Corporation
WhereScape
WiPro
Wolfram Research
Workday
Xplenty
Yellowfin BI
Yseop
Zendesk
Zoomdata
Zucchetti

Chapter 10: Conclusion & Strategic Recommendations
Why is the Market Poised to Grow?
Geographic Outlook: Which Countries Offer the Highest Growth Potential?
Partnerships & M&A Activity: Highlighting the Importance of Big Data
Driving the Development of Digital Therapeutics
Improving Outcomes, Achieving Operational Efficiency and Reducing Costs
Assessing the Impact of Connected Health Solutions
Accelerating the Transition Towards Value-Based Care
The Emergence of Advanced AI (Artificial Intelligence) & Machine Learning Techniques
The Value of Big Data in Precision Medicine
Addressing Privacy & Security Concerns
The Role of Data Protection Legislation
Blockchain: Enabling Secure, Efficient and Interoperable Data Sharing
Recommendations
Big Data Hardware, Software & Professional Services Providers
Healthcare & Pharmaceutical Industry Stakeholders
 


List Of Figures

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


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

Big Data in insurance industry will account for more than $2.4 Billion in 2018, these investments are expected to grow a CAGR of approximately 14%.

USD 2500View Report

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

Big Data investments in pharmaceutical industry will account for nearly $4.7 Billion in 2018, these investments further expected to grow at CAGR 12%.

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

Big Data in insurance industry will account for more than $2.4 Billion in 2018, these investments are expected to grow a CAGR of approximately 14%.

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Big Data in Insurance - Thematic Research

Big Data in Insurance - Thematic ResearchInsurance is a highly data-intensive industry, making it extremely important for insurers to manage large volumes of data from both traditional and non-traditional sources.

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Reports Details

Published Date : Jul 2018
No. of Pages :561
Country :Global
Category :Pharmaceuticals and Healthcare
Publisher :SNS Telecom & IT
Report Delivery By :Email
Report Delivery Time :12 to 24 hours after placing the order.

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