Big Data In The Healthcare & Pharmaceutical Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts

“Big Data” originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data, to solve complex problems.

Amid the proliferation of real-time and historical data from sources such as connected devices, web, social media, sensors, log files and transactional applications, Big Data is rapidly gaining traction from a diverse range of vertical sectors. The healthcare and pharmaceutical industry is no exception to this trend, where Big Data has found a host of applications ranging from drug discovery and precision medicine to clinical decision support and population health management.

SNS Telecom & IT estimates that Big Data investments in the healthcare and pharmaceutical industry will account for nearly $4.7 Billion in 2018 alone. Led by a plethora of business opportunities for healthcare providers, insurers, payers, government agencies, pharmaceutical companies and other stakeholders, these investments are further expected to grow at a CAGR of approximately 12% over the next three years.

The “Big Data in the Healthcare & Pharmaceutical Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of Big Data in the healthcare and pharmaceutical industry including key market drivers, challenges, investment potential, application areas, use cases, future roadmap, value chain, case studies, vendor profiles and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services investments from 2018 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 5 application areas, 37 use cases, 6 regions and 35 countries.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.

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Topics Covered

The report covers the following topics:

Big Data ecosystem
Market drivers and barriers
Enabling technologies, standardization and regulatory initiatives
Big Data analytics and implementation models
Business case, application areas and use cases in the healthcare and pharmaceutical industry
Over 40 case studies of Big Data investments by healthcare providers, insurers, payers, pharmaceutical companies and other stakeholders
Future roadmap and value chain
Profiles and strategies of over 270 leading and emerging Big Data ecosystem players
Strategic recommendations for Big Data vendors, and healthcare and pharmaceutical industry stakeholders
Market analysis and forecasts from 2018 till 2030

Forecast Segmentation

Market forecasts are provided for each of the following submarkets and their subcategories:

Hardware, Software & Professional Services

Hardware
Software
Professional Services
Horizontal Submarkets

Storage & Compute Infrastructure
Networking Infrastructure
Hadoop & Infrastructure Software
SQL
NoSQL
Analytic Platforms & Applications
Cloud Platforms
Professional Services
Application Areas

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

Drug Discovery, Design & Development
Medical Product Design & Development
Clinical Development & Trials
Precision Medicine & Genomics
Manufacturing & Supply Chain Management
Post-Market Surveillance & Pharmacovigilance
Medical Product Fault Monitoring
Clinical Decision Support
Care Coordination & Delivery Management
CER (Comparative Effectiveness Research) & Observational Evidence
Personalized Healthcare & Targeted Treatments
Data-Driven Preventive Care & Health Interventions
Surgical Practice & Complex Medical Procedures
Pathology, Medical Imaging & Other Medical Tests
Proactive & Remote Patient Monitoring
Predictive Maintenance of Medical Equipment
Pharmacy Services
Self-Care & Lifestyle Support
Digital Therapeutics
Medication Adherence & Management
Vaccine Development & Promotion
Population Health Management
Connected Health Communities & Medical Knowledge Dissemination
Epidemiology & Disease Surveillance
Health Policy Decision Making
Controlling Substance Abuse & Addiction
Increasing Awareness & Accessible Healthcare
Health Insurance Claims Processing & Management
Fraud & Abuse Prevention
Proactive Patient Engagement
Accountable & Value-Based Care
Data-Driven Health Insurance Premiums
Marketing & Sales
Administrative & Customer Services
Finance & Risk Management
Healthcare Data Monetization
Other Use Cases
Regional Markets

Asia Pacific
Eastern Europe
Latin & Central America
Middle East & Africa
North America
Western Europe
Country Markets

Argentina, Australia, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany, India, Indonesia, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, UAE, UK, USA

Key Questions Answered

The report provides answers to the following key questions:

How big is the Big Data opportunity in the healthcare and pharmaceutical industry?
How is the market evolving by segment and region?
What will the market size be in 2021, and at what rate will it grow?
What trends, challenges and barriers are influencing its growth?
Who are the key Big Data software, hardware and services vendors, and what are their strategies?
How much are healthcare providers, insurers, payers, pharmaceutical companies and other stakeholders investing in Big Data?
What opportunities exist for Big Data analytics in the healthcare and pharmaceutical industry?
Which countries, application areas and use cases will see the highest percentage of Big Data investments in the healthcare and pharmaceutical industry?

Key Findings

The report has the following key findings:

In 2018, Big Data vendors will pocket nearly $4.7 Billion from hardware, software and professional services revenues in the healthcare and pharmaceutical industry. These investments are further expected to grow at a CAGR of approximately 12% over the next three years, eventually accounting for more than $7 Billion by the end of 2021.
Big Data and advanced analytics are driving a paradigm shift in the healthcare and pharmaceutical industry with multiple innovations ranging from precision medicine and digital therapeutics to the adoption of accountable and value-based care models.
Drug developers are making substantial investments in Big Data and artificial intelligence-driven drug discovery platforms to shorten the process of successfully discovering promising compounds. In addition, Big Data technologies are increasingly being utilized to streamline clinical trials, enabling biopharmaceutical companies to significantly lower costs and accelerate productive trials.
The growing adoption of Big Data technologies has also brought about an array of benefits for hospitals and other healthcare facilities. Based on feedback from healthcare providers worldwide, these include but are not limited to cost savings in the range of 20-30%, an increase in patient access to services by more than 35%, growth in revenue by up to 30%, a reduction in emergency room visits by 10%, a drop in patient wait times by 30-60%, improvements in outcomes by as much as 20%, a 10-50% decline in mortality rates for conditions such as heart failure, and a reduction in the occurrence of hospital acquired and surgical site infections by nearly 60%.

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List of Companies Mentioned

1010 data
AbbVie
Absolutdata
Accenture
ACR (American College of Radiology)
Actian Corporation
Adaptive Insights
Adobe Systems
Advizor Solutions
AeroSpike
Aetna
AFS Technologies
Alation
Algorithmia
Alluxio
Alphabet
ALTEN
Alteryx
Ambient Clinical Analytics
Ambulance Victoria
AMD (Advanced Micro Devices)
Amino
Anaconda
Apixio
Arcadia Data
Arimo
ARM
ASF (Apache Software Foundation)
ASTM (American Society for Testing and Materials)
AstraZeneca
Atomwise
AtScale
Attivio
Attunity
Australian Digital Health Agency
Automated Insights
AVORA
AWS (Amazon Web Services)
Axiomatics
Ayasdi
BackOffice Associates
Bangkok Hospital Group
Basho Technologies
Bayer
BCG (Boston Consulting Group)
Bedrock Data
BetterWorks
Big Panda
BigML
Birst
Bitam
Blue Medora
BlueData Software
BlueTalon
BMC Software
BMS (Bristol-Myers Squibb)
BOARD International
Booz Allen Hamilton
Boxever
CACI International
Cambridge Semantics
Capgemini
Cazena
Centerstone
Centrifuge Systems
CenturyLink
Chartio
Cigna
Cincinnati Children’s Hospital Medical Center
Cisco Systems
Civis Analytics
ClearStory Data
Cloudability
Cloudera
Cloudian
Clustrix
CNIL (Data Protection Regulatory Authority, France)
CognitiveScale
Collibra
Concurrent Technology
Confluent
Contexti
CosmosID
Couchbase
Crate.io
Cray
CSA (Cloud Security Alliance)
CSCC (Cloud Standards Customer Council)
CSIRO (Commonwealth Scientific and Industrial Research Organization)
Databricks
Dataiku
Datalytyx
Datameer
DataRobot
DataStax
Datawatch Corporation
Datos IO
DCRI (Duke Clinical Research Institute)
DDN (DataDirect Networks)
Decisyon
Deep Genomics
DeepMind Technologies
Dell Technologies
Deloitte
Demandbase
Denodo Technologies
Desktop Genetics
Dianomic Systems
Digital Reasoning Systems
Dimensional Insight
DMG (Data Mining Group)
Dolphin Enterprise Solutions Corporation
Domino Data Lab
Domo
Dremio
DriveScale
Druva
DTA (Digital Therapeutics Alliance)
Dundas Data Visualization
DXC Technology
Elastic
Engineering Group (Engineering Ingegneria Informatica)
EnterpriseDB Corporation
eQ Technologic
Ericsson
Erwin
EV? (Big Cloud Analytics)
EXASOL
EXL (ExlService Holdings)
Express Scripts
Exscientia
Facebook
Faros Healthcare
FICO (Fair Isaac Corporation)
Figure Eight
FogHorn Systems
Fractal Analytics
Franz
Fujitsu
Fuzzy Logix
Gainsight
GE (General Electric)
Genomics England
Ginger.io
Glassbeam
GNS Healthcare
Gold Coast Health
GoodData Corporation
Google
Grakn Labs
Greenwave Systems
GridGain Systems
GSK (GlaxoSmithKline)
Guavus
H2O.ai
Hanse Orga Group
HarperDB
HCL Technologies
Hedvig
Hitachi Vantara
HITRUST Alliance
HL7 (Health Level Seven)
HLI (Human Longevity Inc.)
Hortonworks
HPE (Hewlett Packard Enterprise)
Huawei
HVR
HyperScience
HyTrust
IBM Corporation
iDashboards
IDERA
IEC (International Electrotechnical Commission)
IEEE (Institute of Electrical and Electronics Engineers)
Ignite Technologies
IHE (Integrating the Healthcare Enterprise)
Illumina
Imanis Data
Impetus Technologies
INCITS (InterNational Committee for Information Technology Standards)
Incorta
INDS (National Institute of Health Data, France)
InetSoft Technology Corporation
InfluxData
Infogix
Infor
Informatica
Information Builders
Infosys
Infoworks
Insightsoftware.com
InsightSquared
Intel Corporation
Interana
InterSystems Corporation
ISO (International Organization for Standardization)
ITU (International Telecommunication Union)
IU Health (Indiana University Health)
IURTC (Indiana University Research & Technology Corporation)
Jedox
Jethro
Jinfonet Software
Johnson & Johnson
Juniper Networks
KALEAO
KBV/NASHIP (National Association of Statutory Health Insurance Physicians, Germany)
Keen IO
Keyrus
Kinetica
KNIME
Kognitio
Kyvos Insights
LeanXcale
Lexalytics
Lexmark International
Lightbend
Linux Foundation
Logi Analytics
Logical Clocks
Longview Solutions
Looker Data Sciences
LucidWorks
Luminoso Technologies
Maana
Manthan Software Services
MapD Technologies
MapR Technologies
MariaDB Corporation
MarkLogic Corporation
Massachusetts General Hospital
Mathworks
Mayo Clinic
Medtronic
Melissa
MemSQL
Merck & Co.
Merck KGaA
Metric Insights
Microsoft Corporation
MicroStrategy
Ministry of Health, Labor and Welfare, Japan
Minitab
MolecularMatch
MongoDB
Moorfields Eye Hospital
MSQC (Michigan Surgical Quality Collaborative)
Mu Sigma
NCCS (National Cancer Centre Singapore)
NCPDP (National Council for Prescription Drug Programs)
NEC Corporation
NEMA (National Electrical Manufacturers Association)
Neo4j
NetApp
NextBio
NHS (National Health Service, United Kingdom)
NHS England
NHS Scotland
Nimbix
Nokia
Novartis
NTT Data Corporation
Numerify
NuoDB
NVIDIA Corporation
OASIS (Organization for the Advancement of Structured Information Standards)
Objectivity
Oblong Industries
ODaF (Open Data Foundation)
ODCA (Open Data Center Alliance)
ODPi (Open Ecosystem of Big Data)
OGC (Open Geospatial Consortium)
OpenText Corporation
Opera Solutions
Optimal Plus
Optum
OptumLabs
Oracle Corporation
Oxford Nanopore Technologies
Pacific Biosciences
Palantir Technologies
Panasonic Corporation
Panorama Software
PatientsLikeMe
Paxata
Pepperdata
Pfizer
Phocas Software
Pivotal Software
Prognoz
Progress Software Corporation
Proteus Digital Health
Provalis Research
Pure Storage
PwC (PricewaterhouseCoopers International)
Pyramid Analytics
Qlik
Qrama/Tengu
Quantum Corporation
Qubole
Rackspace
Radius Intelligence
RapidMiner
Recorded Future
Red Hat
Redis Labs
RedPoint Global
Reltio
Roche
Royal Philips
RStudio
Rubrik
Ryft
Sailthru
Salesforce.com
Salient Management Company
Samsung Group
Sanofi
SAP
SAS Institute
ScaleOut Software
Seagate Technology
Seattle Children’s Hospital
Sickweather
Sinequa
SingHealth (Singapore Health Services)
SiSense
Sizmek
SnapLogic
Snowflake Computing
Software AG
Splice Machine
Splunk
Sproxil
Strategy Companion Corporation
Stratio
Streamlio
StreamSets
Striim
Sumo Logic
Supermicro (Super Micro Computer)
Syncsort
SynerScope
SYNTASA
Tableau Software
Takeda Pharmaceutical Company
Talend
Tamr
TARGIT
TCS (Tata Consultancy Services)
Teradata Corporation
Thales
Thermo Fisher Scientific
ThoughtSpot
TIBCO Software
Tidemark
TM Forum
Toshiba Corporation
TPC (Transaction Processing Performance Council)
Transwarp
Trifacta
Twitter
U.S. CDC (Centers for Disease Control & Prevention)
U.S. CMS (Centers for Medicare & Medicaid Services)
U.S. Department of Veterans Affairs
U.S. FDA (Food and Drug Administration)
U.S. HHS (Department of Health & Human Services)
U.S. NIST (National Institute of Standards and Technology)
U.S. VHA (Veterans Health Administration)
UCL (University College London) Institute of Ophthalmology
UN (United Nations)
Unifi Software
UnitedHealth Group
University of Illinois at Urbana-Champaign
University of Michigan
University of Pittsburgh
University of Utah Health
Unravel Data
VANTIQ
Vecima Networks
VMware
VoltDB
W3C (World Wide Web Consortium)
WANdisco
Waterline Data
WellDoc
Western Digital Corporation
WhereScape
WiPro
Wolfram Research
Workday
X12
Xplenty
Yellowfin BI
Yseop
Zendesk
Zoomdata
Zucchetti

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Table of Contents

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

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

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

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

Continue….

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