What are the Long-Term Changes We are Likely to See in the Global Big Data in the Healthcare & Pharmaceutical Market?

“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.

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

The “Big Data in the Healthcare & Pharmaceutical Industry: 2017 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of Big Data in the healthcare and pharmaceutical industry including key market drivers, challenges, investment potential, application areas, use cases, future roadmap, value chain, case studies, vendor profiles and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services investments from 2017 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 5 application areas, 36 use cases, 6 regions and 35 countries.

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

The report covers the following topics:

Big Data ecosystem
Market drivers and barriers
Enabling technologies, standardization and regulatory initiatives
Big Data analytics and implementation models
Business case, application areas and use cases in the healthcare and pharmaceutical industry
34 case studies of Big Data investments by healthcare providers, insurers, payers, pharmaceutical companies and other stakeholders
Future roadmap and value chain
Company profiles and strategies of over 240 Big Data vendors
Strategic recommendations for Big Data vendors, and healthcare and pharmaceutical industry stakeholders
Market analysis and forecasts from 2017 till 2030

Forecast Segmentation

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

Hardware, Software & Professional Services

Hardware
Software
Professional Services

Horizontal Submarkets

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

Application Areas

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

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Use Cases

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

Regional Markets

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

Country Markets

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

Key Questions Answered

The report provides answers to the following key questions:

How big is the Big Data opportunity in the healthcare and pharmaceutical industry?
How is the market evolving by segment and region?
What will the market size be in 2020 and at what rate will it grow?
What trends, challenges and barriers are influencing its growth?
Who are the key Big Data software, hardware and services vendors and what are their strategies?
How much are healthcare providers, insurers, payers, pharmaceutical companies and other stakeholders investing in Big Data?
What opportunities exist for Big Data analytics in the healthcare and pharmaceutical industry?
Which countries, application areas and use cases will see the highest percentage of Big Data investments in the healthcare and pharmaceutical industry?

Key Findings

The report has the following key findings:

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

List of Companies Mentioned

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

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Table of Contents

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

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

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

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