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

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

  • SNS Telecom & IT
  • July 2018
  • Healthcare IT
  • 561 pages

Report Description

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

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

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

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

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

Topics Covered

The report covers the following topics:

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

Forecast Segmentation

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

Hardware, Software & Professional Services

  • Hardware
  • Software
  • Professional Services

Horizontal Submarkets

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

Application Areas

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

Use Cases

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

Regional Markets

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

Country Markets

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

Key Questions Answered

The report provides answers to the following key questions:

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

Key Findings

The report has the following key findings:

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

List of Companies Mentioned

  • 1010 data
  • AbbVie
  • Absolutdata
  • Accenture
  • ACR (American College of Radiology)
  • Actian Corporation
  • Adaptive Insights
  • Adobe Systems
  • Advizor Solutions
  • AeroSpike
  • Aetna
  • AFS Technologies
  • Alation
  • Algorithmia
  • Alluxio
  • Alphabet
  • ALTEN
  • Alteryx
  • Ambient Clinical Analytics
  • Ambulance Victoria
  • AMD (Advanced Micro Devices)
  • Amino
  • Anaconda
  • Apixio
  • Arcadia Data
  • Arimo
  • ARM
  • ASF (Apache Software Foundation)
  • ASTM (American Society for Testing and Materials)
  • AstraZeneca
  • Atomwise
  • AtScale
  • Attivio
  • Attunity
  • Australian Digital Health Agency
  • Automated Insights
  • AVORA
  • AWS (Amazon Web Services)
  • Axiomatics
  • Ayasdi
  • BackOffice Associates
  • Bangkok Hospital Group
  • Basho Technologies
  • Bayer
  • BCG (Boston Consulting Group)
  • Bedrock Data
  • BetterWorks
  • Big Panda
  • BigML
  • Birst
  • Bitam
  • Blue Medora
  • BlueData Software
  • BlueTalon
  • BMC Software
  • BMS (Bristol-Myers Squibb)
  • BOARD International
  • Booz Allen Hamilton
  • Boxever
  • CACI International
  • Cambridge Semantics
  • Capgemini
  • Cazena
  • Centerstone
  • Centrifuge Systems
  • CenturyLink
  • Chartio
  • Cigna
  • Cincinnati Children’s Hospital Medical Center
  • Cisco Systems
  • Civis Analytics
  • ClearStory Data
  • Cloudability
  • Cloudera
  • Cloudian
  • Clustrix
  • CNIL (Data Protection Regulatory Authority, France)
  • CognitiveScale
  • Collibra
  • Concurrent Technology
  • Confluent
  • Contexti
  • CosmosID
  • Couchbase
  • Crate.io
  • Cray
  • CSA (Cloud Security Alliance)
  • CSCC (Cloud Standards Customer Council)
  • CSIRO (Commonwealth Scientific and Industrial Research Organization)
  • Databricks
  • Dataiku
  • Datalytyx
  • Datameer
  • DataRobot
  • DataStax
  • Datawatch Corporation
  • Datos IO
  • DCRI (Duke Clinical Research Institute)
  • DDN (DataDirect Networks)
  • Decisyon
  • Deep Genomics
  • DeepMind Technologies
  • Dell Technologies
  • Deloitte
  • Demandbase
  • Denodo Technologies
  • Desktop Genetics
  • Dianomic Systems
  • Digital Reasoning Systems
  • Dimensional Insight
  • DMG  (Data Mining Group)
  • Dolphin Enterprise Solutions Corporation
  • Domino Data Lab
  • Domo
  • Dremio
  • DriveScale
  • Druva
  • DTA (Digital Therapeutics Alliance)
  • Dundas Data Visualization
  • DXC Technology
  • Elastic
  • Engineering Group (Engineering Ingegneria Informatica)
  • EnterpriseDB Corporation
  • eQ Technologic
  • Ericsson
  • Erwin
  • 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

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