Date: Mon, 23 Oct 2017 18:08:09 -0400 From: "Andy Silva" <andy.silva@snsresearchreports.com> To: gnome@freebsd.org Subject: Big Data in the Healthcare & Pharmaceutical Industry: 2017 - 2030 - Opportunities, Challenges, Strategies & Forecasts (Report) Message-ID: <6860374382600559224740@Ankur>
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The Big Data Market: 2017 =96 2030 =96 Opportunities, Challenges, Strategie= s, Industry Verticals & Forecasts (Report) Hello Please find the latest SNS Research report summary to you and your team, "B= ig Data in the Healthcare & Pharmaceutical Industry: 2017 =96 2030 =96 Oppo= rtunities, Challenges, Strategies & Forecasts" Below is the report highligh= t and if you like I can send you sample pages for your details inside.=20 =20 The report presents an in-depth assessment of Big Data in the healthcare an= d pharmaceutical industry including key market drivers, challenges, investm= ent potential, application areas, use cases, future roadmap, value chain, c= ase studies, vendor profiles and strategies. The report also presents marke= t size forecasts for Big Data hardware, software and professional services = investments from 2017 through to 2030. The forecasts are segmented for 8 ho= rizontal submarkets, 5 application areas, 36 use cases, 6 regions and 35 co= untries. The report comes with an associated Excel datasheet suite covering quantita= tive data from all numeric forecasts presented in the report. Report Information: Release Date: August 2017 Number of Pages: 499 Number of Tables and Figures: 117 Key Questions Answered: How big is the Big Data opportunity in the healthcare and pharmaceutical in= dustry=3F How is the market evolving by segment and region=3F What will the market size be in 2020 and at what rate will it grow=3F What trends, challenges and barriers are influencing its growth=3F Who are the key Big Data software, hardware and services vendors and what a= re their strategies=3F How much are healthcare providers, insurers, payers, pharmaceutical compani= es and other stakeholders investing in Big Data=3F What opportunities exist for Big Data analytics in the healthcare and pharm= aceutical industry=3F Which countries, application areas and use cases will see the highest perce= ntage of Big Data investments in the healthcare and pharmaceutical industry=3F Key Findings: The report has the following key findings: In 2017, Big Data vendors will pocket nearly $4 Billion from hardware, soft= ware and professional services revenues in the healthcare and pharmaceutica= l industry. These investments are further expected to grow at a CAGR of mor= e than 15% over the next three years, eventually accounting for over $5.8 B= illion by the end of 2020. Through the use of Big Data technologies, hospitals and other healthcare fa= cilities have been able to achieve cost reductions of more than 10%, improv= ements in outcomes by as much as 20% for certain conditions, growth in reve= nue by 30%, and increase in patient access to services by more than 35%. Big Data technologies are playing a pivotal role in accelerating the transi= tion towards accountable and value-based care models, by enabling the conti= nuous collection, consolidation and analysis of clinical and operational da= ta from healthcare facilities and other available data sources. Addressing privacy and security concerns is necessary in order to fully lev= erage the benefits of Big Data in the healthcare and pharmaceutical industr= y. Therefore, it is essential for key stakeholders to make significant inve= stments in data encryption and cybersecurity, in addition to adopting defen= sible de-identification techniques and implementing strict restrictions on = data use. 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 pharma= ceutical 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 pharmace= utical industry stakeholders Market analysis and forecasts from 2017 till 2030 Report Pricing: =20 Single User License: USD 2,500 Company Wide License: USD 3,500 =20 Ordering Process: =20 Please provide the following information: Report Title - Big Data in the healthcare & Pharmaceutical Industry: 2017 = =96 2030 Report License - (Single User/Company Wide) Name - Email - Job Title - Company - Invoice Address - Please contact me if you have any questions, or wish to purchase a copy. Ta= ble of contents, List of figures and List of companies mentioned in report = are given below for more inside. I look forward to hearing from you. =20 Kind Regards =20 Andy Silva Marketing Executive Signals and Systems Telecom andy.silva@snsreports.com _________________________________________________________________________ Table of Contents: 1 Chapter 1: Introduction 1.1 Executive Summary 1.2 Topics Covered 1.3 Forecast Segmentation 1.4 Key Questions Answered 1.5 Key Findings 1.6 Methodology 1.7 Target Audience 1.8 Companies & Organizations Mentioned =20 2 Chapter 2: An Overview of Big Data 2.1 What is Big Data=3F 2.2 Key Approaches to Big Data Processing 2.2.1 Hadoop 2.2.2 NoSQL 2.2.3 MPAD (Massively Parallel Analytic Databases) 2.2.4 In-Memory Processing 2.2.5 Stream Processing Technologies 2.2.6 Spark 2.2.7 Other Databases & Analytic Technologies 2.3 Key Characteristics of Big Data 2.3.1 Volume 2.3.2 Velocity 2.3.3 Variety 2.3.4 Value 2.4 Market Growth Drivers 2.4.1 Awareness of Benefits 2.4.2 Maturation of Big Data Platforms 2.4.3 Continued Investments by Web Giants, Governments & Enterprises 2.4.4 Growth of Data Volume, Velocity & Variety 2.4.5 Vendor Commitments & Partnerships 2.4.6 Technology Trends Lowering Entry Barriers 2.5 Market Barriers 2.5.1 Lack of Analytic Specialists 2.5.2 Uncertain Big Data Strategies 2.5.3 Organizational Resistance to Big Data Adoption 2.5.4 Technical Challenges: Scalability & Maintenance 2.5.5 Security & Privacy Concerns =20 3 Chapter 3: Big Data Analytics 3.1 What are Big Data Analytics=3F 3.2 The Importance of Analytics 3.3 Reactive vs. Proactive Analytics 3.4 Customer vs. Operational Analytics 3.5 Technology & Implementation Approaches 3.5.1 Grid Computing 3.5.2 In-Database Processing 3.5.3 In-Memory Analytics 3.5.4 Machine Learning & Data Mining 3.5.5 Predictive Analytics 3.5.6 NLP (Natural Language Processing) 3.5.7 Text Analytics 3.5.8 Visual Analytics 3.5.9 Graph Analytics 3.5.10 Social Media, IT & Telco Network Analytics =20 4 Chapter 4: Business Case & Applications in the Healthcare & Pharmaceutica= l Industry 4.1 Overview & Investment Potential 4.2 Industry Specific Market Growth Drivers 4.3 Industry Specific Market Barriers 4.4 Key Applications 4.4.1 Pharmaceutical & Medical Products 4.4.1.1 Drug Discovery, Design & Development 4.4.1.2 Medical Product Design & Development 4.4.1.3 Clinical Development & Trials 4.4.1.4 Precision Medicine & Genomics 4.4.1.5 Manufacturing & Supply Chain Management 4.4.1.6 Post-Market Surveillance & Pharmacovigilance 4.4.1.7 Medical Product Fault Monitoring 4.4.2 Core Healthcare Operations 4.4.2.1 Clinical Decision Support 4.4.2.2 Care Coordination & Delivery Management 4.4.2.3 CER (Comparative Effectiveness Research) & Observational Evidence 4.4.2.4 Personalized Healthcare & Targeted Treatments 4.4.2.5 Data-Driven Preventive Care & Health Interventions 4.4.2.6 Surgical Practice & Complex Medical Procedures 4.4.2.7 Pathology, Medical Imaging & Other Medical Tests 4.4.2.8 Proactive & Remote Patient Monitoring 4.4.2.9 Predictive Maintenance of Medical Equipment 4.4.2.10 Pharmacy Services 4.4.3 Healthcare Support, Awareness & Disease Prevention 4.4.3.1 Self-Care & Lifestyle Support 4.4.3.2 Medication Adherence & Management 4.4.3.3 Vaccine Development & Promotion 4.4.3.4 Population Health Management 4.4.3.5 Connected Health Communities & Medical Knowledge Dissemination 4.4.3.6 Epidemiology & Disease Surveillance 4.4.3.7 Health Policy Decision Making 4.4.3.8 Controlling Substance Abuse & Addiction 4.4.3.9 Increasing Awareness & Accessible Healthcare 4.4.4 Health Insurance & Payer Services 4.4.4.1 Health Insurance Claims Processing & Management 4.4.4.2 Fraud & Abuse Prevention 4.4.4.3 Proactive Patient Engagement 4.4.4.4 Accountable & Value-Based Care 4.4.4.5 Data-Driven Health Insurance Premiums 4.4.5 Marketing, Sales & Other Applications 4.4.5.1 Marketing & Sales 4.4.5.2 Administrative & Customer Services 4.4.5.3 Finance & Risk Management 4.4.5.4 Healthcare Data Monetization 4.4.5.5 Other Applications =20 5 Chapter 5: Healthcare & Pharmaceutical Industry Case Studies 5.1 Pharmaceutical & Medical Device Companies 5.1.1 AstraZeneca: Analytics-Driven Drug Development with Big Data 5.1.2 Bayer: Accelerating Clinical Trials with Big Data 5.1.3 GSK (GlaxoSmithKline): Increasing Success Rates in Drug Discovery wit= h Big Data 5.1.4 Johnson & Johnson: Intelligent Pharmaceutical Marketing with Big Data 5.1.5 Medtronic: Facilitating Predictive Care with Big Data 5.1.6 Merck & Co.: Optimizing Vaccine Manufacturing with Big Data 5.1.7 Merck KGaA: Discovering Drugs Faster with Big Data 5.1.8 Novartis: Digitizing Healthcare with Big Data 5.1.9 Pfizer: Developing Effective and Targeted Therapies with Big Data 5.1.10 Roche: Personalizing Healthcare with Big Data 5.1.11 Sanofi: Proactive Diabetes Care with Big Data 5.2 Healthcare Providers, Insurers & Payers 5.2.1 Aetna: Predicting & Improving Health with Big Data 5.2.2 Bangkok Hospital Group: Transforming the Patient Experience with Big = Data 5.2.3 Gold Coast Health: Reducing Hospital Waiting Times with Big Data 5.2.4 IU Health (Indiana University Health): Preventing Hospital-Acquired I= nfections with Big Data 5.2.5 MSQC (Michigan Surgical Quality Collaborative): Surgical Quality Impr= ovement with Big Data 5.2.6 NCCS (National Cancer Centre Singapore): Advancing Cancer Treatment = with Big Data 5.2.7 NHS Scotland: Improving Outcomes with Big Data 5.2.8 Seattle Children's Hospital: Enabling Faster & Accurate Diagnosis wit= h Big Data 5.2.9 UnitedHealth Group: Enhancing Patient Care & Value with Big Data 5.2.10 VHA (Veterans Health Administration): Streamlining Healthcare Delive= ry with Big Data 5.3 Other Stakeholders 5.3.1 Amino: Healthcare Transparency with Big Data 5.3.2 CosmosID: Advancing Microbial Genomics with Big Data 5.3.3 Express Scripts: Improving Medication Adherence with Big Data 5.3.4 Faros Healthcare: Enhancing Clinical Decision Making with Big Data 5.3.5 Genomics England: Developing the World's First Genomics Medicine Serv= ice with Big Data 5.3.6 Ginger.io: Improving Mental Wellbeing with Big Data 5.3.7 Illumina: Enabling Precision Medicine with Big Data 5.3.8 INDS (National Institute of Health Data, France): Population Health M= anagement with Big Data 5.3.9 MolecularMatch: Advancing the Clinical Utility of Genomics with Big D= ata 5.3.10 Proteus Digital Health: Pioneering Digital Medicine with Big Data 5.3.11 Royal Philips: Enhancing Workflows in ICUs (Intensive Care Units) wi= th Big Data 5.3.12 Sickweather: Sickness Forecasting & Mapping with Big Data 5.3.13 Sproxil: Fighting Counterfeit Drugs with Big Data =20 6 Chapter 6: Future Roadmap & Value Chain 6.1 Future Roadmap 6.1.1 2017 =96 2020: Growing Investments in Real-Time & Predictive Health A= nalytics 6.1.2 2020 =96 2025: Large-Scale Adoption of Precision Medicine 6.1.3 2025 =96 2030: Moving Beyond National-Level Population Health Managem= ent 6.2 Value Chain 6.2.1 Hardware Providers 6.2.1.1 Storage & Compute Infrastructure Providers 6.2.1.2 Networking Infrastructure Providers 6.2.2 Software Providers 6.2.2.1 Hadoop & Infrastructure Software Providers 6.2.2.2 SQL & NoSQL Providers 6.2.2.3 Analytic Platform & Application Software Providers 6.2.2.4 Cloud Platform Providers 6.2.3 Professional Services Providers 6.2.4 End-to-End Solution Providers 6.2.5 Healthcare & Pharmaceutical Industry =20 7 Chapter 7: Standardization & Regulatory Initiatives 7.1 ASF (Apache Software Foundation) 7.1.1 Management of Hadoop 7.1.2 Big Data Projects Beyond Hadoop 7.2 CSA (Cloud Security Alliance) 7.2.1 BDWG (Big Data Working Group) 7.3 CSCC (Cloud Standards Customer Council) 7.3.1 Big Data Working Group 7.4 DMG (Data Mining Group) 7.4.1 PMML (Predictive Model Markup Language) Working Group 7.4.2 PFA (Portable Format for Analytics) Working Group 7.5 IEEE (Institute of Electrical and Electronics Engineers) 7.5.1 Big Data Initiative 7.6 INCITS (InterNational Committee for Information Technology Standards) 7.6.1 Big Data Technical Committee 7.7 ISO (International Organization for Standardization) 7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange 7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms 7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques 7.7.4 ISO/IEC JTC 1/WG 9: Big Data 7.7.5 Collaborations with Other ISO Work Groups 7.8 ITU (International Telecommunications Union) 7.8.1 ITU-T Y.3600: Big Data =96 Cloud Computing Based Requirements and Cap= abilities 7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks 7.8.3 Other Relevant Work 7.9 Linux Foundation 7.9.1 ODPi (Open Ecosystem of Big Data) 7.10 NIST (National Institute of Standards and Technology) 7.10.1 NBD-PWG (NIST Big Data Public Working Group) 7.11 OASIS (Organization for the Advancement of Structured Information Stan= dards) 7.11.1 Technical Committees 7.12 ODaF (Open Data Foundation) 7.12.1 Big Data Accessibility 7.13 ODCA (Open Data Center Alliance) 7.13.1 Work on Big Data 7.14 OGC (Open Geospatial Consortium) 7.14.1 Big Data DWG (Domain Working Group) 7.15 TM Forum 7.15.1 Big Data Analytics Strategic Program 7.16 TPC (Transaction Processing Performance Council) 7.16.1 TPC-BDWG (TPC Big Data Working Group) 7.17 W3C (World Wide Web Consortium) 7.17.1 Big Data Community Group 7.17.2 Open Government Community Group 7.18 Other Initiatives Relevant to the Healthcare & Pharmaceutical Industry 7.18.1 HIPAA (Health Insurance Portability and Accountability Act of 1996) 7.18.2 HITECH (Health Information Technology for Economic and Clinical Heal= th) Act 7.18.3 European Union's GDPR (General Data Protection Regulation) 7.18.4 Australian Digital Health Agency 7.18.5 United Kingdom's ITK (Interoperability Toolkit) 7.18.6 Japan's SS-MIX (Standard Structured Medical Information eXchange) 7.18.7 Germany's xDT 7.18.8 France's DMP (Dossier M=E9dical Personnel) 7.18.9 HL7 (Health Level Seven) Specifications 7.18.10 IHE (Integrating the Healthcare Enterprise) 7.18.11 NCPDP (National Council for Prescription Drug Programs) 7.18.12 DICOM (Digital Imaging and Communications in Medicine) 7.18.13 eHealth Exchange 7.18.14 EDIFACT (Electronic Data Interchange For Administration, Commerce, = and Transport) 7.18.15 X12 & Others =20 8 Chapter 8: Market Analysis & Forecasts 8.1 Global Outlook for Big Data in the Healthcare & Pharmaceutical Industry 8.2 Hardware, Software & Professional Services Segmentation 8.3 Horizontal Submarket Segmentation 8.4 Hardware Submarkets 8.4.1 Storage and Compute Infrastructure 8.4.2 Networking Infrastructure 8.5 Software Submarkets 8.5.1 Hadoop & Infrastructure Software 8.5.2 SQL 8.5.3 NoSQL 8.5.4 Analytic Platforms & Applications 8.5.5 Cloud Platforms 8.6 Professional Services Submarket 8.6.1 Professional Services 8.7 Application Area Segmentation 8.7.1 Pharmaceutical & Medical Products 8.7.2 Core Healthcare Operations 8.7.3 Healthcare Support, Awareness & Disease Prevention 8.7.4 Health Insurance & Payer Services 8.7.5 Marketing, Sales & Other Applications 8.8 Use Case Segmentation 8.9 Pharmaceutical & Medical Products 8.9.1 Drug Discovery, Design & Development 8.9.2 Medical Product Design & Development 8.9.3 Clinical Development & Trials 8.9.4 Precision Medicine & Genomics 8.9.5 Manufacturing & Supply Chain Management 8.9.6 Post-Market Surveillance & Pharmacovigilance 8.9.7 Medical Product Fault Monitoring 8.10 Core Healthcare Operations 8.10.1 Clinical Decision Support 8.10.2 Care Coordination & Delivery Management 8.10.3 CER (Comparative Effectiveness Research) & Observational Evidence 8.10.4 Personalized Healthcare & Targeted Treatments 8.10.5 Data-Driven Preventive Care & Health Interventions 8.10.6 Surgical Practice & Complex Medical Procedures 8.10.7 Pathology, Medical Imaging & Other Medical Tests 8.10.8 Proactive & Remote Patient Monitoring 8.10.9 Predictive Maintenance of Medical Equipment 8.10.10 Pharmacy Services 8.11 Healthcare Support, Awareness & Disease Prevention 8.11.1 Self-Care & Lifestyle Support 8.11.2 Medication Adherence & Management 8.11.3 Vaccine Development & Promotion 8.11.4 Population Health Management 8.11.5 Connected Health Communities & Medical Knowledge Dissemination 8.11.6 Epidemiology & Disease Surveillance 8.11.7 Health Policy Decision Making 8.11.8 Controlling Substance Abuse & Addiction 8.11.9 Increasing Awareness & Accessible Healthcare 8.12 Health Insurance & Payer Services 8.12.1 Health Insurance Claims Processing & Management 8.12.2 Fraud & Abuse Prevention 8.12.3 Proactive Patient Engagement 8.12.4 Accountable & Value-Based Care 8.12.5 Data-Driven Health Insurance Premiums 8.13 Marketing, Sales & Other Application Use Cases 8.13.1 Marketing & Sales 8.13.2 Administrative & Customer Services 8.13.3 Finance & Risk Management 8.13.4 Healthcare Data Monetization 8.13.5 Other Use Cases 8.14 Regional Outlook 8.15 Asia Pacific 8.15.1 Country Level Segmentation 8.15.2 Australia 8.15.3 China 8.15.4 India 8.15.5 Indonesia 8.15.6 Japan 8.15.7 Malaysia 8.15.8 Pakistan 8.15.9 Philippines 8.15.10 Singapore 8.15.11 South Korea 8.15.12 Taiwan 8.15.13 Thailand 8.15.14 Rest of Asia Pacific 8.16 Eastern Europe 8.16.1 Country Level Segmentation 8.16.2 Czech Republic 8.16.3 Poland 8.16.4 Russia 8.16.5 Rest of Eastern Europe 8.17 Latin & Central America 8.17.1 Country Level Segmentation 8.17.2 Argentina 8.17.3 Brazil 8.17.4 Mexico 8.17.5 Rest of Latin & Central America 8.18 Middle East & Africa 8.18.1 Country Level Segmentation 8.18.2 Israel 8.18.3 Qatar 8.18.4 Saudi Arabia 8.18.5 South Africa 8.18.6 UAE 8.18.7 Rest of the Middle East & Africa 8.19 North America 8.19.1 Country Level Segmentation 8.19.2 Canada 8.19.3 USA 8.20 Western Europe 8.20.1 Country Level Segmentation 8.20.2 Denmark 8.20.3 Finland 8.20.4 France 8.20.5 Germany 8.20.6 Italy 8.20.7 Netherlands 8.20.8 Norway 8.20.9 Spain 8.20.10 Sweden 8.20.11 UK 8.20.12 Rest of Western Europe =20 9 Chapter 9: Vendor Landscape 9.1 1010data 9.2 Absolutdata 9.3 Accenture 9.4 Actian Corporation 9.5 Adaptive Insights 9.6 Advizor Solutions 9.7 AeroSpike 9.8 AFS Technologies 9.9 Alation 9.10 Algorithmia 9.11 Alluxio 9.12 Alpine Data 9.13 Alteryx 9.14 AMD (Advanced Micro Devices) 9.15 Apixio 9.16 Arcadia Data 9.17 Arimo 9.18 ARM 9.19 AtScale 9.20 Attivio 9.21 Attunity 9.22 Automated Insights 9.23 AWS (Amazon Web Services) 9.24 Axiomatics 9.25 Ayasdi 9.26 Basho Technologies 9.27 BCG (Boston Consulting Group) 9.28 Bedrock Data 9.29 BetterWorks 9.30 Big Cloud Analytics 9.31 BigML 9.32 Big Panda 9.33 Birst 9.34 Bitam 9.35 Blue Medora 9.36 BlueData Software 9.37 BlueTalon 9.38 BMC Software 9.39 BOARD International 9.40 Booz Allen Hamilton 9.41 Boxever 9.42 CACI International 9.43 Cambridge Semantics 9.44 Capgemini 9.45 Cazena 9.46 Centrifuge Systems 9.47 CenturyLink 9.48 Chartio 9.49 Cisco Systems 9.50 Civis Analytics 9.51 ClearStory Data 9.52 Cloudability 9.53 Cloudera 9.54 Clustrix 9.55 CognitiveScale 9.56 Collibra 9.57 Concurrent Computer Corporation 9.58 Confluent 9.59 Contexti 9.60 Continuum Analytics 9.61 Couchbase 9.62 CrowdFlower 9.63 Databricks 9.64 DataGravity 9.65 Dataiku 9.66 Datameer 9.67 DataRobot 9.68 DataScience 9.69 DataStax 9.70 DataTorrent 9.71 Datawatch Corporation 9.72 Datos IO 9.73 DDN (DataDirect Networks) 9.74 Decisyon 9.75 Dell Technologies 9.76 Deloitte 9.77 Demandbase 9.78 Denodo Technologies 9.79 Digital Reasoning Systems 9.80 Dimensional Insight 9.81 Dolphin Enterprise Solutions Corporation 9.82 Domino Data Lab 9.83 Domo 9.84 DriveScale 9.85 Dundas Data Visualization 9.86 DXC Technology 9.87 Eligotech 9.88 Engineering Group (Engineering Ingegneria Informatica) 9.89 EnterpriseDB 9.90 eQ Technologic 9.91 Ericsson 9.92 EXASOL 9.93 Facebook 9.94 FICO (Fair Isaac Corporation) 9.95 Fractal Analytics 9.96 Fujitsu 9.97 Fuzzy Logix 9.98 Gainsight 9.99 GE (General Electric) 9.100 Glassbeam 9.101 GoodData Corporation 9.102 Google 9.103 Greenwave Systems 9.104 GridGain Systems 9.105 Guavus 9.106 H2O.ai 9.107 HDS (Hitachi Data Systems) 9.108 Hedvig 9.109 Hortonworks 9.110 HPE (Hewlett Packard Enterprise) 9.111 Huawei 9.112 IBM Corporation 9.113 iDashboards 9.114 Impetus Technologies 9.115 Incorta 9.116 InetSoft Technology Corporation 9.117 Infer 9.118 Infor 9.119 Informatica Corporation 9.120 Information Builders 9.121 Infosys 9.122 Infoworks 9.123 Insightsoftware.com 9.124 InsightSquared 9.125 Intel Corporation 9.126 Interana 9.127 InterSystems Corporation 9.128 Jedox 9.129 Jethro 9.130 Jinfonet Software 9.131 Juniper Networks 9.132 KALEAO 9.133 Keen IO 9.134 Kinetica 9.135 KNIME 9.136 Kognitio 9.137 Kyvos Insights 9.138 Lavastorm 9.139 Lexalytics 9.140 Lexmark International 9.141 Logi Analytics 9.142 Longview Solutions 9.143 Looker Data Sciences 9.144 LucidWorks 9.145 Luminoso Technologies 9.146 Maana 9.147 Magento Commerce 9.148 Manthan Software Services 9.149 MapD Technologies 9.150 MapR Technologies 9.151 MariaDB Corporation 9.152 MarkLogic Corporation 9.153 Mathworks 9.154 MemSQL 9.155 Metric Insights 9.156 Microsoft Corporation 9.157 MicroStrategy 9.158 Minitab 9.159 MongoDB 9.160 Mu Sigma 9.161 NEC Corporation 9.162 Neo Technology 9.163 NetApp 9.164 Nimbix 9.165 Nokia 9.166 NTT Data Corporation 9.167 Numerify 9.168 NuoDB 9.169 Nutonian 9.170 NVIDIA Corporation 9.171 Oblong Industries 9.172 OpenText Corporation 9.173 Opera Solutions 9.174 Optimal Plus 9.175 Oracle Corporation 9.176 Palantir Technologies 9.177 Panorama Software 9.178 Paxata 9.179 Pentaho Corporation 9.180 Pepperdata 9.181 Phocas Software 9.182 Pivotal Software 9.183 Prognoz 9.184 Progress Software Corporation 9.185 PwC (PricewaterhouseCoopers International) 9.186 Pyramid Analytics 9.187 Qlik 9.188 Quantum Corporation 9.189 Qubole 9.190 Rackspace 9.191 Radius Intelligence 9.192 RapidMiner 9.193 Recorded Future 9.194 Red Hat 9.195 Redis Labs 9.196 RedPoint Global 9.197 Reltio 9.198 Rocket Fuel 9.199 RStudio 9.200 Ryft Systems 9.201 Sailthru 9.202 Salesforce.com 9.203 Salient Management Company 9.204 Samsung Group 9.205 SAP 9.206 SAS Institute 9.207 ScaleDB 9.208 ScaleOut Software 9.209 SCIO Health Analytics 9.210 Seagate Technology 9.211 Sinequa 9.212 SiSense 9.213 SnapLogic 9.214 Snowflake Computing 9.215 Software AG 9.216 Splice Machine 9.217 Splunk 9.218 Sqrrl 9.219 Strategy Companion Corporation 9.220 StreamSets 9.221 Striim 9.222 Sumo Logic 9.223 Supermicro (Super Micro Computer) 9.224 Syncsort 9.225 SynerScope 9.226 Tableau Software 9.227 Talena 9.228 Talend 9.229 Tamr 9.230 TARGIT 9.231 TCS (Tata Consultancy Services) 9.232 Teradata Corporation 9.233 ThoughtSpot 9.234 TIBCO Software 9.235 Tidemark 9.236 Toshiba Corporation 9.237 Trifacta 9.238 Unravel Data 9.239 VMware 9.240 VoltDB 9.241 Waterline Data 9.242 Western Digital Corporation 9.243 WiPro 9.244 Workday 9.245 Xplenty 9.246 Yellowfin International 9.247 Yseop 9.248 Zendesk 9.249 Zoomdata 9.250 Zucchetti =20 10 Chapter 10: Conclusion & Strategic Recommendations 10.1 Why is the Market Poised to Grow=3F 10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential=3F 10.3 Partnerships & M&A Activity: Highlighting the Importance of Big Data 10.4 Improving Outcomes, Achieving Operational Efficiency and Reducing Costs 10.5 Assessing the Impact of Connected Health Solutions 10.6 Accelerating the Transition Towards Value-Based Care 10.7 The Value of Big Data in Precision Medicine 10.8 Addressing Privacy & Security Concerns 10.9 The Role of Data Protection Legislation 10.10 Blockchain: Enabling Secure, Efficient and Interoperable Data Sharing 10.11 Recommendations 10.11.1 Big Data Hardware, Software & Professional Services Providers 10.11.2 Healthcare & Pharmaceutical Industry Stakeholders =20 List of Figures: =20 Figure 1: Hadoop Architecture Figure 2: Reactive vs. Proactive Analytics Figure 3: Distribution of Big Data Investments in the Healthcare & Pharmace= utical Industry, by Application Area: 2016 (%) Figure 4: Key Characteristics of Genomics and Three Major Sources of Big Da= ta Figure 5: Bayer's Vision of Big Data in Medicine Figure 6: Sickweather's Sickness Forecasting & Mapping Service Figure 7: Counterfeit Drug Identification with Big Data & Mobile Technology Figure 8: Big Data Roadmap in the Healthcare & Pharmaceutical Industry 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 Indus= try: 2017 - 2030 ($ Million) Figure 12: Global Big Data Revenue in the Healthcare & Pharmaceutical Indus= try, by Hardware, Software & Professional Services: 2017 - 2030 ($ Million) Figure 13: Global Big Data Revenue in the Healthcare & Pharmaceutical Indus= try, by Submarket: 2017 - 2030 ($ Million) Figure 14: Global Big Data Storage and Compute Infrastructure Submarket Rev= enue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) Figure 15: Global Big Data Networking Infrastructure Submarket Revenue in t= he Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) Figure 16: Global Big Data Hadoop & Infrastructure Software Submarket Reven= ue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) Figure 17: Global Big Data SQL Submarket Revenue in the Healthcare & Pharma= ceutical Industry: 2017 - 2030 ($ Million) Figure 18: Global Big Data NoSQL Submarket Revenue in the Healthcare & Phar= maceutical Industry: 2017 - 2030 ($ Million) Figure 19: Global Big Data Analytic Platforms & Applications Submarket Reve= nue in the Healthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) Figure 20: Global Big Data Cloud Platforms Submarket Revenue in the Healthc= are & Pharmaceutical Industry: 2017 - 2030 ($ Million) Figure 21: Global Big Data Professional Services Submarket Revenue in the H= ealthcare & Pharmaceutical Industry: 2017 - 2030 ($ Million) Figure 22: Global Big Data Revenue in the Healthcare & Pharmaceutical Indus= try, by Application Area: 2017 - 2030 ($ Million) Figure 23: Global Big Data Revenue in Pharmaceutical & Medical Products: 20= 17 - 2030 ($ Million) Figure 24: Global Big Data Revenue in Core Healthcare Operations: 2017 - 20= 30 ($ Million) Figure 25: Global Big Data Revenue in Healthcare Support, Awareness & Disea= se Prevention: 2017 - 2030 ($ Million) Figure 26: Global Big Data Revenue in Health Insurance & Payer Services: 20= 17 - 2030 ($ Million) Figure 27: Global Big Data Revenue in Healthcare/Pharmaceutical Marketing, = Sales & Other Applications: 2017 - 2030 ($ Million) Figure 28: Global Big Data Revenue in the Healthcare & Pharmaceutical Indus= try, by Use Case: 2017 - 2030 ($ Million) Figure 29: Global Big Data Revenue in Drug Discovery, Design & Development:= 2017 - 2030 ($ Million) Figure 30: Global Big Data Revenue in Medical Product Design & Development:= 2017 - 2030 ($ Million) Figure 31: Global Big Data Revenue in Clinical Development & Trials: 2017 -= 2030 ($ Million) Figure 32: Global Big Data Revenue in Precision Medicine & Genomics: 2017 -= 2030 ($ Million) Figure 33: Global Big Data Revenue in Pharmaceutical/Medical Manufacturing = & Supply Chain Management: 2017 - 2030 ($ Million) Figure 34: Global Big Data Revenue in Post-Market Surveillance & Pharmacovi= gilance: 2017 - 2030 ($ Million) Figure 35: Global Big Data Revenue in Medical Product Fault Monitoring: 201= 7 - 2030 ($ Million) Figure 36: Global Big Data Revenue in Clinical Decision Support: 2017 - 203= 0 ($ Million) Figure 37: Global Big Data Revenue in Care Coordination & Delivery Manageme= nt: 2017 - 2030 ($ Million) Figure 38: Global Big Data Revenue in CER (Comparative Effectiveness Resear= ch) & Observational Evidence: 2017 - 2030 ($ Million) Figure 39: Global Big Data Revenue in Personalized Healthcare & Targeted Tr= eatments: 2017 - 2030 ($ Million) Figure 40: Global Big Data Revenue in Data-Driven Preventive Care & Health = Interventions: 2017 - 2030 ($ Million) Figure 41: Global Big Data Revenue in Surgical Practice & Complex Medical P= rocedures: 2017 - 2030 ($ Million) Figure 42: Global Big Data Revenue in Pathology, Medical Imaging & Other Me= dical Tests: 2017 - 2030 ($ Million) Figure 43: Global Big Data Revenue in Proactive & Remote Patient Monitoring= : 2017 - 2030 ($ Million) Figure 44: Global Big Data Revenue in Predictive Maintenance of Medical Equ= ipment: 2017 - 2030 ($ Million) Figure 45: Global Big Data Revenue in Pharmacy Services: 2017 - 2030 ($ Mil= lion) Figure 46: Global Big Data Revenue in Self-Care & Lifestyle Support: 2017 -= 2030 ($ Million) Figure 47: Global Big Data Revenue in Medication Adherence & Management: 20= 17 - 2030 ($ Million) Figure 48: Global Big Data Revenue in Vaccine Development & Promotion: 2017= - 2030 ($ Million) Figure 49: Global Big Data Revenue in Population Health Management: 2017 - = 2030 ($ Million) Figure 50: Global Big Data Revenue in Connected Health Communities & Medica= l Knowledge Dissemination: 2017 - 2030 ($ Million) Figure 51: Global Big Data Revenue in Epidemiology & Disease Surveillance: = 2017 - 2030 ($ Million) Figure 52: Global Big Data Revenue in Health Policy Decision Making: 2017 -= 2030 ($ Million) Figure 53: Global Big Data Revenue in Controlling Substance Abuse & Addicti= on: 2017 - 2030 ($ Million) Figure 54: Global Big Data Revenue in Increasing Awareness & Accessible Hea= lthcare: 2017 - 2030 ($ Million) Figure 55: Global Big Data Revenue in Health Insurance Claims Processing & = Management: 2017 - 2030 ($ Million) Figure 56: Global Big Data Revenue in Fraud & Abuse Prevention: 2017 - 2030= ($ Million) Figure 57: Global Big Data Revenue in Proactive Patient Engagement: 2017 - = 2030 ($ Million) Figure 58: Global Big Data Revenue in Accountable & Value-Based Care: 2017 = - 2030 ($ Million) Figure 59: Global Big Data Revenue in Data-Driven Health Insurance Premiums= : 2017 - 2030 ($ Million) Figure 60: Global Big Data Revenue in Healthcare/Pharmaceutical Marketing &= Sales: 2017 - 2030 ($ Million) Figure 61: Global Big Data Revenue in Healthcare/Pharmaceutical Administrat= ive & Customer Services: 2017 - 2030 ($ Million) Figure 62: Global Big Data Revenue in Healthcare/Pharmaceutical Finance & R= isk Management: 2017 - 2030 ($ Million) Figure 63: Global Big Data Revenue in Healthcare Data Monetization: 2017 - = 2030 ($ Million) Figure 64: Global Big Data Revenue in Other Healthcare & Pharmaceutical Ind= ustry Use Cases: 2017 - 2030 ($ Million) Figure 65: Big Data Revenue in the Healthcare & Pharmaceutical Industry, by= Region: 2017 - 2030 ($ Million) Figure 66: Asia Pacific Big Data Revenue in the Healthcare & Pharmaceutical= Industry: 2017 - 2030 ($ Million) Figure 67: Asia Pacific Big Data Revenue in the Healthcare & Pharmaceutical= Industry, by Country: 2017 - 2030 ($ Million) Figure 68: Australia Big Data Revenue in the Healthcare & Pharmaceutical In= dustry: 2017 - 2030 ($ Million) Figure 69: China Big Data Revenue in the Healthcare & Pharmaceutical Indust= ry: 2017 - 2030 ($ Million) Figure 70: India Big Data Revenue in the Healthcare & Pharmaceutical Indust= ry: 2017 - 2030 ($ Million) Figure 71: Indonesia Big Data Revenue in the Healthcare & Pharmaceutical In= dustry: 2017 - 2030 ($ Million) Figure 72: Japan Big Data Revenue in the Healthcare & Pharmaceutical Indust= ry: 2017 - 2030 ($ Million) Figure 73: Malaysia Big Data Revenue in the Healthcare & Pharmaceutical Ind= ustry: 2017 - 2030 ($ Million) Figure 74: Pakistan Big Data Revenue in the Healthcare & Pharmaceutical Ind= ustry: 2017 - 2030 ($ Million) Figure 75: Philippines Big Data Revenue in the Healthcare & Pharmaceutical = Industry: 2017 - 2030 ($ Million) Figure 76: Singapore Big Data Revenue in the Healthcare & Pharmaceutical In= dustry: 2017 - 2030 ($ Million) Figure 77: South Korea Big Data Revenue in the Healthcare & Pharmaceutical = Industry: 2017 - 2030 ($ Million) Figure 78: Taiwan Big Data Revenue in the Healthcare & Pharmaceutical Indus= try: 2017 - 2030 ($ Million) Figure 79: Thailand Big Data Revenue in the Healthcare & Pharmaceutical Ind= ustry: 2017 - 2030 ($ Million) Figure 80: Rest of Asia Pacific Big Data Revenue in the Healthcare & Pharma= ceutical Industry: 2017 - 2030 ($ Million) Figure 81: Eastern Europe Big Data Revenue in the Healthcare & Pharmaceutic= al Industry: 2017 - 2030 ($ Million) Figure 82: Eastern Europe Big Data Revenue in the Healthcare & Pharmaceutic= al Industry, by Country: 2017 - 2030 ($ Million) Figure 83: Czech Republic Big Data Revenue in the Healthcare & Pharmaceutic= al Industry: 2017 - 2030 ($ Million) Figure 84: Poland Big Data Revenue in the Healthcare & Pharmaceutical Indus= try: 2017 - 2030 ($ Million) Figure 85: Russia Big Data Revenue in the Healthcare & Pharmaceutical Indus= try: 2017 - 2030 ($ Million) Figure 86: Rest of Eastern Europe Big Data Revenue in the Healthcare & Phar= maceutical Industry: 2017 - 2030 ($ Million) Figure 87: Latin & Central America Big Data Revenue in the Healthcare & Pha= rmaceutical Industry: 2017 - 2030 ($ Million) Figure 88: Latin & Central America Big Data Revenue in the Healthcare & Pha= rmaceutical Industry, by Country: 2017 - 2030 ($ Million) Figure 89: Argentina Big Data Revenue in the Healthcare & Pharmaceutical In= dustry: 2017 - 2030 ($ Million) Figure 90: Brazil Big Data Revenue in the Healthcare & Pharmaceutical Indus= try: 2017 - 2030 ($ Million) Figure 91: Mexico Big Data Revenue in the Healthcare & Pharmaceutical Indus= try: 2017 - 2030 ($ Million) Figure 92: Rest of Latin & Central America Big Data Revenue in the Healthca= re & Pharmaceutical Industry: 2017 - 2030 ($ Million) Figure 93: Middle East & Africa Big Data Revenue in the Healthcare & Pharma= ceutical Industry: 2017 - 2030 ($ Million) Figure 94: Middle East & Africa Big Data Revenue in the Healthcare & Pharma= ceutical Industry, by Country: 2017 - 2030 ($ Million) Figure 95: Israel Big Data Revenue in the Healthcare & Pharmaceutical Indus= try: 2017 - 2030 ($ Million) Figure 96: Qatar Big Data Revenue in the Healthcare & Pharmaceutical Indust= ry: 2017 - 2030 ($ Million) Figure 97: Saudi Arabia Big Data Revenue in the Healthcare & Pharmaceutical= Industry: 2017 - 2030 ($ Million) Figure 98: South Africa Big Data Revenue in the Healthcare & Pharmaceutical= Industry: 2017 - 2030 ($ Million) Figure 99: UAE Big Data Revenue in the Healthcare & Pharmaceutical Industry= : 2017 - 2030 ($ Million) Figure 100: Rest of the Middle East & Africa Big Data Revenue in the Health= care & Pharmaceutical Industry: 2017 - 2030 ($ Million) Figure 101: North America Big Data Revenue in the Healthcare & Pharmaceutic= al Industry: 2017 - 2030 ($ Million) Figure 102: North America Big Data Revenue in the Healthcare & Pharmaceutic= al Industry, by Country: 2017 - 2030 ($ Million) Figure 103: Canada Big Data Revenue in the Healthcare & Pharmaceutical Indu= stry: 2017 - 2030 ($ Million) Figure 104: USA Big Data Revenue in the Healthcare & Pharmaceutical Industr= y: 2017 - 2030 ($ Million) Figure 105: Western Europe Big Data Revenue in the Healthcare & Pharmaceuti= cal Industry: 2017 - 2030 ($ Million) Figure 106: Western Europe Big Data Revenue in the Healthcare & Pharmaceuti= cal Industry, by Country: 2017 - 2030 ($ Million) Figure 107: Denmark Big Data Revenue in the Healthcare & Pharmaceutical Ind= ustry: 2017 - 2030 ($ Million) Figure 108: Finland Big Data Revenue in the Healthcare & Pharmaceutical Ind= ustry: 2017 - 2030 ($ Million) Figure 109: France Big Data Revenue in the Healthcare & Pharmaceutical Indu= stry: 2017 - 2030 ($ Million) Figure 110: Germany Big Data Revenue in the Healthcare & Pharmaceutical Ind= ustry: 2017 - 2030 ($ Million) Figure 111: Italy Big Data Revenue in the Healthcare & Pharmaceutical Indus= try: 2017 - 2030 ($ Million) Figure 112: Netherlands Big Data Revenue in the Healthcare & Pharmaceutical= Industry: 2017 - 2030 ($ Million) Figure 113: Norway Big Data Revenue in the Healthcare & Pharmaceutical Indu= stry: 2017 - 2030 ($ Million) Figure 114: Spain Big Data Revenue in the Healthcare & Pharmaceutical Indus= try: 2017 - 2030 ($ Million) Figure 115: Sweden Big Data Revenue in the Healthcare & Pharmaceutical Indu= stry: 2017 - 2030 ($ Million) Figure 116: UK Big Data Revenue in the Healthcare & Pharmaceutical Industry= : 2017 - 2030 ($ Million) Figure 117: Rest of Western Europe Big Data Revenue in the Healthcare & Pha= rmaceutical Industry: 2017 - 2030 ($ Million) =20 Thank you once again and looking forward to hearing from you. =20 Kind Regards =20 Andy Silva =20 =20 To unsubscribe send an email with unsubscribe in the subject line to: remov= e@snsreports.com =20
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