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Big Data in the Healthcare Pharmaceutical Industry Opportunities Challenges Strategies Forecasts


Big Data in the Healthcare & Pharmaceutical Industry: 2017 – 2030 – Opportunities, Challenges, Strategies & Forecasts

Report code: SDMRTE38147 | Industry: Telecom & IT | Published On: 2020-01-01


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 Research estimates that 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.

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
  
2  Chapter  2:  An  Overview  of  Big  Data
2.1  What  is  Big  Data?
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
  
3  Chapter  3:  Big  Data  Analytics
3.1  What  are  Big  Data  Analytics?
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
  
4  Chapter  4:  Business  Case  &  Applications  in  the  Healthcare  &  Pharmaceutical  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  Digital  Therapeutics
4.4.3.3  Medication  Adherence  &  Management
4.4.3.4  Vaccine  Development  &  Promotion
4.4.3.5  Population  Health  Management
4.4.3.6  Connected  Health  Communities  &  Medical  Knowledge  Dissemination
4.4.3.7  Epidemiology  &  Disease  Surveillance
4.4.3.8  Health  Policy  Decision  Making
4.4.3.9  Controlling  Substance  Abuse  &  Addiction
4.4.3.10  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
  
5  Chapter  5:  Healthcare  &  Pharmaceutical  Industry  Case  Studies
5.1  Pharmaceutical  &  Medical  Device  Companies
5.1.1  AbbVie:  Designing  &  Implementing  Clinical  Trials  with  Big  Data
5.1.2  AstraZeneca:  Analytics-Driven  Drug  Development  with  Big  Data
5.1.3  Bayer:  Accelerating  Clinical  Trials  with  Big  Data
5.1.4  BMS  (Bristol-Myers  Squibb):  Driving  Clinical  Discovery  with  Big  Data
5.1.5  GSK  (GlaxoSmithKline):  Increasing  Success  Rates  in  Drug  Discovery  with  Big  Data
5.1.6  Johnson  &  Johnson:  Intelligent  Pharmaceutical  Marketing  with  Big  Data
5.1.7  Medtronic:  Facilitating  Predictive  Care  with  Big  Data
5.1.8  Merck  &  Co.:  Optimizing  Vaccine  Manufacturing  with  Big  Data
5.1.9  Merck  KGaA:  Discovering  Drugs  Faster  with  Big  Data
5.1.10  Novartis:  Digitizing  Healthcare  with  Big  Data
5.1.11  Pfizer:  Developing  Effective  and  Targeted  Therapies  with  Big  Data
5.1.12  Roche:  Personalizing  Healthcare  with  Big  Data
5.1.13  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  Ambulance  Victoria:  Improving  Patient  Survival  Rates  with  Big  Data
5.2.3  Bangkok  Hospital  Group:  Transforming  the  Patient  Experience  with  Big  Data
5.2.4  Cigna:  Streamlining  Health  Insurance  Claims  with  Big  Data
5.2.5  Gold  Coast  Health:  Reducing  Hospital  Waiting  Times  with  Big  Data
5.2.6  IU  Health  (Indiana  University  Health):  Preventing  Hospital-Acquired  Infections  with  Big  Data
5.2.7  Moorfields  Eye  Hospital:  Diagnosing  Eye  Diseases  with  Big  Data
5.2.8  MSQC  (Michigan  Surgical  Quality  Collaborative):  Surgical  Quality  Improvement  with  Big  Data
5.2.9  NCCS    (National  Cancer  Centre  Singapore):  Advancing  Cancer  Treatment  with  Big  Data
5.2.10  NHS  Scotland:  Improving  Outcomes  with  Big  Data
5.2.11  Seattle  Children's  Hospital:  Enabling  Faster  &  Accurate  Diagnosis  with  Big  Data
5.2.12  UnitedHealth  Group:  Enhancing  Patient  Care  &  Value  with  Big  Data
5.2.13  VHA  (Veterans  Health  Administration):  Streamlining  Healthcare  Delivery  with  Big  Data
5.3  Other  Stakeholders
5.3.1  Amino:  Healthcare  Transparency  with  Big  Data
5.3.2  Atomwise:  Improving  Drug  Discovery  with  Big  Data
5.3.3  CosmosID:    Advancing  Microbial  Genomics  with  Big  Data
5.3.4  Deep  Genomics:  Discovering  Novel  Oligonucleotide  Therapies  with  Big  Data
5.3.5  Desktop  Genetics:  Facilitating  Genome  Editing  with  Big  Data
5.3.6  Express  Scripts:  Improving  Medication  Adherence  with  Big  Data
5.3.7  Faros  Healthcare:  Enhancing  Clinical  Decision  Making  with  Big  Data
5.3.8  Genomics  England:  Developing  the  World's  First  Genomics  Medicine  Service  with  Big  Data
5.3.9  Ginger.io:  Improving  Mental  Wellbeing  with  Big  Data
5.3.10  Illumina:  Enabling  Precision  Medicine  with  Big  Data
5.3.11  INDS  (National  Institute  of  Health  Data,  France):  Population  Health  Management  with  Big  Data
5.3.12  MolecularMatch:  Advancing  the  Clinical  Utility  of  Genomics  with  Big  Data
5.3.13  Proteus  Digital  Health:  Pioneering  Digital  Medicine  with  Big  Data
5.3.14  Royal  Philips:  Enhancing  Workflows  in  ICUs  (Intensive  Care  Units)  with  Big  Data
5.3.15  Sickweather:  Sickness  Forecasting  &  Mapping  with  Big  Data
5.3.16  Sproxil:  Fighting  Counterfeit  Drugs  with  Big  Data
  
6  Chapter  6:  Future  Roadmap  &  Value  Chain
6.1  Future  Roadmap
6.1.1  Pre-2020:  Growing  Investments  in  Real-Time  &  Predictive  Health  Analytics
6.1.2  2020  –  2025:  Data-Driven  Advances  in  Drug  Discovery  &  Precision  Medicine
6.1.3  2025  –  2030:  Moving  Beyond  National-Level  Population  Health  Management
6.2  The  Big  Data  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
  
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  Telecommunication  Union)
7.8.1  ITU-T  Y.3600:  Big  Data  –  Cloud  Computing  Based  Requirements  and  Capabilities
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  Standards)
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  Health)  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  Médical  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  HITRUST  CSF  (Common  Security  Framework)
7.18.16  DTA  (Digital  Therapeutics  Alliance)
7.18.17  X12  &  Others
8  Chapter  8:  Market  Sizing  &  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
8.20.12  Rest  of  Western  Europe
  
9  Chapter  9:  Vendor  Landscape
9.1  1010data
9.2  Absolutdata
9.3  Accenture
9.4  Actian  Corporation/HCL  Technologies
9.5  Adaptive  Insights
9.6  Adobe  Systems
9.7  Advizor  Solutions
9.8  AeroSpike
9.9  AFS  Technologies
9.10  Alation
9.11  Algorithmia
9.12  Alluxio
9.13  ALTEN
9.14  Alteryx
9.15  AMD  (Advanced  Micro  Devices)
9.16  Anaconda
9.17  Apixio
9.18  Arcadia  Data
9.19  ARM
9.20  AtScale
9.21  Attivio
9.22  Attunity
9.23  Automated  Insights
9.24  AVORA
9.25  AWS  (Amazon  Web  Services)
9.26  Axiomatics
9.27  Ayasdi
9.28  BackOffice  Associates
9.29  Basho  Technologies
9.30  BCG  (Boston  Consulting  Group)
9.31  Bedrock  Data
9.32  BetterWorks
9.33  Big  Panda
9.34  BigML
9.35  Bitam
9.36  Blue  Medora
9.37  BlueData  Software
9.38  BlueTalon
9.39  BMC  Software
9.40  BOARD  International
9.41  Booz  Allen  Hamilton
9.42  Boxever
9.43  CACI  International
9.44  Cambridge  Semantics
9.45  Capgemini
9.46  Cazena
9.47  Centrifuge  Systems
9.48  CenturyLink
9.49  Chartio
9.50  Cisco  Systems
9.51  Civis  Analytics
9.52  ClearStory  Data
9.53  Cloudability
9.54  Cloudera
9.55  Cloudian
9.56  Clustrix
9.57  CognitiveScale
9.58  Collibra
9.59  Concurrent  Technology/Vecima  Networks
9.60  Confluent
9.61  Contexti
9.62  Couchbase
9.63  Crate.io
9.64  Cray
9.65  Databricks
9.66  Dataiku
9.67  Datalytyx
9.68  Datameer
9.69  DataRobot
9.70  DataStax
9.71  Datawatch  Corporation
9.72  DDN  (DataDirect  Networks)
9.73  Decisyon
9.74  Dell  Technologies
9.75  Deloitte
9.76  Demandbase
9.77  Denodo  Technologies
9.78  Dianomic  Systems
9.79  Digital  Reasoning  Systems
9.80  Dimensional  Insight
9.81  Dolphin  Enterprise  Solutions  Corporation/Hanse  Orga  Group
9.82  Domino  Data  Lab
9.83  Domo
9.84  Dremio
9.85  DriveScale
9.86  Druva
9.87  Dundas  Data  Visualization
9.88  DXC  Technology
9.89  Elastic
9.90  Engineering  Group  (Engineering  Ingegneria  Informatica)
9.91  EnterpriseDB  Corporation
9.92  eQ  Technologic
9.93  Ericsson
9.94  Erwin
9.95  EVŌ  (Big  Cloud  Analytics)
9.96  EXASOL
9.97  EXL  (ExlService  Holdings)
9.98  Facebook
9.99  FICO  (Fair  Isaac  Corporation)
9.100  Figure  Eight
9.101  FogHorn  Systems
9.102  Fractal  Analytics
9.103  Franz
9.104  Fujitsu
9.105  Fuzzy  Logix
9.106  Gainsight
9.107  GE  (General  Electric)
9.108  Glassbeam
9.109  GoodData  Corporation
9.110  Google/Alphabet
9.111  Grakn  Labs
9.112  Greenwave  Systems
9.113  GridGain  Systems
9.114  H2O.ai
9.115  HarperDB
9.116  Hedvig
9.117  Hitachi  Vantara
9.118  Hortonworks
9.119  HPE  (Hewlett  Packard  Enterprise)
9.120  Huawei
9.121  HVR
9.122  HyperScience
9.123  HyTrust
9.124  IBM  Corporation
9.125  iDashboards
9.126  IDERA
9.127  Ignite  Technologies
9.128  Imanis  Data
9.129  Impetus  Technologies
9.130  Incorta
9.131  InetSoft  Technology  Corporation
9.132  InfluxData
9.133  Infogix
9.134  Infor/Birst
9.135  Informatica
9.136  Information  Builders
9.137  Infosys
9.138  Infoworks
9.139  Insightsoftware.com
9.140  InsightSquared
9.141  Intel  Corporation
9.142  Interana
9.143  InterSystems  Corporation
9.144  Jedox
9.145  Jethro
9.146  Jinfonet  Software
9.147  Juniper  Networks
9.148  KALEAO
9.149  Keen  IO
9.150  Keyrus
9.151  Kinetica
9.152  KNIME
9.153  Kognitio
9.154  Kyvos  Insights
9.155  LeanXcale
9.156  Lexalytics
9.157  Lexmark  International
9.158  Lightbend
9.159  Logi  Analytics
9.160  Logical  Clocks
9.161  Longview  Solutions/Tidemark
9.162  Looker  Data  Sciences
9.163  LucidWorks
9.164  Luminoso  Technologies
9.165  Maana
9.166  Manthan  Software  Services
9.167  MapD  Technologies
9.168  MapR  Technologies
9.169  MariaDB  Corporation
9.170  MarkLogic  Corporation
9.171  Mathworks
9.172  Melissa
9.173  MemSQL
9.174  Metric  Insights
9.175  Microsoft  Corporation
9.176  MicroStrategy
9.177  Minitab
9.178  MongoDB
9.179  Mu  Sigma
9.180  NEC  Corporation
9.181  Neo4j
9.182  NetApp
9.183  Nimbix
9.184  Nokia
9.185  NTT  Data  Corporation
9.186  Numerify
9.187  NuoDB
9.188  NVIDIA  Corporation
9.189  Objectivity
9.190  Oblong  Industries
9.191  OpenText  Corporation
9.192  Opera  Solutions
9.193  Optimal  Plus
9.194  Oracle  Corporation
9.195  Palantir  Technologies
9.196  Panasonic  Corporation/Arimo
9.197  Panorama  Software
9.198  Paxata
9.199  Pepperdata
9.200  Phocas  Software
9.201  Pivotal  Software
9.202  Prognoz
9.203  Progress  Software  Corporation
9.204  Provalis  Research
9.205  Pure  Storage
9.206  PwC  (PricewaterhouseCoopers  International)
9.207  Pyramid  Analytics
9.208  Qlik
9.209  Qrama/Tengu
9.210  Quantum  Corporation
9.211  Qubole
9.212  Rackspace
9.213  Radius  Intelligence
9.214  RapidMiner
9.215  Recorded  Future
9.216  Red  Hat
9.217  Redis  Labs
9.218  RedPoint  Global
9.219  Reltio
9.220  RStudio
9.221  Rubrik/Datos  IO
9.222  Ryft
9.223  Sailthru
9.224  Salesforce.com
9.225  Salient  Management  Company
9.226  Samsung  Group
9.227  SAP
9.228  SAS  Institute
9.229  ScaleOut  Software
9.230  Seagate  Technology
9.231  Sinequa
9.232  SiSense
9.233  Sizmek
9.234  SnapLogic
9.235  Snowflake  Computing
9.236  Software  AG
9.237  Splice  Machine
9.238  Splunk
9.239  Strategy  Companion  Corporation
9.240  Stratio
9.241  Streamlio
9.242  StreamSets
9.243  Striim
9.244  Sumo  Logic
9.245  Supermicro  (Super  Micro  Computer)
9.246  Syncsort
9.247  SynerScope
9.248  SYNTASA
9.249  Tableau  Software
9.250  Talend
9.251  Tamr
9.252  TARGIT
9.253  TCS  (Tata  Consultancy  Services)
9.254  Teradata  Corporation
9.255  Thales/Guavus
9.256  ThoughtSpot
9.257  TIBCO  Software
9.258  Toshiba  Corporation
9.259  Transwarp
9.260  Trifacta
9.261  Unifi  Software
9.262  Unravel  Data
9.263  VANTIQ
9.264  VMware
9.265  VoltDB
9.266  WANdisco
9.267  Waterline  Data
9.268  Western  Digital  Corporation
9.269  WhereScape
9.270  WiPro
9.271  Wolfram  Research
9.272  Workday
9.273  Xplenty
9.274  Yellowfin  BI
9.275  Yseop
9.276  Zendesk
9.277  Zoomdata
9.278  Zucchetti
  
10  Chapter  10:  Conclusion  &  Strategic  Recommendations
10.1  Why  is  the  Market  Poised  to  Grow?
10.2  Geographic  Outlook:  Which  Countries  Offer  the  Highest  Growth  Potential?
10.3  Partnerships  &  M&A  Activity:  Highlighting  the  Importance  of  Big  Data
10.4  Driving  the  Development  of  Digital  Therapeutics
10.5  Improving  Outcomes,  Achieving  Operational  Efficiency  and  Reducing  Costs
10.6  Assessing  the  Impact  of  Connected  Health  Solutions
10.7  Accelerating  the  Transition  Towards  Value-Based  Care
10.8  The  Emergence  of  Advanced  AI  (Artificial  Intelligence)  &  Machine  Learning  Techniques
10.9  The  Value  of  Big  Data  in  Precision  Medicine
10.10  Addressing  Privacy  &  Security  Concerns
10.11  The  Role  of  Data  Protection  Legislation
10.12  Blockchain:  Enabling  Secure,  Efficient  and  Interoperable  Data  Sharing
10.13  Recommendations
10.13.1  Big  Data  Hardware,  Software  &  Professional  Services  Providers
10.13.2  Healthcare  &  Pharmaceutical  Industry  Stakeholders
8.20.12  Rest  of  Western  Europe
  
9  Chapter  9:  Vendor  Landscape
9.1  1010data
9.2  Absolutdata
9.3  Accenture
9.4  Actian  Corporation/HCL  Technologies
9.5  Adaptive  Insights
9.6  Adobe  Systems
9.7  Advizor  Solutions
9.8  AeroSpike
9.9  AFS  Technologies
9.10  Alation
9.11  Algorithmia
9.12  Alluxio
9.13  ALTEN
9.14  Alteryx
9.15  AMD  (Advanced  Micro  Devices)
9.16  Anaconda
9.17  Apixio
9.18  Arcadia  Data
9.19  ARM
9.20  AtScale
9.21  Attivio
9.22  Attunity
9.23  Automated  Insights
9.24  AVORA
9.25  AWS  (Amazon  Web  Services)
9.26  Axiomatics
9.27  Ayasdi
9.28  BackOffice  Associates
9.29  Basho  Technologies
9.30  BCG  (Boston  Consulting  Group)
9.31  Bedrock  Data
9.32  BetterWorks
9.33  Big  Panda
9.34  BigML
9.35  Bitam
9.36  Blue  Medora
9.37  BlueData  Software
9.38  BlueTalon
9.39  BMC  Software
9.40  BOARD  International
9.41  Booz  Allen  Hamilton
9.42  Boxever
9.43  CACI  International
9.44  Cambridge  Semantics
9.45  Capgemini
9.46  Cazena
9.47  Centrifuge  Systems
9.48  CenturyLink
9.49  Chartio
9.50  Cisco  Systems
9.51  Civis  Analytics
9.52  ClearStory  Data
9.53  Cloudability
9.54  Cloudera
9.55  Cloudian
9.56  Clustrix
9.57  CognitiveScale
9.58  Collibra
9.59  Concurrent  Technology/Vecima  Networks
9.60  Confluent
9.61  Contexti
9.62  Couchbase
9.63  Crate.io
9.64  Cray
9.65  Databricks
9.66  Dataiku
9.67  Datalytyx
9.68  Datameer
9.69  DataRobot
9.70  DataStax
9.71  Datawatch  Corporation
9.72  DDN  (DataDirect  Networks)
9.73  Decisyon
9.74  Dell  Technologies
9.75  Deloitte
9.76  Demandbase
9.77  Denodo  Technologies
9.78  Dianomic  Systems
9.79  Digital  Reasoning  Systems
9.80  Dimensional  Insight
9.81  Dolphin  Enterprise  Solutions  Corporation/Hanse  Orga  Group
9.82  Domino  Data  Lab
9.83  Domo
9.84  Dremio
9.85  DriveScale
9.86  Druva
9.87  Dundas  Data  Visualization
9.88  DXC  Technology
9.89  Elastic
9.90  Engineering  Group  (Engineering  Ingegneria  Informatica)
9.91  EnterpriseDB  Corporation
9.92  eQ  Technologic
9.93  Ericsson
9.94  Erwin
9.95  EVŌ  (Big  Cloud  Analytics)
9.96  EXASOL
9.97  EXL  (ExlService  Holdings)
9.98  Facebook
9.99  FICO  (Fair  Isaac  Corporation)
9.100  Figure  Eight
9.101  FogHorn  Systems
9.102  Fractal  Analytics
9.103  Franz
9.104  Fujitsu
9.105  Fuzzy  Logix
9.106  Gainsight
9.107  GE  (General  Electric)
9.108  Glassbeam
9.109  GoodData  Corporation
9.110  Google/Alphabet
9.111  Grakn  Labs
9.112  Greenwave  Systems
9.113  GridGain  Systems
9.114  H2O.ai
9.115  HarperDB
9.116  Hedvig
9.117  Hitachi  Vantara
9.118  Hortonworks
9.119  HPE  (Hewlett  Packard  Enterprise)
9.120  Huawei
9.121  HVR
9.122  HyperScience
9.123  HyTrust
9.124  IBM  Corporation
9.125  iDashboards
9.126  IDERA
9.127  Ignite  Technologies
9.128  Imanis  Data
9.129  Impetus  Technologies
9.130  Incorta
9.131  InetSoft  Technology  Corporation
9.132  InfluxData
9.133  Infogix
9.134  Infor/Birst
9.135  Informatica
9.136  Information  Builders
9.137  Infosys
9.138  Infoworks
9.139  Insightsoftware.com
9.140  InsightSquared
9.141  Intel  Corporation
9.142  Interana
9.143  InterSystems  Corporation
9.144  Jedox
9.145  Jethro
9.146  Jinfonet  Software
9.147  Juniper  Networks
9.148  KALEAO
9.149  Keen  IO
9.150  Keyrus
9.151  Kinetica
9.152  KNIME
9.153  Kognitio
9.154  Kyvos  Insights
9.155  LeanXcale
9.156  Lexalytics
9.157  Lexmark  International
9.158  Lightbend
9.159  Logi  Analytics
9.160  Logical  Clocks
9.161  Longview  Solutions/Tidemark
9.162  Looker  Data  Sciences
9.163  LucidWorks
9.164  Luminoso  Technologies
9.165  Maana
9.166  Manthan  Software  Services
9.167  MapD  Technologies
9.168  MapR  Technologies
9.169  MariaDB  Corporation
9.170  MarkLogic  Corporation
9.171  Mathworks
9.172  Melissa
9.173  MemSQL
9.174  Metric  Insights
9.175  Microsoft  Corporation
9.176  MicroStrategy
9.177  Minitab
9.178  MongoDB
9.179  Mu  Sigma
9.180  NEC  Corporation
9.181  Neo4j
9.182  NetApp
9.183  Nimbix
9.184  Nokia
9.185  NTT  Data  Corporation
9.186  Numerify
9.187  NuoDB
9.188  NVIDIA  Corporation
9.189  Objectivity
9.190  Oblong  Industries
9.191  OpenText  Corporation
9.192  Opera  Solutions
9.193  Optimal  Plus
9.194  Oracle  Corporation
9.195  Palantir  Technologies
9.196  Panasonic  Corporation/Arimo
9.197  Panorama  Software
9.198  Paxata
9.199  Pepperdata
9.200  Phocas  Software
9.201  Pivotal  Software
9.202  Prognoz
9.203  Progress  Software  Corporation
9.204  Provalis  Research
9.205  Pure  Storage
9.206  PwC  (PricewaterhouseCoopers  International)
9.207  Pyramid  Analytics
9.208  Qlik
9.209  Qrama/Tengu
9.210  Quantum  Corporation
9.211  Qubole
9.212  Rackspace
9.213  Radius  Intelligence
9.214  RapidMiner
9.215  Recorded  Future
9.216  Red  Hat
9.217  Redis  Labs
9.218  RedPoint  Global
9.219  Reltio
9.220  RStudio
9.221  Rubrik/Datos  IO
9.222  Ryft
9.223  Sailthru
9.224  Salesforce.com
9.225  Salient  Management  Company
9.226  Samsung  Group
9.227  SAP
9.228  SAS  Institute
9.229  ScaleOut  Software
9.230  Seagate  Technology
9.231  Sinequa
9.232  SiSense
9.233  Sizmek
9.234  SnapLogic
9.235  Snowflake  Computing
9.236  Software  AG
9.237  Splice  Machine
9.238  Splunk
9.239  Strategy  Companion  Corporation
9.240  Stratio
9.241  Streamlio
9.242  StreamSets
9.243  Striim
9.244  Sumo  Logic
9.245  Supermicro  (Super  Micro  Computer)
9.246  Syncsort
9.247  SynerScope
9.248  SYNTASA
9.249  Tableau  Software
9.250  Talend
9.251  Tamr
9.252  TARGIT
9.253  TCS  (Tata  Consultancy  Services)
9.254  Teradata  Corporation
9.255  Thales/Guavus
9.256  ThoughtSpot
9.257  TIBCO  Software
9.258  Toshiba  Corporation
9.259  Transwarp
9.260  Trifacta
9.261  Unifi  Software
9.262  Unravel  Data
9.263  VANTIQ
9.264  VMware
9.265  VoltDB
9.266  WANdisco
9.267  Waterline  Data
9.268  Western  Digital  Corporation
9.269  WhereScape
9.270  WiPro
9.271  Wolfram  Research
9.272  Workday
9.273  Xplenty
9.274  Yellowfin  BI
9.275  Yseop
9.276  Zendesk
9.277  Zoomdata
9.278  Zucchetti
  
10  Chapter  10:  Conclusion  &  Strategic  Recommendations
10.1  Why  is  the  Market  Poised  to  Grow?
10.2  Geographic  Outlook:  Which  Countries  Offer  the  Highest  Growth  Potential?
10.3  Partnerships  &  M&A  Activity:  Highlighting  the  Importance  of  Big  Data
10.4  Driving  the  Development  of  Digital  Therapeutics
10.5  Improving  Outcomes,  Achieving  Operational  Efficiency  and  Reducing  Costs
10.6  Assessing  the  Impact  of  Connected  Health  Solutions
10.7  Accelerating  the  Transition  Towards  Value-Based  Care
10.8  The  Emergence  of  Advanced  AI  (Artificial  Intelligence)  &  Machine  Learning  Techniques
10.9  The  Value  of  Big  Data  in  Precision  Medicine
10.10  Addressing  Privacy  &  Security  Concerns
10.11  The  Role  of  Data  Protection  Legislation
10.12  Blockchain:  Enabling  Secure,  Efficient  and  Interoperable  Data  Sharing
10.13  Recommendations
10.13.1  Big  Data  Hardware,  Software  &  Professional  Services  Providers
10.13.2  Healthcare  &  Pharmaceutical  Industry  Stakeholders
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  Digital  Therapeutics
8.11.3  Medication  Adherence  &  Management
8.11.4  Vaccine  Development  &  Promotion
8.11.5  Population  Health  Management
8.11.6  Connected  Health  Communities  &  Medical  Knowledge  Dissemination
8.11.7  Epidemiology  &  Disease  Surveillance
8.11.8  Health  Policy  Decision  Making
8.11.9  Controlling  Substance  Abuse  &  Addiction
8.11.10  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

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