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.
This research 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 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.
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%.
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
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
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 184.108.40.206 Drug Discovery, Design & Development 220.127.116.11 Medical Product Design & Development 18.104.22.168 Clinical Development & Trials 22.214.171.124 Precision Medicine & Genomics 126.96.36.199 Manufacturing & Supply Chain Management 188.8.131.52 Post-Market Surveillance & Pharmacovigilance 184.108.40.206 Medical Product Fault Monitoring 4.4.2 Core Healthcare Operations 220.127.116.11 Clinical Decision Support 18.104.22.168 Care Coordination & Delivery Management 22.214.171.124 CER (Comparative Effectiveness Research) & Observational Evidence 126.96.36.199 Personalized Healthcare & Targeted Treatments 188.8.131.52 Data-Driven Preventive Care & Health Interventions 184.108.40.206 Surgical Practice & Complex Medical Procedures 220.127.116.11 Pathology, Medical Imaging & Other Medical Tests 18.104.22.168 Proactive & Remote Patient Monitoring 22.214.171.124 Predictive Maintenance of Medical Equipment 126.96.36.199 Pharmacy Services 4.4.3 Healthcare Support, Awareness & Disease Prevention 188.8.131.52 Self-Care & Lifestyle Support 184.108.40.206 Digital Therapeutics 220.127.116.11 Medication Adherence & Management 18.104.22.168 Vaccine Development & Promotion 22.214.171.124 Population Health Management 126.96.36.199 Connected Health Communities & Medical Knowledge Dissemination 188.8.131.52 Epidemiology & Disease Surveillance 184.108.40.206 Health Policy Decision Making 220.127.116.11 Controlling Substance Abuse & Addiction 18.104.22.168 Increasing Awareness & Accessible Healthcare 4.4.4 Health Insurance & Payer Services 22.214.171.124 Health Insurance Claims Processing & Management 126.96.36.199 Fraud & Abuse Prevention 188.8.131.52 Proactive Patient Engagement 184.108.40.206 Accountable & Value-Based Care 220.127.116.11 Data-Driven Health Insurance Premiums 4.4.5 Marketing, Sales & Other Applications 18.104.22.168 Marketing & Sales 22.214.171.124 Administrative & Customer Services 126.96.36.199 Finance & Risk Management 188.8.131.52 Healthcare Data Monetization 184.108.40.206 Other Applications
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 5.3.14Royal 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
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 220.127.116.11 Storage & Compute Infrastructure Providers 18.104.22.168 Networking Infrastructure Providers 6.2.2 Software Providers 22.214.171.124 Hadoop & Infrastructure Software Providers 126.96.36.199 SQL & NoSQL Providers 188.8.131.52 Analytic Platform & Application Software Providers 184.108.40.206 Cloud Platform Providers 6.2.3 Professional Services Providers 6.2.4 End-to-End Solution Providers 6.2.5 Healthcare & Pharmaceutical Industry
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 Mdical 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
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 8.9.1 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 8.15.10Singapore 8.15.11South Korea 8.15.12Taiwan 8.15.13Thailand 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 8.20.10Sweden 8.20.11 UK 8.20.12 Rest of Western Europe
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