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Global Big Data in Healthcare Market, Trends and Forecasts, Till 2035: Distribution by Component, Type of Hardware, Type of Software, Type of Service, Deployment Option, Application Area, Healthcare Vertical, End User, Economic Status, Geography, and Leading Players

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    Report

  • 331 Pages
  • February 2024
  • Region: Global
  • Roots Analysis
  • ID: 5941153

Strategic Healthcare Partnerships Propel Technological Advancements in Big Data Analytics Tools, Driving Market Growth

The global big data in healthcare market size is estimated to grow from USD 67 billion in 2023 to USD 540 billion by 2035, representing a CAGR of 19.06% during the forecast period 2023-2035. The research study consists of current big data in healthcare market trends, detailed competitor’s analysis, key market insights, market impact analysis, and market forecast and opportunity analysis. The growth in the big data analytics in healthcare market size over the next decade is likely to be the result of anticipated increase in adoption of AI-enabled solutions and rise in the demand for personalized medicine.

Big data in healthcare refers to the large amount of unstructured data obtained from various sources, such as medical research / journals, biometric data, electronic medical records, Internet of Medical Things (IoMT), social media, payer records, omics research and data banks. Integrating this diverse and complex unstructured data into traditional databases poses a significant challenge in terms of data structuring and standardization, which is essential to ensure compatibility and enable effective analysis. However, recent advancements in big data analytics tools, artificial intelligence, and machine learning have revolutionized the conversion of big data in healthcare into valuable and actionable information. These technological breakthroughs have revolutionized various aspects of healthcare, enabling data-driven decision-making, improving diagnostics, facilitating personalized treatment approaches and empowering patients with self-service options (including online portals, mobile applications, and wearable devices). Further, big data analytics tools play a crucial role in pharmaceutical R&D by expediting drug discovery and development processes. Driven by the growing demand for business intelligence solutions, surge in unstructured data, and the increasing focus on the development of personalized medicine, the global market for big data in healthcare is poised to experience sustained market growth during the forecast period.

Market Share Insights

The big data in healthcare market research report presents an in-depth analysis of the various companies that are engaged in the global big data in healthcare industry, across different segments, as defined below:

  • Historical Trend: 2018-2022
  • Base Year: 2022
  • Forecast Period: 2023-2035
  • Market Size 2023: $67 Billion
  • CAGR: 19.06%
  • PowerPoint Presentation (Complimentary)
  • Customization Scope: 15% Free Customization
  • Component
    • Hardware (Storage Devices, Servers, and Networking Infrastructure)
    • Software (Electronic Health Record, Practice Management Software, Revenue Cycle Management Software, and Workforce Management Software)
    • Services (Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics)
  • Deployment Option
    • Cloud-based
    • On-premises
  • Application Area
    • Clinical Data Management
    • Financial Management
    • Operational Management
    • Population Health Management
  • Healthcare Vertical
    • Healthcare Services
    • Medical Devices
    • Pharmaceuticals
    • Other Verticals
  • Economic Status
    • High Income Countries
    • Upper-Middle Income Countries
    • Lower-Middle Income Countries
  • End User
    • Clinics
    • Health Insurance Agencies
    • Hospitals
    • Other End Users
  • Geography
    • North America
    • Europe
    • Asia
    • Latin America
    • Middle East and North Africa
    • Rest of the World
  • Key Companies Profiled
    • Accenture
    • Akka Technologies
    • Altamira.ai
    • Amazon Web Services
    • Athena Global Technologies
    • atom Consultancy Services (ACS)
    • Avenga
    • Happiest Minds
    • InData Labs
    • Itransition
    • Kellton
    • Keyrus
    • Lutech
    • Microsoft
    • Nagarro
    • Nous Infosystems
    • NTT data
    • Oracle
    • Orange Mantra
    • Oxagile
    • Scalefocus
    • Softweb Solutions
    • Solix Technologies
    • Spindox
    • Tata Elxsi
    • Teradata
    • Trianz (formerly CBIG Consulting)
    • Trigyn Technologies
    • XenonStack
    • (Full list of 405+ companies and organizations is available in the report)
  • Excel Data Packs (Complimentary)
    • Overall Market Landscape
    • Key Insights
    • Company Competitiveness Analysis
    • Market Forecast and Opportunity Analysis

Key Segments

Market Share by Component

The global big data in healthcare market is categorized into different types of components, such as hardware, services, and software. Owing to the rising adoption of advanced technologies, increased emphasis on data security, and ongoing investments in innovation, the hardware segment currently holds the highest market share in 2023 and is expected to witness healthy growth during the forecast period.

Market Share by Deployment Option

The global big data in healthcare market highlights cloud-based and on-premises deployment options. The cloud-based deployment option occupies the highest share in 2023 and is expected to remain dominant during the forecast period. This can be attributed to the multitude of benefits offered by cloud-based deployment, including scalability and flexibility, cost-effectiveness, ease of implementation and maintenance, and data accessibility and collaboration, among other advantages.

Market Share by Area of Application

The global big data in healthcare market is segmented into clinical data management, financial management, operational management, and population health management. Among these segments, population health management is likely to grow at a higher CAGR as compared to other application areas in the coming years. This can be attributed to the increasing demand for disease management strategies (which can be facilitated through the implementation of big data) as well as the shifting focus of healthcare industry towards value-based care.

Market Share by Geography

This segment highlights the distribution of big data in healthcare market across various geographies, such as North America, Europe, Asia, Latin America, Middle East and North Africa, and rest of the world regions. According to projections, the big data analytics in healthcare market in North America is likely to capture majority (57%) of the share, and this trend is unlikely to change in the future. It is worth highlighting that the market in Asia is expected to grow at a relatively healthy CAGR (21.29%), during the forecast period, 2023-2035.

Key Market Insights

The report features an extensive study of the current market landscape, market size and future opportunities within the big data in healthcare market, during the given forecast period. The market report highlights the efforts of several stakeholders engaged in this rapidly emerging market segment of the healthcare industry. Key takeaways of the big data analytics in healthcare market report are briefly discussed below.

Advantages of Big Data Analytics in Healthcare Market

The emerging applications of big data analytics in healthcare market are transforming the way healthcare is delivered, offering numerous benefits and opportunities. By harnessing the power of big data, healthcare professionals can gain valuable insights and improve various aspects of patient care. Big data analytics tools facilitate the development of personalized medicine by analyzing patient data to identify patterns and make precise diagnoses. It also allows disease prevention and early intervention through predictive analytics, helping to mitigate risks and improve population health. Additionally, big data analytics solutions play a crucial role in optimizing healthcare operations, resource allocation, and improving patient outcomes.

Competitive Landscape of Big Data Analytics Services

The current market landscape features the presence of over 405 companies offering a variety of big data analytics services, ranging from consulting, implementation, data management and storage to technical support and component maintenance services. Overall, the big data analytics in healthcare market seems to be well-fragmented, featuring the presence of very small, small, mid-sized, large, and very large companies having the required expertise to offer big data services across different healthcare verticals, including pharmaceutical, medical devices, healthcare services and other verticals. It is worth mentioning that around 65% of the players offering big data analytics services are based in North America.

Market Trends: Strategic Partnerships Driving Technological Advancements in Big Data Analytics Tools in Healthcare

Stakeholders in the big data analytics industry have forged several partnerships with other healthcare organizations to drive technological advancements in the healthcare industry. In October 2023, Marengo Asia Hospital entered into a strategic partnership with Oracle to adopt Oracle’s enterprise resource planning solution in order to optimize former’s internal processes and obtain a unified view of business operations. Earlier in June 2023, Google Cloud and Mayo Clinic forged an alliance to introduce generative artificial intelligence into the healthcare industry. The primary objectives of this partnership were to enhance clinical workflows, simplify information retrieval for clinicians and researchers, and ultimately improve patient outcomes. Such big data in healthcare market trends are expected to drive market growth during the forecast period.

Key Drivers in the Big Data in Healthcare Market

Several factors, such as the growing need to store, process, and analyze large volumes of healthcare data, have led to the adoption of big data analytics solutions in the healthcare industry. With the advent of digital solutions, including electronic medical records and wearable devices, healthcare organizations are generating large amounts of data on a daily basis. In fact, on an average, a hospital can generate around 50 petabytes of patient data and operational data per day. Furthermore, the amount of data generated by the healthcare industry is anticipated to grow at an exponential rate, with a CAGR of more than 35% until 2025. The ability to effectively analyze and derive insights from this vast amount of unstructured data is crucial for improving operational efficiency, and decision-making in the healthcare industry.

The focus on population health management is another driver for the big data in healthcare market. As healthcare shifts from a fee-for-service model to a value-based care model, there is a greater emphasis on preventive care and public health. This shift highlights the importance of leveraging big data in healthcare to analyze demographic data, generate insights, and drive positive outcomes for patients. Additionally, big data analytics plays a crucial role in optimizing care management and addressing the complex issue of social determinants of health. All these factors, along with the increasing adoption of artificial intelligence enabled healthcare solutions are anticipated to fuel the big data in healthcare market growth during the forecast period.

Market Size of the Big Data in Healthcare Market

Driven by the rapid advancements in technology and the emerging trend of digital transformation across the healthcare industry, big data in healthcare market is anticipated to grow at an CAGR of 19.06% during the forecast period 2023-2035. The market growth is primarily fueled by the keenness shown by the healthcare providers to adopt data-driven solutions, coupled with the substantial support and initiatives from governments worldwide.

North America Holds the Largest Share of the Big Data in Healthcare Market

Presently, close to 60% of the market opportunity is created by the demand for big data analytics solutions in North America. This can be attributed to strong government support for big data analytics, particularly in the US, which has accelerated the adoption rates across various sectors, including healthcare. For instance, in August 2023, the House of Representatives passed the Military Construction, Veterans Affairs, and Related Agencies Appropriations Bill, which allocated an Information Technology (IT) budget of around USD 6.4 billion for the Department of Veterans Affairs (VA) for fiscal year 2024. Notably, USD 1.3 billion of this budget was dedicated towards modernizing country’s EHR systems. Additionally, the All of Us Research program, a biobank initiative focused on precision medicine research is further driving the demand for big data analytics solutions in North America. These big data in healthcare market trends reinforce the expectation that North America will maintain its position as the leading market for big data analytics services during the forecast period, growing at a CAGR of 18.52%.

Leading Companies in the Big Data in Healthcare Market

Examples of key companies engaged in the big data in healthcare industry (which have also been captured in this market report, arranged in alphabetical order) include Accenture, Akka Technologies, Altamira.ai, Amazon Web Services. Athena Global Technologies, atom Consultancy Services (ACS), Avenga, Happiest Minds, InData Labs, Itransition, Kellton, Keyrus, Lutech, Microsoft, Nagarro, Nous Infosystems, NTT data, Oracle, Orange Mantra, Oxagile, Scalefocus, Softweb Solutions, Solix Technologies, Spindox, Tata Elxsi, Teradata, Trianz (formerly CBIG Consulting), Trigyn Technologies, and XenonStack. This market report includes an easily searchable excel database of all the companies offering big data analytics services for a variety of applications in healthcare industry worldwide.

Recent Developments in Big Data in Healthcare Market

Several recent developments have taken place in the big data in healthcare industry, some of which have been outlined below. These developments substantiate the overall big data in healthcare market trends that the analyst has outlined in the analyses:

  • In October 2023, the New York City Department of Health, and Mental Hygiene (DOHMH) launched the Center for Population Health Data Science, with the aim of enhancing city’s public health management infrastructure.
  • In October 2023, the Advanced Research Projects Agency for Health (ARPA-H) announced a USD 50 million investment in six research projects. The primary focus of these research projects was to address existing vulnerabilities in cybersecurity for healthcare systems.
  • In May 2023, Fifth Third Bancorp acquired Big Data Healthcare (BDHC) in order to enhance former’s healthcare revenue cycle capabilities.
  • In April 2023, Duke Health and SAS Institute partnered to improve health equity and optimize health outcomes through the use of artificial intelligence and data analytics.
  • In March 2023, Google Cloud launched Open Health Stack, an open-source platform for healthcare app developers, and announced several artificial intelligence partnerships to improve clinical workflows, patient outcomes, and healthcare innovation.
  • In February 2023, Mayo Clinic Platform and TripleBlind expanded their partnership to effectively de-identify sensitive data and safeguard patient privacy, while simultaneously fostering digital transformations in the healthcare industry.

Report Coverage

The big data in healthcare market report presents an in-depth analysis of big data in healthcare market trends, highlighting the capabilities of various service providers engaged in this market, across different segments. Amongst other elements, the market report includes:

  • A preface providing an introduction to the full report, Big Data in Healthcare Market, 2018-2022 (Historical Trends) and 2023-2035 (Forecasted Estimates).
  • An outline of the systematic research methodology adopted to conduct the study on big data analytics in healthcare market, providing insights on the various assumptions, methodologies, and quality control measures employed to ensure accuracy and reliability of these findings.
  • An overview of economic factors that impact the overall big data in healthcare market, including historical trends, currency fluctuation, foreign exchange impact, recession, and inflation measurement.
  • An executive summary of the insights captured during research. It offers a high-level view on the current big data in healthcare market trends and the likely evolution of big data in healthcare market in the mid-long term.
  • A general overview of big data and its diverse types (structured data, unstructured data, and semi-structured data). Additionally, the chapter highlights different types of big data analytics services and their applications in healthcare industry. Further, it discusses the future perspective of big data analytics in healthcare market, demonstrating how big data analytics services can revolutionize the healthcare industry and offer lucrative business opportunities to service providers.
  • A detailed assessment of big data in healthcare service providers, based on several relevant parameters, including year of establishment, company size (in terms of employee count), location of headquarters (North America, Europe, Asia, Middle East and North Africa, Latin America and rest of the world), business model (big data as a service and big data services), type of offering (big data consulting, big data implementation, big data analytics, big data management, big data support and maintenance), type of big data analytics offered (diagnostic / descriptive analytics, predictive analytics and prescriptive analytics), type of big data storage solution offered (data lake and data warehouse), deployment option (cloud-based and on-premises), application area (clinical data management, financial management, operational management, and population health management) and end user (healthcare provider, contract research organizations / pharmaceutical companies and others).
  • An insightful analysis, highlighting the contemporary big data in healthcare market trends through different representations, based on relevant parameters, such as company size and location of headquarters; company size and business model; type of offerings and location of headquarters; type of big data storage solution offered and deployment option; type of big data analytics offered and application area; company size, application area and end user.
  • A detailed competitiveness analysis of big data in healthcare service providers based on supplier strength (in terms of years of experience) and portfolio strength (in terms of number of offerings, type of big data analytics services offered, type of big data storage solution offered, deployment option, and end user).
  • Elaborate profiles of leading players and tabulated profiles of other prominent players (shortlisted based on proprietary company competitiveness) offering big data analytics solutions across various geographies. Each elaborate profile features an overview of the company (including information on year of establishment, number of employees, location of headquarters and leadership team), details related to its financial information (if available), big data analytics offerings and capabilities, and recent developments and an informed future outlook.
  • An in-depth analysis of the factors that can impact the market growth of big data analytics in healthcare. It also highlights the key drivers, potential restraints, emerging opportunities, and existing challenges within this industry.
  • A detailed estimate of the current market size, current opportunity, and the future growth potential of the big data in healthcare market over the next decade. Based on multiple parameters, such as likely adoption trends and through primary validations, the analyst has provided an informed estimate on the market evolution during the forecast period 2023-2035. The report also features the likely distribution of the current and forecasted opportunity within the cell line development market. Further, in order to account for future uncertainties and to add robustness to the model, the analyst has provided three forecast scenarios, namely conservative, base, and optimistic scenarios, representing different tracks of the industry’s growth.
  • Detailed projections of the current and future big data in healthcare market across various components, such as hardware, services, and software.
  • Detailed projections of the current and future big data in healthcare market across various types of hardware, such as storage devices, networking infrastructure and servers.
  • Detailed projections of the current and future big data in healthcare market across various types of software, such as electronic health record, practice management software, revenue cycle management software, and workforce management software.
  • Detailed projections of the current and future big data in healthcare market across various types of services, such as descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
  • Detailed projections of the current and future big data in healthcare market across various deployment options, namely cloud-based and on-premises.
  • Detailed projections of the current and future big data in healthcare market across various application areas, such as clinical data management, financial management, operational management, and population health management.
  • Detailed projections of the current and future big data in healthcare market across various healthcare verticals, such as healthcare services, medical devices, pharmaceuticals, and other verticals.
  • Detailed projections of the current and future big data in healthcare market across various end users, such as clinics, health insurance agencies, hospitals, and other end users.
  • Detailed projections of the current and future big data in healthcare market across various types of economy, such as high-income countries, upper-middle income countries, and lower-middle income countries.
  • Detailed projections of the current and future big data in healthcare market across various geographies, such as North America, Europe, Asia, Latin America Middle East and North Africa, and rest of the world.
  • Detailed projections of the historical and current revenues of the leading players engaged in the big data in healthcare market.

One of the key objectives of this market report was to estimate the current market size, opportunity and the future growth potential of the big data in healthcare market, over the forecast period. Based on multiple parameters, likely adoption trends and through primary validations, the analyst has provided an informed estimate on the market evolution during the forecast period 2023-2035. The market report also features the likely distribution of the current and forecasted opportunity within the big data in healthcare market across various segments, such as component (hardware, software and services), type of hardware (storage devices, networking infrastructure and server), type of service (descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics), type of software (electronic health record, practice management software, revenue cycle management software, and workforce management software), deployment option (cloud-based and on-premises), application area (clinical data management, financial management, operational management, and population health management), healthcare vertical (healthcare services, medical devices, pharmaceuticals, and other verticals), end user (clinics, health insurance agencies, hospitals, and other end users), economic status (high income countries, upper-middle income countries, and lower-middle income countries), geography (North America, Europe, Asia, Middle East and North Africa, Latin America and rest of the world), and leading players.

In order to account for future uncertainties associated with some of the key parameters and to add robustness to the model, the analyst has provided three market forecast scenarios, namely conservative, base, and optimistic scenarios, representing different tracks of the industry’s evolution.

The opinions and insights presented in the market report were influenced by discussions held with stakeholders in the industry. The report features detailed transcripts of interviews held with the following industry stakeholders:

  • Chief Executive Officer and Founder, Mid-sized Company, India
  • Chief Executive Officer and Co-Founder, Mid-sized Company, India
  • Chief People Officer and Co-Founder, Small company, US
  • Vice President, Large Company, US
  • Business Head, Mid-sized Company, India
  • Senior IT Inside Sales Lead, Small Company, India
  • Senior Manager, Mid-sized Company, US
  • Delivery Manager, Mid-sized Company, US
  • Strategy, Research and Analyst Relations Manager, Large Company, India
  • Business Development Manager, Mid-sized Company, US
  • Business Development Associate, Mid-sized Company, US
  • Business Development Specialist Advisor, Large Company, US
  • Business Development Executive, Small Company, Armenia
  • Business Consultant, Mid-sized Company, India

Further, all actual figures have been sourced and analyzed from publicly available information forums and primary research discussions. Financial figures mentioned in this market report are in USD, unless otherwise specified.

Table of Contents

1. PREFACE
1.1. Introduction
1.2. Market Share Insights
1.3. Key Market Insights
1.4. Report Coverage
1.5. Key Questions Answered
1.6. Chapter Outlines
2. RESEARCH METHODOLOGY
2.1. Chapter Overview
2.2. Research Assumptions
2.3. Project Methodology
2.4. Forecast Methodology
2.5. Robust Quality Control
2.6. Key Considerations
2.6.1. Demographics
2.6.2. Economic Factors
2.6.3. Government Regulations
2.6.4. Supply Chain
2.6.5. COVID Impact / Related Factors
2.6.6. Market Access
2.6.7. Healthcare Policies
2.6.8. Industry Consolidation
2.7. Key Market Segmentations
3. ECONOMIC AND OTHER PROJECT SPECIFIC CONSIDERATIONS
3.1. Chapter Overview
3.2. Market Dynamics
3.2.1. Time Period
3.2.1.1. Historical Trends
3.2.1.2. Current and Forecasted Estimates
3.2.2. Currency Coverage
3.2.2.1. Major Currencies Affecting the Market
3.2.2.2. Impact of Currency Fluctuations on the Industry
3.2.3. Foreign Exchange Impact
3.2.3.1. Evaluation of Foreign Exchange Rates and Their Impact on Market
3.2.3.2. Strategies for Mitigating Foreign Exchange Risk
3.2.4. Recession
3.2.4.1. Historical Analysis of Past Recessions and Lessons Learnt
3.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
3.2.5. Inflation
3.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
3.2.5.2. Potential Impact of Inflation on the Market Evolution
4. EXECUTIVE SUMMARY
4.1. Chapter Overview
5. INTRODUCTION
5.1. Chapter Overview
5.2. Overview of Big Data
5.2.1. Types of Big Data
5.2.1.1. Structured Data
5.2.1.2. Unstructured Data
5.2.1.3. Semi-Structured Data
5.2.2. Management and Storage of Big Data
5.3. Big Data Analytics
5.3.1. Types of Big Data Analytics
5.3.1.1. Descriptive Analytics
5.3.1.2. Diagnostic Analytics
5.3.1.3. Predictive Analytics
5.3.1.4. Prescriptive Analytics
5.4. Applications of Big Data in Healthcare
5.5. Future Perspective
6. OVERALL MARKET LANDSCAPE
6.1. Chapter Overview
6.2. Big Data in Healthcare Service Providers: Overall Market Landscape
6.3. Analysis by Year of Establishment
6.4. Analysis by Company Size
6.5. Analysis by Location of Headquarters
6.6. Analysis by Type of Business Model
6.7. Analysis by Type of Offering
6.8. Analysis by Type of Big Data Analytics Offered
6.9. Analysis by Type of Big Data Storage Solution Offered
6.10. Analysis by Deployment Option
6.11. Analysis by Application Area
6.12. Analysis by End User
7. KEY INSIGHTS
7.1. Chapter Overview
7.2. Big Data in Healthcare Service Providers: Key Insights
7.2.1 Analysis by Year of Establishment and Company Size
7.2.2. Analysis by Company Size and Location of Headquarters
7.2.3. Analysis by Type of Offering and Company Size
7.2.4. Analysis by Type of Big Data Analytics Offered and Application Area
7.2.5. Analysis by Company Size, Application Area and End User
8. COMPANY COMPETITIVENSS ANALYSIS
8.1. Chapter Overview
8.2. Assumptions and Key Parameters
8.3. Methodology
8.4. Big Data in Healthcare Service Providers: Company Competitiveness Analysis
8.4.1. Big Data in Healthcare Service Providers based in North America
8.4.1.1. Small Service Providers based in North America
8.4.1.2. Mid-sized Service Providers based in North America
8.4.1.3. Large Service Providers based in North America
8.4.1.4. Very Large Service Providers based in North America
8.4.2. Big Data in Healthcare Service Providers based in Europe
8.4.2.1. Small Service Providers based in Europe
8.4.2.2. Mid-sized Service Providers based in Europe
8.4.2.3. Large and Very Large Service Providers based in Europe
8.4.3. Big Data in Healthcare Service Providers based in Asia and Rest of the World
8.4.3.1. Small Service Providers based in Asia and Rest of the World
8.4.3.2. Mid-sized Service Providers based in Asia and Rest of the World
8.4.3.3. Large Service Providers based in Asia and Rest of the World
8.4.3.4. Very Large Service Providers based in Asia and Rest of the World
9. COMPANY PROFILES: BIG DATA IN HEALTHCARE SERVICE PROVIDERS IN NORTH AMERICA
9.1. Chapter Overview
9.2. Detailed Company Profiles of Leading Players in North America
9.2.1. Amazon Web Services
9.2.1.1. Company Overview
9.2.1.2. Financial Information
9.2.1.3. Big Data Offerings and Capabilities
9.2.1.4. Recent Developments and Future Outlook
9.2.2. Microsoft
9.2.2.1. Company Overview
9.2.2.2. Financial Information
9.2.2.3. Big Data Offerings and Capabilities
9.2.2.4. Recent Developments and Future Outlook
9.2.3. Oracle
9.2.3.1. Company Overview
9.2.3.2. Financial Information
9.2.3.3. Big Data Offerings and Capabilities
9.2.3.4. Recent Developments and Future Outlook
9.2.4. Teradata
9.2.4.1. Company Overview
9.2.4.2. Financial Information
9.2.4.3. Big Data Offerings and Capabilities
9.2.4.4. Recent Developments and Future Outlook
9.3. Short Company Profiles of Other Prominent Players in North America
9.3.1 Itransition
9.3.1.1. Company Overview
9.3.1.2. Big Data Offerings and Capabilities
9.3.2 Nous Infosystems
9.3.2.1. Company Overview
9.3.2.2. Big Data Offerings and Capabilities
9.3.3 Oxagile
9.3.3.1. Company Overview
9.3.3.2. Big Data Offerings and Capabilities
9.3.4 Softweb Solutions
9.3.4.1. Company Overview
9.3.4.2. Big Data Offerings and Capabilities
9.3.5 Solix Technologies
9.3.5.1. Company Overview
9.3.5.2. Big Data Offerings and Capabilities
9.3.6 Trianz (formerly CBIG Consulting)
9.3.6.1. Company Overview
9.3.6.2. Big Data Offerings and Capabilities
10. COMPANY PROFILES: BIG DATA IN HEALTHCARE SERVICE PROVIDERS IN EUROPE
10.1. Chapter Overview
10.2. Detailed Company Profiles of Leading Players in Europe
10.2.1. Accenture
10.2.1.1. Company Overview
10.2.1.2. Financial Information
10.2.1.3. Big Data Offerings and Capabilities
10.2.1.4. Recent Developments and Future Outlook
10.2.2. Keyrus
10.2.2.1. Company Overview
10.2.2.2. Financial Information
10.2.2.3. Big Data Offerings and Capabilities
10.2.2.4. Recent Developments and Future Outlook
10.3. Short Company Profiles of Other Prominent Players in Europe
10.3.1 Akka Technologies
10.3.1.1. Company Overview
10.3.1.2. Big Data Offerings and Capabilities
10.3.2 Altamira.ai
10.3.2.1. Company Overview
10.3.2.2. Big Data Offerings and Capabilities
10.3.3 atom Consultancy Services (ACS)
10.3.3.1. Company Overview
10.3.3.2. Big Data Offerings and Capabilities
10.3.4 Avenga
10.3.4.1. Company Overview
10.3.4.2. Big Data Offerings and Capabilities
10.3.5 Lutech
10.3.5.1. Company Overview
10.3.5.2. Big Data Offerings and Capabilities
10.3.6 Nagarro
10.3.6.1. Company Overview
10.3.6.2. Big Data Offerings and Capabilities
10.3.7 Scalefocus
10.3.7.1. Company Overview
10.3.7.2. Big Data Offerings and Capabilities
10.3.8 Spindox
10.3.8.1. Company Overview
10.3.8.2. Big Data Offerings and Capabilities
11. COMPANY PROFILES: BIG DATA IN HEALTHCARE SERVICE PROVIDERS IN ASIA AND REST OF THE WORLD
11.1. Chapter Overview
11.2. Detailed Company Profiles of Leading Players in Asia and Rest of the World
11.2.1. Tata Elxsi
11.2.1.1. Company Overview
11.2.1.2. Big Data Offerings and Capabilities
11.2.1.3. Recent Developments and Future Outlook
11.2.2. Kellton
11.2.2.1. Company Overview
11.2.2.2. Financial Information
11.2.2.3. Big Data Offerings and Capabilities
11.2.2.4. Recent Developments and Future Outlook
11.3. Short Company Profiles of Other Prominent Players in Asia and Rest of the World
11.3.1 Athena Global Technologies
11.3.1.1. Company Overview
11.3.1.2. Big Data Offerings and Capabilities
11.3.2 Happiest Minds
11.3.2.1. Company Overview
11.3.2.2. Big Data Offerings and Capabilities
11.3.3 InData Labs
11.3.3.1. Company Overview
11.3.3.2. Big Data Offerings and Capabilities
11.3.4 NTT data
11.3.4.1. Company Overview
11.3.4.2. Big Data Offerings and Capabilities
11.3.5 OrangeMantra
11.3.5.1. Company Overview
11.3.5.2. Big Data Offerings and Capabilities
11.3.6 Trigyn Technologies
11.3.6.1. Company Overview
11.3.6.2. Big Data Offerings and Capabilities
11.3.7 XenonStack
11.3.7.1. Company Overview
11.3.7.2. Big Data Offerings and Capabilities
12. MARKET IMPACT ANALYSIS: DRIVERS, RESTRAINTS, OPPORTUNITIES AND CHALLENGES
12.1. Chapter Overview
12.2. Market Drivers
12.3. Market Restraints
12.4. Market Opportunities
12.5. Market Challenges
12.6. Conclusion
13. GLOBAL BIG DATA IN HEALTHCARE MARKET
13.1. Chapter Overview
13.2. Key Assumptions and Methodology
13.3. Global Big Data in Healthcare Market, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
13.3.1. Scenario Analysis
13.3.1.1. Conservative Scenario
13.3.1.2. Optimistic Scenario
13.4. Key Market Segmentations
14. BIG DATA IN HEALTHCARE MARKET, BY COMPONENT
14.1. Chapter Overview
14.2. Key Assumptions and Methodology
14.3. Big Data in Healthcare Market: Distribution by Component, 2018, 2023 and 2035
14.3.1. Big Data Hardware: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
14.3.2. Big Data Software: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
14.3.3. Big Data Services: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
14.4. Data Triangulation and Validation
15. BIG DATA IN HEALTHCARE MARKET, BY TYPE OF HARDWARE
15.1. Chapter Overview
15.2. Key Assumptions and Methodology
15.3. Big Data in Healthcare Market: Distribution by Type of Hardware, 2018, 2023 and 2035
15.3.1. Storage Devices: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
15.3.2. Servers: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
15.3.3. Networking Infrastructure: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
15.4. Data Triangulation and Validation
16. BIG DATA IN HEALTHCARE MARKET, BY TYPE OF SOFTWARE
16.1. Chapter Overview
16.2. Key Assumptions and Methodology
16.3. Big Data in Healthcare Market: Distribution by Type of Software, 2018, 2023 and 2035
16.3.1. Electronic Health Record: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
16.3.2. Revenue Cycle Management Software: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
16.3.3. Practice Management Software: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
16.3.4. Workforce Management Software: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
16.4. Data Triangulation and Validation
17. BIG DATA IN HEALTHCARE MARKET, BY TYPE OF SERVICE
17.1. Chapter Overview
17.2. Key Assumptions and Methodology
17.3. Big Data in Healthcare Market: Distribution by Type of Services, 2018, 2023 and 2035
17.3.1. Diagnostic Analytics: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
17.3.2. Descriptive Analytics: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
17.3.3. Predictive Analytics: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
17.3.4. Prescriptive Analytics: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
17.4. Data Triangulation and Validation
18. BIG DATA IN HEALTHCARE MARKET, BY DEPLOYMENT OPTION
18.1. Chapter Overview
18.2. Key Assumptions and Methodology
18.3. Big Data in Healthcare Market: Distribution by Deployment Option, 2018, 2023 and 2035
18.3.1. Cloud-based Deployment: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
18.3.2. On-premises Deployment: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
18.4. Data Triangulation and Validation
19. BIG DATA IN HEALTHCARE MARKET, BY APPLICATION AREA
19.1. Chapter Overview
19.2. Key Assumptions and Methodology
19.3. Big Data in Healthcare Market: Distribution by Application Area, 2018, 2023 and 2035
19.3.1. Operational Management: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
19.3.2. Clinical Data Management: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
19.3.3. Financial Management: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
19.3.4. Population Health Management: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
19.4. Data Triangulation and Validation
20. BIG DATA IN HEALTHCARE MARKET, BY HEALTHCARE VERTICAL
20.1. Chapter Overview
20.2. Key Assumptions and Methodology
20.3. Big Data in Healthcare Market: Distribution by Healthcare Vertical, 2018, 2023 and 2035
20.3.1. Healthcare Services: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
20.3.2. Pharmaceuticals: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
20.3.3. Medical Devices: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
20.3.4. Other Verticals: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
20.4. Data Triangulation and Validation
21. BIG DATA IN HEALTHCARE MARKET, BY END USER
21.1. Chapter Overview
21.2. Key Assumptions and Methodology
21.3. Big Data in Healthcare Market: Distribution by End User, 2018, 2023 and 2035
21.3.1. Hospitals: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
21.3.2. Health Insurance Agencies: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
21.3.3. Clinics: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
21.3.4. Other End Users: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
21.4. Data Triangulation and Validation
22. BIG DATA IN HEALTHCARE MARKET, BY ECONOMIC STATUS
22.1. Chapter Overview
22.2. Key Assumptions and Methodology
22.3. Big Data in Healthcare Market: Distribution by Economic Status, 2018, 2023 and 2035
22.3.1. High Income Countries: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.1.1. US: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.1.2. Canada: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.1.3. Germany: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.1.4. UK: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.1.5. UAE: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.1.6. South Korea: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.1.7. France: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.1.8. Australia: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.1.9. New Zealand: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.1.10. Italy: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.1.11. Saudi Arabia: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.1.11. Nordic Countries: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.2. Upper-Middle Income Countries: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.2.1. China: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.2.1. Russia: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.2.1. Brazil: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.2.1. Japan: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.2.1. South Africa: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.3. Lower-Middle Income Countries: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.3.3.1. India: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
22.4. Data Triangulation and Validation
23. BIG DATA IN HEALTHCARE MARKET, BY GEOGRAPHY
23.1. Chapter Overview
23.2. Key Assumptions and Methodology
23.3. Big Data in Healthcare Market: Distribution by Geography, 2018, 2023 and 2035
23.3.1. North America: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
23.3.2. Europe: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
23.3.3. Asia: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
23.3.4. Middle East and North Africa: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
23.3.5. Latin America: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
23.3.6. Rest of the World: Historical Trends (2018-2022) and Forecasted Estimates (2023-2035)
23.4. Data Triangulation and Validation
24. BIG DATA IN HEALTHCARE MARKET, REVENUE FORECAST OF LEADING PLAYERS
24.1. Chapter Overview
24.2. Key Assumptions and Methodology
24.3. Microsoft: Revenue Generated from Big Data in Healthcare Offerings FY 2018 - FY 2023
24.4. Optum: Revenue Generated from Big Data in Healthcare Offerings FY 2018 - FY 2023
24.5. IBM: Revenue Generated from Big Data in Healthcare Offerings FY 2018 - FY 2023
24.6. Oracle: Revenue Generated from Big Data in Healthcare Offerings FY 2018 - FY 2023
24.7. Allscripts: Revenue Generated from Big Data in Healthcare Offerings FY 2018 - FY 2023
25. CONCLUSION
25.1. Chapter Overview
26. EXECUTIVE INSIGHTS
26.1. Chapter Overview
26.2. Emorphis Technologies
26.2.1. Company Snapshot
26.2.2. Interview Transcript
26.3. Estenda Solutions
26.3.1. Company Snapshot
26.3.2. Interview Transcript
26.4. DataToBiz
26.4.1. Company Snapshot
26.4.2. Interview Transcript
26.5. Growth Acceleration Partners
26.5.1. Company Snapshot
26.5.2. Interview Transcrip
26.6. W2S Solutions
26.6.1. Company Snapshot
26.6.2. Interview Transcript
26.7. OrangeMantra
26.7.1. Company Snapshot
26.7.2. Interview Transcript
26.8. Soulpage IT Solutions
26.8.1. Company Snapshot
26.8.2. Interview Transcript
26.9. TechMango
26.9.1. Company Snapshot
26.9.2. Interview Transcript
26.10. Tata Elxsi
26.10.1. Company Snapshot
26.10.2. Interview Transcript
26.11. OpenXcell
26.11.1. Company Snapshot
26.11.2. Interview Transcript
26.12. ThirdEye Data
26.12.1. Company Snapshot
26.12.2. Interview Transcript
26.13. NTT Data
26.13.1. Company Snapshot
26.13.2. Interview Transcript
26.14. CodeRiders
26.14.1. Company Snapshot
26.14.2. Interview Transcript
26.15. Xenon Stack
26.15.1. Company Snapshot
26.15.2. Interview Transcript
27. APPENDIX I: TABULATED DATA28. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS
LIST OF FIGURES
Figure 2.1 Research Methodology: Project Methodology
Figure 2.2 Research Methodology: Forecast Methodology
Figure 2.3 Research Methodology: Robust Quality Control
Figure 2.4 Research Methodology: Key Market Segmentation
Figure 3.1 Lessons Learnt from Past Recessions
Figure 4.1 Executive Summary: Overall Market Landscape
Figure 4.2 Executive Summary: Global Market for Big Data in Healthcare by Component, Type of Hardware, Type of Software, Type of Service, and Deployment Option
Figure 4.3 Executive Summary: Global Market for Big Data in Healthcare by Application Area, Healthcare Vertical, End User, Economic Status, Geography and Leading Players
Figure 5.1 Types of Big Data Analytics
Figure 5.2 Applications of Big Data in Healthcare
Figure 6.1 Big Data in Healthcare Service Providers: Distribution by Year of Establishment
Figure 6.2 Big Data in Healthcare Service Providers: Distribution by Company Size
Figure 6.3 Big Data in Healthcare Service Providers: Distribution by Location of Headquarters (Region)
Figure 6.4 Big Data in Healthcare Service Providers: Distribution by Location of Headquarters (Country)
Figure 6.5 Big Data in Healthcare Service Providers: Distribution by Type of Business Model
Figure 6.6 Big Data in Healthcare Service Providers: Distribution by Type of Offering
Figure 6.7 Big Data in Healthcare Service Providers: Type of Big Data Analytics Offered
Figure 6.8 Big Data in Healthcare Service Providers: Type of Big Data Storage Solution Offered
Figure 6.9 Big Data in Healthcare Service Providers: Distribution by Deployment Option
Figure 6.10 Big Data in Healthcare Service Providers: Distribution by Application Area
Figure 6.10 Big Data in Healthcare Service Providers: Distribution by End User
Figure 7.1 Big Data in Healthcare Service Providers: Distribution by Year of Establishment and Company Size
Figure 7.2 Big Data in Healthcare Service Providers: Distribution by Company Size and Location of Headquarters
Figure 7.3 Big Data in Healthcare Service Providers: Distribution by Type of Offering and Company Size
Figure 7.4 Big Data in Healthcare Service Providers: Distribution by Type of Big Data Analytics Offered and Application Area
Figure 7.5 Big Data in Healthcare Service Providers: Distribution by Company Size, Application Area and End User
Figure 8.1 Company Competitiveness Analysis: Small Service Providers based in North America
Figure 8.2 Company Competitiveness Analysis: Mid-sized Service Providers based in North America (I/II)
Figure 8.3 Company Competitiveness Analysis: Mid-sized Service Providers based in North America (II/II)
Figure 8.4 Company Competitiveness Analysis: Large Service Providers based in North America (I/II)
Figure 8.5 Company Competitiveness Analysis: Large Service Providers based in North America (II/II)
Figure 8.6 Company Competitiveness Analysis: Very Large Service Providers based in North America
Figure 8.7 Company Competitiveness Analysis: Small Service Providers based in Europe
Figure 8.8 Company Competitiveness Analysis: Mid-sized Service Providers based in Europe
Figure 8.9 Company Competitiveness Analysis: Large and Very Large Big Service Providers based in Europe
Figure 8.10 Company Competitiveness Analysis: Small Service Providers based in Asia and Rest of the World
Figure 8.11 Company Competitiveness Analysis: Mid-sized Service Providers based in Asia and Rest of the World (I/II)
Figure 8.12 Company Competitiveness Analysis: Mid-sized Service Providers based in Asia and Rest of the World (II/II)
Figure 8.13 Company Competitiveness Analysis: Large Big Service Providers based in Asia and Rest of the World
Figure 8.14 Company Competitiveness Analysis: Very Large Service Providers based in Asia and Rest of the World
Figure 9.1 Amazon Web Services: Annual Revenues, FY 2018 - 9M FY 2023 (USD Billion)
Figure 9.2 Microsoft: Annual Revenues, FY 2018 - Q1 FY 2024 (USD Billion)
Figure 9.3 Oracle: Annual Revenues, FY 2018 - Q1 FY 2024 (USD Billion)
Figure 9.4 Teradata: Annual Revenues, FY 2018 - 9M FY 2023 (USD Billion)
Figure 10.1 Accenture: Annual Revenues, FY 2018 - FY 2023 (USD Billion)
Figure 10.2 Keyrus: Annual Revenues, FY 2018 - H1 FY 2023 (USD Million)
Figure 11.1 Tata Elxsi: Annual Revenues, FY 2018 - H1 FY 2023 (INR Billion)
Figure 11.2 Kellton: Annual Revenues, FY 2018 - FY 2023 (INR Billion)
Figure 12.1 Big Data in Healthcare Market Drivers
Figure 12.2 Big Data in Healthcare Market Restraints
Figure 12.3 Big Data in Healthcare Market Opportunities
Figure 12.4 Big Data in Healthcare Market Challenges
Figure 13.1 Global Market for Big Data in Healthcare, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 13.2 Global Market for Big Data in Healthcare, Forecasted Estimates (2023-2035): Conservative Scenario (USD Billion)
Figure 13.3 Global Market for Big Data in Healthcare, Forecasted Estimates (2023-2035): Optimistic Scenario (USD Billion)
Figure 14.1 Big Data in Healthcare Market: Distribution by Component, 2018, 2023 and 2035 (USD Billion)
Figure 14.2 Big Data in Healthcare Market for Hardware, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 14.3 Big Data in Healthcare Market for Software, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 14.4 Big Data in Healthcare Market for Services, Historical Trends (2018-2022) and Forecasted Estimates
Figure 15.1 Big Data in Healthcare Market: Distribution by Type of Hardware, 2018, 2023 and 2035 (USD Billion)
Figure 15.2 Big Data in Healthcare Market for Storage Devices, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 15.3 Big Data in Healthcare Market for Servers, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 15.4 Big Data in Healthcare Market for Networking Infrastructure, Historical Trends (2018-2022) and Forecasted Estimates
Figure 16.1 Big Data in Healthcare Market: Distribution by Type of Software, 2018, 2023 and 2035 (USD Billion)
Figure 16.2 Big Data in Healthcare Market for Electronic Health Records, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 16.3 Big Data in Healthcare Market for Revenue Cycle Management Software, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 16.4 Big Data in Healthcare Market for Practice Management Software, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 16.5 Big Data in Healthcare Market for Workforce Management Software, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 17.1 Big Data in Healthcare Market: Distribution by Type of Service, 2018, 2023 and 2035 (USD Billion)
Figure 17.2 Big Data in Healthcare Market for Diagnostic Analytics, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 17.3 Big Data in Healthcare Market for Descriptive Analytics, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 17.4 Big Data in Healthcare Market for Predictive Analytics, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 17.5 Big Data in Healthcare Market for Prescriptive Analytics, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 18.1 Big Data in Healthcare Market: Distribution by Deployment Option, 2018, 2023 and 2035 (USD Billion)
Figure 18.2 Big Data in Healthcare Market for Cloud-based Deployment, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 18.3 Big Data in Healthcare Market for On-premises Deployment, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 19.1 Big Data in Healthcare Market: Distribution by Application Area, 2018, 2023 and 2035
Figure 19.2 Big Data in Healthcare Market for Operational Management, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 19.3 Big Data in Healthcare Market for Clinical Data Management, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 19.4 Big Data in Healthcare Market for Financial Management, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 19.5 Big Data in Healthcare Market for Population Health Management, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 20.1 Big Data in Healthcare Market: Distribution by Healthcare Vertical, 2018, 2023 and 2035
Figure 20.2 Big Data in Healthcare Market for Healthcare Services, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 20.3 Big Data in Healthcare Market for Pharmaceuticals, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 20.4 Big Data in Healthcare Market for Medical Devices, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 20.5 Big Data in Healthcare Market for Other Verticals, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 21.1 Big Data in Healthcare Market: Distribution by End User, 2018, 2023 and 2035 (USD Billion)
Figure 21.2 Big Data in Healthcare Market for Hospitals, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 21.3 Big Data in Healthcare Market for Health Insurance Agencies, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 21.4 Big Data in Healthcare Market for Clinics, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 21.5 Big Data in Healthcare Market for Other End Users, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.1 Big Data in Healthcare Market: Distribution by Economic Status, 2018, 2023 and 2035
Figure 22.2 Big Data in Healthcare Market in High Income Countries, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.3 Big Data in Healthcare Market in the US, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.4 Big Data in Healthcare Market in Canada, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.5 Big Data in Healthcare Market in Germany, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.6 Big Data in Healthcare Market in the UK, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.7 Big Data in Healthcare Market in the UAE, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.8 Big Data in Healthcare Market in South Korea, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.9 Big Data in Healthcare Market in France, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.10 Big Data in Healthcare Market in Australia, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.11 Big Data in Healthcare Market in New Zealand, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.12 Big Data in Healthcare Market in Italy, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.13 Big Data in Healthcare Market in Saudi Arabia, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.14 Big Data in Healthcare Market in Nordic Countries, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.15 Big Data in Healthcare Market in Upper-Middle Income Countries, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.16 Big Data in Healthcare Market in China, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.17 Big Data in Healthcare Market in Russia, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.18 Big Data in Healthcare Market in Brazil, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.19 Big Data in Healthcare Market in Japan, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.20 Big Data in Healthcare Market in South Africa, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 22.21 Big Data in Healthcare Market in India, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 23.1 Big Data in Healthcare Market: Distribution by Geography, 2018, 2023 and 2035 (USD Billion)
Figure 23.2 Big Data in Healthcare Market in North America, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 23.3 Big Data in Healthcare Market in Europe, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 23.4 Big Data in Healthcare Market in Asia, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 23.5 Big Data in Healthcare Market in Middle East and North Africa, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 23.6 Big Data in Healthcare Market in Latin America, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 23.7 Big Data in Healthcare Market in Rest of the World, Historical Trends (2018-2022) and Forecasted Estimates (2023-2035) (USD Billion)
Figure 24.1 Microsoft: Revenue Generated from Big Data in Healthcare Offerings, FY 2018 - FY 2023 (USD Billion)
Figure 24.2 Optum: Revenue Generated from Big Data in Healthcare Offerings, FY 2018 - FY 2023 (USD Billion)
Figure 24.3 IBM: Revenue Generated from Big Data in Healthcare Offerings, FY 2018 - FY 2023 (USD Billion)
Figure 24.4 Oracle: Revenue Generated from Big Data in Healthcare Offerings, FY 2018 - FY 2023 (USD Billion)
Figure 24.5 Allscripts: Revenue Generated from Big Data in Healthcare Offerings, FY 2018 - FY 2023 (USD Billion)
LIST OF TABLES
Table 5.1 Comparison between Data Lake and Data Warehouse
Table 6.1 List of Big Data in Healthcare Service Providers
Table 6.2 Big Data in Healthcare Service Providers: Information on Type of Offering and Type of Big Data Analytics Offered
Table 6.3 Big Data in Healthcare Service Providers: Information on Type of Big Data Storage Solution Offered and Deployment Option
Table 6.4 Big Data in Healthcare Service Providers: Information on Application Area and End User
Table 8.1 Company Competitiveness Analysis: Big Data In Healthcare Service Providers based in North America
Table 8.2 Company Competitiveness Analysis: Big Data In Healthcare Service Providers based in Europe
Table 8.3 Company Competitiveness Analysis: Big Data In Healthcare Service Providers Based in Asia and Rest of the World
Table 9.1 Big Data in Healthcare Service Providers in North America: List Companies Profiled
Table 9.2 Amazon Web Services: Company Snapshot
Table 9.3 Amazon Web Services: Big Data Offerings and Capabilities
Table 9.4 Amazon Web Services: Recent Developments and Future Outlook
Table 9.5 Microsoft: Company Snapshot
Table 9.6 Microsoft: Big Data Offerings and Capabilities
Table 9.7 Microsoft: Recent Developments and Future Outlook
Table 9.8 Oracle: Company Snapshot
Table 9.9 Oracle: Big Data Offerings and Capabilities
Table 9.10 Oracle: Recent Developments and Future Outlook
Table 9.11 Teradata: Company Snapshot
Table 9.12 Teradata: Big Data Offerings and Capabilities
Table 9.13 Teradata: Recent Developments and Future Outlook
Table 9.14 Itransition: Company Snapshot
Table 9.15 Itransition: Big Data Offerings and Capabilities
Table 9.16 Nous Infosystems: Company Snapshot
Table 9.17 Nous Infosystems: Big Data Offerings and Capabilities
Table 9.18 Oxagile: Company Snapshot
Table 9.19 Oxagile: Big Data Offerings and Capabilities
Table 9.20 Softweb Solutions: Company Snapshot
Table 9.21 Softweb Solutions: Big Data Offerings and Capabilities
Table 9.22 Solix Technologies: Company Snapshot
Table 9.23 Solix Technologies: Big Data Offerings and Capabilities
Table 9.24 Trianz (formerly CBIG Consulting): Company Snapshot
Table 9.25 Trianz (formerly CBIG Consulting): Big Data Offerings and Capabilities
Table 10.1 Big Data in Healthcare Service Providers in Europe: List Companies Profiled
Table 10.2 Accenture: Company Snapshot
Table 10.3 Accenture: Big Data Offerings and Capabilities
Table 10.4 Accenture: Recent Developments and Future Outlook
Table 10.5 Keyrus: Company Snapshot
Table 10.6 Keyrus: Big Data Offerings and Capabilities
Table 10.7 Keyrus: Recent Developments and Future Outlook
Table 10.8 Akka Technologies: Company Snapshot
Table 10.9 Akka Technologies: Big Data Offerings and Capabilities
Table 10.10 Altamira.ai: Company Snapshot
Table 10.11 Altamira.ai: Big Data Offerings and Capabilities
Table 10.12 atom Consultancy Services (ACS): Company Snapshot
Table 10.13 atom Consultancy Services (ACS): Big Data Offerings and Capabilities
Table 10.14 Avenga: Company Snapshot
Table 10.15 Avenga: Big Data Offerings and Capabilities
Table 10.16 Lutech: Company Snapshot
Table 10.17 Lutech: Big Data Offerings and Capabilities
Table 10.18 Nagarro: Company Snapshot
Table 10.19 Nagarro: Big Data Offerings and Capabilities
Table 10.20 Scalefocus: Company Snapshot
Table 10.21 Scalefocus: Big Data Offerings and Capabilities
Table 10.22 Scalefocus: Company Snapshot
Table 10.23 Scalefocus: Big Data Offerings and Capabilities
Table 11.1 Big Data in Healthcare Service Providers in Asia and Rest of the World: List Companies Profiled
Table 11.2 Tata Elxsi: Company Snapshot
Table 11.3 Tata Elxsi: Big Data Offerings and Capabilities
Table 11.4 Kellton: Company Snapshot
Table 11.5 Kellton: Big Data Offerings and Capabilities
Table 11.6 Athena Global Technologies: Company Snapshot
Table 11.7 Athena Global Technologies: Big Data Offerings and Capabilities
Table 11.8 Happiest Minds: Company Snapshot
Table 11.9 Happiest Minds: Big Data Offerings and Capabilities
Table 11.10 InData Labs: Company Snapshot
Table 11.11 InData Labs: Big Data Offerings and Capabilities
Table 11.12 NTT Data: Company Snapshot
Table 11.13 NTT Data: Big Data Offerings and Capabilities
Table 11.14 OrangeMantra: Company Snapshot
Table 11.15 OrangeMantra: Big Data Offerings and Capabilities
Table 11.16 Trigyn Technologies: Company Snapshot
Table 11.17 Trigyn Technologies: Big Data Offerings and Capabilities
Table 11.18 XenonStack: Company Snapshot
Table 11.19 XenonStack: Big Data Offerings and Capabilities
Table 26.1 Emorphis Technologies: Company Snapshot
Table 26.2 Estenda Solutions: Company Snapshot
Table 26.3 DataToBiz: Company Snapshot
Table 26.4 Growth Acceleration Partners: Company Snapshot
Table 26.5 W2S Solutions: Company Snapshot
Table 26.6 OrangeMantra: Company Snapshot
Table 26.7 Soulpage IT Solutions: Company Snapshot
Table 26.8 TechMango: Company Snapshot
Table 26.9 Tata Elxsi: Company Snapshot
Table 26.10 OpenXcell: Company Snapshot
Table 26.11 ThirdEye Data: Company Snapshot
Table 26.12 NTT Data Services: Company Snapshot
Table 26.13 CodeRiders: Company Snapshot
Table 26.14 Xenon Stack: Company Snapshot
Table 27.1 Big Data in Healthcare Service Providers: Distribution by Year of Establishment
Table 27.2 Big Data in Healthcare Service Providers: Distribution by Company Size
Table 27.3 Big Data in Healthcare Service Providers: Distribution by Location of Headquarters
Table 27.4 Big Data in Healthcare Service Providers: Distribution by Type of Business Model
Table 27.5 Big Data in Healthcare Service Providers: Distribution by Type of Offering
Table 27.6 Big Data in Healthcare Service Providers: Type of Big Data Analytics Offered
Table 27.7 Big Data in Healthcare Service Providers: Type of Big Data Storage Solution Offered
Table 27.8 Big Data in Healthcare Service Providers: Distribution by Deployment Option
Table 27.9 Big Data in Healthcare Service Providers: Distribution by Application Area
Table 27.10 Big Data in Healthcare Service Providers: Distribution by End User
Table 27.11 Big Data in Healthcare Service Providers: Distribution by Year of Establishment and Company Size
Table 27.12 Big Data in Healthcare Service Providers: Distribution by Company Size and Location of Headquarters
Table 27.13 Big Data in Healthcare Service Providers: Distribution by Type of Offering and Company Size
Table 27.14 Big Data in Healthcare Service Providers: Distribution by Type of Big Data Analytics Offered and Application Area
Table 27.15 Big Data in Healthcare Service Providers: Distribution by Company Size, Application Area and End User
Table 27.16 Global Market for Big Data in Healthcare, Historical Trends (2018-2022) (USD Billion)
Table 27.17 Global Market for Big Data in Healthcare, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.18 Big Data in Healthcare Market for Hardware, Historical Trends (2018-2022) (USD Billion)
Table 27.19 Big Data in Healthcare Market for Hardware, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.20 Big Data in Healthcare Market for Software, Historical Trends (2018-2022) (USD Billion)
Table 27.21 Big Data in Healthcare Market for Software, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.22 Big Data in Healthcare Market for Services, Historical Trends (2018-2022) (USD Billion)
Table 27.23 Big Data in Healthcare Market for Services, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.24 Big Data in Healthcare Market for Storage Devices, Historical Trends (2018-2022) (USD Billion)
Table 27.25 Big Data in Healthcare Market for Storage Devices, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.26 Big Data in Healthcare Market for Servers, Historical Trends (2018-2022) (USD Billion)
Table 27.27 Big Data in Healthcare Market for Servers, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.28 Big Data in Healthcare Market for Networking Infrastructure, Historical Trends (2018-2022) (USD Billion)
Table 27.29 Big Data in Healthcare Market for Networking Infrastructure, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.30 Big Data in Healthcare Market for Electronic Health Record, Historical Trends (2018-2022) (USD Billion)
Table 27.31 Big Data in Healthcare Market for Electronic Health Record, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.32 Big Data in Healthcare Market for Revenue Cycle Management Software, Historical Trends (2018-2022) (USD Billion)
Table 27.33 Big Data in Healthcare Market for Revenue Cycle Management Software, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.34 Big Data in Healthcare Market for Practice Management Software, Historical Trends (2018-2022) (USD Billion)
Table 27.35 Big Data in Healthcare Market for Practice Management Software, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.36 Big Data in Healthcare Market for Workforce Management Software, Historical Trends (2018-2022) (USD Billion)
Table 27.37 Big Data in Healthcare Market for Workforce Management Software, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.38 Big Data in Healthcare Market for Diagnostic Analytics, Historical Trends (2018-2022) (USD Billion)
Table 27.39 Big Data in Healthcare Market for Diagnostic Analytics, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.40 Big Data in Healthcare Market for Descriptive Analytics, Historical Trends (2018-2022) (USD Billion)
Table 27.41 Big Data in Healthcare Market for Descriptive Analytics, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.42 Big Data in Healthcare Market for Predictive Analytics, Historical Trends (2018-2022) (USD Billion)
Table 27.43 Big Data in Healthcare Market for Predictive Analytics, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.44 Big Data in Healthcare Market for Prescriptive Analytics, Historical Trends (2018-2022) (USD Billion)
Table 27.45 Big Data in Healthcare Market for Prescriptive Analytics, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.46 Big Data in Healthcare Market for Cloud-based Deployment, Historical Trends (2018-2022) (USD Billion)
Table 27.47 Big Data in Healthcare Market for Cloud-based Deployment, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.48 Big Data in Healthcare Market for On-premises Deployment, Historical Trends (2018-2022) (USD Billion)
Table 27.49 Big Data in Healthcare Market for On-premises Deployment, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.50 Big Data in Healthcare Market for Operational Management, Historical Trends (2018-2022) (USD Billion)
Table 27.51 Big Data in Healthcare Market for Operational Management, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.52 Big Data in Healthcare Market for Clinical Data Management, Historical Trends (2018-2022) (USD Billion)
Table 27.53 Big Data in Healthcare Market for Clinical Data Management, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.54 Big Data in Healthcare Market for Financial Management, Historical Trends (2018-2022) (USD Billion)
Table 27.55 Big Data in Healthcare Market for Financial Management, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.56 Big Data in Healthcare Market for Population Health Management, Historical Trends (2018-2022) (USD Billion)
Table 27.57 Big Data in Healthcare Market for Population Health Management, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.58 Big Data in Healthcare Market for Healthcare Services, Historical Trends (2018-2022) (USD Billion)
Table 27.59 Big Data in Healthcare Market for Healthcare Services, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.60 Big Data in Healthcare Market for Pharmaceuticals, Historical Trends (2018-2022) (USD Billion)
Table 27.61 Big Data in Healthcare Market for Pharmaceuticals, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.62 Big Data in Healthcare Market for Medical Devices, Historical Trends (2018-2022) (USD Billion)
Table 27.63 Big Data in Healthcare Market for Medical Devices, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.64 Big Data in Healthcare Market for Other Verticals, Historical Trends (2018-2022) (USD Billion)
Table 27.65 Big Data in Healthcare Market for Other Verticals, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.66 Big Data in Healthcare Market for Hospitals, Historical Trends (2018-2022) (USD Billion)
Table 27.67 Big Data in Healthcare Market for Hospitals, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.68 Big Data in Healthcare Market for Health Insurance Agencies, Historical Trends (2018-2022) (USD Billion)
Table 27.69 Big Data in Healthcare Market for Health Insurance Agencies, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.70 Big Data in Healthcare Market for Clinics, Historical Trends (2018-2022) (USD Billion)
Table 27.71 Big Data in Healthcare Market for Clinics, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.72 Big Data in Healthcare Market for Other End Users, Historical Trends (2018-2022) (USD Billion)
Table 27.73 Big Data in Healthcare Market for Other End Users, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.74 Big Data in Healthcare Market in High Income Countries, Historical Trends (2018-2022) (USD Billion)
Table 27.75 Big Data in Healthcare Market in High Income Countries, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.76 Big Data in Healthcare Market in Upper-Middle Income Countries, Historical Trends (2018-2022) (USD Billion)
Table 27.77 Big Data in Healthcare Market in Upper-Middle Income Countries, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.78 Big Data in Healthcare Market in Lower-Middle Income Countries, Historical Trends (2018-2022) (USD Billion)
Table 27.79 Big Data in Healthcare Market in Lower-Middle Income Countries, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.80 Big Data in Healthcare Market in North America, Historical Trends (2018-2022) (USD Billion)
Table 27.81 Big Data in Healthcare Market in North America, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.82 Big Data in Healthcare Market in Europe, Historical Trends (2018-2022) (USD Billion)
Table 27.83 Big Data in Healthcare Market in Europe, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.84 Big Data in Healthcare Market in Asia, Historical Trends (2018-2022) (USD Billion)
Table 27.85 Big Data in Healthcare Market in Asia, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.86 Big Data in Healthcare Market in Latin America, Historical Trends (2018-2022) (USD Billion)
Table 27.87 Big Data in Healthcare Market in Latin America, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.88 Big Data in Healthcare Market in Middle East and North Africa, Historical Trends (2018-2022) (USD Billion)
Table 27.89 Big Data in Healthcare Market in Middle East and North Africa, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.90 Big Data in Healthcare Market in Rest of the World, Historical Trends (2018-2022) (USD Billion)
Table 27.91 Big Data in Healthcare Market in Rest of the World, Forecasted Estimates (2023-2035), Conservative, Base and Optimistic Scenarios (USD Billion)
Table 27.92 Big Data in Healthcare Market: Distribution by Leading Players, 2018-2023 (USD Billion)

Companies Mentioned (Partial List)

A selection of companies mentioned in this report includes, but is not limited to:

  • 314e Corporation
  • 3Pillar Global
  • 4-Serv Solutions
  • 47Billion
  • A-1 Technology
  • AaNeel Infotech
  • Ab Ovo
  • Accely
  • Accenture
  • ACE Technologies
  • Acropolis Infotech
  • Actowiz Solutions
  • ADVANCE.AI
  • Aegis Softtech
  • Affirma
  • AFour Technologies
  • Agile Global Solutions
  • Agilisium
  • Agnito Technologies
  • AINQA
  • AiRo Digital Labs
  • AITOBI
  • Akka Technologies
  • AllCode
  • Allied Digital
  • Alltech Consulting Services
  • Aloha Technology
  • Altair Engineering
  • Altamira.ai
  • Alteryx
  • Altysys
  • Amalfi Analytics
  • Amatis
  • Amazon Web Services
  • Amdocs
  • Amitech
  • Ampcus
  • Ana-Data Consulting
  • Andersen
  • Aqurate
  • ArborMetrix
  • ARHS
  • Aridhia Digital Research Environment
  • Aroopa
  • ARQ Group
  • ARRIA NLG
  • Artezio
  • Artha Solutions
  • ARTIFICIAL NEURONS
  • Assimilate Technologies
  • ASSYST
  • Athena Global Technologies
  • Atlas Systems
  • atom Consultancy Services (ACS)
  • Atomo
  • Atos
  • Aurous Consultancy
  • Ausy (a subsidiary of Randstad Digital)
  • Avancer
  • Avenga
  • AvidBeam
  • Aviskaran
  • Axians
  • Axiom Technology
  • Axtria
  • B12 Consulting
  • Bahwan CyberTek
  • Basil Systems
  • BCS Technology International
  • Beaconfire
  • Bell Integrator
  • Bestarion
  • Big Data Healthcare
  • BigDataGuys
  • BIT Studios
  • BLACKCOFFER
  • BlueCloud Technologies
  • BluEnt
  • BMB
  • Bodhtree Consulting
  • Brighter Consultancy
  • BUCHANAN & EDWARDS
  • Bursys
  • C2S Consulting
  • CACI
  • CBNITS
  • CDRP Technologies
  • CIGNEX
  • Cilio Automation Factory
  • Cirata (formerly WANdisco)
  • Cisco
  • Clairvoyant
  • Clarasys
  • Classic Informatics
  • ClearScale
  • Cloudera (formerly Hortonworks)
  • CLOVITY
  • Coderiders
  • Codibly
  • Cogitativo
  • Cogito Tech
  • Comptech Associates
  • Computools
  • Conneqt
  • Corex Solutions
  • Corot Systems
  • Cosmonet Solutions
  • COSO IT
  • Cotocus
  • Cquensys
  • Crest Infosystems
  • CTI Corporate Technologies
  • Cuelogic
  • Dasher Technologies
  • Data Aces
  • Databricks
  • DATAECONOMY
  • DataRiver
  • DataToBiz
  • DataZymes
  • Deevita
  • Definitive Healthcare
  • DEKRA
  • Delane Consulting and Talent Management
  • Depex Technologies
  • Dexlock
  • DGB Technologies
  • DICEUS
  • Digiteum
  • DLT Solutions
  • doodleblue Innovations
  • dotSolved Systems
  • DREAMZTECH
  • DWP Global
  • e& etisalat
  • eCommQuest
  • Egen Solutions (formerly SpringML)
  • EIT Professionals
  • EJADA SYSTEMS
  • ELEKS
  • Emorphis
  • Empeek
  • Entefy
  • Enterprise Engineering
  • Enterprise Software Solutions (ESS)
  • EPAM Systems
  • EPSoft Technologies
  • ESDS Software Solution
  • Estenda Solutions
  • Everest Consultants
  • Evoke Technologies
  • Exadel
  • EXAFLUENCE
  • Excelacom
  • Exceltic
  • Fayrix
  • Fingent
  • Fission Labs
  • Fusemachines
  • Futurism Technologies
  • Geniusee
  • Genzeon
  • GGK Tech
  • Global Alliant
  • Google Cloud
  • GrayMatter Software Services
  • Grid Dynamics
  • GSPANN
  • Happiest Minds
  • Hewlett Packard Enterprise
  • Hidden Brains InfoTech
  • Hitachi Vantara
  • HTC global service
  • Huwaei Technologies
  • IBM
  • Iflexion
  • Illumination Work
  • InData Labs
  • Indium Software
  • Indus Net Technologies (INT.)
  • Infodat
  • Infomerica
  • Infometry
  • Informatic Technologies
  • Infystrat
  • Innominds
  • Innovecs
  • INOXOFT
  • Intellectsoft
  • Intellibee
  • InterraIT
  • Intersoft Data Labs
  • INTERSOG
  • InterSystems
  • Intetics
  • Intone
  • IQVIA
  • IT KeySource
  • ITC Infotech
  • ITConnectsUS
  • Iteris
  • ITRadiant Solutions
  • Itransition
  • Itrex
  • IYRIX
  • Jetsoftpro
  • Kainos
  • Kanerika
  • KARYA Technologies
  • Kellton
  • Keyrus
  • KGS Technology
  • KMM Technologies
  • Kodehash
  • L.E.K Consulting
  • LexisNexis
  • LUMEN
  • Lutech
  • Makeen Technologies
  • Marlabs
  • MashPoint
  • Microsoft
  • Miquido
  • mLogica
  • Mobile programming
  • Mobisoft Infotech
  • Modak Analytics
  • Motherson Technology Services
  • Motivity Labs
  • Nagarro
  • Nalashaa
  • NANO-X IMAGING
  • Naviasys
  • NEC Corporation
  • Nemo IT Solutions
  • Nettechnocrats IT Services
  • NEX Softsys
  • NexDegree
  • NextShift
  • Nitor Infotech
  • NIX United
  • Nous Infosystems
  • Novel Patterns
  • Nowasys
  • NTT data
  • Omnipoint Services
  • ONEDATA SOFTWARE SOLUTIONS
  • Operisoft Technologies
  • Oracle
  • Orange Mantra
  • Orion Governance
  • OSIZ TECHNOLOGIES
  • OTS Solutions
  • Oxagile
  • Payoda Technology
  • Pegasus One
  • PixelPlex
  • Plain Concepts
  • Plexus Tech
  • plInfosys
  • Polestar Insights
  • Precision Medicine Group
  • PREDICTif Solutions
  • Preezma
  • Press Ganey
  • PRI Global
  • Princeton IT services
  • PruTech Solutions
  • Pure Data
  • PureSoftware
  • Pythian
  • QAT Global
  • QSS Technosoft
  • Qualitest
  • Quantium
  • QUANTTA ANALYTICS
  • Quantum Integrators
  • Quantzig
  • Qubole
  • R Systems
  • RACKSPACE TECHNOLOGY
  • RADcube
  • Rapidops
  • Rapidsoft Technologies
  • RCG Global Services
  • Real-Time Technology Solutions
  • Reltio
  • Resolve Tech Solutions
  • RevInfotech
  • Rigel Networks
  • Rolta India
  • Royal Cyber
  • RPA Infotech (RPAI)
  • RxEOB
  • S-PRO
  • Saal.ai
  • Sails Software
  • SAPSOL Technologies
  • SAS Institute
  • Savana
  • Saviance
  • SB Italia
  • Scalefocus
  • Scality
  • ScienceSoft
  • Scripta Insights
  • Seasia Infotech
  • Security of the Third Millennium (S3K)
  • Seer Interactive
  • Seqster
  • ServusTech
  • Seshaasai
  • SHC Technologies
  • Shimento
  • Sigma Data Systems
  • Sigmaways
  • Silver Touch Technologies
  • Skoruz
  • smartData
  • SmarTek21
  • SNAK Consultancy services
  • Snowflake
  • Soft Strategy
  • Softrams
  • SoftServe
  • Softvan
  • Softweb Solutions
  • Solix Technologies
  • SoulPage IT Solutions
  • SPEC INDIA
  • Spectos
  • SpiceOrb
  • Spikewell
  • Spindox
  • SPINEOR
  • SpinSys
  • Sprinterra
  • SpurTree Technologies
  • Sqilline
  • SREYO
  • Statswork
  • Stefanini Group
  • Strateq
  • Sun Technologies
  • Sunwin Intelligent
  • Svitla
  • System Soft technologies
  • SystemDomain
  • Sysvine Technologies
  • T-Systems International
  • TATA Consultancy Services
  • Tata Elxsi
  • TECHCUSHY SOFTWARE SOLUTIONS
  • Techlene Software Solutions
  • Techmango Technology Services
  • TechnoGen
  • TechTier
  • Techwave
  • Tectonic
  • TEG Analytics
  • TekClan
  • Tekrevol
  • Teksun
  • Telefonica
  • Tellius
  • Telstra Health UK
  • Tenzai
  • Teradata
  • Terawe
  • Terralogic
  • The Digital Group (T/DG)
  • The Select Group
  • ThinkPalm
  • ThirdEye Data
  • THOUGHTi
  • Tibil Solutions
  • TP&P Technology
  • Transorg Analytics
  • Trianz (formerly CBIG Consulting)
  • Trigyn Technologies
  • Trillion Technology Solutions
  • Trimane
  • Trinus
  • UB Technology Innovations (UBTI)
  • UnifyCloud
  • USEReady
  • V2soft
  • Value Innovation Labs
  • VARTEQ
  • VATES
  • VDOIT Technologies
  • Veersa
  • ViSolve
  • Visvero
  • VITRANA
  • VMware
  • W2S Solutions
  • Wavelabs Technologies
  • Wavicle Data Solutions
  • Wissen Technology
  • WNS
  • X-Byte
  • XenonStack
  • XICOM TECHNOLOGIES
  • Xoriant
  • Yalantis
  • Zen & Art
  • Zivaro

Methodology

 

 

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Table Information