The Global Big Data in Healthcare Market is expected to reach US$ 17,278.13 million by 2022, and the market is projected to grow at a CAGR of ~ 20.69 % during the forecast period 2015-2022.
Big data refers to the enormous amount of structured as well as unstructured data, which helps organizations to improve their decision making processes. The term big data might seem to refer huge volume of data, but that is not necessarily the case. It may also refer to technology analytics services such as descriptive, predictive or prescriptive that any organization utilize it to handle the data sets. Unstructured data refers to the data that does not have a standardized form. Around 80% of the data in the business organizations is unstructured and around 60% of the patient care information is in the unstructured format.
Big data provides insights to improve learning outcomes of research by comparing different data sets and thus helps researchers to understand pattern and make predictions. Other advantages are the competitive advantage by combining data from social media and existing customer records to target customers for pricing and promotional strategies and to prepare themselves for the future demand which is possible by analyzing customer data in real time which introduces new capabilities such as location based services and new marketing campaigns to increase revenue, analytics for a deeper level of customer relationships, pricing accuracy, and for efficient loss prevention by reducing the fraudulent claims, etc.
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Further pharmaceutical and biotechnological companies are harnessing the power of big data for product cross selling, financial risk management, regulatory compliance management, and others. Big data analytics in pharmaceutical manufacturing will also help to better the forecast production demand, understand the plant’s performance, and provide faster support services to the customers. Manufacturers are able to view product quality and observe a machine’s performance in real time. In healthcare, big data has added benefits such as patient safety. Data from the U.S. healthcare systems alone has reached 150 Exabyte in 2011. Big data in healthcare is overwhelming not only because of its volume but also because of its diversity (clinical and financial). With understanding about the trends and the patterns within the data, healthcare systems can improve quality care of patients and curb healthcare costs.
Some of key players profiled in the report are Cerner Corporation, Cognizant, Dell, GE Healthcare, Mckesson, Optum, Philips, Siemens, Xerox and others.
The Global Big Data in Healthcare Market has been segmented on the basis of components and services. The components is segmented into hardware and software type. The hardware segment is further sub-segmented into routers firewalls, virtual private network (VPN), data storage (data centers/storage on premise, storage on cloud/storage on demand), email servers, and wireless & access points. The software type is sub-segmented into electronic health record (EHR) software, practice management software, revenue cycle management software, workforce management software, and others. The software deployment type is sub-segmented into software on premise and software on demand.
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On the basis of analytic service type, the market is segmented into descriptive analytics, predictive analytics, and prescriptive analytics. On the basis of analytic service applications, the market is segmented into clinical data analytics, financial analytics, and operational analytics. The clinical data analytics is sub-segmented into quality care, population health management, clinical decision support, precision health, reporting, and compliance. The financial analytics is sub-segmented into claims processing, revenue cycle, management, and risk assessment. The operational analytics is sub-segmented into workforce analytics and supply chain analytics.
1.2 Scope of Study
1.2.1 Research Objective
1.2.2 Assumptions & Limitations
1.3 Market Structure
2 Research Methodology
2.1 Research Process
2.2 Primary Research
2.3 Secondary Research
3 Market dynamics
3.4 Macroeconomic Indicators
4 Market factor analysis
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