Big Data in Healthcare Market is Expected to Gain Popularity Across the Globe by 2026

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Big data in healthcare market revenue is expected to grow at lucrative rate over the forecast period primarily due to increased digitalization in industrial process. Digitization is transforming industrial processes, functions, products, and services. Advent of e-commerce has resulted in a sudden spurt in data being generated by various companies. Also, with decades of research and development activities across the healthcare industry, there is a large amount of data, referred to as ‘big data’, piled up with host organizations. It is therefore essential to ensure optimum utilization of this data for reducing costs, providing targeted products and services, and avoiding duplication of work. Big data solution helps simplifying and analyze complex and diverse data, a task that cannot be achieved using conventional software and hardware. Moreover, such kind of data cannot be managed with trivial data management tools and methods. Various tools such as Pentaho, Hadoop, Cloudera, and MongoDB are available in the market for the same. These tools help in data cleaning, mining, and analyzing of big data. There are also various data visualization tools such as Tableau, Silk, and Chartio that help in presenting the data in a more convenient and readable form.

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Fragmented Market:

There are various small and global players offering big data software and services. The small regional players offer big data services at low costs, which creates a high entry barrier for global players looking to establish their footprint in emerging markets. The market is highly fragmented, with various players offering services differentiated in terms of pricing and inclusions.

Competitor Analysis:

Some of the key companies in the global big data in healthcare market include

Hewlett-Packard Co.,Teradata Corporation,Opera Solutions,Cognizant Technology Solutions,Siemens Healthcare Gmbh,Mu Sigma,Splunk Inc.,Cloudera,EMC,IBM,Calpont Corporation,Oracle Corporation

Globally, big data in healthcare market scenario can be segmented on the basis of application, analytics type, delivery model, and end users.

Big Data in Healthcare Market Segmentation:

By Analytics Type

Descriptive Analytics

Predictive Analytics

Prescriptive Analytics

By Application

Clinical Analytics

Financial Analytics

Operational and Administrative Analytics

Population Health Analytics

By Delivery Model

On-premise

On-demand

By End User

Payers

Providers

Third party care organizations

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Dearth of Skilled Manpower to Inhibit Growth of Big Data in Healthcare:

Although, the market is huge in the developed nations, particularly in the U.S., dearth of trained personnel for analyzing big data is projected to limit the growth of this market in North America. Moreover, factors such as decreasing profit margins, slowdown in the global economy, and lack of skilled manpower is inhibiting growth of big data implementation in the healthcare industry. Some of the other challenges for big data in healthcare include data fragmentation, security issues, and lack of standardization. Furthermore, big data solution providers need to ensure compliance with regulations laid by the Health Insurance Portability and Accountability Act of 1996 (HIPAA). These factors are especially prevalent in developing economies of Asia Pacific and Latin America due to lack of effective healthcare infrastructure and inadequate supportive government regulations. Increasing cost pressure and lack of trained professionals dissuades healthcare providers from implementing expensive systems supporting big data solutions. However, improvement in IT infrastructure in developing economies over the near future is expected to be a major contributor for increase in demand for big data.

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About Author

Cynthia Carrier is a graduate of Texas A&M, where she played volleyball and annoyed a lot of professors. Now as Plains Gazette's entertainment and Lifestyle Editor, she enjoys writing about delicious BBQ, outrageous style trends and all things Texas.