Using Big Data Analytics in Genomics and Health Care Industry

 The primary purpose of using big data in all healthcare industries is to create competitive, comparative, and effective performance and improvements. At least three features of big data, volume, variety, and velocity, are widely used in this industry. (Roski, 2014, p. 1) On the other hand, the major challenge was the concept of consent, data ownership, and control of healthcare data in a big data platform. 

Big data architectural framework in the health care industry

There is a big difference between traditional big data analytics in the healthcare system and big data analytics. The significant difference "lies in how processing is executed." (Raghupathi, 2014, p. 4) The traditional data analysis was based on structured database systems, whereas the modern healthcare data analytics is related to the "business intelligence tool installed on a stand-alone system, such as a desktop or laptop. Because big data is by definition large, the processing is broken down and executed across multiple nodes." (Raghupathi, 2014)

This graph shows the complexity of big data analysis in the healthcare industry.

Big data and personal genomics in the health care industry

One of the most critical parts of healthcare data is Genomic information which includes "individualized strategies for diagnostic or therapeutic decision-making by utilizing patients' genomic information." (He, et., 2017, para. 1) The Genomic data structure looks like the following flowchart structure extracted from a peer-reviewed article entitled "Big Data Analytics for Genomic Medicine" (He, et., 2017)

Big data in Genomic data science uncovered many essential hidden elements of an individual's genomic information, including "hidden patterns, unknown correlations, and other insights through examining large-scale various data sets."  (He, et., 2017, para. 1) Therefore, big data is used as a powerful tool of study for healthcare researchers to "decode the functional information hidden in DNA sequences." (Genome.Gov, 2021)

The following image shows the amount of data collected from the Genomics project in reality, where each shark represents 100,000,000 GB of data. (Genome.Gov, 2021)

Software called "Aligners - Whole-Genome Alignment" is one of the best tools for healthcare big data Genome researchers that "determine where individual pieces of DNA sequence lie on each part of a reference genome sequence." (Genome.Gov, 2021)
Big data framework and analysis in Genome data science in the future can bring more hidden information inside humans, animals, and plants. From my point of view, Quantum computing using big data analytics in Genome data science will be the next healthcare revolution.


Reference

Genome.Gov. (2021). Genomic Data Science Fact Sheet. Genome.Gov. Retrieved 2022, from https://www.genome.gov/about-genomics/fact-sheets/Genomic-Data-Science#:%7E:text=The%20Big%20Picture,data%20within%20the%20next%20decade.

He, K., Ge, D., & He, M. (2017). Big Data Analytics for Genomic Medicine. In International Journal of Molecular Sciences (Vol. 18, Issue 2, p. 412). MDPI AG. https://doi.org/10.3390/ijms18020412

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. In Health Information Science and Systems (Vol. 2, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1186/2047-2501-2-3

Roski, J., Bo-Linn, G. W., & Andrews, T. A. (2014). Creating value in health care through big data: opportunities and policy implications. Health affairs, 33(7), 1115-1122.