Humanizing Big Data: Recognizing the Human Aspect of Big Data.

Area of Science:

• Genomics
• Bioinformatics
• Data Science

Background:

• Big data, characterized by large volumes from diverse sources, is increasingly utilized for predictive analytics.
• Genetic data, encompassing clinical testing, recreational testing (e.g., ancestry), and research studies, constitutes a significant type of big data.
• The analysis of this genetic big data faces challenges including non-systematic data collection, diverse assay technologies, and variable variant classification/interpretation.

Purpose of the Study:

• To evaluate the challenges associated with big data analysis in genomics.
• To address the complexities of translating sophisticated genomic technologies to the individual level.
• To emphasize the human element in genetic data, focusing on counseling, consent, and interpretation.

Main Methods:

• Review of challenges in big data analysis.
• Discussion of advanced genomic technologies like microarrays and next-generation sequencing.
• Focus on the practical application and human considerations of genetic/genomic testing.

Main Results:

• Identified challenges in analyzing massive genomic datasets.
• Highlighted the need to consider the human aspect of genetic data sources.
• Emphasized the importance of understanding assay limitations and interpretation.

Conclusions:

• Translating genomic big data to individual impact requires addressing analytical challenges.
• Adequate genetic counseling and informed consent are crucial in all genetic testing settings.
• Understanding the strengths and limitations of genetic assays and their interpretation is vital for responsible application.