Genomic data from scientific experiments by pharmaceutical companies is a type of Big Data that fits into the 7Vs of Big Data characteristics in the following ways:
Volume: Pharmaceutical companies generate a massive amount of genomic data from their scientific experiments. This is because they need to sequence the DNA of millions of patients and healthy individuals in order to identify genetic variants that are associated with diseases.
Variety: Genomic data is very varied. It includes data from a variety of sources, such as blood samples, tissue samples, and tumors. It also includes data from a variety of technologies, such as DNA sequencing machines and microarrays.
Velocity: Genomic data is generated at a very high velocity. This is because pharmaceutical companies are constantly conducting new scientific experiments.
Veracity: Genomic data can be challenging to verify. This is because it is often generated by complex sequencing machines and algorithms.
Value: Genomic data is very valuable. It can be used to develop new drugs, diagnose diseases, and predict a person’s risk of developing certain diseases.
Variability: Genomic data can be very variable. This is because the amount of data that is generated on a given day can vary depending on the number of experiments that are being conducted.
Visualization: Genomic data can be visualized in a number of ways, such as through charts, graphs, and heatmaps. Visualization can help to make genomic data more understandable and actionable.