Big Data Analytics and the Future of Biotech Industry
Blog | September 1, 2017
The biotech industry has experienced an explosion of data within the last few years due to the advancement in technology and biosciences. The role of data science has evolved with an exponential increase in data relating to genome sequencing, environmental monitoring, pharmaceutical compound combinations, and predictive diagnosis. Analysis of such large volumes of data speeds up discoveries in genetic research, GMOs, drug discoveries, and the human microbiome.
Here are some of the areas where big data analytics are impacting the biotech industry:
Genomics
Big data analytics has completely transformed the field of modern genomics. It took more than a decade and billions of dollars to sequence the first complete human genome. Analysis of 3 million base pairs and gene interactions would not be possible without big data analytics. Big data analytics has transformed the biotech industry by making genomic technology commercially feasible both in terms of cost and time.
Speak with an analytics expert for more information on big data and its impact on the biotech industry.
Drug Discovery
Drug discovery is a very tedious process. Pharma companies have to screen millions of compounds and interactions between them, then test it in preclinical trials, and still have a low chance of success. Big data analytics makes it possible to analyze the libraries of millions of compounds and build predictive models to find drugs with a higher chance of success. Apart from that, big data analytics has also transformed the drug trial process with the introduction of real-world evidence. This could significantly improve the drug discovery process within the biotech industry.
Agriculture
Data gathered from GPS technology and multiple GPS enabled tractors can help farmers to cope with the changing environmental condition by implementing precision farming. Data analytics is also changing the landscape of the biotech industry with its contribution to genetic research in creating GMOs. Such engineered crops can be modified with inputs from data analytics to improve crop yield and survive changing conditions.
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