The uses of data analytics in biotechnology
Biotechnology is a broad field of biology that leverages various biological systems to develop products that can fundamentally transform the way we do things.
That said, biotech is not a modern concept. It has existed for thousands of years with ancient civilisations using early forms to produce crops and brew alcoholic beverages.
Today, the biotech industry has grown leaps and bounds and has accumulated a considerable amount of scientific data through research. Being in an industry where data is crucial, it’s not hard to see why biotech companies use data analytics.
Modern data analytics tools have enabled biotech researchers to create predictive analytics models and get insights about the most effective ways to achieve their desired goals and objectives.
In addition, biotech companies can use data analytics to help them get a better understanding of their market and predict various situations they may encounter in the future.
In this post, we explore some of the common uses of data analytics in the field of biotechnology.
Genomics is a branch of biotechnology that plays a role in developing forensic technologies and identifying how genetic factors may contribute to health conditions.
This branch of biotechnology generally processes large datasets to obtain insights, as researchers have to identify and classify genes from millions of genome bases. Traditionally, this process has been the most expensive and time-consuming.
For instance, The Human Genome Project, a major international effort to map the entire human genome, took thirteen years and billions of dollars to complete.
Today, thanks to modern data analytics, biotechnology companies can decode entire genomes in a much shorter timeline and at a much lower cost than before.
With data analytics tools, medical researchers can get insights on genetic mutations and gene sequences and use this information to find relationships between genes and the effect of new drugs.
Also, data analytics allow researchers to study the human genome to answer complex medical questions like why some diseases are more likely to affect a certain race of people or why some individuals develop particular illnesses after a certain age.
Data analytics in genomics can also help identify the passing of certain genes within families, which can help find cures for inherited diseases and disabilities.
With data analytics, scientists can conduct studies on different crops on a molecular level to discover ways to achieve the best crop yield.
Data analytics can also help develop GMOs, giving rise to genetically engineered crops that are resistant to diseases and can survive challenging conditions.
Data analytics isn’t just useful for researchers but can also help farmers, as it allows them to study crops and identify the best practices for growing them, determine prices for their harvest and find out the availability of crop necessities, such as fertiliser and tools.
Biotechnology also plays a critical role in conserving the environment.
Data analytics can help biotech companies to create products that don’t affect the environment negatively.
For instance, through data-powered insights, scientists have been able to create alternatives to everything from single-use plastics to bricks using sustainable and biodegradable materials such as mushrooms and other plant-based elements.
The possibilities of data analytics in biotechnology are endless
Data analytics has opened new doors in the field of biotechnology.
Thanks to data analytics, research and development that took years can now be completed in just a few months and researchers have access to biological, social and environmental insights that can be used to develop better and sustainable products.
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