At a Glance
- Genomics is an intricate facet of biotechnology that requires storing and analyzing humongous amounts of data. Cutting-edge data science trends have brought a transformation in the way genomics is studied worldwide.
- Biotechnology deals with the extraction, reaction and manipulation of molecules in living beings. These processes will expedite safely and systematically with the help of the advancements in data science.
- Every day, there is a humongous amount of data generated by various private and government-funded biotech firms. Data science is crucial in establishing interconnectivity amongst these biotechnology entities to share the knowledge database.
- Companies are faced with newer variants of diseases every few years and this requires gene sequencing of microorganisms at a much faster rate to help in drug and vaccine development. Newer technologies developed in the field of data science can be pivotal to help with the endeavor.
Industries have advanced with the knowledge and application of biotechnology. From facilitating clinical trials of drugs and vaccines to genetic alterations in crops for better yield, biotechnology has been crucial in transforming the way we live our lives. Now, how does biotechnology cross paths with data science? The answer to this question is multi-layered. The last decade has seen unprecedented advancements in the way data has been leveraged, stored, analyzed, shared and applied to derive statistics, build forecast models and solve the mysteries of science. Data science is emerging in biotechnology with the tools and techniques that help scientists systematize their findings and expedite their experiments for better and safer results. Data science is the crucial bridge between knowledge and information. The application of data science is rapidly moving from simply analyzing the existing information to deriving solutions for the future.
By 2021, 66% of analytics processes will no longer simply discover what happened and why; they will also prescribe what should be done.
With data science becoming an integral part of how businesses operate, biotechnologists and related industries need to understand its emergence and crucial role. By understanding the applications of data-related tools and techniques, biotechnology can experience positive growth at a much faster rate. Let us look at some of the emerging pivotal data science applications in biotechnology.
1.Field of Advanced Medicinal Development: The field of modern medicine has gained momentum in terms of R&D with data science in biomedical research. Computerized medical records, big data to arrive at plausible diagnoses, automated medicinal kits, genetic coding, computerized molecular studies for drug and vaccine development, etc., are the technological boons of data science that have made it possible for companies to make unprecedented advancements in the field of medicine.
2.Developments in the Field of Agriculture:Scientists across the globe tout the systematic use of algorithms, computerized statistics and data analysis capabilities to advance the genetic studies of various crops. This has made it possible for them to experiment on the plants at a molecular level in the lab to shortlist the best yield. This saves time, effort and money while giving the best results with the combination of botanists, biotechnologists and data scientists. Apart from scientists, data science is also useful for farmers. Many mobile applications are being developed for the farmers to study best practices to grow crops, compare the prices and availability of seeds, fertilizers, and other farming essentials in their locality and get expert advice online. The data generated and leveraged in these apps can join together to create a library of references for the large farming community.
3.Mitigating the Damage to the Environment:Biotechnology is genuinely a way of giving back to nature by modifying existing systems and deriving new ones to minimize environmental damages. Now, one might ask what role data science can play in establishing ecological sustainability. Well, data is at the core of knowing the problem areas- factories with concerning amounts of emissions, agricultural sites that are in dire need of water conservation, landfills with immense amounts of non-degradable waste, etc. With the help of computerized tools, this data can be collected, stored and categorized to implement solutions. The solutions and best practices can also be shared via data science tools with other such areas, thus creating a chain of positive environmental practices.
Figure 1:Leveraging the Power of Data Science Applications in Biotechnology Industry
The areas discussed above are prime examples of how data science applications are emerging in the biotechnology industry. Experts worldwide are working hard towards coming up with newer tools, techniques and solutions with which data can change the way biotechnology research is performed and results are derived. The coming decade will be vital in changing the way we apply data-driven analysis and solutions to bring about positive transformation in biotechnology and its related fields.
Key Takeaways
- Data science has moved on from merely becoming an analytical field to becoming a supportive pillar for reasoning and research in the biotechnology industry.
- Leveraging data with the latest tools and techniques is helping bio technicians in expediting research and development processes while mitigating manual errors.
- Biotechnology applications in medicine, agriculture, environment and more such fields have transformed with the help of advancements in data science.
- While data science is emerging in biotechnology, there are still many milestones to be achieved to unleash its unlimited potential.
Start Your Digital Transformation Journey with Assessment
