Using Big Data Analytics in the Biotech Industry

The power of data in contemporary business and science is immense. No industry can really make progress without the analysis of massive information libraries that give us fresh insights and enable new breakthroughs.

Biotech is not an exception here as it relies heavily on big data analytics. But how does data science influence the biotech industry? What are the most common use cases of big data in biotechnology? If you are interested in seeing the answers, keep reading to learn more about this amazing topic.

Biotech and Big Data in a Nutshell

In order to make the article clear even for data science newbies, we want to explain the fundamentals of biotechnology and big data.

By definition, biotechnology represents the manipulation (as through genetic engineering) of living organisms or their components to produce useful usually commercial products such as pest-resistant crops, new bacterial strains, or novel pharmaceuticals. The global biotechnology market size is expected to reach $727 billion by 2025, growing steadily at a 7.4% rate.

#1 Ranking: Read how InetSoft was rated #1 for user adoption in G2's user survey-based index Read More

On the other hand, big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

As such, big data is critical to the success of biotechnology projects because biotech experts depend on massive data libraries. For example, a rough approximation of the human body data storage amounts to staggering 150 zettabytes (or 150 trillion gigabytes) of information.

These and many other data volumes in biotech are so enormous that everyday information processing tools could never generate any meaningful results. This is where big data shows up as a genuine driver of the biotech industry.

10 Use Cases of Big Data in Biotechnology

Now you know the key concepts, but it's just the tip of the iceberg. At this point, it is necessary to pinpoint the most important use cases of big data in biotechnology. Here are the top 10 examples:

1. Genomics

We open the list with the obvious choice because genomics is the brand ambassador of big data in biotechnology. Genomics is the science of human genes, so it's obvious why big data is needed here. Genome sequencing is getting faster every day, but it still takes years to uncover the entire genome.

view demo icon
View a 2-minute demonstration of InetSoft's easy, agile, and robust BI software.

2. Agriculture

Farmers are struggling to adjust to the growing impact of climate change, so they started using big data analytics as the new means of data interpretation. They can rely on GPS and weather forecasts to develop so-called precision farming, while GMO enables agriculture companies to improve their products.

3. Pharmaceutical drug discovery

The pharmaceutical industry is gigantic as it focuses on thousands of illnesses and health conditions. A single medicament can consist of dozens of compounds, while testing and experimenting might even include millions of ingredients. Without big data analytics, finding a cure for any disease in a reasonable timeframe would be close to impossible.

4. Crowdsourced data analytics

Contemporary consumers leave digital footprints all over the Internet, particularly on social networks and websites. It's a huge privilege for healthcare companies because they can discover the latest trends and draw meaningful information directly from the source, i.e. from the crowd of consumers and/or patients.

5. Drug safety

Testing new drugs for safety has never been easier as now big data platforms can quickly access and interpret millions of electronic records. This eliminates intuition and guesswork from drug safety procedures since healthcare providers can make data-driven decisions based on earlier track records. This way, big data actually saves people's lives and makes new medicine much safer.

view gallery
View live interactive examples in InetSoft's dashboard and visualization gallery.

6. Drug recycling

Drug recycling is the idea that healthcare organizations or consumers with unused drugs can transfer them in a safe and appropriate way to another consumer who needs them. This process is often conducted in a shady environment within low-quality supply chains, so patients' health may be in danger. Big data has the ability to monitor this procedure and control each contributor in real-time, thus turning drug recycling into a much safer process.

7. Business research and development

Biotech companies also use big data analytics for research purposes and development. For instance, data scientists can design advanced algorithms to extrapolate useful information from trusted sources such as industry websites, science magazines, and hundreds of other data libraries.

8. Data visualization

People perceive and understand visual information super-quickly, but it's not easy to design meaningful charts with so much information in the field such as genomics. This is where big data steps in to support biotech scientists and create visualization platforms. One of the finest examples comes in the form of the Integrative Genomics Viewer, a high-performance visualization tool for interactive exploration of large and integrated genomic datasets.

9. Electronic clinical research

Apart from drug discovery, a number of biotech stakeholders work together to conduct electronic clinical research. Big data allows them to combine multiple e-libraries and gain new insights from healthcare systems, pharmaceutical companies, software developers, and other relevant contributors.

10. Fake drug prevention

Finally, big data also contribute to the discovery and prevention of fake drug trade. The problem is serious as it jeopardizes the lives of thousands of people all over the globe, but data scientists are doing their best to identify fraudulent transactions and stop scammers from selling fake medicine.

Read the top 10 reasons for selecting InetSoft as your BI partner.

The Bottom Line

Biotech scientists have always relied on data for research purposes, but the rise of big data opened new boundaries and helped the entire industry to skyrocket in the last decade. In this post, we discussed the basics of data science in biotech and pinpointed some of the most interesting use cases. Big data is the present and the future of the biotech industry, so stay tuned because we are going to write more about the most exciting breakthroughs in this field.