Scientist in laboratory performing biotech industry experiment

Why Big Data is Revolutionizing the Biotech Industry – Part I

The biotech industry has always relied on data analytics, business intelligence, and technology advancements to drive R&D and commercial profits. The biotech industry generated approximately 133 billion dollars of revenue in 2015, but future profits and breakthroughs will likely depend on computing and data science. The past years have produced endless amounts of data that biotech industry scientists and researchers can use to overcome challenges and uncover opportunities.

Pharma Automation

A pharma research group may review millions of compounds before selecting the appropriate ones for pre-clinical trials. While there are software tools available that streamline the discovery process, the journey to successful drug discovery consumes enormous amounts of time and money. Big Data-based predictive modeling uses virtual libraries that contain terabytes of data and hundreds of millions of compounds to identify the compounds that will most likely experience success. These predictive modeling programs compare the trial criteria and desired outcomes against the target disease and chemical structures. Pharma automation reduces risks, saves money and offers faster research-to-market cycles.


Crowdsourcing is commonly used for outsourcing labor, entrepreneurial projects, and Kickstarter campaigns. Some pharma R&D companies have created online gaming platforms that involve disease profiles, research challenges and solving medical puzzles. Patient-driven research works through online tools and surveys that empower health care consumers to conduct their own studies, upload their own medical data and contribute knowledge about their meds, conditions and symptoms to benefit the world and the medical community. For example, a genomics company can easily generate huge amounts of data from target demographic patients by crowdsourcing patients to participate in specific research.

Sentiment Analysis

Sentiment analysis is a Big Data tool to analyze social networking posts and comments. Organizations primarily use it for marketing, advertising, and public relations research. For example, many companies use it to understand consumers’ reactions to new products, media announcements, and general customer service. However, social media platforms contain millions of health-related comments because health care consumers are sharing personal and public information about diseases and medical conditions. Some pharma and biotechnology companies are creating online patient communities and social media platforms to centralize, gather and uncover new trends. When used together with crowdsourcing, these tools provide sources of free labor and infinite information.


The Human Genome Project took over a decade of worldwide research and support to identify the 20,000 plus genes and sequence all three billion genome bases. This global project cost billions of dollars, but today’s biotechnology companies now have access to Big Data solutions that can decode entire genomes for just thousands of dollars. The genomics market has helped create software companies that use tools and frameworks to conduct massive computing tasks to analyze genetic, medical and biological data. These companies often work with computer hardware giants to improve their application performance and their Big Data analysis results.

Discovering Genetic Biomarkers

There are genomic analysis tools that identify DNA code variants and the genetic biomarkers of disease risk factors. Some Big Data analytics and informatics systems are capable of integrating multiple data types together for enhanced results. The ability to correlate large data warehouses, full of clinical, proteomic, phenotypic and genomic data, provides a clearer understanding of disease factors, symptoms and development. There are software solutions that allow researchers to view sequence alignments and disease data alongside clinical findings. There are even machine learning technology that leverages diverse datasets to create predictive models and biomarker signatures that can be used to identify ideal participants for clinical trials.

Big Data is generating results in different industries because innovative companies are using it to unlock value, trends, and insights. The biotech industry is quickly adapting to Big Data solutions because they have both public and private information sources that offer research, safety, and quality improvement opportunities. Part two will cover how Big Data is specifically impacting drug safety and research.