We have long been in an age where there is too much information for one single person to analyze. What we now call “data analytics”- consisting of greater volumes of data, smarter analytics, and more advanced technologies- has been an indispensable tool for businesses to make informed decisions in the modern world.
The pharmaceutical industry is a highly regulated one with new drug approvals requiring years of R & D, testing, and refining. The life sciences industry is one of the most data-centric organizations and it generates large quantities of data in the form of images, video, audio, and documents. The data for a single drug can run into petabytes and constantly increase with every new data set that is generated. A recent study by Aberdeen Group indicated that the life sciences industry is moving to a “Big Data-driven business” with new drug approvals requiring years of R&D and testing.
Data analytics in the Drug discovery process:
The drug discovery process is a long and expensive process. The process involves screening for chemicals that might be used to treat a disease using a broad variety of assays. In 1938, Sandoz Labs in Switzerland was the first company to produce synthetic vitamin C, but the process was not cost-efficient. In the 1990s, new technology allowed the company to optimize its process and lower the cost by 30%. The company held tie-ups with research organizations to use its technology to develop drugs for HIV, cancer, diabetes, and other diseases. But the tie-ups didn’t work out. In 2000, the company conducted a study to find out why the tie-ups did not work out. The study revealed that the drug research organizations were not looking at the data in the right way. The study prompted Sandoz to develop a new data analysis software, called Sandoz Insight, to help organizations make better decisions. The software helped Sandoz increase its sales by 50% and helped it win new business.
BI & Data analytics in clinical trials and precision medicine:
Business intelligence is a key part of the next generation of healthcare delivery and research, as it helps to reduce costs, time, and inefficiencies. The data-driven research and development (R&D) of precision medicine is a key example of how business intelligence can be used to improve the healthcare industry. Precision medicine is a new approach to disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. It’s based on the understanding that most health issues are caused by a complex interaction between genetic, environmental, and behavioral factors, and that a one-size-fits-all approach to healthcare simply doesn’t work well.
Data analytics in Research and development:
Clinical trials are the costliest step in pharmaceutical research and development (R&D). Over 70% of developmental costs are due to clinical trials, which are also the slowest step in R&D. In fact, it takes about 12 years for a drug to get from the initial discovery to the market. This is a long time when you consider that many new drugs fail in clinical trials, and those that pass cost a lot of money to develop. It’s no surprise that pharmaceutical companies are looking for ways to improve their R&D efficiency and lower their developmental costs.
Conclusion: Business intelligence is playing a key role in helping the pharmaceutical industry to achieve efficiencies and halt costs to keep the industry moving forward. The pharmaceutical industry is a key supplier of consumer products and services globally. In 2014, the pharmaceutical industry contributed more than US$1.1 trillion to the global economy. With the number of prescriptions generated on the