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The use of data science in pharma has far-reaching implications for those deciding upon a degree in this type of field. The pharmaceutical industry constantly makes use of important statistical information gathered from a wide variety of sources. What is data science? How is data science used in the field of pharmaceuticals and other similar areas? How could a degree in data science enhance your career? The paragraphs that follow will answer these questions.

What is Data Science?

Data science is a growing field in which many of today’s students are developing an interest. Data science is an inter-disciplinary field of study that focuses on the scientific methods used in gathering, analyzing, and applying data and statistics. Data science is a unique field because it brings together many different types of areas of study to accomplish its end result. For example, computer science, advanced mathematics, statistics, and many other areas of study are all used in data science professions. As a result, a degree in data science opens up an almost limitless array of job opportunities in all sorts of different sectors that rely on this expertise.

How is Data Science Used in Big Pharma?

The pharmaceutical industry is an area that is seeing constant expansion. As more technologically advanced prescription medications are brought into existence, the need for professionals who are able to understand and make use of related data and statistics has also seen a huge increase. How is data science used in the pharmaceutical industry? Let’s consider some of the many types of data required in this field on a daily basis.

Data Usage in the Pharmaceutical Industry

The statistics below indicate how often and to what degree data science and similar information is used in the creation of new drug therapies within the pharmaceutical industry.

* It takes approximately 8 years for a new drug to be approved by the FDA.

* Each new drug approved costs an average of $500 million dollars.

* One one out of approximate 10,000 compounds discovered actually makes it to the approval stage.

* Only three out of 20 new drug therapies make enough profit to cover the losses experienced when the drug is undergoing testing.

These facts are merely a demonstration of the different areas where statistical information is gathered, studied, and then put to use within the pharmaceutical industry in order to further expand upon the medical treatments we have to offer patients today.

Collaboration between Big Pharma and Big Data

In times past, there was a bit of a discrepancy in making use of big data to further advance the development of new pharmaceutical drugs and other medical therapies. However, in recent years it has become more clear how dependent these two fields are on one another. This has led to an increased push toward the marriage of these two exciting fields. Whereas drug manufacturing companies used to heavily guard their privacy, they now understand how the use of big data can boost their success. Everyone from academic collaborators to healthcare professionals and patients themselves is continuing to push for these two important fields to work together toward increased productivity and success for both.

Using Predictive Analytics Models in Pharma

The use of predictive models has grown drastically in recent times. There is a mass appeal in being able to use current data to reliably predict future trends and outcomes, therefore offering the ability to be able to cater to future demands before they even arise.

Much of the money spent in big pharma is used in the screening process before the drug even makes it to the clinical trial stage. This ends up becoming a lengthy and expensive process, while sick individuals are waiting for new drugs to be approved that could benefit their condition. Now, data science is being used to shorten this previously lengthy process and hopefully lower the expense involved as well. Using predictive analytics, companies can put primary focus on specific products and ingredients in drug therapies most likely to be effective. These decisions will be based on a variety of gathered data that will help them choose among the hundreds of options available.

Data Science for Better Clinical Trials

Clinical trials are one of the most frustrating parts of getting a new drug approved. Clinical trials can drag on for extended periods of time and are expensive, but necessary, to fulfill. Theoretically, data science holds the key to the technology needed to shorten this process and make it more cost effective as well. Here are several ways data science can make this possible.

* Selecting Patients

Now companies can use a variety of data gathered from various sources to choose appropriate patients to take part in their clinical trials. This information can come from social media, genetic testing profiles, and public health databases.

* Monitoring Progress in Real-Time

Great care must be taken to monitor every step through the entire course of a clinical trial. Not only do patient outcomes have to be carefully monitored, but the actual policies and procedures outlined in the way the clinical trial is handled must also be.  Improved technologies  in the field of data science can make all of this easier.

* Increase Drug Safety and Reduce Serious Side Effects

Of course, a huge concern with any clinical trial is the safety of the participants and the existence of serious drug-related side effects. New technologies in data science may eventually be able to alert researchers to potential drug side effects, interactions, and contraindications before the issues even come up. This can prevent countless serious side effects or even fatalities during the testing phase for new medications.

Data Science in Sales and Marketing

In past years, sales and marketing for pharmaceutical drug companies were performed primarily on foot by paid representatives who would tirelessly visit doctor’s offices and medical centers throughout the country. This action isn’t really necessary anymore due to advances in data science. Now at least 25% of the marketing performed by drug companies is done digitally. Additionally, almost all sales and marketing teams rely heavily on targeted analytics to drive sales, improve spending, and enhance their overall bottom line.

Predictive analytics allow companies to determine which medical professionals are more likely to take an interest in a specific drug based on data gathered and analyzed before hand. This can allow for the creation of extremely targeted sales techniques driven to provide a greater degree of success. Additionally, today’s drug representatives are now equipped with smart electronic devices that have access to important real-time analytics designed to help them make the sale. This makes their time much more productive and ensures a greater degree of success.

Data Science for Better Follow-up with Patients

Drug companies have nothing useful if they don’t have constant follow-up and the important data that this provides. Follow-up allows the drug companies to know exactly how their drugs are being used, the health of the patients, and how the consumers view the new drug. In times past, getting thousands of different opinions on new drug therapies from the patients who were using them proved to be time-consuming and messy data to have to sort through. Advances in data science can lead to the gathering of this essential data in a format that’s easy to read, analyze, and make use of. With this technology, drug interactions can be noted before they become serious and affect the lives of thousands of people. Data can also be organized that will let companies know the driving force behind those who stop using their medications.

Data science is an ever-expanding area of study that affects and is used by nearly all modern day professionals. If you’re considering a degree in data science, it’s important to be aware of the various ways it can be used in the pharmaceutical industry. The use of data science in pharma will continue to grow as pharmaceutical companies continue to bring about progressive drugs and other treatments.

Related resource: 30 Best Online Master’s in Data Science Degree Programs 2017


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