Big Data in life Science
Life science or medical device companies using ‘Big Data”?
Leading companies across the board are utilizing and relying on computers more and more. And with good reason, the executives of these companies are typically dealing with extensive data sets that seem to get bigger each year. Implementing ways to source, sort, and utilize data about customers, operations, and finances will decide which companies thrive. “Big data” is, in simplified terms, taking information gathered usually from larger corporations and converting that information into usable and valuable insights.
The Life science industry has been a significant stage for this new wave of data utilization. After the emergence of Covid-19 displayed a lack of quick and effective data-handling measures. Companies scrambled to measure up to this new standard, and many saw great success; for example, Merck, Alexion, Bristol-Meyers Squibb, Sanofi, and Pfizer all utilize data to a very high degree and implemented efficient systems to “fill in the gaps” of their already existing systems, which allowed them to meet the demands from the public and government regarding this new standard for medical data.
How exactly does this data help them?
Data acquisition and implementing that data is an essential part of running a successful business. Typically, data is seen as a way to better service, grow customer relationships, and manage risks. Data that answers questions such as “how much have we made this quarter compared to the previous year,” “is there a better way to target this demographic,” or “which of these adjacent industries could we pursue” should be utilized by every company under the sun. The insights provided by even these basic forms of data are precious to any company and are quickly becoming necessary in today's marketplace.
What are the results? Does it help them reduce costs, do things more efficiently, and reduce time to get products to market?
However, data can do way more for your life science company than just exposing shortcomings and identifying potential profit. Taking data to the highest levels, “big data,” life science and healthcare companies will get the opportunity to dig deep into sub-par operations and systems, reduce costs of research & development, and accelerate drug discovery. With a heightened reliance on data, life science companies should have no issues connecting with new clients, discovering new growth opportunities, or expanding their market.
FAQ:
Q: What are the titles of the positions they typically hire in data science?
A: Data scientists in life science companies have many of the same skills as a data scientist from any other industry, with a few noteworthy differences, such as a higher understanding of scientific research than commercial know-how.
1. Data scientist
2. Data Analyst
3. Data Engineer
4. Business intelligence developer
According to LinkedIn, around 24k people with these job titles are working in the biotech industry, 24k in the pharmaceutical industry, and 8k in the medical devices fields.
Q: About how many data science employees do companies typically employ?
A: Larger companies hire around 500-900 data scientists for their global rosters, while smaller companies have anywhere from 20-70 data science employees.
Squibb =521 out of 38K employees globally
Pfizer = 856 out of 100k global employees
Merck =527 out of 47k global employees
Caris life sciences =22 out 1.3k total employees
Tempus = 68 out of 1.6k total employees
Foundation medicine =27 out of 1.9k total employees
Q: What are the most common degrees that data science candidates have?
A: A bachelor’s in data science or computer science; however, many companies will still prefer a master's.
Q: What's the typical compensation range for data science candidates?
A: Between $60,000 and $130,000
Conclusion:
According to the bureau of labor statistics, the projected percent employment change for 2021-2031 predicts that the data scientist’s role will grow by 35.8 %. With the bureau of labor statistics also identifying data scientists as one of the three computer and mathematical occupations listed amongst the ten fastest growing occupations, we know that the data scientist role is here to stay and will be relied upon to a higher degree. Data scientists are filling a gap in many companies’ data acquisition to data presentation process, and we see the demand for these positions increase. With no end to the rising demand, we recommend acquiring some data scientists now, before they are in even higher demand.
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