Big data has made a massive impact on healthcare, but securing big data talent isn’t easy

Big data continues to make a serious impact on the healthcare industry, and information is coming from a diverse range of directions: internet-connected medical devices and IoT initiatives, research and development data collected by pharmaceutical companies, clinical trials data, and more, McKinsey & Company reported. Big data analytics in healthcare could potentially predict epidemics, help patients avoid preventable diseases, reduce treatment costs and improve quality of life overall. Full-scale implementation and usage are still in process, but big data has definitely become a major initiative.

While big data and healthcare analytics gain steam, talent remains an issue. According to, 50 percent of organizations look for data science experts to support, lead and provide expertise, but haven’t been able to hire the staff they need to operationalize big data analytics programs.

Why big data talent is so hard to find

So why is big data talent so hard to find in healthcare? It could be a combination of factors:

  • Qualified data scientists are few and far between.
  • Data scientists are in demand throughout every vertical. McKinsey noted that in 2018, fewer than 200,000 data scientists are available to fill 490,000 data science jobs. Simply put, there’s a lot of work to do, and not enough people to do it.
  • Big data professionals with healthcare-specific experience are in incredibly high demand.
  • There’s already a shortage of big data scientists overall. It’s a tall order to find big data scientists with healthcare expertise, or at the very least, experience. It’s not enough to know big data. In the healthcare industry, organizations look for a unique variety of an already unique skillset. Expertise with data mining and analysis is critical, but so is understanding its context. Healthcare nomenclature is industry-specific, and data types aren’t the same as other industries. The ideal data analytics professional for a hospital will have to be familiar with healthcare concepts and know how to interact with clinicians, commented.
  • Big data initiatives require advanced technology, but many healthcare organizations run legacy technology.
  • Hospitals may desire talent to help make an ongoing big data analytics project successful, but still have legacy systems in place that were born in the mainframe era, explained. Data scientists possess advanced skillsets; they’re good at math, possess programming skills, understand business intelligence and are used to open data APIs and open source software. It’s hard to attract individuals who have developed their professional skills in an innovative environment to a hospital with old technology platforms.
  • 31 percent of healthcare organizations don’t have a clear picture of where to start with their healthcare analytics initiatives. This makes putting together an analytics department more important and more difficult at the same time.
  • Hospitals and providers are facing difficulties with workflows, health information exchange, interoperability and more. The barriers are making it hard to define a concrete big data analytics program, which makes it hard to bring in experts, even though experts are the key to breaking the barriers and getting programs organized.

What should healthcare do about it?

There’s no easy fix, but there are steps healthcare organizations can take to make positions more attractive and begin to fill the skills gap. It might be an uphill battle for a while, but hospitals and providers have options.

  • If hiring experts from outside of the organization isn’t preferred, there’s always the option of training internal tech personnel. With a solid training program, mentoring options and an infrastructure that will foster growth, it would be possible to use the expertise of one or two healthcare-seasoned data scientists to build a flourishing big data analytics team.
  • Healthcare organizations are under pressure to put together strong big data analytics programs and strategies, particularly in the face of advancing in front of competition. It sounds counterintuitive, but it will pay (literally) to take the time to carefully plan out how to mine and operationalize actionable insights. If healthcare analytics goals are clear, it will be easier to secure data science and infrastructure architectural expertise that’s needed to support a healthy, enduring program.

Flexential updated our healthcare study in 2018. Part of our findings defined the following fundamental conclusions related to the ongoing growth of IoT, telemedicine and big data:

  • Application and device choice are key concerns. CIOs choose to integrate new technologies with caution due to security worries, but often have little control.
  • Devices and applications are producing increasing amounts of data. The majority of organizations are still figuring out what to do with it and how to best leverage it.
  • Big data is emerging as a major initiative with “population health” and connected devices, but implementation & usage is not yet strong.

If your IT team has questions about the infrastructural changes you may need to make to support your big data program, contact us at to speak with one of our experts.