What’s the big deal with big data?

It seems as though big data is entering every aspect of our life at the moment.

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Through the companies we’re used to hearing about such as Google, Facebook, and Amazon, but also in more unique instances: the 2013 Boston Marathon, Malaysia Airlines Flight 370, Edward Snowden and the National Security Agency. The reality is, once you begin to understand what big data is, you start to notice it everywhere.1

Essentially, big data describes data sets that are so large and complex that traditional data-processing application software are insufficient to deal with them. Instead, such data can be analyzed computationally to reveal patterns, trends and associations, typically relating to human behavior and interactions.2,3 However, while everyone has heard something about it, it seems that big data as a term is not that well understood, with many confusing it with the Internet of Things (IoT), artificial intelligence (AI) or machine learning.4

While related in terms of technology, the IoT instead refers to a network of internet-connected devices (for instance your smartphone, laptop, or Fitbit); AI is a non-human method of using algorithms to analyze the big data created by the devices in the IoT; while machine learning is the process by which these computers use information gathered from big data to act, without being explicitly programmed.5,6,7 Some may argue that big data is therefore the backbone of our digital world—but if the general public don’t truly understand it, they are unlikely to be able to see the extent of its potential.

Big data is already revolutionizing industries, and healthcare isn’t far behind. For decades, the foundation of most medical research has been the collection and analysis of data: regarding who gets sick, how it happened, and why. Nowadays, information can be easily shared across healthcare disciplines, and almost every smartphone out there has sensors that can monitor your health, so both the quantity and variety of accessible health data is growing exponentially.8

We are reaching the stage where we have the capacity to understand patients holistically, from the early stages of their life, with the drive to identify any warning signs of potential illness early enough so that the treatment needed is simpler, and less pricey.8,9 Thanks to the recent surge in wearables like the Apple Watch (which is now being used to identify early signs of atrial fibrillation), the wellness sector is driving us closer and closer to this reality—and chronic disease management isn’t far behind. Data from new connected health devices and apps are being compiled into databases, providing objective, accurate information on chronic diseases such as asthma. Such data, in collaboration with AI, could offer the potential for identifying patterns, predicting health outcomes, optimizing treatment strategies, and pushing the boundaries of endotype discovery.8,10

We are already starting to witness success stories relating to big data across the US. The non-profit organization UNC Healthcare in North Carolina recently developed a new system, which allows physicians to rapidly access and analyze patient data using natural-language processing (similar to that used in IBM Watson technology). Using their patients’ electronic health records (EHRs), they can extract insights and predictors of readmission risk, which allows them to identify high-risk patients, understand why they’ve been hospitalized, and take preventative action.11

Another success is Premier, the largest US healthcare alliance: comprising of a network of over 2,700 hospitals and health systems, 90,000 non-acute care facilities, and 400 physicians. The alliance has successfully compiled the largest clinical, financial, supply chain and operational comparative database, which provides members with information on clinical outcome measures, resource utilization and transaction-level cost data. With such valuable information, the alliance can now make informed strategic decisions that can improve processes and outcomes. And this was demonstrated after just one performance improvement initiative involving 330 hospitals— nearly 30,000 lives were saved, and healthcare spending reduced by almost $7 billion.12

However, for all its benefits, big data in healthcare still presents major challenges. Deciding how these data can and should be used, while maintaining security and patients’ right to privacy, is no mean feat. No matter how useful big data may be in the healthcare space, it can only be used in a secure and trusting environment—a topic that we explore further in our most recent whitepaper. Despite this, the general consensus is that the positive potential of big data far outweighs the negative.9 As long as we learn how to preserve security and privacy, the full potential of big data can be unlocked, and change the face of healthcare as we know it.

Discover more about the benefits and pitfalls of big data in our latest whitepaper in the respiratory_care v2.0 series, Small steps towards big data, and stay informed on the latest in healthcare innovation with our updates.


  1. “I see big data. All the time. It’s everywhere.” Available at: https://www.sas.com/en_us/insights/articles/data-management/i-see-big-data.html. Accessed: June 2018.
  2. Big data. What it is and why it matters. Available at: https://www.sas.com/en_gb/insights/big-data/what-is-big-data.html. Accessed: June 2018.
  3. Big data. Changing the way businesses compete and operate. Available at: http://www.ey.com/Publication/vwLUAssets/EY_-_Big_data:_changing_the_way_businesses_operate/$FILE/EY-Insights-on-GRC-Big-data.pdf. Accessed: June 2018.
  4. Havas Lynx social listening. Date: June 2018.
  5. Big Data, The Internet of Things (IoT) and Artificial Intelligence (AI). Available at: https://www.seriun.co.uk/iot-big-data-ai/. Accessed: June 2018.
  6. What is the Internet of Things? WIRED explains. Available at: http://www.wired.co.uk/article/internet-of-things-what-is-explained-iot. Accessed: June 2018.
  7. Machine learning versus AI: what’s the difference? Available at: http://www.wired.co.uk/article/machine-learning-ai-explained. Accessed: June 2018.
  8. How Big Data Is Transforming The Healthcare Sector. Available at: https://www.linkedin.com/pulse/how-big-data-transforming-healthcare-sector-bernard-marr/. Accessed: June 2018.
  9. How Big Data Is Changing Healthcare. Available at: https://www.forbes.com/sites/bernardmarr/2015/04/21/how-big-data-is-changing-healthcare/. Accessed: June 2018.
  10. Belgrave D et al. J Allergy Clin Immunol 2017; 139(2): 400–407.
  11. UNC Health Care Uses IBM Analytics to Manage Medical Data and Improve Patient Care. Available at: https://www-03.ibm.com/press/us/en/pressrelease/42179.wss.Accessed: June 2018.
  12. Data-driven healthcare organizations use big data analytics for big gains. Available at: https://www-03.ibm.com/industries/ca/en/healthcare/documents/Data_driven_healthcare_organizations_use_big_data_analytics_for_big_gains.pdf. Accessed: June 2018.

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