Definition

Big Data itself is not a technology, but rather a collective term for digital data sets that are so large and complex that they are difficult or impossible to manage using traditional software and/ or hardware, nor can they be managed using conventional data management methods [1,2]. The large amounts of data can lead to new insights on patterns by generating, combining and analysing alternatives. The data comes from internal and external sources, with various formats and locations. Examples of relevant data sources in healthcare are medical files, research literature, communication, biometric data, transactional data, sensor data, websites and social media [3]. The data can also be used for other purposes than those for which it was originally gathered [1,4].

Applications & benefits

The use of data is gaining increasing importance in the digital era. Analyses of data sets can identify associations, patterns and trends, and for example detect diseases in an earlier stage. Using this knowledge, healthcare professionals and other stakeholders in the healthcare system can improve the efficiency and effectiveness of diagnoses, enabling prevention strategies and/or medical interventions, services and policy to be better linked with individual patients [1,5,6]. This ultimately results in higher-quality healthcare, lower costs and improved patient results. Researchers have suggested that Big Data could lower American healthcare costs by around 12 to 17% [7]. Big Data also offers potential advantages for translational research in the field of healthcare and well-being. This can help bridge the gap in knowledge about the progression and disease processes of both common and rare diseases [8]. Finally, it can help discover the effects on population levels, such as off-target effects and the side-effects of medication or the prevention of comorbidity [9,10].

Market

Wikibon predicts that the worldwide market for Big Data will grow in the coming years to a value of almost 93 billion USD in 2026. This amounts to an annual growth of 14.4% [11,12]. Healthcare is one of the sectors responsible for this growth. Big Data is expected to contribute towards improved quality and lower costs [13].

Driving forces

There are several factors that are stimulating the growth of the Big Data market [1,5]:

• A greater availability of data due to digitisation and new technologies such as the Internet of Things, holistic tracking and high-throughput techniques, including next generation sequencing.
• Faster connections, processes and larger storage options.
• Increasing demand for transparency, and insight into health data.
• Personalized care and medicine demand the analysis of huge amounts of complex data [13].

Growing availability of (medical) data
Growing connectivity and improved data infrastructure
New technological capabilities

Hindering forces

Increasing emphasis on privacy sensitivity
Lack of expertise
Lack of awareness and reluctance among potential users

Despite the possibilities, there are still many challenges concerning the use of Big Data. One of the main challenges still seems to be the potential intrusion into individual privacy. Most medical data is anonymous, but the risk of data becoming de-anonymised is increasing due to data from various sources being combined [14].

The question remains as to whether people worry about privacy in their daily lives, given the ease with which many publicise personal information on social media. Privacy seems to be less and less something we have a right to, and increasingly something that has to be paid for [14,16]. Another challenge lies in the centralisation of knowledge. ‘Information is power’ and the danger is that Big Data will result in private monopolies being created on data and knowledge. As such, society is becoming increasingly dependent on private parties [16]. Despite the financial opportunities that data presents, willingness to invest in research on data applications seems to be limited, while this is so very important in healthcare [17]. After all, technology is not perfect. All too often, analyses result in incorrect correlations or unstructured data sets that result in a ‘rubbish in, rubbish out’ effect [18]. Deep data might be one possible solution (see inset: Deep Data). Finally, a certain degree of acceptance will also be needed. Will doctors be willing to accept a diagnosis made by a computer? After all, doctors bear the final responsibility and are used to making diagnoses autonomously, based on their own clinical insights [15,16]. Currently, there is still a lack of ‘experts’ who can implement Big Data in healthcare [7,13,19].

Conclusion

With all the potential and rapid developments to improve the quality of analyses, Big Data will bring about many changes in healthcare. It will become increasingly easy to make diagnoses and treatments more personal and to use them predictively.