Using Big Data in Healthcare

Background

The term ʻbig dataʼ refers to high volume and complexity data, which have become extremely difficult to process using traditional techniques. One of the earliest definitions of big data was provided by Laney (2001), describing the new data growth challenges as three-dimensional (Volume, Velocity, Variety). The three-dimensional model, which stands as the most adopted definition up to date, regards big data as data that are massive in size (Volume), need to be quickly acquired and processed (Velocity), and are unstructured, loosely coupled and cannot be handled by traditional RDBMS (Variety). Another 'V' term that frequently accompanies the definition of big data is Veracity, indicating the need of processing clean and quality data, in order to achieve meaningful results from the big data use (cf. e.g., Buhl et al., 2013).

Nowadays, various sectors have transformed their way of functioning and they are collecting and exploiting large amounts of data, that may offer groundbreaking insights. Traditional services, such as weather forecasting, now require the manipulation of large amounts of data in order to provide better models, more accurate predictions, profiling and decision support. Many other paradigms exist where big data analytics can reshape well established operations. Power grids, for example, are beginning to evolve into smart grids, which are able to analyze data acquired from smart home appliances and smart sensors in order to sustain resources and energy and prevent service downtime during peak hours (Balac et al., 2013).