Content area
Full Text
Introduction
According to a report by International Data Corporation (IDC) in 2011, the total volume of created and copied data in the world was 1.8 ZB, which had increased by nearly nine times within five years (Gantz and Reinsel, 2011). This figure is expected to double every two years (Chen et al., 2012). In 2012, 2.5 quintillion bytes of data were created on a daily basis, and 90 per cent of this data were created within two years (IBM, 2013). This data explosion led to the popularity of the “big data” concept. In the past, new technological developments typically first appeared in technical and academic publications. The fast evolution of big data technologies and the quick and widespread adoption by the industry left little time for academic investigation of big data in facilities management (FM), which implies that a coherent understanding of the concept and its nomenclature has yet to be developed (Gandomi and Haider, 2015).
The Gartner IT Glossary defines big data as “high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight, decision-making and process automation” (Gartner IT Glossary, 2019). Industry analyst, Doug Laney, articulated that volume, variety and velocity (3 V’s) are the three dimensions of challenges inherent in the big data concept (Laney, 2001). Gandomi and Haider (2015) provide further descriptions of the three dimensions. Volume refers to the magnitude of data, but the definition of volume differs depending on factors, such as time, type of data and the industry. Variety refers to the structural heterogeneity in a dataset, which can be structured, semi-structured, or unstructured. Among these, unstructured data constitute 95 per cent of big data. Velocity refers to the rate at which data are generated and analyzed. The limitations of big data utilization are dependent upon the size, sector and location of a company and these limitations also evolve over time. What may be deemed big data today may not meet the threshold in the future because technology and storage capacity will change. The dimensions of big data are interdependent each other. Apart from the 3 V’s (velocity, volume and variety), four other dimensions that are considered relevant to big data are value (usefulness), variability (diversity of data flow), complexity (degree...