I. Introduction
The term Big Data refers to large-scale information management and analysis technologies that exceed the capability of traditional data processing technologies [1]. Gartner defines Big Data as “high volume, velocity and/or variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation” [1]. In fact the huge size is not the only property of Big Data. Only if the information has the characteristics of Volume, Velocity and/or Variety we can talk about Big Data [2] as shown in Figure 1. Volume refers to the fact that we are dealing with ever-growing data expanding beyond terabytes into petabytes, and even exabytes (1 million terabytes). Variety refers to the fact that Big Data is characterized by data that often come from heterogeneous sources such as machines, sensors and unrefined ones, making the management much more complex. Finally, the third characteristic, that is velocity that, according to Gartner [3], “means both how fast data is being produced and how fast the data must be processed to meet demand”. In fact in a very short time the data can become obsolete. Dealing effectively with Big Data “requires to perform analytics against the volume and variety of data while it is still in motion, not just after” [2]. IBM [4] proposes the inclusion of veracity as the fourth Big Data attribute to emphasize the importance of addressing and managing the uncertainty of some types of data. Striving for high data quality is an important Big Data requirement and challenge, but even the best data cleansing methods cannot remove the inherent unpredictability of some data, like the weather, the economy, or a customer's actual future buying decisions. The need to acknowledge and plan for uncertainty is a dimension of Big Data that has been introduced as executives seek to better understand the uncertain world around them. The amount of available data has exploded significantly in the past years, due to the fast growing number of services and users producing vast amounts of data [5]. In particular, the Internet of Things (IoT) has given rise to new types of data, emerging for instance from the collection of sensor data and the control of actuators. The explosion of devices that have automated and perhaps improved the lives of all of us has generated a huge mass of information that will continue to grow exponentially. For this reason the need to store, manage, and treat the ever increasing amounts of data that comes via the Internet of Things has become urgent. With the amount of data being produced every day, there is the need to unlock the unnamed fifth V of big data: VALUE. According to analysts with Forrester [6], most organizations today use less than 5% of the data thats available to them. As our capability to collect data has increased, our ability to store, sort and analyze it has diminished. In this context, Big Data becomes immensely important, making possible to turn into this amount of data in information, knowledge, and, ultimately, wisdom.
Big data characteristics