Opening Chapter Case: The Emerging Field of Crisis Analytics - Part 1 The closing chapter case discusses crisis analytics, the integration of big data with crisis management. Within crisis management, big data can facilitate the preparation, management, and learning processes involved in addressing organizational crisis events Hence, crisis analytics has become a sub-field of the crisis management discipline.336 Big Data Defined The term big data was first used to describe how multiple datasets can be used to address problems associated with visualizing data in the science and engineering fields Cox & Ellsworth, 1997). Furthermore, the data may be stored in various geographic locations. The problem for scientists and engineers was how to access smaller segments of the data in a format that facilitated problem-solving. However, local computer capacities may not be able to handle extensive data sets. More recently, the definition proposed by IBM maintains that big data is characterized by the three Vs: Volume, variety, and velocity (Adams, 2017): . Volume refers to a large amount of data generated across many venues, such as mobile devices, social media, and the Internet of Things (loT) (Adams, 2017). The concept of volume also implies that the data cannot be processed on a single machine or with traditional computational software (Choi & Lambert, 2017; Qadir et al., 2016). . Variety refers to the nature of the data (e.g., structured or unstructured) and the richness of the data representation (e.g., text format, image, or video) (Emmanouil & Nikolas, 2015). For example, social media data has contributed to the growing level of unstructured data. Velocity refers to the speed at which data is generated. Some data, such as streaming videos or Tweets are created quickly, while other data, such as inspection forms used to process and predict accidents, accrue more slowly (Bernini, 2017). Velocity can also refer to the speed at which the data is transformed into more useable information (Adams, 2017; Fertier et al., 2016). Three Vs have been added to augment the definition and characteristics of big data: . Veracity refers to the authenticity of the data. For example, is the data accurate and consistent with reality (Fertier et al., 2016; Qadir et al., 2016)? Data that is not trustworthy presents problems with its ability to help decision-makers. Value refers to the usefulness of the data. Much of the data within the realm of big integrity. data is useless because it is either inaccessible, the dataset is too large, or it lacks . Visualization refers to the ability to depict big data information in a visual format. Although not a characteristic of big data, it is essential because data that cannot be communicated visually lacks value. An application of visualization would include crisis mapping (Qadir et al., 2016).337 Dig Data Described Describing big data may also prove useful. The Big Data for Development report (UN Global Pulse, 2012) offers several characteristics (Watson et al., 2017): 1. Big data is often digitally generated and does not require human activity. For example, the Internet of Things (loT) is continually creating data using a series of ones and zeros and, thus, is manipulated by computers and modules. 2. It is a byproduct of digital interactions with service providers. 3. It is collected automatically in IT systems that extract and store it. 4. It is trackable geographically, for example, through smartphone use. 5. It is continuously analyzed, meaning it is being reviewed systematically to ensure human well-being. The UN Global Pulse report assumes that data s generated primarily through digital systems. However, others have noted big data can reside in banks of data generated manually, such as safety inspection reports (Schultz, 2015). Also, big data is not always passively produced. Many sources are initiated by customers who buy products or services and then relay experiences via social networking sites (Drosio & Stanek, 2016). Big Data Sources Big data emanates from individual, government (public sector), and business (private sector) sources (Watson et al., 2017). However, because these three sources overlap, some have classified big data from their technological roots. In their taxonomy of crisis analytics, Qadir and colleagues (2016) note data sources regarding six techno categories: data exhaust, online activity, sensing technologies, public data, and crowdsourced data. These are described next. Data exhaust. Much of what is considered big data originates from digital sources with mechanisms that create their data as part of the operating system. Data exhaust describes the process whereby data is a byproduct of a device doing "something" or service rendered, such as processing payments or using a telephone provider (Lokanathan & Gunaratine, 2015). Online activity. This source is far-reaching in that it includes many venues, including search activity and social media usage. Both activities result in different outcomes withSTAPLES Postscript 338 social media encompassing an interaction aspect between senders and receivers. The deep web is the dark underbelly which traditional search engines may not reach (BrightPlanet, 2015). Online activity occurs over all three sources of big data, individuals, governments, and business. Sensing technologies. Sensors gather and communicate data to a central processor. For example, modern automobiles have many sensors that generate data about how the vehicle is running to the vehicle module (i.e., computer), which commands the car's systems. Sensing technologies exist across all three big data sources. Public data. Governments collect an abundance of data, including census data, birth and death certificates, and personal and socio-economic data (Qadir et al., 2016). Although the government collects a lot of data, much of it is not readily available, and hence, is not very useful. Crowdsourcing. Data can be collected from large groups of people for specific questions or problems. The rationale is that the collective opinion of a large group of people can be as reliable as the opinions of experts (Qadir, 2016), a phenomenon known as the wisdom of crowds (Surowiecki, 2005). In addition to the sources noted previously, many companies store data from their day-to-day operations. For example, safety inspection and audit reports can accumulate, both in written and digital forms (Bernini, 2017; Marsh, 2016; Mathis, 2016). Given this backdrop, the links between data analytics and crisis management are clear. Case Discussion Questions 1. How can the existence of big data create crises in organizations? 2. What potential uses do you see for data analytics in the crisis management field: Introduction The opening chapter case illustrates one of the new trends e management, crisis analytic sis