Conceptualization, Operationalization, and Validation of the Digital Data Stream Readiness Index

Conceptualization, Operationalization, and Validation of the Digital Data Stream Readiness Index

Elisabetta Raguseo (Politecnico di Torino, Torino, Italy), Federico Pigni (Grenoble Ecole de Management, Grenoble, France) and Gabriele Piccoli (Louisiana State University, Baton Rouge, United States & University of Pavia, Pavia, Italy)
Copyright: © 2018 |Pages: 21
DOI: 10.4018/JGIM.2018100106

Abstract

This article describes how in their search for value creation, companies are investing considerable resources in so-called “Big Data” initiatives. A peculiar aspect of these initiatives is the increasing availability of real-time streams of data. Successfully leveraging these streams to extract value is emerging as a critical competence for the modern firm. Despite the significant attention received, scholarly research on Digital Data Stream (DDS) remains insufficient. More importantly, there are no specialized definitions and measurement instruments that can move the field forward by initiating a cumulative research tradition. This article can provide clarification on key definitions, differentiating DDS from Big Data. Drawing on the organizational readiness concept, the DDS readiness index develops as a measure of organizational readiness to exploit real-time digital data. This article will conceptualize, define, operationalize and validate the index. By identifying the four dimensions of mindset, skillset, dataset and toolset as the elements of the DDS readiness index and discussing its managerial and research implications
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1. Introduction

Every day, people generate digital data through tweets, clicks, videos and the plethora of sensors that are embedded in their devices. Instruments and machines such as smart meters, manufacturing sensors, equipment logs, and vehicle tracking systems, automatically and continuously, generate digital data. Big data is “a holistic approach to manage, process and analyze 5 Vs (i.e., volume, variety, velocity, veracity and value) in order to create actionable insights for sustained value delivery, measuring performance and establishing competitive advantage (Fosso Wamba et al., 2015, p. 6). It is the umbrella term for this evolving trend. Recent research suggests that Big Data is a driver of business success across a wide range of industries (McAfee & Brynjolfsson, 2012). Organizations are investing considerable resources in Big Data initiatives in search of value creation opportunities (Chen, Chiang, & Storey, 2012), in driving their digital business strategy (Bharadwaj, El Sawy, Pavlou, & Venkatraman, 2013) and in making better informed business decisions (Eastburn & Boland, 2015).

Despite the expected impacts of Big Data and their organizational outcomes, there has been only limited research focusing on investigating the adoption and usage of Big Data in firms (Baesens, Bapna, Marsden, Vanthienen, & Zhao, 2016), or framing the organizational transformational outcomes (George, Haas, & Pentland, 2014). While the body of literature on Big Data is constantly increasing, little is known about the factors determining or moderating their adoption and, in particular, on the preconditions leading to their successful outcome.

Part of the problem resides in the intrinsic polysemy of the term Big Data not representing a single technology, technique or initiative, but rather a trend across many areas of business and technology. We observe that most definitions of Big Data (e.g., Fosso Wamba, Akter, Edwards, Chopin, & Gnanzou, 2015) stress the dimension of volume - the size of the managed data sets. Such focus implies the development of new data management techniques (e.g., non-relational databases), increased and scalable data processing infrastructures and software (e.g., distributed and parallel tools, data visualization tools). Big Data, is then a term that refers, at the same time, to initiatives related to data management, processing, analysis and visualization. This “all encompassing” definition makes it difficult to develop a cumulative tradition of research in the area (Chen et al., 2012). Thus, it is critical for academics to also explore specific aspects of the general Big Data trend.

In this article, we refer to the related, but more focused, concept of Digital Data Streams (DDSs). DDSs, defined as the flows of digitally encoded data, available in real time and describing a related class of events (Piccoli & Pigni, 2013; Piccoli, Rodriguez, & Watson, 2015), fit in the historical evolution of computing, from batch processing, to online transactions processing, to the continuous processing of streaming data (Watson, Wixom, Hoffer, Anderson-Lehman, & Reynolds, 2006). We focus on DDSs for three reasons. First, DDSs are a key organization resource that firms can leverage for competitive advantage. A world characterized by vast amounts of real-time digital data flows, beyond the historical data that companies have typically leveraged (Davenport, 2014; Watson et al., 2006). The emerging world of the Internet-of-Things, for example, is replete with DDSs (French & Shim, 2016). Second, the DDS concept has the appropriate level of focus and precision, thus enabling precise definition and rigorous academic inquiry. Third, DDSs are a promising level of analysis for understanding the organizational impacts of real-time digital data, thereby allowing managers to dissect events in real-time, to shorten the decision cycle, and deepen their understanding of customers at the same time (Piccoli et al., 2015).

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