Four steps to realizing business value from digital data streams

Based on four case studies and a follow-up survey, we have identifiied the key success factors for realizing value from DDS (digital data stream) investments. But managers need to pay attention to the combinations of success factors. A key fiinding is that value realization is improved when the agility of the resource allocation process is appropriate for the levels both of DDS platform maturity and of commitment from datadriven top management. We present a four-step sequence and a decision framework for putting the optimum combination of success factors in place

Realizing value from business analytics platforms: The effects of managerial search and agility of resource allocation processes

Few organizations have been able to realize value from their investments in business analytics. This could be due to an inadequate understanding of the pattern of investments required to realize value from business analytics. Specifically, we propose that business analytics requires an upfront investment of infrastructure capital to build a mature platform, followed by multiple investments of innovation capital to create value through competitive actions informed by analytics-enabled insights. Drawing on dynamic capabilities and digital options literatures, we develop a model in which the effect of investments in maturity of the business analytics platform on organizational value is moderated by the agility of the process allocating resources for innovation, and by the efforts expended by line managers to search and select the insights. The model is tested on data collected from a survey of line managers. The findings support the proposed model. Implications and plans for future research are discussed

Exploring the Digital Transformation Based on Big Data with Ubiquitous Internet of Everything

Digital technologies present both game-changing opportunities for and existential threats to companies. Digital services in consumer-facing organizations offer novelty value propositions, closer consumer relationships and higher automation of consumer-facing processes. Facing big digital data streams generated by ubiquitous Internet of Everything(IoE) and savvy customers with mobile computing and social media, this paper focuses on digital transformation journeys seeking digital capabilities and digital leadership to upgrade organizational performance, one is discovering big data value, the other is dual methods with agile. The finding provides practical implications that can help guide practitioners in digital transformation

Towards a reconceptualization of data in organizations: a literature review

Information Systems researchers have sought to demonstrate the strategic value of data in organizations through robust evidence. Over time, the ways data benefit organizations have evolved and become more diverse, yet definitions of data and their value propositions have not kept up and remain disconnected. The field still lacks clear understanding of various roles data play in organizations and how to define them. This paper presents a comprehensive review of related literature in the Information Systems and Management fields from the past two decades. We first conduct a systematic literature search and organize them into key research themes by the purpose of data use. We then propose a reconceptualization of data that takes into account their distinct features. Our aim is to provide an explanation for the unique nature of data and the diverse sources of their value in organizations

Conceptualization, operationalization, and validation of the digital data stream Readiness Index

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

AAPOR Report on Big Data

In recent years we have seen an increase in the amount of statistics in society describing different phenomena based on so called Big Data. The term Big Data is used for a variety of data as explained in the report, many of them characterized not just by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, the way in which they are collected, and disseminated are fundamental. The change constitutes a paradigm shift for survey research.There is a great potential in Big Data but there are some fundamental challenges that have to be resolved before its full potential can be realized. In this report we give examples of different types of Big Data and their potential for survey research. We also describe the Big Data process and discuss its main challenges

Tap ‘Follow’ #FitFam: A Process of Social Media Microcelebrity

The practice of microcelebrity in social media has become part of the internet’s mainstream, and has led to the rise of influencers–trusted tastemakers in an industry niche–who are playing increasingly larger cultural and economic roles. Scholars have examined this topic since Senft introduced it in 2001, shedding light on strategies and practices of popular influencers, as well as the cultural milieu contributing to microcelebrity practices. Missing from the literature, however, is an explanation of how these popular microcelebrities reached their social media influencer status. Thus, through phenomenological interviews with 24 participants in multiple areas of the fitness sector, this study presents a general seven-step process by which these individuals became microcelebrities and leveraged their followings. Three findings are particularly noteworthy. First, a process detailing how influencers reached their status contributes to our theoretical understanding of microcelebrity by offering contextual factors and general steps experienced by influencers. Second, although microcelebrity practices are characterised by intentional self-commodification, most influencers in this study began their careers accidentally. Third, social media may be altering the traditional career paths of fitness professionals, especially as it relates to educations and credentials, which can be substituted with body capital. Future research may utili(z)e this process as a framework to investigate specific influencer strategies over time or at certain career stages, the meaning ascribed to influencers and microcelebrity practices, and influencer motivation related to individual context. Findings also encourage continued examination of social media’s effects on the fitness industry as a whole