Vertigo Over the Seven V's of Big Data

Chartier, Tim

doi:10.1002/jcaf.22145

Cited by 4 publications

(5 citation statements)

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“…Volume is the size of the data (Ghasemaghaei, 2021), while veracity deals with the reliability and insightfulness of the data (Wamba et al, 2017), and variety refers to different sources of data (Akter et al, 2017). Value denotes the worth of hidden insights inside the BD (Chartier, 2016) regarding, for example, economically, digitally, and environmentally sustainable products, and velocity is the data generation speed (Talwar et al, 2021).…”

Section: Bd and Analyticsmentioning

confidence: 99%

“…In contrast, the functional dimensions portray the utilization of innovative technologies for collecting, organizing, distributing, and analyzing heterogeneous data sets (Daniel, 2019). While McAfee and Brynjolfsson et al (2012) identify three characterized patterns that involve 3V's: volume, variety, and velocity, Chartier (2016) includes four additional V's, including validity, veracity, value, and visibility.…”

Section: Bd and Analyticsmentioning

confidence: 99%

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Moving Toward Economic and Digital Sustainability in Marketing Analytics

Haverila,

Al-Azad,

Haverila

et al. 2024

Journal of Global Information Management

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Despite analytical advancements, firms have yet to realize the full potential of big data marketing analytics (BDMA) because the poor quality data restricts customer predictions and insightful decisions. Technology and market uncertainty create challenges in understanding business needs, choosing analytical tools, determining customer insights, and market trends. This study aims to assess the quality of marketing analytics, including technology and information quality. Data were collected from 236 North American respondents working in firms with at least limited experience in the deployment of BDMA. The analysis tool was PLS-SEM. The findings supported the hypothesis that technology and market uncertainty negatively influence the quality of analytical outcomes. This study makes a significant theoretical and methodological contribution to BDMA literature by assessing the quality of analytics as an integrated formative construct.

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Section: Bd and Analyticsmentioning

confidence: 99%

Section: Bd and Analyticsmentioning

confidence: 99%

Moving Toward Economic and Digital Sustainability in Marketing Analytics

Haverila,

Al-Azad,

Haverila

et al. 2024

Journal of Global Information Management

View full text Add to dashboard Cite

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“…The most common approach has been to define "big data" through characteristics that pertain to data, also called Vs. While the initial definition involved three Vs that are volume, variety, and velocity (Russom, 2011;McAfee and Brynjolfsson, 2012); the most recent version includes four additional Vs: validity, veracity, value, and visibility (Livingstone, 2013;Chartier, 2016). There also exist a number of definition variants 600 BPMJ 23,3…”

Section: Bdads: Definitions and Viewpointsmentioning

confidence: 99%

Let’s stop trying to be “sexy” – preparing managers for the (big) data-driven business era

Carillo¹

2017

BPMJ

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Purpose The purpose of this paper is to analyze the inadequacies of current business education in the tackling of the educational challenges inherent to the advent of a data-driven business world. It presents an analysis of the implications of digitization and more specifically big data analytics (BDA) and data science (DS) on organizations with a special emphasis on decision-making processes and the function of managers. It argues that business schools and other educational institutions have well responded to the need to train future data scientists but have rather disregarded the question of effectively preparing future managers for the new data-driven business era. Design/methodology/approach The approach involves analysis and review of the literature. Findings The development of analytics skills shall not pertain to data scientists only, it must rather become an organizational cultural component shared among all employees and more specifically among decision makers: managers. In the data-driven business era, managers turn into manager-scientists who shall possess skills at the crossroad of data management, analytical/modeling techniques and tools, and business. However, the multidisciplinary nature of big data analytics and data science (BDADS) seems to collide with the dominant “functional silo design” that characterizes business schools. The scope and breadth of the radical digitally enabled change, the author are facing, may necessitate a global questioning about the nature and structure of business education. Research limitations/implications For the sake of transparency and clarity, academia and the industry must join forces to standardize the meaning of the terms surrounding big data. BDA/DS training programs, courses, and curricula shall be organized in such a way that students shall interact with an array of specialists providing them a broad enough picture of the big data landscape. The multidisciplinary nature of analytics and DS necessitates to revisit pedagogical models by developing experiential learning and implementing a spiral-shaped pedagogical approach. The attention of scholars is needed as there exists an array of unexplored research territories. This investigation will help bridge the gap between education and the industry. Practical implications The findings will help practitioners understand the educational challenges triggered by the advent of the data-driven business era. The implications will also help develop effective trainings and pedagogical strategies that are better suited to prepare future professionals for the new data-driven business world. Originality/value By demonstrating how the advent of a data-driven business era is impacting the function and role of managers, the paper initiates a debate revolving around the question about how business schools and higher education shall evolve to better tackle the educational challenges associated with BDADS training. Elements of response and recommendations are then provided.

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“…Although automated medical decision making is not new (Gleser & Collen, 1972: 180-181), the speed of information growth is said to have exceeded Moore's law (Chen & Zhang, 2014: 314, 318, 320) and its value is believed to have exceeded oil's (The Economist, 2017: 7). Therefore, large amounts of data are being processed and another ‗V', for Value, is attributed to Big Data (Chartier, 2016).…”

Section: Introduction (To Automated Decision Making -In Health and Elsewhere)mentioning

confidence: 99%

The Long Road Toward Tracking the Trackers and De-biasing: A Consensus on Shaking the Black Box and Freeing From Bias

Bouchagiar¹

2019

RES

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Automated decision making is both promising and threatening. Processing the biggest data possible may lead to societal advances but also violate human rights. There is, then, an acute need to protect individuals without impeding major benefits. Non-human agents may be biased; and they may not lend themselves to easy explanations. Instead of focusing on interpreting models, there seems to be a shift toward a concept of risk assessments. Opaque systems are aimed at predicting, or forecasting, future situations. This challenges human values and ethical principles. Even though incorporating ethics in machines is an old subject of legal discussion, consensus has not yet been reached; for theories and values may be controversial. This paper examines whether there could be an agreement on fundamental principles. A commonly understood basis could allow for fair and proportionate mechanisms to address crucial aspects of partiality and opacity in automated decision making. It could trigger a shift toward a concept of ‘tracking the trackers’ and a discussion on a ‘right to an unbiased decision maker’.

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Vertigo Over the Seven V's of Big Data

Cited by 4 publications

References 0 publications

Moving Toward Economic and Digital Sustainability in Marketing Analytics

Moving Toward Economic and Digital Sustainability in Marketing Analytics

Let’s stop trying to be “sexy” – preparing managers for the (big) data-driven business era

The Long Road Toward Tracking the Trackers and De-biasing: A Consensus on Shaking the Black Box and Freeing From Bias

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