Can big data improve firm decision quality? The role of data quality and data diagnosticity

Ghasemaghaei, Maryam; Calic, Goran

doi:10.1016/j.dss.2019.03.008

Cited by 123 publications

(84 citation statements)

References 80 publications

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“…Thus, these authors have demonstrated the emergent need for firms to have effective data quality mechanisms to be able to derive sufficient value from BDA application. Ghasemaghaei and Calic [47] used OLT and the DQD compiled by Wang and Strong [45] to explain the effect of BDA on data quality categories. They found that while many organisations have invested in BDA application, they need to pay more attention to the quality of their data in order to enhance the quality of the solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The indicators to measure BDT (velocity, veracity, value, and variability) were self-developed based on the definitions proposed by Arockia et al [32]. The accuracy, believability, completeness, timeliness, and ease of operation constructs, each with four indicators, were adapted from [8,47], and [48]. BDA application, with four indicators, was adapted from [23] and [55].…”

Section: Research Instrumentmentioning

confidence: 99%

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Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

et al. 2021

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The popularity of big data analytics (BDA) has boosted the interest of organisations into exploiting their large scale data. This technology can become a strategic stimulation for organisations to achieve competitive advantage and sustainable growth. Previous BDA research, however, has focused more on introducing more traits, known as Vs for big data traits, while ignoring the quality of data when examining the application of BDA. Therefore, this study aims to explore the effect of big data traits and data quality dimensions on BDA application. This study has formulated 10 hypotheses that comprised of the relationships of big data traits, accuracy, believability, completeness, timeliness, ease of operation, and BDA application constructs. This study conducted a survey using a questionnaire as a data collection instrument. Then, the partial least squares structural equation modelling technique was used to analyse the hypothesised relationships between the constructs. The findings revealed that big data traits can significantly affect all constructs for data quality dimensions and that the ease of operation construct has a significant effect on BDA application. This study contributes to the literature by bringing new insights to the field of BDA and may serve as a guideline for future researchers and practitioners when studying BDA application.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Research Instrumentmentioning

confidence: 99%

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

et al. 2021

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“…Merendino et al (2018) conducted several in-depth interviews with directors of UK-based companies and concluded that there was a shortfall in cognitive capabilities related to BDA, and that the directors' decisionmaking will be improved by sharing the knowledge within their teams. Other authors have pointed out towards the mediating role of data quality (veracity) in the whole process of data collection, processing, analyzing and decision-making (Ghasemaghaei and Calic (2019) and Janssen et.al. (2017)).…”

Section: Introductionmentioning

confidence: 99%

How can Big Data contribute to improve the financial performance of companies?

Barilla

Reynoso

2020

REMEF

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Objetivo: Proponemos una metodología integral que combina un conjunto de herramientas del análisis de Big Data (BDA) con el análisis prospectivo, análisis de riesgo y análisis estratégico con la finalidad de mejorar el desempeño financiero de la empresa medido a través de Key Performance Indicators (KPI)} Metodología: La metodología está compuesta por cinco (5) etapas: modelación financiera, prospectiva, análisis de riesgo que incluye BDA, análisis estratégico y monitoreo Resultados: Esta metodología permite dirigir el BDA hacia la caracterización de las variables críticas que crean valor para la empresa, diseñar estrategias contingentes y evaluar su impacto en los indicadores financieros seleccionados (KPI) todo esto de forma multidimensional. Recomendación: Se requiere un monitoreo constante del modelo para generar diferentes formas de innovación y flexibilidad en la empresa y mejorar su desempeño financiero. Limitación: El éxito de la metodología depende de la habilidad de la empresa para mejorar, adaptarse, ajustarse o innovar para ganar, sostener o reconfigurar una ventaja competitiva. A esta habilidad se le denomina capacidades dinámicas orientadas a procesos (PODC) Originalidad: La metodología propuesta es integral ya que permite la inclusión de diversas áreas de la empresa con el objetivo de mejorar su desempeño financiero representado por los KPIs. Asimismo, el análisis se puede realizar para áreas específicas y unidades de negocio. Conclusión: La metodología propuesta promueve la innovación y la flexibilidad que mejorarán el desempeño financiero de la compañía siempre que exista un buen ajuste entre las actividades de Big Data, la estructura organizacional, el compromiso de la alta gerencia y el apoyo para el desarrollo de PODC.

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“…Sin embargo, la originalidad de este estudio no es una afrenta a la literatura existente, sino una oportunidad para que los académicos de análisis de datos incluyan DIQ en sus investigaciones futuras. Las organizaciones están buscando cómo aprovechar el poder del big data para mejorar el proceso de toma de decisiones (Ghasemaghaei & Calic, 2019;Janssen, Voort, & Wahyudi, 2017;Quinn, Dibb, Simkin, Canhoto, & Analogbei, 2016). De acuerdo a NewVantage Partners LLC (2019), el 77% de las 1000 compañías con más ingresos de los Estados Unidos, indican que la "adopción comercial" de iniciativas de big data sigue representando un desafío para sus organizaciones, de hecho, el 92% de ellas están aumentando su ritmo de inversión en big data.…”

Section: Conclusiones E Implicaciones Del Capítulounclassified

“…Las nuevas formas de datos requieren tecnologías novedosas que sean capaces de manejar los desafíos que plantean los grandes volúmenes, diversos y de rápido movimiento (Ghasemaghaei & Calic, 2019;X. Wang et al, 2015).…”

Section: Capacidad Tecnológica Y Calidad De La Informaciónunclassified

Analítica de datos y calidad de la información para la toma de decisiones del marketing

Serazzi¹

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Can big data improve firm decision quality? The role of data quality and data diagnosticity

Cited by 123 publications

References 80 publications

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

How can Big Data contribute to improve the financial performance of companies?

Analítica de datos y calidad de la información para la toma de decisiones del marketing

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