Analytics: Key to Go from Generating Big Data to Deriving Business Value

Arora, Deepali; Malik, Piyush

doi:10.1109/bigdataservice.2015.62

Cited by 18 publications

(9 citation statements)

References 46 publications

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“…The potential to extract actionable insights from Big Data has gained increased attention of researchers in academia as well as several industrial sectors. The ability to generate value from large volumes of data is an art which combined with analytical skills needs to be mastered in order to gain competitive advantage in business (Arora & Malik, 2015).…”

Section: Annual Scientific Productionmentioning

confidence: 99%

Social media and e-commerce: A scientometrics analysis

Javid¹,

Nazari²,

Ghaeli³

2019

10.5267/j.ijdns

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The purpose of this research is to investigate the status and the evolution of the scientific studies on the effect of social networks on e-commerce. The study seeks to address the status of a set of scientific productions of researchers in the world indexed in Scopus based on scientometrics indicators. In total, 1926 articles were found and the collected data were analyzed using quantitative and qualitative indicators of scientometrics with bibliometrix R software package. The findings show that researches have grown exponentially since 2009 and the trend has continued at relatively stable rates. Thematic analysis shows that the subject had a significant but not well-developed research field .There is a high rate of cooperation with a rich research network among institutions in United States, European and Asian countries. Studies also show that research interest in this area is prevalent in developed countries. In addition, the lack of funds and complex analytical tools may be due to lack of studies in developing countries, especially in Africa. The study of the global trend of research through scientometrics helps managers and researchers in identifying countries and institutions with the greatest potential for scientific production, which allows them to develop their professions.

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Section: Annual Scientific Productionmentioning

confidence: 99%

Social media and e-commerce: A scientometrics analysis

Javid¹,

Nazari²,

Ghaeli³

2019

10.5267/j.ijdns

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“…here we climb to the next level of the DIKW hierarchy. Thus, big data analytic capabilities have gained much attention recently (Akter and Fosso Wamba, 2016;Arora and Malik, 2015;Fosso Wamba et al, 2017). Analytical capabilities are human centric, such as building a predictive analytics model, or interpreting a business need into an algorithm.…”

Section: Capability Creation Processmentioning

confidence: 99%

A recipe for big data value creation

Ylijoki

Porras

2018

BPMJ

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Purpose The purpose of this paper is to present a process-theory-based model of big data value creation in a business context. The authors approach the topic from the viewpoint of a single firm. Design/methodology/approach The authors reflect current big data literature in two widely used value creation frameworks and arrange the results according to a process theory perspective. Findings The model, consisting of four probabilistic processes, provides a “recipe” for converting big data investments into firm performance. The provided recipe helps practitioners to understand the ingredients and complexities that may promote or demote the performance impact of big data in a business context. Practical implications The model acts as a framework which helps to understand the necessary conditions and their relationships in the conversion process. This helps to focus on success factors which promote positive performance. Originality/value Using well-established frameworks and process components, the authors synthetize big data value creation-related papers into a holistic model which explains how big data investments translate into economic performance, and why the conversion sometimes fails. While the authors rely on existing theories and frameworks, the authors claim that the arrangement and application of the elements to the big data context is novel.

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“…In the work it is found that the interactivity and the quality of posts may be much more successful than the bare number of posts and of fans. Finally, the work in [5] suggests to fruitful employ social big data analytics to identify strategies for retaining customers before they decide to leave a company for a competitor. This fits perfectly the logic of the customer lifetime value, defined as the present value of all existing and future cash flows generated from a customer [95].…”

Section: Managing Customer Relationshipmentioning

confidence: 99%

A glimpse on big data analytics in the framework of marketing strategies

2017

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Mining and analyzing the valuable knowledge hidden behind the amount of data available in social media is becoming a fundamental prerequisite for any effective and successful strategic marketing campaign. Anyway, to the best of our knowledge, a systematic analysis and review of the very recent literature according to a marketing framework is still missing. In this work, we intend to provide, first and foremost, a clear understanding of the main concepts and issues regarding social big data, as well as their features and technologies. Secondly, we focus on marketing, describing an operative methodology to get useful insights from social big data. Then, we carry out a brief but accurate classification of recent use cases from the literature, according to the decision support and the competitive advantages obtained by enterprises whenever they exploit the analytics available from social big data sources. Finally, we outline some open issues and suggestions in order to encourage further research in the field

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Analytics: Key to Go from Generating Big Data to Deriving Business Value

Cited by 18 publications

References 46 publications

Social media and e-commerce: A scientometrics analysis

Social media and e-commerce: A scientometrics analysis

A recipe for big data value creation

A glimpse on big data analytics in the framework of marketing strategies

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