Data Analytics and Big Data

Sedkaoui, Soraya

doi:10.1002/9781119528043

Cited by 32 publications

(27 citation statements)

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“…According to Gartner, this concept brings a family of tools that respond to challenges related to the 3 Vs model that includes the analyzing process of a large amount of data generated in a variety of types (structured, semi structured and unstructured) and produced in real time. Therefore, typically, these three Vs are used to define big data (Sedkaoui, 2018a).…”

Section: Big Data: An Overviewmentioning

confidence: 99%

“…According to Frizzo-Barker et al (2016), this phenomenon could potentially change the way companies think about data infrastructure, business intelligence and analysis and information system strategy (Sedkaoui 2018a). Companies will be able to enhance their decision-making process and therefore their performance by exploring the potential of data analysis (McAfee and Brynjolfsson 2011).…”

Section: Big Data: An Overviewmentioning

confidence: 99%

“…The new wave is based on the phenomenon of big data and the possibilities that can be created by analytical methods (Ohlhorst 2013 ;Morabito 2015 ;Henke et al 2016 ;Foster et al 2017 ;Sedkaoui 2018a). Without data analysis, companies cannot fulfill their customers' requirements in real time.…”

Section: How Sharing Economy Businesses Can Seize the Opportunities Omentioning

confidence: 99%

“…Also, to fully exploit the potential of big data, companies need powerful tools to process, analyze, and store the quantities of data they produce and collect daily. Various types of tools and technologies have been developed to streamline the data analysis process and create an ideal environment for the application of different data analysis algorithms (Sedkaoui, 2018a). Hadoop, Spark, Python, Matlab, R, and other tools and features for managing and analyzing big data offer a variety of algorithmic applications.…”

Section: Big Data Algorithms For Sharing Economy Businessesmentioning

confidence: 99%

“…All sharing platforms and applications rely on data and analysis to develop practices and determine who to target. These data are increasingly used today because of the conjunction of a number of factors, such as (Sedkaoui, 2018a):…”

Section: Introductionmentioning

confidence: 99%

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How Data Analytics Drive Sharing Economy Business Models?

Sedkaoui¹,

Benaichouba²

2019

Proceedings of the 52nd International Academic Conference, Barcelona

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Several studies and reports published by Mckinsey, Gartner, Cesco, PwC, etc., confirm that data analytics offers companies more value and allows them to the creation of new and innovative ideas. This is why the data-driven approach has been the subject of considerable publicity in recent years. This approach has given rise to the emergence of many business models, all of which have created their own way of doing things. This is the case of many emergent business models who have noticed that several assets (goods or services) are not exploited effectively by the parties that hold them. We buy many products that we use only for a certain period and then put them aside. What if we could find one or more people who might need it? This is the question that these innovative business models had taken into account. They could see potential monetary benefits in these different resources, simply by facilitating their sharing. Some succeed by bursting the value chains and shaking up the established players: Uber for taxis, BlaBlaCar for interurban carpooling, Airbnb for accommodation, etc., and this is, of course, just the beginning, because the trend is accelerating. These are fascinating ideas that have led to the emergence of the sharing economy. But, one thing is clear, the ideas created by Uber, Airbnb, BlaBlaCar, etc. cannot be realized without determining what allows their development (how?) and, of course, the target (for whom?). These companies use the data to determine what to develop and target, to create untapped sharing market opportunities. Many researchers have found the potential of large amounts of data produced and collected by many sharing platforms. The analysis of these quantities not only helps to promote the performance of these models or operationalize their activities, but also to predict economic results such as inflation, unemployment, housing prices, etc. All sharing platforms and applications rely on data and analysis to develop practices and determine who to target. These data are increasingly used today because of the conjunction of a number of factors, such as: • The constant decrease in data storage costs; • The increase of the computing power; • The production of large amounts of data, which is largely unstructured and requires different operating techniques and which cannot be preceded by traditional methods. Being able to generate value, in the context of the sharing economy, and make big data more profitable is based on the ability of companies to analyze the amount available data. The challenge, therefore, lie in the ability to extract value from the amount volume of data produced in real-time continuous streams with multiple form and from multiple sources. In another word, the key to explore data and uncover secrets from it is to find and develop applicable ways in such a way 459 https://iises.net/proceedings/international-academic-conference-barcelona/front-page 23 September 2019, IISES International Academic Conference, Barcelona IISES to extract knowledge that can conduct any bu...

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Section: Big Data: An Overviewmentioning

confidence: 99%

Section: Big Data: An Overviewmentioning

confidence: 99%

Section: How Sharing Economy Businesses Can Seize the Opportunities Omentioning

confidence: 99%

Section: Big Data Algorithms For Sharing Economy Businessesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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