Building an Analytics Culture to Boost a Data-Driven Entrepreneur's Business Model

Sedkaoui, Soraya; Khelfaoui, Mounia

doi:10.4018/978-1-5225-7277-0.ch014

Cited by 4 publications

(14 citation statements)

References 29 publications

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“…In other words, this economy is known as collaborative consumption that people sharing of resources rather than having individual ownership (Sundararajan, 2016). The term itself describes a phenomenon that is based on the sharing of access or on the supply of goods or services, labor, and underutilized financial resources with sources of demand, resource providers and services, and digital platforms (Sedkaoui and Khelfaoui, 2019a).…”

Section: Figure 1: Sharing Economy Companies By Sectormentioning

confidence: 99%

“…When technologies become cheaper and easier to use, they transform businesses. This is the case with big data technologies, with a substantial reduction in data processing and storage costs (Sedkaoui and Khelfaoui, 2019a).…”

Section: Data-driven Business Modelsmentioning

confidence: 99%

“…In this category we find all companies relying on the use of big data to derive value. For example, we can mention Uber which uses data related to the location of its clients, its drivers, trips, traffic to determine the price (Sedkaoui and Khelfaoui, 2019b).…”

Section: Recommendation As a Servicementioning

confidence: 99%

“…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 (Botsman 2014;Sedkaoui and Khelfaoui, 2019a), all of which have created their own way of doing business. This is the case of many emergent business models who have noticed that several assets (goods or services) are not exploited effectively by those that hold them.…”

Section: Introductionmentioning

confidence: 99%

“…and, of course, the target (for whom?). These companies use data to determine what to develop and whom to target in order to create opportunities in untapped markets for sharing (Sedkaoui and Khelfaoui, 2019a).…”

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: Figure 1: Sharing Economy Companies By Sectormentioning

confidence: 99%

Section: Data-driven Business Modelsmentioning

confidence: 99%

Section: Recommendation As a Servicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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