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Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we

Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: Understand if there is monetization potential in autonomous vehicle data Create a financial model of what detailing the viability of AV data monetization Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data. First, in order to brainstorm how this data could be monetized, we generated potential use cases, defined probable customers of these use cases, and how the data could generate value to customers as a means to understand what the "price" of autonomous vehicle data might be. While we came up with an extensive list of potential data monetization use cases, we evaluated our list of use cases against six criteria to narrow our focus into the following five: Government, Insurance Companies, Mapping, Marketing purposes, and Freight. Based on our research, we decided to move forward with the insurance industry as a proof of concept for autonomous vehicle data monetization. Based on our modeling, we concluded there is a significant market for autonomous vehicle data monetization moving forward. Data accessibility is a key driver in how profitable a particular company and their competitors can be in this space. In order to effectively monetize this data, it would first be important to understand the method by which a company obtains access to the data in the first place. Ultimately, based on our analysis, Company X has positioned itself well to take advantage of the new trends in autonomous vehicle technology. With more strategic investments and innovation, Company X can be a key benefactor of this unprecedented space in the near future.
ContributorsShapiro, Brandon (Co-author) / Quintana, Alex (Co-author) / Sigrist, Austin (Co-author) / Clark, Rachael (Co-author) / Carlton, Corrine (Co-author) / Simonson, Mark (Thesis director) / Reber, Kevin (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we

Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: 1. Understand if there is monetization potential in autonomous vehicle data 2. Create a financial model of what detailing the viability of AV data monetization 3. Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data.
ContributorsCarlton, Corrine (Co-author) / Clark, Rachael (Co-author) / Quintana, Alex (Co-author) / Shapiro, Brandon (Co-author) / Sigrist, Austin (Co-author) / Simonson, Mark (Thesis director) / Reber, Kevin (Committee member) / School of Accountancy (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description

Hundreds of thousands of archaeological investigations in the United States conducted over the last several decades have documented a large portion of the recovered archaeological record in the United States. However, if we are to use this enormous corpus to achieve richer understandings of the past, it is essential that

Hundreds of thousands of archaeological investigations in the United States conducted over the last several decades have documented a large portion of the recovered archaeological record in the United States. However, if we are to use this enormous corpus to achieve richer understandings of the past, it is essential that both CRM and academic archaeologists change how they manage their digital documents and data over the course of a project and how this information is preserved for future use. We explore the nature and scope of the problem and describe how it can be addressed. In particular, we argue that project workflows must ensure that the documents and data are fully documented and deposited in a publicly accessible, digital repository where they can be discovered, accessed, and reused to enable new insights and build cumulative knowledge.

Cientos de miles de investigaciones arqueológicas en los Estados Unidos realizado en las últimas décadas han documentado una gran parte del registro arqueológico recuperado en los Estados Unidos. Sin embargo, si vamos a utilizar este enorme corpus para lograr entendimientos más ricos del pasado, es esencial que CRM y los arqueólogos académicos cambian cómo administran sus documentos digitales y los datos en el transcurso de un proyecto y cómo se conserva esta información para uso en el futuro. Exploramos la naturaleza y el alcance del problema y describimos cómo se pueden abordarse. En particular, sostenemos que los flujos de trabajo de proyecto deben asegurarse que los documentos y datos son totalmente documentados y depositados en un repositorio digital de acceso público, donde puede ser descubiertos, acceder y reutilizados para activar nuevos conocimientos y construir conocimiento acumulativo.

ContributorsMcManamon, Francis P. (Author) / Kintigh, Keith W. (Author) / Ellison, Leigh Anne (Author) / Brin, Adam (Author)
Created2017-08