Business intelligence adoptions of DevOps methodologies = Adopción de metodologías DevOps para la inteligencia de negocio

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      <subfield code="a">Antoñanzas Martínez, Guillermo</subfield>
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      <subfield code="a">Business intelligence adoptions of DevOps methodologies</subfield>
      <subfield code="b">= Adopción de metodologías DevOps para la inteligencia de negocio</subfield>
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      <subfield code="a">Madrid</subfield>
      <subfield code="b">Universidad Politécnica de Madrid, Escuela Técnica Superior de Ingenieros Informáticos</subfield>
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Supervisor: Dr. Jurgen Vinju; Co-supervisor: Lina Ochoa Benegas</subfield>
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      <subfield code="a">For this Master Thesis, the author wants to know what is the state of the Business Intelligence (BI) tools in the Development and Operation (DevOps) maturity level scale. To the best of your knowledge, no analysis like this exists in the state of the art. Moreover, the communication between the DevOps teams is fundamental for any project and it is even more relevant to those that keep growing, like how BI projects tend to be. To this aim, he will take an in-depth look into the most relevant and most used BI tools and the features they have, with reference to the DevOps models and its maturity levels, as well as some additional features that are of interest to the community (e.g. license, activity, maintainability). He will analyze their characteristics in the DevOps categories and obtain the theoretical maximum maturity levels for each tool to show the gaps that the BI tools currently have. Multiple DevOps Maturity Models will be used to widen the scope of the analysis and have a better representation of how tools implement DevOps.
Additionally, he will analyze real cases of BI projects from the internship company, NTTData, to see if the theoretical maximum maturity levels are reached and propose changes to improve those levels</subfield>
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