Examinando por Materia "Maturity levels"

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    Diagnóstico de madurez de la PMO de ARUS
    (Universidad EAFIT, 2021) Angulo Martínez, Mario Germán; Areiza Sierra, Juan Esteban; Pabón Ramírez, Carolina
    This research describes how the organizational maturity level in project management of ARUS PMO was diagnosed, through an adaptation of the OPM PMI standard. To make the diagnosis, a survey was used as a data collection instrument, which was formulated to people direct and indirectly related to PMO and the organizational project portfolio management. The main results were documented, tabulated and analyzed holistically, by sections according to the stages that the projects go through, by groups according to the position of the people and their relationship with the ARUS PMO. As the main result, it was determinate that the ARUS PMO has a maturity level of 3, which means the ARUS PMO is OPM defined by the organization, which is characterized by being a PMO with a defined process, roles, accountabilities, and project management can be predicted. However, during the results analysis, improvements and opportunities were detected, so that means that ARUS PMO can reach a maturity level 4, OPM quantitatively management maturity level; for that, the ARUS PMO must identify the life cycle that the project follows, in order to improve their planning and execution; moreover, it is also recommended that the PMO adapt to market trends and start using agile frameworks and tools that allow process automation, so PMO will be adapt faster to the changes that customer requires.
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    A fuzzy ELECTRE structure methodology to assess big data maturity in healthcare SMEs
    (Springer Verlag, 2019-10-01) Peña A.; Bonet I.; Lochmuller C.; Tabares M.S.; Peña A.; Bonet I.; Lochmuller C.; Tabares M.S.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    Advances in technology and an increase in the amount and complexity of data that are generated in healthcare have led to an indispensable revolution in this sector related to big data. Analytics of information based on multimodal clinical data sources requires big data projects. When starting big data projects in the healthcare sector, it is often necessary to assess the maturity of an organization with respect to big data, i.e., its capacity in managing big data. The assessment of the maturity of an organization requires multicriteria decision making as there is no single criterion or dimension that defines the maturity level regarding big data but an entire set of them. Based on the ISO 15504, this article proposes a fuzzy ELECTRE structure methodology to assess the maturity level of small- and medium-sized enterprises in the healthcare sector. The obtained experimental results provide evidence that this methodology helps to determine and compare maturity levels in big data management of organizations or the evolution of maturity over time. This is also useful in terms of diagnosing the readiness of an organization before starting to implement big data initiatives or technologies. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.