Crisp-dm/smes: A data analytics methodology for non-profit smes
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Springer
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The exponential increase in information due to technological advances and the development of communications has created the need to make decisions based on the data analysis. This trend has opened the doors to new approaches to data understanding and decision-making. On the one hand, companies need to follow data analytic methodologies to manage large volumes of information with big data tools. On the other hand, there are non-profit small and medium-sized enterprises (SMEs) that make efforts to address data analytics according to their different sources and types. They find challenges such as lack of knowledge in methodological and software tools, which allow timely deployment for decision-making. In this paper, we propose a data analytics methodology for non-profit SMEs. The design of this methodology is based on CRISP-DM as a reference framework, is represented by Software Process Engineering Metamodel (SPEM) and is characterized by being simple, flexible, and low implementation costs. © Springer Nature Singapore Pte Ltd. 2020.
Palabras clave
CRISP-DM, Data analytics, Non-profit SMEs, Cost engineering, Decision making, Profitability, Software design, CRISP-DM, Exponential increase, Implementation cost, Non-profit, Reference frameworks, Small and medium-sized enterprise, Software process engineering metamodel, Technological advances, Data Analytics