Abstract-As companies generate and handle increasingly large amounts of data along with the Big Data era, several model of data lifecycle have been proposed to deal with this situation. The analysis, the management and the use of data becomes more complicated or almost impossible in some cases for the companies. To transform these data to a knowledge, the choice of the adequate lifecycle that matches with the company expectations becomes essential.For this goal, this paper aims to be a guide to assist companies to choose a lifecycle that fits their data management vision. For this, we identify the relevant criteria of selection cycles and defined a rating system to each of these criteria. In this paper, we study the available lifecycles of data in the literature that we consider relevant. As a result of this study, we classify these cycles following two types: first analysis oriented phases and the second based on relevant criteria.

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Several organizations from different sectors depend increasingly on knowledge extracted from huge volumes of data generated by different sources, such as IoT, sensors and databases. At the core of data lifecycle, data reliability, analytics, security, scalability and use are important concerns. Coping with these issues in handling data requires understanding the challenges associated with it. Analysis process and storage devices have been widely studied. However, very few studies have explored the collect data phase. In this study we aim to analyse more the collect phase of data lifecycle to provide an optimized and smart approach. This paper aim to provide the right method to follow in data collect phase within different domain according to client needs and requirements. It provides not only a detailed view of the main steps, but also based on a prior literature review on different existing methods. This allowed us subsequently to establish a correspondence with the SLR method on which we based our method. We use an explicit example to illustrate the steps of our method.

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