This study aims to propose a model based on philosophical thoughts of Dr S.R Ranganathan and the lean-startup method for the execution of big data analytics (BDA) in libraries. The research paves a way to understand the role and required competencies of Library and Information Science (LIS) professionals for the implementation of BDA in libraries.,In the BDA analytics context, a session with a proposed model was presented to the audience to get the response of librarians about the required competencies and skills. The research tool was developed based on the literature review to know the role of LIS professionals and their required competencies/skills for BDA. The questionnaire was distributed in the BDA session to collect the responses of the participating audience on the variables that focused on the role and core competencies of LIS professionals in BDA. In the analysis of results, the independent t-test was applied to know the mean value of the overall response rate.,The findings show that perceptions of LIS professionals in the understanding of BDA ranked high in data privacy, data availability, data organization and data literacy. Digital data curation, policies supervision and providing the data consultancy also showed a significant relationship among these variables. Besides, the correlation between the required skills for BDA, metadata skills, data ethics, data acquisition, data cleaning, data organization, data analysis, digital curation, data clustering, data protection rules and digital visualization also showed a beneficial relationship.,This study also helps to understand the perspective of LIS professionals for the implementation of BDA in libraries and to fill the literature gap in the respective.

中文翻译：

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图书馆员基于精益启动模型在图书馆中实施大数据分析的观点

这项研究旨在基于SR Ranganathan博士的哲学思想和精益启动方法在图书馆中执行大数据分析（BDA）提出一个模型。该研究为了解图书馆和信息科学（LIS）专业人员在图书馆实施BDA的作用和所需的能力铺平了道路。在BDA分析背景下，向受众介绍了一个具有建议模型的会议，以使读者图书馆员对所需能力和技能的回应。该研究工具是在文献综述的基础上开发的，旨在了解LIS专业人员的作用及其对BDA的要求能力/技能。该问卷在BDA会议上分发，以收集与会人员对变量的回答，这些变量关注LIS专业人员在BDA中的作用和核心能力。在结果分析中，应用了独立的t检验来了解总体响应率的平均值。研究结果表明，LIS专业人员对BDA的理解在数据隐私，数据可用性，数据组织和数据素养。数字数据策划，政策监督和提供数据咨询也显示出这些变量之间的显着关系。此外，BDA所需技能，元数据技能，数据伦理，数据获取，数据清理，数据组织，数据分析，数字策展，数据聚类，