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Analytics Canvas – A Framework for the Design and Specification of Data Analytics Projects

机译:Analytics画布–数据分析项目的设计和规范框架

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Artificial intelligence and data analytics are key technologies in current digitization efforts. However, companies still have huge problems to identify their benefit and use within their production system. One of the main reasons is the need to involve many different stakeholders like IT specialists, engineers, data scientists and management. This requires a communication across different domains. Within the scientific community, the conceptual design of data science projects are generally not looked at. Current methods focus on the implementation of such projects.In this paper, we propose the Analytics Canvas. It is a semi-formal specification technique for describing an analytics use case and the necessary data infrastructure during the early planning and specification of an analytics project. As a method for system architects, the Canvas allows a clear description and differentiation of the roles of stakeholders in an analytics project as well as their points of contact. It thus can be used as a basis for transparent interdisciplinary communication and structuring the stakeholders’ tasks on a conceptual level. The Analytics Canvas uses an intuitive, visual representation of existing and future requirements along five layers in an analytics project, and provides concrete modeling rules. Based on specific use cases, we illustrate the typical approach in defining analytics projects by using the Canvas.
机译:人工智能和数据分析是当前数字化工作中的关键技术。但是,公司在确定其收益和在生产系统中的使用方面仍然存在巨大的问题。主要原因之一是需要让许多不同的利益相关者参与,例如IT专家,工程师,数据科学家和管理人员。这需要跨不同域的通信。在科学界内部,通常不会关注数据科学项目的概念设计。当前的方法侧重于此类项目的实现。在本文中,我们提出了Analytics Canvas。它是一种半正式的规范技术,用于在分析项目的早期规划和规范期间描述分析用例和必要的数据基础结构。作为系统架构师的一种方法,Canvas可以对利益相关者在分析项目中的角色及其联系点进行清晰的描述和区分。因此,它可以用作透明的跨学科交流的基础,并在概念上构建利益相关者的任务。 Analytics(分析)画布在分析项目的五个层次上使用了直观的可视化表示形式,以现有和将来的需求表示,并提供了具体的建模规则。基于特定的用例,我们说明了使用Canvas定义分析项目的典型方法。

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