Applying Scrum in Data Science Projects

Abstract

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Data Analytics Project Methodologies

“…Furthermore, recent studies also showed the application of existing agile methods for doing data analytics projects [17,23,24]. The use of agile methods is common in software development.…”

“…It is used because it facilitates volatile requirements and allows to quickly react to changing environments [17,23]. Agile methods applied in data analytics consist of Scrum and Kanban [24,25]. Scrum is an iterative process with defined events, artefact and roles to deliver value in time-boxed sprints [26].…”

“…Another study evaluated a design of a Scrum data analytics model. The design consisted of Scrum artefacts, events and roles that were applied on CRISP-DM [24]. Next, there is the Kanban method.…”

The impact of conceptual structures on transaction and enterprise architecture practices

“…Through the AR cycles, the design artefacts are contextualized by referring to practical problems which are identified in the real world and detailed in the methodological approach. Using Action Research, the following key questions are addressed and evaluated ex ante in individual steps, using the DSRM principles of evaluation (described in Section 9.1), given the defined evaluation criteria and summarised in Following the ex ante evaluation and the determination of a general solution Section 7.9, details the artefacts of the AREA KMS and the Protégé artefacts which were developed in an iterative process using development methods loosely coupled on Scrum (Baijens et al, 2020).…”

“…The Protégé artefacts were developed in an iterative process using development principles loosely coupled on Scrum methods to provide for early results. In comparison with other agile methods, Scrum focuses on constant iteration which delivers quick incremental results (Baijens et al, 2020).…”