Overview
This documentation describes the conception, development and evaluation of a data visualisation product. The goal of the project is to …
The methodological procedure of this work follows the process model shown in Figure 1, which is based on the concepts on data product development outlined in [1] and the data science project-template on github.
It comprises the following four main phases with the corresponding documentation artefacts:
- Project Understanding → Project Charta: Context analysis, stakeholder and user analysis (based on the Value Proposition Canvas), formulation of objectives, success criteria, constraints and the visualization concept.
- Data Acquisition and Exploration → Data Report: Identification and acquisition of relevant data sources, followed by exploratory data analysis to understand data quality and structure.
- Visual Encoding and Design → Visualization Design Report: Documentation of the data-to-visual mapping, layout and composition, interaction design, visual style and the resulting prototype(s).
- Evaluation → Evaluation Report: Assessment of the developed prototype based on success criteria outlined in the project charta, evaluation of stakeholder feedback, checkpoint decision and, if applicable, gathering additional requirements and planning of the deployment.
- Deployment → Deployment Report: Description of the software architecture and deployment process, as well as the integration of the visualization product into the target environment and processes.
References
1.
Doemer M, Kempf D. Is it ops that make data science scientific? Archives of Data Science, Series A (Online First). 2022;8(2):12 S.