Have you ever found yourself struggling to make sense of complex data sets or longed for an easy way to convey your business insights or research results? Most traditional tools either have a steep learning curve or too little flexibility that exploratory analysis requires. Whether you’re a student, researcher, or business analyst, data work should be intuitive and evident at a glance.
VisualizeIt is a data visualisation platform making it easy to transform raw data into meaningful and interactive graphics. With chart-based and timeline visuals, data tales can be examined, narrated, and relayed effortlessly. Whether creating a public dashboard or merely visualising your own dataset, VisualizeIt offers an elastic and interactive solution. Ranging from drag-and-drop interface to powerful filtering and exporting features, the site empowers one to communicate inferences concisely without involving much technical knowledge.
Name: Yi Zong
Student number: 48208479
The tool has an easy-to-use interface for users to upload, process, and visualize data in various chart types and with interactive features. For educational, workplace, or personal use, VisualizeIt allows users to grasp and communicate results more effectively through insightful and interactive visualizations.
The MVP will support the following features:
The system should allow developers to add new chart types, data processing tools, or dashboard widgets with ease without much effort in modifying the existing codebase. Modular design and well-defined APIs will be used to provide extensible architecture.
Reliability is a measure of the system performing as intended with changing datasets and user interactions. The platform must ensure accuracy and consistency of visuals whether data is changed or updated. This will do away with misleading conclusions or presentation errors.
Simultaneous multiple users generating and working on visualizations should be accommodated by the system. Implemented by a small group of studies or a whole company analytics team, it should maintain consistent performance. Horizontal scaling and load balancing will be utilized to make it responsive.
Extensibility of the system will be gauged by how easy it is for developers to add new visualization types or data connectors. This will be measured through internal developer testing and code reviews, ensuring modularity and interface contract adherence.
Functionality will be verified through automated system testing, like edge cases including missing values or improper formats. Stress tests on data of differing sizes will ensure how reliably the system runs without crashing or providing wrong results.
Scalability of VisualizeIt will be experimented using K6 to imitate large numbers of users concurrently using the system. The performance benchmarks will be recorded during incremental loading and horizontal scaling being on. System behavior will be determined using response time and error rate.