**6.2 Future work**

In this project, a complete visual analytics tool was presented that can help the users in finding hidden relationships in their data by providing various machine learning and visualization techniques. However, the tool is still in its initial phase and requires more features in order to cater to all the needs of a data analyst.

In the initial ViDAS implementation, there was an intention to include dashboards, which would feature the coordinated views, cross-filtering i.e. filtering/ selecting data in one chart would reflect relevant changes in other linked charts. Dash, an extension to the Plotly charting library, was explored to create these coordinated views but unfortunately, this could not be completed due to time constraints and may be added in the future.

Another interesting feature that could be implemented in the future is the functionality to select desired fields in the overview tab. During the overview tab view, the user may find multiple correlated fields that are important to visualize. These fields can be intuitively selected from the overview visualization to be used in the desired visualizations. Furthermore, an area that can be significantly improved further is the processing of date/time data during the visualizations. Currently, dates are treated as any other dimension or ignored. There could possibly be sophisticated parsing by using parts of dates such as years and months. Also, interactivity within the visualizations can be further improved as Plotly features a handful of interaction widgets that can be added.

The functionality of 'Smart Analytics' can be integrated into the tool to help the user in applying the machine learning models. In some cases, the raw data can be unstructured and complex which may pose a challenge in deciding which machine learning models to apply. In such cases, the smart analytics feature can use data mining techniques to explore the type of data in the data set and suggest the recommended machine learning models to apply based on the data type and complexity. Additionally, more ML nodes can be introduced that offer flexibility in dealing with complex data. Forecasting can be used with time-series data to predict future values depending on the past data-set.

Furthermore, due to the current situation of a pandemic, the evaluation of ViDAS could not be performed to the full extent. The evaluation was conducted by using only the stakeholders as the focus group. In the future, a complete evaluation should be conducted that includes a mixed focus group comprising of the domain experts as well as the non-technical users.

These new features combined with the in-depth evaluation of ViDAS can result in a better and thorough visual analytics tool that provides a user-friendly experience and helps its users in dealing with raw and complex data by allowing data analytics as well as interactive visualizations.
