We use Plotly Dash Enterprise mainly for creating dashboards using Python. With Plotly's support of Python, it helps us to develop interactive dashboards according to the customer use case and the kind of applications that are required.
We have Federal Reserve Economic Data as well as Bureau of Labor Survey data sets for our economic data. We take this data on a per state basis or on a per county basis monthly to detect or determine economic government data sets, such as unemployment rate and employment rates in the manufacturing sector. We take that data using their APIs, and once we have this data in our database, we use Plotly to create dashboards with interactive visualizations that help our analytics team to make decisions and tune our machine learning model accordingly.
We have both internal and external use cases with Plotly Dash Enterprise. With our machine learning model, we develop interactive dashboards to have a picture of how things are going in terms of the employment rate and other economic data sets. Also, with our clients, who are hiring companies, we project this data to them to compare their statistics with the provided government data set. Since we are a private company, they evaluate their performance against the government provided data.
Integration with Plotly Dash Enterprise involves only the databases that we have, and interaction depends solely on the controls, meaning we have drop-downs, radio buttons, and other interface elements. We utilize multiple visualizations along with different types of charts that Plotly helps us to interact with.
The ability to develop dashboards using Python has been our great use case with Plotly Dash Enterprise. With this capability, we are able to create a GitHub repository or a central version control system that helps us manage different versions of the dashboards. If we need to improve something, we simply go back to a previous version and make immediate changes if necessary. Furthermore, we also have the ability to control how our dashboards look and design them according to our own use cases, achieving the required scalability with the help of the enterprise version.
Since we have ties with hiring companies that require high scalability, Plotly Dash Enterprise helps us achieve that. With the GitHub version control system, we have created a repository containing our dashboard code. With the help of Plotly, we integrate our dashboards with GitHub to provide us much more control over how our dashboards look and manage different versions of them simultaneously.
We use Python mainly with Plotly Dash Enterprise, which is an added use case instead of doing a drop-down and using Power BI. Coding provides us with much more ability to design custom visualizations tailored to our specific needs. Plotly Dash Enterprise helps us achieve a much more interactive and vivid form of visualization for our organization, which helps us drive better results and analytics. It also helps us derive decisions that are beneficial for our use cases and create different versions for different sets of companies that we partner with.
The main advantage we have is that we manage different forms of files or different forms of data that we have stored, including semi-structured, structured, and unstructured formats. With the help of Plotly Dash Enterprise, we tackle these challenges and create a unified data frame or dataset that helps us achieve a common goal. We are not restricted to any form of data. No matter the data format, we can handle it clearly with the help of Python libraries and scale our visualizations to another level.
The main improvement I can think of is that while creating charts, it gives you a certain format of how it could look. If you want to create something extra and go more vivid and creative with how the actual chart would look, it allows for that option but could be improved to be more artistic or aesthetically pleasing. This sort of format is missing, and I think it would be beneficial to the analytics team if it can be more interactive, with the capability of D3.js, and give us more control over how our actual dashboard would look to achieve a more aesthetic appearance. The strict format of how you can shape those charts and that extra nuance you need to keep in code to get the exact possible results are the reasons behind my rating. The rest of the features provided by Plotly are extremely good.
We have been using Plotly Dash Enterprise for nearly two to three years.
It's a great tool to incorporate in your organization to develop dashboards that help your analytics team derive better decisions and generate more business profits. It gives you much more control with Python and helps you interact with multiple file formats to easily bring them to a common platform, such as a Pandas DataFrame or PySpark DataFrame. Plotly Dash Enterprise helps you create the visualizations you want and achieve better results. I would rate this product an 8 out of 10.