Introduction to Modern Dashboarding Tools

February 19, 2024  ·  3m read

In today’s data-driven world, the significance of effectively visualising and interacting with data cannot be overstated. Imagine having the best engine in the world without a car to put it in. Or Imagine having a complex, and highly valuable piece of machinery like a car to help you drive to your destination but not have the correct dashboard indicators. I.e. you’re pushing down the pedal, but how fast are you accelerating?

Analytics dashboards have become essential tools, transforming complex datasets into actionable insights. As we delve into the realm of dashboarding tools like Shiny, Dash, Bokeh, Panel, and Notebooks, it’s important to understand their roles and the evolution that has led to their current state.

The Evolution of Dashboards

The term “dashboard” dates back to the days of horse-drawn carriages, referring to a wooden board that protected passengers from mud “dashed” up by the horses’ hooves. As technology advanced, so did the concept of the dashboard. Today, it stands as a digital interface, providing real-time, interactive data visualisation, crucial for decision-making in various industries.

Original Dashboard

In the context of data science and business intelligence, a dashboard is more than a display tool. It’s an interactive platform that offers insights into vast datasets, allowing users to discern trends, make predictions, and drive strategic decisions.

Why Dashboards Matter in Data Science

Tools like Notebooks, Shiny and Dash empower data scientists to create prototypes and Minimum Viable Products swiftly (see Figure 2). This capability is invaluable for testing hypotheses and presenting data-driven insights in an understandable format.

Plotly and Dash Visualisation

Dashboards allow users to interact with data, adjust parameters, and see the impacts in real-time. This rapid prototyping not only accelerates the process of sharing insights but also fosters a dynamic environment for immediate user feedback and interaction. Users can explore data in real-time, adjust parameters, and gain a deeper understanding of data patterns, leading to quicker and more informed decision-making. By providing domain experts with easy, consumable access to data, they can test and iterate more quickly, arriving at a solution in a fraction of the time.

A Glimpse into Popular Dashboarding Tools

Shiny (R-based): Shiny stands out for its integration with R, making it a favourite among R users for building web apps directly from R scripts. Dash (Python-based): Dash offers Python users a way to create interactive web applications with ease. It’s known for its simplicity and effectiveness. Bokeh and Panel (Python-based): These tools provide extensive capabilities for interactive plotting and dashboard creation in Python. Notebooks (Jupyter and Google Colab): They offer an interactive environment where code, visualisations, and narrative text are combined, ideal for prototyping and data analysis.

The popularity of dashboarding tools has seen a significant increase in recent years as businesses and individuals strive to effectively visualise and analyse data. Shiny, with its seamless integration with R, has become a favourite among R users, enabling them to build web apps directly from their R scripts. Dash, on the other hand, has gained traction among Python users for its simplicity and effectiveness in creating interactive web applications. Bokeh and Panel have also emerged as powerful Python-based tools, offering extensive capabilities for interactive plotting and dashboard creation. Additionally, the use of notebooks, such as Jupyter and Google Colab, has become widespread due to their ability to combine code, visualisations, and narrative text, making them ideal for prototyping and data analysis.

Start History


Understanding the array of dashboarding tools available is crucial for any data scientist or analyst. The choice between Shiny, Dash, Bokeh, Panel, and Notebooks often depends on the project’s specific requirements and the team’s proficiency in R or Python. As we progress in this series, we’ll dive deeper into each of these tools, exploring their features, strengths, and ideal use cases.

Stay tuned for our upcoming articles, where we’ll explore the nuances of these dashboarding tools, helping you make informed decisions on the right tool for your data visualisation needs.

Keep an eye out for part two where we will take a look at Shiny and Dash - The R and Python champions!