Welcome back to my Dashboard Week journey! Today marked the third day of the challenge, and I had an exciting task at hand: diving into the wealth of data provided by the “City of Melbourne” open data source. After some careful consideration, I decided to focus my analysis on the intriguing dataset titled “Live Music Venues.” This dataset encompassed various fields, including the names and types of venues, their geographical coordinates, addresses, and websites.

The purpose of my analysis was clear—to identify the live music venue closest to my workplace. After a long day of work, there’s nothing quite like the prospect of unwinding with some live music in a vibrant atmosphere. Armed with the dataset, I was determined to discover the ideal place for my post-work adventures.

To begin my quest, I sought to gather additional information about each venue’s capacity. Recognizing the importance of this factor in determining the ideal venue, I incorporated supplementary data containing information about the capacity of each establishment. Utilizing Alteryx, I successfully joined the primary dataset with the supplementary data, thus enhancing the richness of my analysis.

With the data ready for exploration, I turned to Tableau to create an interactive dashboard that would allow me—and future users—to delve into the world of live music venues in Melbourne. The centerpiece of my dashboard was a captivating map, meticulously plotted with the locations of all the venues. Users could easily visualize the distribution of live music hotspots across the city.

To further enhance the functionality of the dashboard, I incorporated an ingenious feature—an adjustable buffer centered around my workplace. This ingenious addition enabled users to customize the radius of their search, helping them find venues within their preferred distance. As I personally wanted to find a venue within a short distance from my workplace, I set the buffer range accordingly.

Beyond the visual appeal and customization options, I sought to make the dashboard as informative and user-friendly as possible. Consequently, I provided users with the ability to filter venues by their specific types. Whether someone desired an intimate jazz club or a lively rock venue, the dashboard catered to their musical preferences.

One of my favorite aspects of the dashboard was the integration of live website links for each venue. By simply clicking on the URL, users could effortlessly visit the website of their chosen venue. This seamless transition from the dashboard to the actual website further enriched the user experience, allowing for deeper exploration and even potential ticket purchases.

During the creation of this blog, an unexpected discovery added an extra layer of excitement to my dashboard project. While exploring the live music venues in Melbourne, I stumbled upon a particular venue that immediately caught my attention. Intrigued by the vibrant reviews and captivating images on its website, I swiftly made up my mind to make this venue my go-to spot for the upcoming weekend. I simply couldn’t resist the allure of a potential new favorite hangout.

In conclusion, my exploration of the “Live Music Venues” dataset from the “City of Melbourne” open data source has been both enriching and exciting. The journey to find the perfect post-work hangout spot has led me to create a captivating and user-friendly dashboard, complete with a customizable buffer, filtering options, and seamless website integration. Not only did this project enable me to analyze the dataset and visualize the venues on a map, but it also provided me with an unexpected personal benefit—a fantastic venue to visit this weekend.

So, if you find yourself in Melbourne and seeking a lively musical escape after a tiring day, look no further than my dashboard’s recommendations. Who knows? You might just stumble upon your new favorite haunt, just as I did. Cheers to the power of data exploration and the exciting surprises it can uncover!

The Data School
Author: The Data School