And so we have finally arrived at the last day of Dashboard Week. It has been a huge week for everyone, but looking back, I am really proud of what I produced given the intensity and time restrictions throughout the week. Not only has this week taught me how to prioritise tasks and outcomes more efficiently and realistically, but it has also been a massive learning experience getting to creatively combine all the tools and teachings learnt over the last 4 months to achieve desired features and outcomes. So, without further ado, let’s me break down my final dashboard for this week.
Thinking of an Angle
The data we were given lists all off the Oscar award winners and nominees for each category for each year, going back to around 1920, so it was important to narrow in on what I wanted to investigate. Being a professional musician, naturally I gravitated to the awards film composers. Specifically, I wanted to look at all of the nominees and winners for original scores and original songs from 1970 to present. Also, I wanted to investigate the impact that film composition has on the chance of a film to win Best Picture. In order to do that, I needed to create a relationship to investigate how many films a award winning composer has composed for, that have won the best picture award. Ideally, this would need to be supported by analysis investigating the number of best picture films that have award winning composers against the number of best picture films that don’t have award winning composers to truly generate a sound conclusion, but given we only had half a day for this challenge, I decided to prioritise producing the MVP first.
Providing Context to the Analysis
As film composers have varying career lengths, one could say the number of awards nominated for/won is a product of time. On the other hand, if a composer has only been around for 2 years, and has been nominated/won 2 awards, then their success rate would be 100& based on their career length. However. even though numerically that would rank them above a composer who has been around for 20 years, but only won 10 awards, that mightn’t necessarily be true in the real world. For example, in this data, John Williams (composer for Star Wars, Indiana Jones, etc.) has been nominated/won far more awards than any other composers in the data. However, since his award-career is 54 years, his success ratio considers him not even in the top 10 composers.
Ideally, if time permitted, I would’ve liked to continue on developing my calculation for weighting the ratio dependent on career length, number of film composed for that have won awards, and number of specific music awards won, as this would’ve provided a more wholistic context to the rankings. However, in the interest of time and producing an MVP, I used the number of awards to career length ration calculation as the measure, and then coloured by number of awards (with the darkest colour being the most awards). This was done to provide an instant visual indication so the viewers can understand the context for the ranking.
Final Thoughts
Well, Dashboard Week has certainly been a journey and a half. Although extremely intense and tiring, it was incredibly rewarding, both in the sense that we produce something tangible each day, we constantly learn how to tackle unique problems, and we get to use our own creativity in finding insights and developing dashboards. This week has encouraged me to continue to push the boundaries and constantly improve not only my visualisation skills, but also my data manipulation and story-finding skills. Keep an eye out on my Tableau Public and NovyPro pages for more content!
Author: Ben Devries
Ben graduated with a Bachelor of Music Performance (Honours) from the Sydney Conservatorium of Music in 2023. For the last few years, Ben spent his time working as a professional jazz saxophonist which led him all around the world performing in cities such as London, San Fransisco, and of course, Sydney. But despite his musical background, Ben’s interest in data analytics came from his passion for problem solving and understanding the little details of how and why things work. From there, Ben went on to discover the Data School Down Under, and throughout the interview process became further inspired not only by the logic and flexibility of data, but also the ability for data to provide valuable insights to help solve complex business problems and present meaningful stories. Ben is excited to join Data School Down Under, and hopes to utilise his creativity, improvisational skills, and ability to draw connections upon diverse areas of information learnt as a musician within his new career in data analytics. In his spare time, Ben still enjoys playing his saxophone, as well as downhill longboarding, and spending time with his family.