Hello everyone,
This is my first time participating in #dataDNA, organized by Onyx Data, ZoomCharts, and Data Career Jumpstart. I'm excited to share my dashboard for the April Challenge, based on the IMDb dataset.
I've learned many lessons from my first encounter with a large Excel dataset (one million records), which presented many challenges during data cleansing and transformation, as well as writing DAX to calculate metrics. I'll share these lessons in my next post.
My overall approach was to analyze trends in the movie industry over 100 years, spanning 8 eras, focusing on revenue, genres, producers, and countries. I analyzed both raw revenue and revenue adjusted for inflation, using the CPI of the US to standardize all years to 1927 dollars.
Addressing the 3 questions posed by the challenge, here are my findings:
💰 Revenue and budget have increased over time, along with the number of movies and the diversification of genres.
📆 2017 and 2016 were the top 2 years in terms of revenue, despite inflation, with their rankings exchanging between each other.
🎥 Stan Lee is the highest grossing revenue producer. However, when considering producers with high revenue relative to budget (i.e., high Revenue to Budget ratio), Bob Eick and Kevin J. Foxe with the movie "The Blair Witch Project" stand out. Notably, these films were released in 1999, a time when there were fewer highly technically demanding movies in competition.
🏇 The popularity ranking of Action and Adventure genres has significantly increased over the years, while Thriller and Horror genres have seen the opposite trend.
The details are in my LinkedIn post: https://www.linkedin.com/posts/tran-ht-le_datadna-activity-7188589741179650049-98hr?utm_source=share...
eyJrIjoiNDc1ZGNiMDktYWU4NC00OTVhLWFhOTQtOGY0NDBmZTZjYWM1IiwidCI6ImRmODY3OWNkLWE4MGUtNDVkOC05OWFjLWM4M2VkN2ZmOTVhMCJ9
