Putting the Database Together

For the last post of my introductory blogs, I want to discuss how I’ll be setting up my database. As mentioned in my previous post, the main focus of my blog throughout the rest of the course will be examining demographics and sales of Nike and Adidas, the two largest shoe retailers, to determine the trajectory of the sneaker industry in coming years.

If I we’re to build a database to store this database, I would prefer a relational database in an effort to keep my data consistent for querying and analyzing.

 

In some of my posts, I will be examining resale values of popular shoes from each company over the years to gauge the brands popularity and value over time. Below is a high level entity relationship diagram for what my database may look like to store original sale and resale data to make comparisons.

Screen Shot 2018-04-27 at 8.17.46 PM.png

In this instance, many resale sites can have many shoes, and many shoes can appear on many resale sites. However, I will specifically be gathering my data from StockX which tracks all sales of sneakers on the secondary market. This type of data will likely be easy for me to collect and will likely be my starting point for my research. I will likely do this for the top 25 shoes of all time from each brand. My potential data can be found here by clicking “View All Sales” Beneath “Last Sales” to see the number of shoes sold in each size at what price. This should give me enough data to make a reasonable assumption about resale values of the shoes from each brand.

In upcoming posts, I will use data I have regarding store openings and closures over the course of the last few years, as well as market share data and sales data. I believe these data sets will help me to gauge the upcoming years of the sneaker industry and determine which brand will lead for years to come.

 

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