Planning: Data Journeys (Continued)

 

Armed with my 6 buckets, the next step was to define the data I was going to capture under each category.  Some things were easy.  Seasons? Four of them (if we’re not counting resort wear). Boom. Stores I shop at? Quick look at my web browsing history. Done.  Then, I got to Item Type and Style and Color . . . and got stuck.  How much detail should I go into? Was it important to distinguish between a blouse and a collared button-up? Should I stick to primary colors or do I want to know how many fuchsia vs. hot pink items I owned? (Answer: one of each).  In the spirit of complete disclosure, I wasted a lot of time on this before realizing that the answer was right in front of me.   A lot of my favorite store websites had nice, user-friendly filters for the exact same things I was trying to define. If Nordstrom and Anthropologie had already done the work for me, why reinvent the color wheel? 

 

With a little tweaking and a couple of additions, voila!

Type	Style/Cut	Color	Purpose	Season	Rating	Brand/Store
Pants Jeans Shorts	Cropped/Ankle Boot cut Straight-leg Skinny Trouser/Wide-leg	Beige Black Blue Brown Green Gray Metallic Off-White Orange Pink Purple Red White Yellow Multi	Work Play Work & Play Dress-Up	Spring Summer Fall Winter	Hate it Meh Like it, Don’t Love it Wardrobe Staple Feel Like a Million Bucks	AG American Apparel Ann Taylor/Ann Taylor Loft Anthropologie Banana Republic Club Monaco Everlane Express Gap H&M J. Crew Madewell Nordstrom Old Navy Paige Urban Outfitters Zara Other
Skirt Dress	A-line Pencil Mini Maxi
Blouse T-Shirt Sweater	Cardigan Collared Button-Up Long-Sleeve Short-Sleeve Sleeveless/Tank Top Tunic
Blazer Coat

Not going to lie, my inner data-nerd is pretty excited by this table.  Probably more than is healthy. Now, I’m going to remind myself I actually socialize with people and don’t just talk to myself on the internet all day.