There’s something oddly humbling about standing in front of a packed closet and realizing you still have “nothing to wear.”

As I stared at the rows of shirts, dresses, and jeans (some I hadn’t worn in years) I couldn’t help but draw data parallels to my closet… which was its own dataset. Messy, redundant, and full of null values.

So, I decided to treat it like a data problem.

If I can clean, model, and transform millions of rows of data — surely I could handle a hundred hangers.

STEP 1: EXTRACT – PULL OUT THE PIECES THAT MATTER

In data, extraction means pulling from a messy source and capturing what’s worth analyzing.

In closets, it means facing the pile and being brutally honest with yourself… and your consumption problems 🥲

I pulled everything out: clothes, shoes, purses, even those “just in case” items that hadn’t seen daylight in years.

This was my data extraction phase, full transparency.

As I started sorting, I noticed a pattern.

The pieces that truly stayed with me weren’t the trend-driven ones I’d bought on impulse — they were the classics. The high-quality basics I’d invested in over the years: the crisp white button-up shirt, the houndstooth work pants , the heavy grey cardigan. Many of my favorite pieces from Everlane and Zara.

It reminded me of the difference between fast data and clean data.

Fast fashion can be tempting, just like downloading flashy datasets for quick results but in the long run, it’s always the timeless pieces that hold their value.

ELT QUERY

SELECT * FROM closet WHERE joy = TRUE AND quality = ‘timeless’;

Step 2: Load — Stage Your Data (and Your Rack)

Once I’d extracted my favorite pieces, I loaded them onto a single clothing rack — my staging table. I went on Amazon the day before and bought one for $30 and telling myself: everything you choose has to fit on here. Instead of putting everything back in my closet, I needed to force myself to part ways with pieces I haven’t touched in so long and free up space that I could use for other storage like my yarn collection – ha.

Just like loading data into a staging environment, this step helped me visualize patterns and relationships.

I began noticing color palettes (my “columns”), favorite fits (my “key values”), and duplicates (“Do I really need three beige bottoms?”).

The rack became my mini data warehouse.

Step 3: Transform — Preparing for the Next Phase

In data, transformation is where separate tables come together through joins to create new meaning.

In fashion, the same principle applies. My base outfit, a classic striped button-up and wide-leg denim is like my primary table.

From there, I layered on new pieces: a denim vest, a paisley patterned jacket, a gingham coat. Styling instead of just “wearing”. Each addition felt like a join, combining two clean, distinct datasets to create a new, more insightful result.

🧮 A left join: keeping the core outfit, adding a jacket that changes the tone.

✳️ An inner join: when the base and layer perfectly align, polished yet effortless.

🧤 A cross join: when pattern meets pattern, bold, unexpected, but still connected.

Every transformation kept the base intact — proof that when your foundation is strong, creativity has infinite combinations.

Step 4: Optimize — Maintain and Measure

A capsule wardrobe isn’t just a one-time cleanup. It’s database maintenance.

I’ve learned it’s important to:

• Revisit each season (scheduled refresh).

• Add only what complements what I already own (controlled data inputs).

• Retire pieces that no longer align with my lifestyle (data depreciation).

And just like a well-designed data model, it’s made my life more efficient.

Capsule Wardrobe: The Loaded Dataset

(Every ETL project needs a final dataset — this one just happens to hang on a clothing rack.)

After the extract phase, here’s what officially made it into my Fall/Winter 2025 Capsule Wardrobe — the timeless pieces I’ve collected, loved, and worn through the years. Each one feels intentional, classic, and true to my style.

The Final Pieces

Tops & Layers

• 6 button-ups
• 6 tops
• 2 polos
• 3 sweaters
• 1 cardigan
• 2 jackets

The “primary keys” — the foundation of every future outfit join.

Bottoms

• 4 skirts
• 4 pants
• 4 pairs of denim

Most are neutral, but the occasional statement color (hello, red skirt) keeps things interesting.

Dresses

• 3 classic dresses
• 2 tunic shirt dresses

Functionality > Flash: These are versatile, comfortable, and can easily move from casual to polished.

Footwear

• 2 pairs of boots
• 3 pairs of clogs (yes… I love clogs)
• 1 pair of flats
• 1 pair of sneakers

Balanced Load: Equal parts practicality and personality — because good footwear is basically good indexing.

Color Story: Classic Meets Creative

As I stepped back and looked at my final capsule, the color story felt like a reflection of me — grounded yet expressive. The foundation is built on soft neutrals: beige, cream, tan, and black — the kind of timeless tones that quietly do the heavy lifting, much like clean, reliable data. But then there are the pops of red and green, my visual outliers that make the dataset interesting. They’re bold, unapologetic, and full of life.

The mix of patterns — from paisley to gingham to classic stripes and leopard — adds just the right level of texture and personality. Together, it’s the perfect balance between classic and fun, a wardrobe that feels both analytical and artistic.

wrapping up

What surprised me most wasn’t how many clothes I had but how freeing it felt to simplify.

Decluttering my closet mirrored the process of decluttering my life, my workspace, and even my creative energy.

When we clear out what’s no longer serving us, whether it’s old data, cluttered dashboards, or unworn clothes, we make room for clarity.

For intention.

For transformation.

Because sometimes, the best insights don’t come from adding more, they come from refining what’s already there.

And yet, there’s one category I refuse to normalize or declutter, my handbags and purses.

They’re my beautiful exceptions to the rule and my little “data anomalies.” Each one carries a story, a moment, or a milestone.

If the rest of my closet is a clean, optimized dataset, my handbag collection is the carefully preserved archive, the one I’ll never delete.

But let’s be real… I’m probably gonna fail hard at this capsule thing because it’s too hard when you love pretty things 😭