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Why you should look to data to achieve the style you can’t afford

M. Lapuerta is a Harvard graduate, tech-fashion gal who used data to let people know how to dress. On her Instagram account @databutmakeitfashion she breaks down all the things you need to know about the runway to look objectively good. While fashion is undoubtedly something creative and abstract, trends are still ruling a big part of the fashion kingdom – and trends can be analysed. We asked Made some questions and found out why data actually goes well together with fashion, how data can help you achieve the aesthetics of your favourite high-fashion brand and which the coming trends are according to – you guessed it – data.

How did you come up with the idea of analysing trends with data?

When I interned at Google last summer, I started learning about how you can teach a computer to detect objects in photos. So, I decided to start teaching a computer to recognize specific fashion trends (such as "dad sneaker" or "crossbody bag", which were quite popular at the time). 

I always knew I wanted to study computer science but I was also always interested in fashion and runway shows. My differing interests and the way I looked or dressed always made me stand out from my computer science peers, and I always thought it to be a weakness. In fashion-tech, however, this way in which I stick out becomes my strength. 

What have you learnt through collecting and analysing fashion data?

There are ways that you can make style trends and fashion objective, but the foundation of fashion lies in creativity and artistry. I think that anyone who is looking to explore fashion-tech needs to come into the industry with an appreciation for its craft, otherwise software solutions won't be entirely effective. For example, in order for me to even know what trends I should be looking for in images, I study a designer's background and past work to know what I should be hunting for in their new collection.

The fashion industry isn't as superfluous as people might think it to be. In light of the COVID-19 epidemic, there isn't a major fashion house which hasn't transferred its resources to create sanitizer or protective gear, or which hasn't made a significant donation to a relief organization. There's a lot of meaning behind-the-scenes of fashion collections that isn't so effectively conveyed through just trend analytics, but I'm working on letting that show through my data. Lastly, I've learned that there are so many ways in which computer science skills can be integrated into industries other than big-tech; so many ways in which women can pursue engineering without having to conform to what everyone else perceives engineering to be.

Some people would say fashion is something subjective and thus maybe doesn’t go along with data. What’s your thoughts on that?

Fashion is definitely something more subjective and the last thing I want to do is diminish the industry's creativity. I've previously written about ways in which software and data analytics can help fashion maintain its creativity, during a time where the industry is becoming more business- or tech-based. Additionally, as more and more fashion companies are filing bankruptcy due to an inability to adapt to e-commerce (Barney's NY, True Religion, J. Crew), data analytics is becoming more important than ever to help high-fashion brands survive and adapt to changing consumer habits.

Why should we look to data when picking out outfits?

One of the ways in which data helps me pick outfits is knowing what high-fashion brands are objectively pushing out each season. For example, I may not be able to afford a piece of clothing straight from the Dior runway, but if I have the knowledge of what trends Dior is promoting, I can dress according to their style regardless. 

Knowing what is objectively in or out makes getting dressed exciting, since you have a better idea of what your favourite brands look like each season and you can mirror that style closely.

Are you yourself considering your data when getting dressed?

I don't base everything I wear off of the numbers I collect, since I have a personal style and aesthetic as well. Sometimes, the trends I see a brand or collection push out don't necessarily mirror my own taste. However, what does inspire me when getting dressed is all the time I spent looking at these photos of runway shows. For example, a grand majority of high-fashion collections used midi over maxi dresses this season. I've personally never been really into maxi-length dresses, so it's exciting to learn about this new silhouette and experiment how I can integrate it into my closet.

Do you experience that the trends you predict with your data usually turn out to be visible trends even without looking at data? For some trends, definitely. For example, it was a no-brainer that "dad sneaker" was so popular, because that's something I had been seeing all over Instagram and Pinterest at the time. What is cool, though, is to track how these hyper-popular trends change from season to season. For example, of the Fall 2020 runway collections I analyzed, I don't believe a single one of them had a dad sneaker walk down the runway, or even a sneaker at all. So, it is definitely interesting to analyze how these trends are objectively changing in high-fashion collections.

So, according to data, which are the biggest trends right now?

Some prominent trends I analyzed from the Fall 2020 runway shows were monochrome outfits, blazers, midi-length skirts and dresses, and sleeveless tops. Graphic tees, not so much.

What do you think about the future of data in fashion?

I think data can help the high-fashion industry keep up with fast-fashion brands who are dominating e-commerce and social-media-consumer markets. While data is certainly helping fast-fashion brands rip off and capitalize off of high-fashion trends, data can also help high-fashion designers protect the integrity of their designs. My hope is that software and analytics helps high-fashion brands thrive and survive, while helping preserve the creative and artistic foundation of the industry.

- Cornelia Falknäs

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