Big Data and Big Fashion
Kay Mattern
3/1/2021
The article was published on: November 2, 2018
Article Summary
The fashion industry is huge with $2.4 trillion a year and annual growth between 5.5 and 7 percent, but the industry overall has been adopting slowly to recent technological advances. The compatibility between big data and the fashion industry is rooted in three key principles: extremely high volumes of data, veracity, and variety. Traditional retail analytics are flawed in the way that companies keeping their data to themselves so the industry lacked competitive analysis, insights, prices and other crucial data. Now, machine learning and artificial intelligence are changing the future of the fashion industry. For example, pattern recognition (a subset of artificial intelligence) is being used to detect counterfeit merchandise and pirated purchases, which account for nearly 2.5% of global imports. Additionally, artificial intelligence is being used to augment the design lifecycle and generate new ideas. In particular, IBM’s Cognitive Prints, a suite of AI tools, assists designers by sorting through vast amounts of image data looking for similarities and patterns. Overall, fashion industry could fundamentally change through the implementation of more technology in its design processes and likely will in the near future.
Fashion Industry Statistics
| Global.Sales.In.Trillions | Average.Annual.Growth.Percentage |
|---|---|
| 2.4 | 6.25 |
Author Information
Andrej Kovacevic is a writer and digital evangelist who strives to share what he’s learned through years of marketing and business experience. Andrej contributes to a range of technology-focused publications on topics such as natural language processing and neural networks. He writes articles on the applications of big data and analytics to fashion, crime, education and beyond.
Why did I choose this article?
I have always dreamed of having a job that I loved and was passionate about. As an aspiring data scientist, I have the opportunity to work in nearly any industry as big data becomes integrated in everything that we do. I have always been interested in fitness and fashion, and more specifically fitness apparel, and would be excited to apply my data science skills in a related career.
Discussion
Before reading this article, I knew little about how data science and the fashion industry are intertwined. This article describes the basics of how data science can be used to combat issues in not only the fashion industry but the retail sector in general. For example, machine learning and artificial intelligence can be used to detect counterfeit merchandise and pirated products. In addition, machine learning and artificial intelligence can generate new styles and patterns or replicate them from past eras. Overall, this article was useful for introducing two main applications of data science in the fashion industry and the type of analyses an aspiring data scientist, like me, could contribute to in the future.