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TRADITIONAL VS. DATA FASHION TREND FORECASTING: AN EXAMINATION USING DGSN AND EDITED by Mikayla DuBreuilA thesis submitted to the Faculty of the University of Delaware in partial fulfillment of the

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What is traditional vs big-data fashion?

Traditional fashion refers to established methods of design, production, and retail, focusing on seasonal collections and physical stores. Big-data fashion leverages data analytics to understand consumer trends, preferences, and behaviors, allowing for rapid response to market changes.

Who is required to file traditional vs big-data fashion?

Traditional fashion designers and retailers are required to file under conventional industry regulations. In contrast, companies utilizing big data in fashion must comply with data protection regulations and possibly additional reporting requirements related to analytics and consumer data.

How to fill out traditional vs big-data fashion?

Filling out traditional fashion typically involves submitting design portfolios, production schedules, and sales forecasts. Big-data fashion requires reporting on data analytics practices, data sources, and consumer insights to showcase data-driven decision-making.

What is the purpose of traditional vs big-data fashion?

The purpose of traditional fashion is to uphold heritage and artistic expression, while big-data fashion aims to enhance operational efficiency, improve consumer targeting, and foster innovation through data insights.

What information must be reported on traditional vs big-data fashion?

Traditional fashion reporting requires details on manufacturing processes, sales data, and inventory levels. Big-data fashion must report on data sources, analytics methodologies, consumer behavior insights, and compliance with data privacy laws.

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