Big Data and the 5 Vs in the fashion industry

           The phrase "big data" refers to a collection of unstructured, organized, and unpredictable data collected from several sources in diverse forms with a large amount that cannot be managed with current data management approaches (Ramadan, 2017, p. 1-2). There are five parameters that define big data:

- Volume: The storage, structuring, and processing of large amounts of data is a key difficulty in big data.  In fact, by 2020, the information volume is predicted to have increased by 50 times.

- Variety: It signifies that the information is unorganized or semi-structured. Data might be in the document format, graphs, emails, audio, text, photos, video, logs, etc.

- Velocity: This term is most commonly used to describe data flowing from sensors, websites, and video streaming.

- Value: It refers to how useful the data to be evaluated is. To put it another way, is it worthwhile to delve into the data? This is a difficult topic to answer since data analysis is expensive in terms of both money and time.

- Veracity: It relates to data integrity, which includes data noise and correctness. This is another element that might influence the data analysis method, as well as the amount of time and data necessary to make conclusions.

5Vs of Big Data (Science Direct, 2015)

        Despite the clothing sector has always depended on instinct and imagination to guide its design, purchasing, and merchandising decisions, it has been experimenting with Big Data for several years. Big data is used in the fashion brand sector to improve forecasting precision in strategy development and decision-making, as well as to expand market and customer analytical skills (Lee, Ju, and Lee, 2021, p. 3). Big Data may be used in the fashion sector for a variety of goals, including market discovery, trend research, customer comprehension, generating high-ticket buys, promoting new designers, assessing influencers' impact, and increasing cross-selling (Silva, Hassani, and Madsen, 2020, p. 21). The clothing sector's lifeblood is discovering and establishing trends, and before, trend popularity was determined only by historical sales. Consumers are increasingly expecting and demanding highly personalized purchasing experiences, so looking at previous sales isn't enough. However, Big Data analytics on the previous season's purchase behaviour might assist determine which fashion features or components buyers would respond to favourably in the future.

For instance, Zara uses Big Data on a daily basis to analyse and comprehend client requests, which are then converted into real designs, allowing the company to stay on top of consumer expectations and desire for the newest fashion trends. Furthermore, they expect the freshest runway designs to be accessible in stores right away. This rapid fashion movement was first aided by the first phase of Big Data analytics, which enabled fashion firms to get the proper price, stock, discounts, colours, and size in order to fulfil demand (Silva, Hassani, and Madsen, 2020, p. 22).

            Fashion businesses appear to be using Big Data to the benefit of their brands by forecasting trends, reducing wastage, enhancing the consumer experience, providing better quality control, and shortening the supply chains. It is vital to improving management by gathering data and interpreting big data in order to determine the reason for a defective product, equipment failure, and production delays, as well as to locate people and items using smart sensors (Lee, Ju, and Lee, 2021, p. 7).

            Fashion is a very sensitive sector to shifting consumer expectations, as well as one that is evolving toward greater customization. Buyers want a variety of alternatives tailored to their personal preferences, settings, and cultural and musical inspirations. As the desire for customisation grows, so does the need for ‘mass customization' of clothes in order to avoid excess inventory (Big Data Made Simple, 2020).

 

(Silva, Hassani, and Madsen, 2020)

Big Data's Effect on the Clothing Sector 

            Big data is essential for success in any high-level sector, including clothing. It is increasingly being used in the design industry for inventory network the board, client behaviour research, inclinations, and sentiments (Jain, 2021).  

• Personalisation Creates a Growing Necessity for Big Data: Fashion is a sector that is extremely sensitive to shifting demands and is also evolving toward more significant personalization.

• Significant Data to Meet Customer Expectations: Producers are already employing online data to get knowledge on certain categories. These include texture selection, which is intricately linked to emotions and seasons.

• Driving Crucial Decisions: The information acquired allows fashion businesses to make key decisions related to both online and offline buying.

• Predictions for Product Demands: For some businesses, predicting what will sell well is quite straightforward.

• Vast Data and Marketers: Predictive innovation will achieve more than just identifying important tones, trims, and styles. It will also let businesses identify powerhouses with whom they may collaborate prior to the previous 'blast.' Because internet media has become widespread, finding powerful partners is crucial in the modern design business.

 

Big Data's Advantages in the Fashion Industry 

        The fashion industry is experiencing changes in the way producers develop and promote their products as a result of the advancement of big data. Regardless of status or culture, design appeals to practically everyone on the earth, yet different clients seek different clothing items. What this massive amount of data adds to the picture is crucial information for creators who want to create goods that will sell well.

The gender divide: For all creators, deciding whether to sell women's or men's apparel is a crucial option. Big data illustrates how and when people shop, how big their purchases are on average, and how many items they buy. This information may assist in determining which lines are more important for company success, and whether to go with women or menswear could be a major decision.

The most popular shading patterns: The design industry is driven by current global trends, but style companies also need to learn which tones are popular with their customers. This is the point at which a large amount of data is used to uncover the most popular shading patterns among purchasers, allowing the plan to be easily updated and modified by the interest.

The cost of each item of apparel is as follows: The evaluation of pieces of clothing is another important component of the accomplishment equation. Each piece of apparel is assigned a price to be displayed in stores, and a wealth of data makes it easier to establish a standard value that will boost sales. Planners should be aware of these fees in order to pass them on to specific customers.

New item categories emerge: Every firm must support the development of new things that will be useful in the future. Large amounts of data can also assist in this regard by revealing which goods should be pursued after and which should be avoided.

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Manon Bally

 

References:

Big Data Made Simple (2020) Big data and fashion: How’s it changing the industry?. [online] Available at: <https://bigdata-madesimple.com/big-data-fashion-changing-industry/> (Accessed 9 November 2021).

Jain, S., (2021) Big Data in Fashion Industry: Impact, Benefits and Application. [online] Textilelearner. Available at: <https://textilelearner.net/big-data-in-fashion-industry/> (Accessed 9 November 2021).

Ramadan, R.A. (2017) Big Data Tools – An Overview. Int J Comput Softw Eng 2: 125, pp. 1-15. doi: https://doi.org/10.15344/2456-4451/2017/125

Sae-Eun Lee, Naan Ju and Kyu-Hye Lee (2021) ‘Visioning the Future of Smart Fashion Factories Based on Media Big Data Analysis’, Applied Sciences, 11(7549), pp. 1-12. doi: 10.3390/app11167549.

Silva, E. S., Hassani, H. and Madsen, D. Ø. (2020) ‘Big Data in fashion: transforming the retail sector’, Journal of Business Strategy, 41(4), pp. 21–27. Available at: https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib,cookie,url&db=edb&AN=143659036&site=eds-live (Accessed: 9 November 2021).