Karthi Krishna
Final Year,
Department of Fashion Technology,
Sona College of Technology,
Salem, Tamil Nadu.
&
Dr. A.C. Kaladevi
Professor,
Department of Computer Science and Engineering,
Sona College of Technology,
Salem, Tamil Nadu.
1. Introduction: As apparel and fashion industry is moving toward virtual and digital supply chain it requires adopt more new technology and software to achieve the goal. It this place big data analytics plays a major role in developing the goal of digital fashion supply chain. As we all know now days customers are mostly preferring to do go with online shopping than traditional offline shopping. Once customers use online platform for purchasing fashion products more dates are generated in verities of produced. These different varieties of dates and its applications are discussed in detail in this article.
2. Properties of Digital Data:
2.1 Condition: It deals with the rules which should be followed during data analysis such as, what kind of methodology should be followed during the process to attain the desired result.
2.2 Composition:
It deals with the what kind of structure does the data made of, types and nature of data etc.
2.3 Context: It deals with the result generation and how the method how the data is answered by the query.
3. Classification of Digital Data:
3.1 Unstructured data: This type of data does not follow any structured model or it does not follow any definitive structure or form. Almost nowadays approximately 90% of data handled in a firm fall under unstructured data. For example, Images, videos, voice recordings fall under this category.
3.2 Semi-Structured data: This type of data falls under both structured and unstructured data. It has both the characteristics of structured and unstructured data model. For example: Email, XML files, HTML etc.
3.2 Structured data: This variety of data module has a proper structure. A programmer or a user should follow a structured method to send query about a data from a data base.
4. Big data – Definition: “Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”
-by Gartner IT Glossary
5. Properties of Big Data:
5.1 Volume: Big data could process very huge volumes of data from Bits to Yottabytes.
1
Bits
0 or 1
2
Bytes
8 bits
3
Kilobytes
1024 bytes
4
Megabytes
10242 bytes
5
Gigabytes
10243 bytes
6
Terabytes
10244 bytes
7
Petabytes
10245 bytes
8
Exabytes
10246 bytes
9
Zettabytes
10247 bytes
10
Yottabytes
10248 bytes
Table: 1 Growth of digital data
5.2 Velocity: Big data processing is high speed compared to traditional structured data processing.
5.3 Variety: Big data can process all three types of digital data.
6. Benefits of Big Data:
More data
More accurate analysis
Greater confidence in decision making
Greater operational efficiencies, cost reduction, time reduction, new product development, and optimized offerings, etc.
7. Application of Big Data in Retail Stores:
7.1 Customer relationship management (CRM): It helps to observe how customers interact with the respective fashion brand. It helps the fashion brand to formulate the sales strategy and customer’s experience with the brand which ultimately results in increase in sales volume.
7.2 Cost saving: With the data generated by the customers we can observe the least sold product or least interacted part of the store. With that data we can invest the money in the proper areas and product through which we can maximize the profit.
7.3 Formulating Effective marketing strategy: By observing the data generated by the customers with the brand we can observe how frequent they we engaged with the particular brand and study the psychology of their customer and formulate the better marketing strategy to attract new customers and increase sales.
7.4 Identifying the target market:
Trends defer from country to country. Before a brand plans a launch of a collection, they must study people’s preference with their choice of product and analyse their needs with respect to the brand with the data collected a brand should analyse who their target customers across the globe. This helps a brand for brand extension across the globe.
7.5 Inventory planning:
Every month brand will review sales report hoe much SKUs have been sold in the month. Which range and which collection is sold is analysed by a data analytics team at head office team. According to the stock clearance each store next batch of collection is manufactured in the factory with JIT principle.
8. Conclusion:
As we all know nowadays entire world relies on smart devices. Usage of these devises produce huge volume of data in various forms which is considered as Big data. Understanding Big data in fashion retailing will help a brand for increment in their sales and their strategies for brand extension across the globe.
Jain, Sheenam, et al. “Big data in fashion industry.” IOP Conference Series: Materials Science and Engineering. Vol. 254. No. 15. IOP Publishing, 2017.
Acharya, Abhilash, et al. “Big data, knowledge co-creation and decision making in fashion industry.” International Journal of Information Management 42 (2018): 90-101.
Madsen, Dag Øivind, Emmanuel Sirimal Silva, and Hossein Hassani. “The application of big data in fashion retailing: a narrative review.” International Journal of Management Concepts and Philosophy 13.4 (2020): 247-274.
Founder & Editor of Textile Learner. He is a Textile Consultant, Blogger & Entrepreneur. Mr. Kiron is working as a textile consultant in several local and international companies. He is also a contributor of Wikipedia.
