Is big data a revolutionary breakthrough in the fast textile era? Can we link the success story of Zara to big data analytics?

We are living in a fast-moving world with clocks ticking at Nano-seconds, and, the textile industry is no different. It is trying to cope up in this era of fast textiles. The success story of Inditex, a parent company of Zara is in line with the same. It produces over 840 million garments a year. The numbers are huge and this has only been possible due to the data analytics that is deployed at their end. 

Fig. Zara’s outlet

With digitalization at its best, every company has access to data these days. It is how a company uses this data to optimize new designs in the least possible time which gives it an edge over others. However, when we talk about the textile industry, we deal with big data. Big data refers to data that cannot be processed using traditional data mining techniques. This is so because big data is very large and time-sensitive. 

In the textile world, big data plays a significant multi-dimensional role. Few of them are:

  • Cost advantages: Cutting edge tools like Hadoop and cloud-based analytics can help to store a large amount of volatile data at a lower cost.
  • High Speed: New data can be immediately recognized by tools like Hadoop and this can help the business to analyze the data and stay ahead of its fellow competitors.
  • A better understanding of customers- Through advance analytics and sentiment analysis, it becomes much easier to understand the basic customer choices and design the item accordingly. Also, through sentiment analysis, a company can get a brief idea about its reputation in the market and work on it accordingly.

When we talk about big data, it can be defined by 4 Vs:

  • Volume-Big data always deals with large volume of data. This is due to the fact that volume of data is built up from unstructured sources like social media interaction.
  • Velocity- Velocity refers to the speed at which data comes in and how quickly it is analysed and utilised.
  • Variety- Variety refers to the different type of data that can come in. Since data is extracted through different mediums like video, mail, audio and so on, and hence, variety is large
  • Veracity- Veracity refers to the uncertainty of data. Due to high veracity, it makes it difficult for companies to react quickly and come to any conclusion.

And as it is clear from the above characteristics that textile data can easily be termed as big data. 

Now the question arises how exactly is this data used? To go by example, let us take a sneak peek at how Zara, which is known worldwide for its fast-moving trends deploys big data analytics to beat its competitors.

Fig. Use of data at Zara

A detailed procedure that is used is given below:

  • Before clothing leaves the centralized warehouse, it is tagged with an RFID microchip. This RFID chip enables stockists to know where each inventory is and which inventory needs replenishing. This has  made their inventory and stock takes 80% faster than before.
  • Real-time tracking of inventory happens from the time it leaves the centralized warehouse until the time it is sold to the customer. This real-time data helps to keep track of the more preferred styles and can give a picture to the supply chain team.
  • There is a data processing center that operates round the clock and collects real-time data from stores all around the world. This real-time data helps to keep track of the inventory levels in each store and the speed at which each design moves from shelf to the point of sale. Hence, better inventory-management and distribution is achieved.
  • Once sales data is ready, Zara works on getting customer data to focus more on customized production rather than mass production. This is what makes Zara stand out as a brand as compared to its other competitors. It sells over 11,000 distinct items per year versus its competitors that sell around  4,000. Zara produces over 50 percent of the items during the season on a real-time basis based on customer demands and popularity. This is all achieved using analytics that is performed on real-time data that is recorded above and performing a sentimental analysis of its customers. 

Future scope

With Zara already exploiting big data analytics to move from mass production to customized production, there is still scope when it comes to customization. Right now, the system is not fully customized. However, with a technological breakthrough, a system supporting fully customized products based on the individual preference will come into the picture.

The system will mainly consist of two things, a recommender system, and a search engine. The engine provides a platform through which customers can put forward their queries and based on these queries, the recommender system offers a product to the customers. The system will know the textile big data. Textile data can be broadly classified in the following categories:

  1. Material
  2. Textile Design 
  3. Body Data
  4. Colour
  5. Product Design
Fig. Overview of proposed system

Based on the customer’s body type and garment preference, a virtual designer will recommend colour and design that suits the customer the best. This recommendation will be done by a virtual designer that will be using big data applications for the same. If the customer likes the customized design, they can finalize it, or else new recommendations will be provided by the virtual designer. These recommendations will also take into consideration latest market trends.


We have seen above how Zara has overtaken its competitors by partially moving from mass production to customized production. It does not only increase customer satisfaction but also reduces inventory that is left out after each season. It further makes supply chain and inventory management more robust and fast. Hence, if more companies move towards analytics-driven technology, it will be a long term benefit.



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