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Big Data (Part 3): Dressed for success – matching fashion with demand

STX_blog_fashion3

August was always an exciting time at my childhood home. We were excited that was school was starting in September and mom was relieved that summer was coming to an end. I remember the annual trips to the local department stores to buy school clothes every year. It was always exciting to pick out a new school clothing and a new winter coat. With only a few stores to choose from, many of us wore similar clothes and coats when classes started.

As consumers, we have far more fashion and store options today. There are specialty stores at the mall, big box outlets, membership stores and specialty online portals. With so many more clothing designers than in years past, retailers are also inundated with fashion choices. The question becomes, “how does the fashion chain – from textile suppliers and clothing manufacturers to the retailers themselves – choose what to carry?”

They all rely on big data to make critical decisions. Let’s go to the start of the chain: the textile manufacturer. It may analyze previous years’ orders, competitive intelligence, purchasing trend data, and raw material and manufacturing costs. While tracking analytics on one data source is relatively easy, capturing and analyzing multiple data sources can be a tremendous challenge – a point underscored in a 2012 research report from Gartner. In its analysis, Gartner found that big data processing challenges don’t come from analysis or a single data set or source but rather from the complexity of interaction between two or more data sets.

“When combining large assets and new asset types, how they relate to each other becomes more complex,” the Gartner report explains. “As more assets are combined, the tempo of record creation and the qualification of the data within use cases becomes more complex.”

The next link is the clothing companies that create the fashion. They have a much more complex job, using big data to analyze fashion trends and improve their decision-making. Information such as historical sales, weather predictions, demographic data and economic details help them chose the right colors, sizes and price points for the clothing they make.

Swim Suits and Snow Parkas
This is where we, as consumers, come into the picture. Just as I did many years ago, people still shop for school and winter clothing this time of year. The clothes on the racks at our favorite retailer or from an online catalog were chosen and ordered 6-9 months ago. Take Kohl’s. The nationwide retailer uses a blend of geographic weather prediction data sources to know where to best sell those snow parkas versus swim suits, economic and competitive data to price it right, demographic data sources to better predict the required sizes and customer demand, and market trends data sources to better forecast the colors and styles that will sell best. The more accurately Kohl’s buyers can predict consumer behavior using big data, the less the retailer will need to discount overstock, and the higher its sales and profit. 

As I stated in my previous blog posts, the Hadoop® architecture is a great tool for efficiently storing and processing the growing amount of data worldwide, but Hadoop is only as good as the processing and storage performance that supports it. As with flu strain and weather predictions, the more data you can quickly and efficiently analyze, the more accurate your prediction. When it comes to weather and flu vaccines, these predictions can help save lives, but in the fashion industry it is all about improving the bottom line.

Whether in fashion, medical, weather or other fields , the use of Hadoop for high levels of speed and accuracy in big data analysis requires computers with application acceleration. One such tool is Seagate Nytro™ Application Acceleration.

This post is part of a three-part series on real world uses for big data
Part 1 examines big data for weather forecasting.
Part 2 examines big data in the medical industry.
Part 3 examines big data in the fashion industry.

 

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