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When was the last time you bought apparel or accessories that were not on sale? Customers never tire of a good sale, but for the fashion industry anything on sale means that someone in the manufacturing process made a mistake. Fashion is an exciting, but a volatile and dynamic business. From misinterpreting the popularity and potential of a trend to miscalculated inventory, such challenges have compelled the industry to think of efficiency. Though the fashion industry is one of the slower adopters of big data, the industry is moving forward. Over the past couple of years the industry has invested in Business Intelligence systems, in-store data capture systems and point-of-sales data capture.

Unfortunately, the pace at which the industry is switching to more savvy BI tools is adversely affecting the decision making process. Even today, the fashion industry is largely driven by gut feeling and instinctive development of design. Designers have always combined intuition and creativity with macro-level knowledge of popular colors, styles, patterns etc. The absence of evidence demands more tangible factors that give reasons for the success or failure of a particular style. Despite the huge role creativity plays in designing a fashion line, profit and business success needs metrics that provide empirical analysis. And this goes beyond moodboards and focus group sessions for choosing the next season's styles, fabrics, colors, materials etc.

Fashion itself is infamous for being changeable, and is driven more by fads and styles than by statistical data. Big Data can help fashion companies ride the crest of each trend ahead of competition. Fashion companies are now able to access or gather larger amounts of customer insights, with a greater depth and clarity of detail and derive inputs by analyzing much more than historical sales data and focus groups. And these insights can help creative design as well as supply chain and pricing decisions.

There are some fashion websites today that go beyond sales trends or focus groups. These websites collect and share data not just on what people are buying, but also what they're wearing. Behavioral data allows fashion companies to correlate purchases with consumer trends.

For fashion businesses, in particular, social media has immense marketing potential. By tapping into vast online conversations, savvy companies can get access to how customers put together brands online. Don't forget that a simple "Like", or word of mouth shared via social media, is more powerful than any survey or focus group. Big data tools can help you extract vast amounts of data and analyze all conversations about trends, successful designs, preferences, and fashion faux pas.

Until now, consumer demand for fashion has been predicted and driven by fashion experts and legacy analytics models which are less sophisticated. Focus groups also churn out small data which may not represent true consumer trends. Big data is changing the way actual consumer preferences are gathered and analyzed. It gives the fashion industry access to individual preferences with far greater detail. Businesses can base their decisions on valuable insights into what styles may perform better and even into what styles will sell better in specific locations. The predictive insights of big data go beyond understanding customers, and can help fashion businesses select creative talent and improve the effectiveness of their marketing and advertising as well.

With analytics, structured and unstructured data can now be processed with the help of PLM tools, and be preserved for future reference. A PLM tool can help plan the entire product range, or in the fashion context the clothing range that is responsive to the demand, and can even forecast the demand for the particular type of design. However for a relatively accurate forecast and strategy, it helps to have a big data analytics solution that syncs seamlessly with all aspects of business. There are some companies in the fashion industry that are already doing this.

One such application of using insights for predicting fashion trends is ITC Infotech's Style Performance Analytics (SPA) solution, which combines PLM, ERP and BI to revolutionize the process of fashion design. ITC Infotech's SPA analyzes past designs and brings out the aspects that have been popular and feeds this information back into the design process. In this way, using fact-based decision making, the fashion line becomes more adapted to the customer's preferences.