本季时尚界流行什么？大数据

    2000年代初，当时还在澳大利亚从事时装设计工作的茱利亚•法勒发现了一个恼人的问题：手头上的信息源太少了，没法帮她及时了解和响应最新的流行趋势。

    “我们掌握着前一季产品业绩的内部数据，也可以访问一些能够给人启发的时尚网站，但是没法知道我们错过了哪些机会，也没有具体数据告诉我们怎样才能改进我们的产品搭配。”她回忆道。

    由于不知道向谁求助，法勒干脆决定自己开发一套解决方案。她挑选的时机再恰当不过。当时。一系列被合称为“大数据”的方法和技术刚刚开始席卷整个科技行业。

    没过多久，法勒的头衔就变成了Editd公司联合创始人。另一名负责技术的联合创始人吉夫•瓦茨目前担任这家公司的CEO。他们的目标是帮助全球服装零售商、品牌和供应商在正确的时间、以正确的价格交付正确的产品。

    法勒表示：“每次你看到一个产品打折，那都是由于错误的决策导致的。它导致这个行业出现了大量损耗，我希望解决这个问题。”

    Editd公司号称拥有目前全世界最大的服装数据库。凭借120台服务器和几百兆兆字节的数据，该公司不仅向客户提供各类服装数据，还提供实时分析与各种其它工具。总部设在伦敦的Editd公司目前拥有27名员工和600万美元的资本，快时尚品牌Gap和塔吉特百货(Target)等大公司都是它的客户。瓦茨声称，Editd公司目前已经盈利，不过他拒绝透露该公司的具体收入。

    530亿个数据点

    Editd的成功秘诀之一是，它汇总了来自全球各种来源的流行时尚数据和销售信息——从零售网站、社交媒体，到设计师的T台走秀报告，再到流行博客——然后设法实时获取这些数据。该公司的数据库包含了至少530亿个来自时尚行业的数据点，有些信息可以追溯到四年前。它还涵盖了全球1000多个零售商，同时拥有1500多万张高清图片。它的“社交监控”功能监控着全球80多万名有影响力的时尚潮人和专家的社交活动。

    为了随时读取这些数据，Editd公司把大部分数据储存在内存而不是硬盘里，对此瓦茨解释道：“这是非常重要的。我们需要以任何可能的方式读取和查询所有数据，它必须具有超强的响应力。”

    另外，它必须足够简单易懂，让外行也能知道数据的意义。瓦茨表示：“用户不必非得是一名数据学家才能理解这些数据的含义。”

    借助于Editd提供的服务，从事新品规划、采购、贸易和战略规划等工作的服装业从业者几乎可以在任何设备上设置他们自己的“社交监控器”。Editd的服务涵盖男装、女装、童装、配饰和美容等多个领域。由于输出端的信息是可以定制的，所以一家高端服装店负责牛仔服的业务员所看到的数据，与一家平价服装连锁店的女款针织衫采购员所看到的数据是截然不同的。

    When Julia Fowler was working as a fashion designer in Australia back in the early 2000s, she found herself frustrated by the lack of information available to help her understand and respond to the latest trends.

    “We had internal data on the performance of previous seasons’ products and access to inspirational trend sites,” she recalls, “but no way to understand opportunities we’d missed or concrete data on how we could improve our product assortment.”

    With nowhere to turn, Fowler decided to take it upon herself to develop a solution to the problem. Her timing was just right: A methodology and series of technologies collectively called “big data” was beginning to swell in the technology industry.

    Fowler has since swapped her title of designer for that of co-founder at Editd (pronounced “edited” and stylized in all caps), a company she launched five years ago with technical co-founder Geoff Watts, who now serves as the company’s CEO. Their mission: to help the world’s apparel retailers, brands, and suppliers deliver the right products at the right price and the right time.

    “Every time you see a product on discount, it’s because the wrong decisions were made,” Fowler says. “This leads to a lot of wastage in the industry. I wanted to fix that problem.”

    Editd says it now has the biggest apparel data warehouse in the world. It offers that data up to customers along with real-time analytics and an assortment of other tools, powered by 120 servers and hundreds of terabytes of data. The London-based company, which has 27 employees and $6 million in investment, counts Gap and Target among its customers. It’s also profitable, Watts says, though he declined to disclose the company’s revenues.

    53 billion data points

    Part of Editd’s secret sauce is the way it aggregates fashion trend and sales information from a wide variety of sources around the globe—from retail sites, social media, designer runway reports, and blogs covering trends—and then makes it accessible in real time. The company’s dataset includes no fewer than 53 billion data points on the fashion industry dating back more than four years. It covers more than 1,000 retailers around the globe and boasts 15 million high-resolution images. Its Social Monitor feature, an aggregated dashboard of social activity by fashion influencers and experts, includes more than 800,000 people.

    To keep its data readily accessible, Editd stores most of it in memory, not on disk. “That’s really important,” Watts explains. “We need to access all of that and query that in any possible way. It needs to be super-responsive.”

    It also needs to be easy for a layperson to grasp. “People shouldn’t have to be data scientists to understand the insights,” Watts adds.

    With Editd’s service, apparel professionals in merchandising, buying, trading, and strategy can set up and tailor their own dashboards and monitor whatever they choose from virtually any device. The service spans menswear, womenswear, children’s apparel, accessories, and beauty. Because the output can be customized, a denim merchandiser at a premium retailer, for instance, would see a very different set of data than a women’s knitwear buyer at a mass-market chain store.