????2000年代初，當時還在澳大利亞從事時裝設計工作的茱利亞?法勒發現了一個惱人的問題：手頭上的信息源太少了，沒法幫她及時了解和響應最新的流行趨勢。

????“我們掌握著前一季產品業績的內部數據，也可以訪問一些能夠給人啟發的時尚網站，但是沒法知道我們錯過了哪些機會，也沒有具體數據告訴我們怎樣才能改進我們的產品搭配。”她回憶道。

????由于不知道向誰求助，法勒干脆決定自己開發一套解決方案。她挑選的時機再恰當不過。當時。一系列被合稱為“大數據”的方法和技術剛剛開始席卷整個科技行業。

????沒過多久，法勒的頭銜就變成了Editd公司聯合創始人。另一名負責技術的聯合創始人吉夫?瓦茨目前擔任這家公司的CEO。他們的目標是幫助全球服裝零售商、品牌和供應商在正確的時間、以正確的價格交付正確的產品。

????法勒表示：“每次你看到一個產品打折，那都是由于錯誤的決策導致的。它導致這個行業出現了大量損耗，我希望解決這個問題。”

????Editd公司號稱擁有目前全世界最大的服裝數據庫。憑借120臺服務器和幾百兆兆字節的數據，該公司不僅向客戶提供各類服裝數據，還提供實時分析與各種其它工具。總部設在倫敦的Editd公司目前擁有27名員工和600萬美元的資本，快時尚品牌Gap和塔吉特百貨(Target)等大公司都是它的客戶。瓦茨聲稱，Editd公司目前已經盈利，不過他拒絕透露該公司的具體收入。

????530億個數據點

????Editd的成功秘訣之一是，它匯總了來自全球各種來源的流行時尚數據和銷售信息——從零售網站、社交媒體，到設計師的T臺走秀報告，再到流行博客——然后設法實時獲取這些數據。該公司的數據庫包含了至少530億個來自時尚行業的數據點，有些信息可以追溯到四年前。它還涵蓋了全球1000多個零售商，同時擁有1500多萬張高清圖片。它的“社交監控”功能監控著全球80多萬名有影響力的時尚潮人和專家的社交活動。

????為了隨時讀取這些數據，Editd公司把大部分數據儲存在內存而不是硬盤里，對此瓦茨解釋道：“這是非常重要的。我們需要以任何可能的方式讀取和查詢所有數據，它必須具有超強的響應力?！?/p>

????另外，它必須足夠簡單易懂，讓外行也能知道數據的意義。瓦茨表示：“用戶不必非得是一名數據學家才能理解這些數據的含義?！?/p>

????借助于Editd提供的服務，從事新品規劃、采購、貿易和戰略規劃等工作的服裝業從業者幾乎可以在任何設備上設置他們自己的“社交監控器”。Editd的服務涵蓋男裝、女裝、童裝、配飾和美容等多個領域。由于輸出端的信息是可以定制的，所以一家高端服裝店負責牛仔服的業務員所看到的數據，與一家平價服裝連鎖店的女款針織衫采購員所看到的數據是截然不同的。

????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.