我是創始人李巖：很抱歉！給自己產品做個廣告，點擊進來看看。
本文為燈塔大數據原創內容，歡迎個人轉載至朋友圈，其他機構轉載請在文章開頭標注：
“轉自：燈塔大數據；微信：DTbigdata”
暢銷書作家、Keynote主講嘉賓、頂尖商業及數據專家
不知道你能不能感覺到，我們每個人都在創造歷史。大數據有著無比強大的力量，能夠給各行各業乃至整個社會帶來巨大變革。
從普通人生活的日常瑣事，到治療癌癥的方法選擇，再到應對人類社會面臨的威脅，大數據將改變每個行業，改變我們生活中的方方面面。現在我們可以很肯定的說，大數據已經在悄然改變我們的生活了。
有人認為大數據的流行不過是曇花一現，但是他們錯了。大數據不會改變，也不會消失，并且大數據的應用也會繼續發展。我們現在稱之為“大數據”的東西，幾年之后就會成為一種標準和慣例。
通常來說，大數據是指收集和使用大量多種多樣的數據。我是一名咨詢師，我每天都和公司企業、政府部門打交道，做一些大數據項目，幫助他們收集、儲存并分析大量的數據，給他們提供改進的建議。
在工作中，我發現很多公司不知道如何將大數據轉化為商業價值，當然也有一些公司做的很好，比如Uber（優步）和Netflix。
編者注
：Netflix是一家美國公司，在美國、加拿大提供互聯網隨選流媒體播放，定制DVD、藍光光碟在線出租業務，全球十大視頻網站之一，愛看電影和英美劇的朋友應該比較熟悉。
Uber為客戶提供基于智能手機應用程序的出租車預定服務，客戶通過這個平臺可以聯系到愿意接送他們出行的司機。
1）大數據眾包原則
Uber的整個商業模型就是建立在大數據眾包原則上的：愿意接單的車主可以聯系乘客，然后帶他們去目的地。這種形式為人們提供了極大的便利，尤其是在那些公共交通并不發達的地區。
2）公共交通網絡深度分析
用戶每在Uber上完成一個訂單，Uber就會記錄一次并且實時監控這個數據，uber會利用這些數據來確定用戶需求，分配資源。Uber還會對城市的公共交通網絡進行深度分析，來確定哪些區域公共交通覆蓋較少，在哪些地方增加服務量，同時還可以把出租車服務和公交或者鐵路進行對接。
3）波浪定價
Uber擁有龐大的城市車主數據庫，當一個乘客想要打車的時候，他們就能馬上匹配最合適的車主來接駕。Uber公司研發了多種算法來實時監測交通狀況和行程時間，這就意味著打車的價格會隨著需求進行調整，交通狀況差的情況下，行程時間就會相應變長。很多司機在打車需求高峰的時候出來接單，需求不高的時候待在家里。
Uber公司已經為他們這種基于大數據的定價機制申請了專利，他們稱之為“波浪定價”。這是一種“動態定價”的形式，與酒店業、航空公司根據需求調整價格是一個道理。不過Uber可不是像它們那樣只是到了周末或者假期的時候就漲價，而是有一定的預測模型來實時估計需求量大小。
4）優步池
數據還主導著該公司的UberPool（優步池，就是所謂的拼車）服務。Uber的官微上介紹說，他們的數據會顯示，哪個城市的哪些人行程非常相似，起點和目的地接近，出行時間也差不多一致，這樣他們就能合并這些訂單，當然是在客戶同意的前提下。
除此之外，Uber還有一些其他項目正在測試之中，或者等待發布，像UberChopper，一鍵叫“機”服務，為乘客聯系直升機，真正實現“打飛的”，Uber-Fresh外賣服務，Uber Rush快遞服務。
電視電影服務供應商Netflix，據稱占據美國互聯網流量高峰的三分之一，該網站目前在全世界50多個國家和地區擁有超過6500萬觀眾，每天總計播放超過1億小時的電視節目和電影。Netflix收集和管理這些觀看數據，以了解觀眾的觀看需求和習慣。但是僅僅這些數據并不能稱為真正意義上的“大數據”，只有把這些數據和尖端分析技術相結合，才使Netflix成為一個真正的大數據公司。
1）推薦引擎
盡管Netflix已經在各個方面都應用大數據分析技術，但是Netflix的主要目的還是要預測用戶喜歡的視頻類型。大數據分析可以幫助分析用戶的喜好這大大加快了“推薦引擎”的發展，
2）建立預測模型
起初分析師手上缺少用戶數據，所以分析上受限制。一旦流成為主要傳輸方式，分析師就能輕松地獲得客戶的更多數據。這些數據讓Netflix能夠建起自己的預測模型，持續不斷地為用戶提供他們感興趣的電影。只有讓用戶感覺到方便和信賴，才能留住他們繼續訂閱網站視頻。
3）添加標簽
Netflix還采用添加標簽的形式給用戶推薦電影。公司專門雇傭了一批人來觀看所有的視頻，然后根據視頻內容，給視頻添加分類標簽。Netflix會根據你的觀影記錄里視頻標簽，來推薦有相似標簽的電影。
2015年4月，Netflix致股東的一封信上說，他們的大數據戰略已經獲得回報。與2014年同期相比，2015年第一季度，新增訂閱觀眾490萬人。僅在2015年第一季度，Netflix會員就觀看了超過100億小時的視頻。隨著Netflix的大數據戰略發展下去，這一數字還會繼續增長。
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這兩家公司是利用大數據獲得優勢的兩個范例，大數據也幫助它們在行業中獲得領先地位。
How Uber And Netflix Turn Big Data Into Real Business Value
Best-Selling Author, Keynote Speaker and Leading Business and Data Expert
Whether we are aware of it or not, we are currently witnessing history being made. Big data is a movement that has the power and the potential to completely transform every aspect of business and society.
From the way we go about our daily lives to the way we treat cancer and protect our society from threats, big data will transform every industry, every aspect of our lives. We can say this with authority because it is already happening.
Some believe big data is a fad, but they could not be more wrong. The hype will fade, and even the name may disappear, but the implications will resonate and the phenomenon will only gather momentum. What we currently call big data today will simply be the norm in just a few years’ time.
Big data refers generally to the collection and utilization of large or diverse volumes of data. In my work as a consultant, I work every day with companies and government organizations on big data projects that allow them to collect, store, and analyze the ever-increasing volumes of data to help improve what they do.
In the course of that work, I’ve seen many companies doing things wrong — and a few getting big data very right, including Netflix and Uber.
Netflix: Changing the way we watch TV and movies
The streaming movie and TV service Netflix are said to account for one-third of peak-time Internet traffic in the US, and the service now have 65 million members in over 50 countries enjoying more than 100 million hours of TV shows and movies a day. Data from these millions of subscribers is collected and monitored in an attempt to understand our viewing habits. But Netflix’s data isn’t just “big” in the literal sense. It is the combination of this data with cutting-edge analytical techniques that makes Netflix a true Big Data company.
Although Big Data is used across every aspect of the Netflix business, their holy grail has always been to predict what customers will enjoy watching. Big Data analytics is the fuel that fires the “recommendation engines” designed to serve this purpose.
At first, analysts were limited by the lack of information they had on their customers. As soon as streaming became the primary delivery method, many new data points on their customers became accessible. This new data enabled Netflix to build models to predict the perfect storm situation of customers consistently being served with movies they would enjoy.
Happy customers, after all, are far more likely to continue their subscriptions.
Another central element to Netflix’s attempt to give us films we will enjoy is tagging. The company pay people to watch movies and then tag them with elements the movies contain. They will then suggest you watch other productions that were tagged similarly to those you enjoyed.
Netflix’s letter to shareholders in April 2015 shows their Big Data strategy was paying off. They added 4.9 million new subscribers in Q1 2015, compared to four million in the same period in 2014. In Q1 2015 alone, Netflix members streamed 10 billion hours of content. If Netflix’s Big Data strategy continues to evolve, that number is set to increase.
Uber: Disrupting car services in the sharing economy
Uber is a smartphone app-based taxi booking service which connects users who need to get somewhere with drivers willing to give them a ride.
Uber’s entire business model is based on the very Big Data principle of crowdsourcing: anyone with a car who is willing to help someone get to where they want to go can offer to help get them there. This gives greater choice for those who live in areas where there is little public transport, and helps to cut the number of cars on our busy streets by pooling journeys.
Uber stores and monitors data on every journey their users take, and use it to determine demand, allocate resources and set fares. The company also carry out in-depth analysis of public transport networks in the cities they serve, so they can focus coverage in poorly served areas and provide links to buses and trains.
Uber holds a vast database of drivers in all of the cities they cover, so when a passenger asks for a ride, they can instantly match you with the most suitable drivers. The company have developed algorithms to monitor traffic conditions and journey times in real time, meaning prices can be adjusted as demand for rides changes, and traffic conditions mean journeys are likely to take longer. This encourages more drivers to get behind the wheel when they are needed – and stay at home when demand is low.
The company have applied for a patent on this method of Big Data-informed pricing, which they call “surge pricing”. This is an implementation of “dynamic pricing” – similar to that used by hotel chains and airlines to adjust price to meet demand – although rather than simply increasing prices at weekends or during public holidays it uses predictive modelling to estimate demand in real time.
Data also drives (pardon the pun) the company’s UberPool service. According to Uber’s blog, introducing this service became a no-brainer when their data told them the “vast majority of [Uber trips in New York] have a look-a-like trip – a trip that starts near, ends near and is happening around the same time as another trip”.
Other initiatives either trialed or due to launch in the future include UberChopper, offering helicopter rides to the wealthy, Uber-Fresh for grocery deliveries and Uber Rush, a package courier service.
These are just two companies using Big Data to generate a very real advantage and disrupt their markets in incredible ways. I’ve compiled dozens more examples of Big Data in practice in my new book of the same name, in the hope that it will inspire and motivate more companies to similarly innovate and take their fields into the future.
翻譯：燈塔大數據
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