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“转自：灯塔大数据；微信：DTbigdata”
畅销书作家、Keynote主讲嘉宾、顶尖商业及数据专家
不知道你能不能感觉到，我们每个人都在创造历史。大数据有着无比强大的力量，能够给各行各业乃至整个社会带来巨大变革。
从普通人生活的日常琐事，到治疗癌症的方法选择，再到应对人类社会面临的威胁，大数据将改变每个行业，改变我们生活中的方方面面。现在我们可以很肯定的说，大数据已经在悄然改变我们的生活了。
有人认为大数据的流行不过是昙花一现，但是他们错了。大数据不会改变，也不会消失，并且大数据的应用也会继续发展。我们现在称之为“大数据”的东西，几年之后就会成为一种标准和惯例。
通常来说，大数据是指收集和使用大量多种多样的数据。我是一名咨询师，我每天都和公司企业、政府部门打交道，做一些大数据项目，帮助他们收集、储存并分析大量的数据，给他们提供改进的建议。
在工作中，我发现很多公司不知道如何将大数据转化为商业价值，当然也有一些公司做的很好，比如Uber（优步）和Netflix。
编者注
：Netflix是一家美国公司，在美国、加拿大提供互联网随选流媒体播放，定制DVD、蓝光光碟在线出租业务，全球十大视频网站之一，爱看电影和英美剧的朋友应该比较熟悉。
Uber为客户提供基于智能手机应用程序的出租车预定服务，客户通过这个平台可以联系到愿意接送他们出行的司机。
1）大数据众包原则
Uber的整个商业模型就是建立在大数据众包原则上的：愿意接单的车主可以联系乘客，然后带他们去目的地。这种形式为人们提供了极大的便利，尤其是在那些公共交通并不发达的地区。
2）公共交通网络深度分析
用户每在Uber上完成一个订单，Uber就会记录一次并且实时监控这个数据，uber会利用这些数据来确定用户需求，分配资源。Uber还会对城市的公共交通网络进行深度分析，来确定哪些区域公共交通覆盖较少，在哪些地方增加服务量，同时还可以把出租车服务和公交或者铁路进行对接。
3）波浪定价
Uber拥有庞大的城市车主数据库，当一个乘客想要打车的时候，他们就能马上匹配最合适的车主来接驾。Uber公司研发了多种算法来实时监测交通状况和行程时间，这就意味着打车的价格会随着需求进行调整，交通状况差的情况下，行程时间就会相应变长。很多司机在打车需求高峰的时候出来接单，需求不高的时候待在家里。
Uber公司已经为他们这种基于大数据的定价机制申请了专利，他们称之为“波浪定价”。这是一种“动态定价”的形式，与酒店业、航空公司根据需求调整价格是一个道理。不过Uber可不是像它们那样只是到了周末或者假期的时候就涨价，而是有一定的预测模型来实时估计需求量大小。
4）优步池
数据还主导着该公司的UberPool（优步池，就是所谓的拼车）服务。Uber的官微上介绍说，他们的数据会显示，哪个城市的哪些人行程非常相似，起点和目的地接近，出行时间也差不多一致，这样他们就能合并这些订单，当然是在客户同意的前提下。
除此之外，Uber还有一些其他项目正在测试之中，或者等待发布，像UberChopper，一键叫“机”服务，为乘客联系直升机，真正实现“打飞的”，Uber-Fresh外卖服务，Uber Rush快递服务。
电视电影服务供应商Netflix，据称占据美国互联网流量高峰的三分之一，该网站目前在全世界50多个国家和地区拥有超过6500万观众，每天总计播放超过1亿小时的电视节目和电影。Netflix收集和管理这些观看数据，以了解观众的观看需求和习惯。但是仅仅这些数据并不能称为真正意义上的“大数据”，只有把这些数据和尖端分析技术相结合，才使Netflix成为一个真正的大数据公司。
1）推荐引擎
尽管Netflix已经在各个方面都应用大数据分析技术，但是Netflix的主要目的还是要预测用户喜欢的视频类型。大数据分析可以帮助分析用户的喜好这大大加快了“推荐引擎”的发展，
2）建立预测模型
起初分析师手上缺少用户数据，所以分析上受限制。一旦流成为主要传输方式，分析师就能轻松地获得客户的更多数据。这些数据让Netflix能够建起自己的预测模型，持续不断地为用户提供他们感兴趣的电影。只有让用户感觉到方便和信赖，才能留住他们继续订阅网站视频。
3）添加标签
Netflix还采用添加标签的形式给用户推荐电影。公司专门雇佣了一批人来观看所有的视频，然后根据视频内容，给视频添加分类标签。Netflix会根据你的观影记录里视频标签，来推荐有相似标签的电影。
2015年4月，Netflix致股东的一封信上说，他们的大数据战略已经获得回报。与2014年同期相比，2015年第一季度，新增订阅观众490万人。仅在2015年第一季度，Netflix会员就观看了超过100亿小时的视频。随着Netflix的大数据战略发展下去，这一数字还会继续增长。
这两家公司是利用大数据获得优势的两个范例，大数据也帮助它们在行业中获得领先地位。
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.
翻译：灯塔大数据
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