我是数据原住民，我要你满足我！

以下英文内容来自First Round Review，由网易创业Club重新删减排序整理后加上原创中文解说，不是翻译哟。为了便于理解，我们把中文内容放在相应英文段落的前面。最后，跳过全部英文也不影响阅读的完整性。

原文作者Monica Rogati曾是智能手环Jawbone的数据产品负责人。Rogati对于数据产品的开发策略有自己独到的见解和实践经验。Jawbone所在的智能手环领域是可穿戴设备、大数据、物联网的交叉领域之一。

本文涉及的主要问题是：数据产品的用户预期将推进不亚于移动设备浪潮的数据消费浪潮的到来；推出数据产品所应注意的关键事项，包括：对数据量和种类的要求、软硬件要到位、数据的干净程度、迭代的快速程度、用户体验的把握和相关思考。

文/网易创业Club 傅昊

我是数据原住民，技术能为我做啥？

数据原住民的英文是“data native”，与之对应的概念是设备原住民，“device native”。

所谓“设备原住民”是说，从小就使用移动设备的一代人，他们与设备的互动模式是天然建立起来的。00后就是典型的设备原住民，他们从小使用手机、Pad、PC，对于设备的交互预期（expectation）非常清晰。但这并不代表他们对数据也具有类似的预期。

类比而言，“数据原住民”就是从接触数据产品起就能获得数据有效反馈和服务的这一代人。

我们可以从两类用户对产品的预期来做个对比，原文中的这句话非常到位：

Digital nativeswere concerned about what they could do with technology.

Now data nativeswant to know what technology can do for them.

设备原住民的预期是，我能用设备/技术做什么？

数据原住民的预期是，设备/技术能为我做什么？

也就是说，随着数据产品的普及，未来的一代消费者将会更多的依赖于数据产品对自己的各种决策提供快速、有效的支持。

换句话说，好的数据型产品应该有能力根据不同的外部环境和对用户的理解及时的满足用户对产品的预期。

为了不过于抽象，简单举几个场景例子：

A）内容型产品可以根据用户使用习惯、所处位置、浏览偏好、浏览时间等维度的数据精准推送相关内容。

B）温度控制器根据天气状况、用户出行数据等维度的数据自动调节房间温度、湿度、光亮等等。

C）健康管理产品，能够精准把握用户健康水平，给出具体可行的健康方案。现在的手环还没有实现这样的愿景，因为的确非常复杂。

D）我们观察到市面上开始有多家风投投资于个人助理产品了。个人助理产品的代表作是美国的Magic。所谓“个人助理”就是订票、买咖啡、点外卖、代购、打车、酒店预订等等服务的综合移动接入工具。从我们的理解看，做一个集成入口的形式不是困难的事情，真正要解决的问题正是我们这里讨论的人与数据交互问题。除了被动的让用户提出要求，设置工具行为之外，个人助理工具有没有能力为用户主动提供不同生活场景下的解决方案可能将会是未来真正的胜负手因素之一。

目前的现实是，现在的数据型产品都还远远没到满足“数据原住民”预期的水平，这个领域很可能正在酝酿着巨大的发展势能。

数据产品正在引发的场景变革将是与PC、互联网、移动端设备引发的变革是可比的又一次浪潮。

如果这个观点为真，我们要提醒创业者和投资人从“数据原住民”这个角度来理解近在眼前的新机遇。

Being a data native goes beyond tech savvy or digitalengagement. It’s not just that you like your information served up on screensor being comfortable with the tools. The digital revolution happened when thebalance tipped in favor of people growing up surrounded and shaped by computersand the Internet. Rogati believes we’re in the middle of a similar yet separaterevolution that’s entirely about data.

“A data native is someone who expects their world to notjust be digital, but to be smart and to adjust immediately to their taste andhabits,” she says. “For example, a magazine should not only be digital andinteractive — it should be personalized. It should tell you what you need toknow based on your interests, location, preferences. The expectations haveshifted.”

Digital nativeswere concerned about what they could do with technology. Now data natives wantto know what technology can do for them.

This attitude has been hastened by the explosion innetwork devices. According to Cisco’s most recent study, the number ofnetworked devices will be triple the global human population by 2019. McKinseysays the Internet of Things is predicted to crack open the economy with a newmarket worth $6.2 trillion by 2025. Just three years ago, Home Depot offered100 different smart home devices. Now it stocks well over 600.

The wristbands Rogati worked on at Jawbone were designedto ride this wave, not only by helping people dutifully quantify themselves andmotivating them, but also turning on their coffee makers when they wake up,switching on the AC and turning lights off when they fall asleep.

It’s an interesting quagmire. Traditionally, newtechnology has expanded people’s notion of what’s possible. Now the crowd is already dreaming of products no one knows how tobuild yet.

让数据型产品变得更聪明

谈完了数据型产品的现状和未来的潜在发展趋势，我们看一下Rogati对数据产品设计、开发过程中所应把握节点的建议。

数据型产品的定义是：

通过从用户本人、其他人和外部环境收集的数据为用户提供背景相关及个性化数据服务的产品。

Data products provide context and personalization usingdata collected from you, others and the world.

如何实现数据型服务的及时性、个性化、环境相关性？下面是几条可以借鉴的建议。

A)你要有数据/ Start with Data

巧妇难为无米之炊，工具做不了没数之算。

数据日志的记录非常重要。对于数据的要求不仅仅是在数量上“大”，数据的层次、种类、在不同场景维度上的收集频次也要足够多。

对于用户行为的记录一定是在合适频率下的多维度收集日志。至于如何做日志就需要数据团队的insight来判断了。

Before you can run any analysis, build a recommendersystem, or start training a machine learning model, you need numbers todissect. The goal shouldn’t just be tocollect massive amounts of data, but more so a wide variety, says Rogati.This means you should instrument your app to log as much as you possibly can,because some data can be lost forever. For example, “It’s not enough to logthat a user clicked on a product recommendation — you have to know what elsewas being recommended, the order of each item, and position on the screen, ”says Rogati. “You need to record versions of your algorithms, parameters,strings that are exposed to the users because all of that could change in acouple months.”

B）数据流要可靠/ Reliable DataFlow

软硬件的基础设施要铺设到位。及时把预期内的数据反馈给用户是一切的基础，就像人们预期餐厅端上来的炒饭是热的是一个意思。

The best instrumentation and the best machine learningalgorithms don’t help if you don’t have a reliable data flow. “If you dropevents or if your infrastructure isn’t sufficiently fault tolerant or scalable,you’re looking at the wrong numbers,” she says.

The moment youshow data back to users, the consequences of breaking your data flow aresevere. It’s missed sales because you didn’t make good recommendations. It’syour app crashing because a queue was backed up. It’s losing the user’s trust.

C）数据要干净，迭代要迅速/ Clean Data — andFast Iterations

数据科学家团队需要通过人工判断对进入算法的数据进行梳理和清洁。用英文讲，这个过程被称为data munging或者data wrangling。

如果进入算法的数据不够“干净”的话，所得到的数据处理结果可能会和预期相差甚远。

我们可以这么理解data wrangling这件事，这是数据科学家数据算法进行训练的过程。科学家通过把梳理过的数据代入算法来检视结果，然后不断快速迭代代入数据的组成结构。

这个过程可能涉及到更深层的数据梳理、数据的可视化处理、数据聚合处理、完善统计学模型等等。

Many articles have been written about theimportance of data wrangling and cleanup. “Data scientists spend 80% of theirtime cleaning data” is heard often enough that it inspired its own parody.Rogati, however, wants to see data scientists embrace it:

Data prep is notbeneath you. Good data prep is detective work; it takes intuition, experience,ingenuity and pragmatism.

“The effort is well worth it — it can havea bigger impact on your results than your choice of algorithms,” she says.

The real challenge is that you can’tpossibly anticipate all the different ways your data is wrong — which makes fast iteration (both at the low level datawrangling and at shipping products) absolutely imperative.

D）优质的用户交互和体验/The human in theloop

产品本身smart并不够，还应该让用户切身体会到产品的smart。所有优质的数据型产品都应该符合人类的使用直觉并不断向用户提供更加优质的数据解决方案。

比如，智能手环或健康设备要记录用户吃了什么、多大量这样的数据时是需要用户打字输入还是语音输入还是图像识别呢？不同的场景使用方式对于用户体验的差别是极大的。打字输入最容易实现，但用户长期体验最差，和“数据原住民”的理念背道而驰。图像识别最友好，工程难度最大，但直觉上更有吸引力。

其实，这一系列的用户体验需要考虑的是后端的数据算法跟前端设备及用户使用场景的配合问题。

这是一个闭环的流程：数据的收集、处理、反馈···

数据的处理是算法和数据梳理团队的任务，面向用户的数据收集和反馈则需要配合后台算法不断变得更加优质、自动、可用。

从这个层面看，数据型产品在切入市场时需要找到至少一个能够有效收集并反馈优质数据解决方案的场景类别。然后再推进其他相关场景下的数据解决方案。

For data products, the user experience is a gatingfactor. “The user interaction needs to be smooth, intuitive and robust becauseit’s being handed to people who are going to misinterpret it, click on the‘wrong’ things, or have different expectations.”

Great userexperience and great data are what make products feel smart.

Those are the two sides of the data product coin and theyreinforce each other to create a virtuous feedback cycle. What does this looklike in practice? You want easy to use software and hardware that canseamlessly integrate into people’s lives so that it generates higher volume andbetter quality data.

Rogati provides an apt example from her experience: “Sayyou’re logging your meals in your Jawbone mobile app — quick autocompletebecomes really important because it helps people get the job done faster. Thefaster people can log their info, the more they’ll do it and the more quality,consistent data you’ll end up with. You’ll avoid misspellings or usingthousands of different version for the same concept if you can autocompletepeople’s thoughts right away.”

Harkening back to that virtuous cycle, more and betterdata is what, in turn, makes autocomplete (and data products in general) workbetter and faster — and makes it feel smooth and smart to users. “This is whythe best data products need reliable data flows, fast iteration, and tight, implicitfeedback loops — all in the service of a better user experience that feelstruly ‘smart’.”

总结

数据原住民只是一个概念，但随着人类对数据预期阈值的提高，未来的生活、消费和决策场景与人类行为很可能会再次发生全人类范围里的大幅变化。

能否把握住这个大的趋势是一件非常有意思的事情。

想一想，会不会有一点小激动呢？

当然，咳咳，作为理性的人，我们不能这么容易激动的。

那啥，B2B领域的数据原住民可能会和C端同样重要哟。