数据库更行通知

by Casper Sermsuksan

由Casper Sermsuksan

哪个更好？ 数据驱动还是数据通知？ (Which is better? Data-Driven or Data-Informed?)

I recently spoke at the Tech in Asia Product Development Conference in Jakarta about being data-driven versus data-informed. Post-conference I was approached a few times to discuss more and speak at other events about the topic. Since people who attended my talk found it useful, I thought I’d share it with the global community.

我最近在雅加达举行的亚洲技术在亚洲产品开发会议上谈到了数据驱动与数据通知。 会后，有几次我被邀请来讨论更多内容，并在有关该主题的其他活动上发言。 由于参加我的演讲的人认为它有用，所以我想与全球社区分享。

The term data-driven has become a buzzword, adding on to the list of other cool, techie jargon. This includes disruptive, pivoting, design thinking, and many more. So, let me first attempt to get us all on the same page.

数据驱动一词已成为流行语，并加入了其他很酷的技术行话 。 这包括破坏性，枢纽性，设计思想等等。 因此，让我首先尝试让我们所有人都在同一页面上。

什么是数据驱动的？ (What’s Data-Driven?)

Being data-driven means letting data be the center of a team and company’s decision making process. Data then plays an essential part of your company. Decision makers reply mostly and sometimes solely on data. Some decisions can be made without human being involved.

数据驱动意味着让数据成为团队和公司决策过程的中心。 数据便成为公司的重要组成部分。 决策者大多对数据作出回应，有时仅对数据作出回应。 无需人工参与就可以做出某些决定。

Some of the conversations that came up from a data-driven company are:

来自数据驱动公司的一些对话是：

• “Let’s A/B test this, and implement the version with the better result”
  “让我们的A / B对此进行测试，并以更好的结果实现该版本”
• “Don’t talk to me unless you have have data”
  “除非有数据，否则不要跟我说话”
• “We’ll just follow what the data tells us”
  “我们将只遵循数据告诉我们的内容”

The data-driven organization will implement this process across teams and functions. This means that every team such as product, marketing, customer success, and operations uses data intensively for all their decisions.

数据驱动型组织将跨团队和职能部门实施此过程。 这意味着每个团队(例如产品，市场营销，客户成功和运营)都将数据密集地用于所有决策。

有什么好处？ (What are the benefits?)

它消除了决策中涉及的许多人为偏见 (It eliminates a lot of human bias involved in decision making)

In fact, a mentor of mine Misha Chellam created flashcards to help you memorize them. Data helps get the team members and stakeholders on the same page with less of their own judgement.

实际上，我的导师Misha Chellam创建了抽认 卡来帮助您记住它们。 数据有助于减少团队成员和利益相关者在同一页面上的判断。

减少了时间消耗，因为我们将人排除在外 (It is less time-consuming because we take human out of the equation)

How many of you have been in a 3-hour long meeting that people argue back and forth with no sign of compromise? Even better if you work in at a startup. Sometimes we discuss and argue for the sake of doing it, then the HiPPO made the final decision. Yep, I know how you feel.

在长达3小时的会议中，你们当中有多少人在没有妥协迹象的情况下来回争论？ 如果您在初创公司工作，那就更好了。 有时，我们为了这样做而进行讨论和争论，然后HiPPO做出了最终决定。 是的，我知道你的感觉。

In fact, a study from MIT has proven the benefits of being data-driven. Prof. Erik Brynjolfsson and his colleagues studied 179 large publicly-traded companies and concluded that those companies are 5% more productive and profitable than their competitors.

实际上，麻省理工学院的一项研究证明了数据驱动的好处。 Erik Brynjolfsson教授及其同事研究了179家大型上市公司，并得出结论，这些公司的生产效率和利润比竞争对手高5％。

但是，数据驱动也有其缺点。 (However, being data-driven also has its drawbacks.)

需要大量数据才能使决策准确 (It requires a huge amount of data for the decisions to be accurate)

You have to watch out for outlier distortion. Outliers are a data points which are different from your average data. In gaming, positive outliers are considered “whales.” Those are customers who either make big purchases or make them very often. They generate a significant amount of revenue for the company.

您必须当心异常失真。 离群值是与您的平均数据不同的数据点。 在游戏中，正离群值被视为“ 鲸鱼” 。 那些是大笔购买或经常购买的客户。 它们为公司带来了可观的收入。

At my previous company, we even coined the term, “whale hunting.” In other industries, it might be those customers who use your product to solve a different problem than most of your customers.

在我以前的公司中，我们甚至创造了“捕鲸”一词。 在其他行业中，可能是那些使用您的产品来解决与大多数客户不同的问题的客户。

Their behaviors are so different they should be excluded from your data to make decisions about your average customers. A few data points together with the outliers can lead your company toward the direction that you don’t want to go.

他们的行为如此不同，应将其从您的数据中排除，以便做出有关普通客户的决策。 一些数据点与异常值一起可以将您的公司引向您不想要的方向。

它需要具有数据科学知识和大量资源的人才能受益 (It requires someone with data science knowledge and a lot of resources to be beneficial)

For startups and organizations that just started a Data Science department, it might be hard to be completely data-driven due to lack of capabilities and resources. Current employees might not have enough knowledge to build the infrastructure. Some might not even have anyone who has expertise with data.

对于刚刚成立数据科学部门的初创企业和组织，由于缺乏功能和资源，可能很难完全由数据驱动。 当前的员工可能没有足够的知识来构建基础结构。 有些人甚至可能没有专门的数据专家。

At my previous company, we had 10 data scientists. In my current position at Kulina, we have 0.5 person (myself and our head of tech).

在我以前的公司中，我们有10位数据科学家。 在我目前在Kulina的职位上，我们有0.5个人(我自己和我们的技术主管)。

我们收集数据的方式仍然存在偏见 (There is still a bias in the way we gather data)

Most of times, what people say is not what they do. So if we gather data by asking them implicitly, we risk making decisions on the wrong information.

大多数时候，人们所说的不是他们所做的 。 因此，如果我们通过隐式询问数据来收集数据，则可能会冒着对错误信息做出决策的风险。

For example, during the scandalous time of Uber and the #deleteuber movement, most of my friends said they would never use Uber again. When Lyft (its main competitor in the U.S.) has a surcharge, they went back and ordered Uber for their regular rides!

例如，在Uber和#deleteuber运动的丑闻时期，我的大多数朋友都说他们将不再使用Uber。 当Lyft(在美国的主要竞争对手)收取附加费时，他们回去并命令Uber进行常规骑行！

让我们从我的经验来看一个例子 (Let’s look at an example from my experience)

One great example is when we were trying to figure out which social media platform we should spend more time and money to engage with our users. We sent out a survey asking them, and the result was quite surprising. We saw Facebook on top of the list, as expected. Yet, what puzzled us was the fact that Google+ was second on the list (no hard feeling to the team at Google).

一个很好的例子是，当我们试图找出哪个社交媒体平台时，我们应该花更多的时间和金钱与用户互动。 我们发出了询问他们的调查，结果令人惊讶。 正如预期的那样，我们在列表中看到了Facebook。 然而，令我们感到困惑的是，Google +名列第二(这对Google团队而言并不难)。

A few team members asked if I would agree with letting them spend more time building our Google+ presence. “Maybe we didn’t know our users well,” a team member mentioned.

一些团队成员问我是否同意让他们花更多时间建立Google+形象。 一位团队成员说：“也许我们不太了解我们的用户。”

Being data-driven, I would probably green-light this. But being stubborn as usual, I discussed with another product marketing manager on what could’ve gone wrong.

受数据驱动，我可能会对此表示赞同。 但是像往常一样固执，我与另一位产品营销经理讨论了可能出了什么问题。

We came to a conclusion that our users might not understand the difference between Google and Google+. We looked deeper into the way the question was asked.

我们得出的结论是，我们的用户可能不了解Google和Google+之间的区别。 我们更深入地研究了问题的提出方式。

Instead of using the icon, we used the wording Google+ in the questionnaire. If you didn’t pay close attention to the survey, you may have thought we were asking if you use Google!

我们在问卷中使用的不是Google+，而是图标。 如果您没有密切关注调查，您可能以为我们在问您是否使用Google！

什么是数据明智的决定？ (What is a data-informed decision?)

Making data-informed decision takes data only as a factor that can be dismissed from time to time. This kind of decision making allows for other factors such as customer experience, gut feeling, brand consistency and the HiPPO to take the lead.

做出基于数据的决策仅将数据作为可以不时消除的因素。 这种决策可以考虑其他因素，例如客户体验，直觉，品牌一致性和HiPPO。

好处是数据面临挑战 (The benefit is that data is put to be challenged)

Because we don’t purely make our decisions based on data, we can use it to create a hypothesis. No matter how good the data is, it has its limitations. It is only a snapshot of reality that doesn’t paint the full picture of our customer journey and behavior. We need to use other factors to help make better decisions, including our own judgement.

因为我们不仅仅基于数据来做出决策，所以我们可以使用它来创建假设。 无论数据多么好，都有其局限性。 这只是现实的快照，并不能全面描绘我们的客户历程和行为。 我们需要利用其他因素来做出更好的决策，包括我们自己的判断。

One example of this happened at my previous company. The nature of our gaming business was having great revenue on the weekend because that’s when our users played games. On a Monday, one of the new product directors approached me about a low weekend revenue, and he was very worried.

一个例子发生在我以前的公司。 我们的游戏业务本质是在周末获得大量收入，因为那是我们的用户玩游戏的时间。 星期一，一位新产品总监向我询问周末的收入很低，他非常担心。

With some experience and guessing I suspected that our revenue was down because we ran a sale the prior weekend. The same effect occurred for the past five times we ran sales.

凭着一些经验和猜测，我怀疑我们的收入下降了，因为我们在上周末进行了一次销售。 在过去五次销售活动中，发生了相同的影响。

If we look at the same cycle before, our average was actually better. We figured out later that it was because of one of the marketing campaigns that we launched to encounter this specific problem.

如果我们以前看相同的周期，我们的平均值实际上更好。 后来我们发现这是由于我们开展的一项营销活动遇到了这一特定问题。

Facebook的Newsfeed版本移动了关键指标，实际上是在没有数据驱动的情况下完成的 (Facebook’s Newsfeed version that moved the key metrics was actually done without being data-driven)

Adam Mosseri and his team back then decided to create the newsfeed without data. In fact, it got a lot of negative reactions that users were angry and even created a Facebook group I Automatically Hate The New Facebook Homepage. Watch his talk here.

亚当·莫塞里 ( Adam Mosseri)和他的团队当时决定创建没有数据的新闻源。 实际上，它引起了很多负面React，用户很生气，甚至创建了一个Facebook组“ 我自动讨厌新的Facebook主页” 。 在这里观看他的演讲。

“At the end of the day, we have to have a gut to make bold decisions without data.” — Adam Mosseri

“最终，我们必须有勇气才能在没有数据的情况下做出大胆的决定。” —亚当·莫塞里(Adam Mosseri)

这并不意味着以数据为依据的决策没有缺点 (This doesn’t mean that data-informed decisions don’t have its drawback)

It requires a lot of time to discuss and analyze, and conclusions could not be formed easily. But remember at the end of the day:

讨论和分析需要大量时间，结论不容易形成。 但是请记住，在一天结束时：

“All the data in the world won’t fix a fundamentally bad product.” — Andy Carvell

“世界上的所有数据都无法解决根本上不好的产品。” — 安迪·卡维尔

To be innovative and build the right product for our customers, we cannot just sit in the room looking at rows of data and charts.

为了创新并为我们的客户制造合适的产品，我们不能只是坐在房间里看着数据和图表的行。

Hint: Data doesn’t have to always be big data.

提示：数据不一定总是大数据。

The key to building products is to:

制造产品的关键是：

• Gather qualitative feedback by talking to your customers
  通过与客户交流来收集定性反馈
• Observe customers struggles through usability tests
  通过可用性测试观察客户的挣扎
• Figure out the jobs customers are hiring you for
  找出客户要雇用您的工作

所以，我对您的建议是 (So, my recommendations to you is)

For smaller companies, you should always aim to be more data-driven. You may not utilize the data you collect, or even collect them at all. The more data you have, the better hypotheses you will generate.

对于较小的公司，您应该始终以更多的数据驱动为目标。 您可能不使用收集的数据，甚至根本不收集数据。 您拥有的数据越多，生成的假设就越好。

For larger companies, make bold decisions that not only move the key metrics but also improve the customer experience. We should use data to help us make better decisions, but don’t rely on it a hundred percent!

对于大型公司，做出大胆的决定，不仅可以移动关键指标，还可以改善客户体验。 我们应该使用数据来帮助我们做出更好的决策，但不要百分百依赖它！

You can also see the slides which contains slightly different information for my talk here.

您也可以在此处查看幻灯片，其中包含与我的演讲略有不同的信息。

翻译自: https://www.freecodecamp.org/news/which-is-better-data-driven-or-data-informed-cba4d2c9cf42/

数据库更行通知