引导分区 pbr 数据分析

by Tim Abraham

蒂姆·亚伯拉罕(Tim Abraham)

如何在1小时内引导您的分析 (How to bootstrap your analytics in 1 hour)

Even though most startups understand how critical data is to their success, they tend to shy away from analytics — especially early on.

即使大多数初创公司了解关键数据对他们的成功有多么重要，但他们往往会回避分析，尤其是在早期。

This partially stems from the myth that if you want to have good analytics, you should carve out around 25% of your engineering resources to fund it. To a founder with a vision, a 25% distraction from executing on that vision — in exchange for better insight into how they’re doing — just isn’t worth it.

这部分源于一个神话，即如果您想拥有出色的分析功能，则应花掉大约25％的工程资源来为其提供资金。 对于有远见的创始人而言，从执行该远见上分散25％的精力(以换取对他们的工作方式的更好了解)是不值得的。

But setting up some basic analytics for your product isn’t nearly as difficult as you think. Certainly nowhere near 25% of engineering budget. While figure may be true for mature companies with many complicated data pipelines, a small company can easily roll something up with minimal costs.

但是为您的产品设置一些基本分析并没有您想的那么困难。 当然远远不及工程预算的25％。 尽管对于拥有许多复杂数据管道的成熟公司而言，这一数字可能是正确的，但小型公司可以轻松地以最小的成本汇总一些东西。

To prove it, I’m going to show you how you can spend 1 hour setting up a system that should be adequate for the next 6 months of your company’s life.

为了证明这一点，我将向您展示如何花1个小时来建立一套系统，该系统应足以满足公司生命周期的接下来6个月的需求。

First, let’s talk about what I mean by “having analytics.” I think the minimum requirements are:

首先，让我们谈谈“拥有分析”的含义。 我认为最低要求是：

• Simple access to your key metrics for everyone in your company方便公司中每个人访问您的关键指标
• A nightly stats email or slack message going out to your team每晚统计信息电子邮件或闲暇消息发送给您的团队
• A few hosted dashboards to put up on monitors in the office一些托管的仪表板可放置在办公室的显示器上
• A place where anyone in your company can explore data (regardless of technical acumen)公司中任何人都可以浏览数据的地方(无论技术敏锐度如何)

In other words, you need metrics and a way to spread them throughout your organization. Let’s begin with the metrics part.

换句话说，您需要度量标准以及一种将其分布到整个组织中的方法 。 让我们从指标部分开始。

您的指标已经在数据库中 (Your metrics are already in your database)

Since pie is way more delicious than widgets, let’s imagine you’ve just started a pie delivery company. You decide that — at a minimum — the success of your business will depend on:

由于派比小部件更美味，让我们想象一下您刚刚成立了派派公司 。 您决定(至少)业务的成功取决于：

1. Your ability to get potential pie consumers您吸引潜在馅饼消费者的能力
2. Your ability to sell pies to those consumers您向那些消费者出售馅饼的能力

If you can reliably grow both 1 and 2, you won’t have too much else to worry about. You decide based on that to track:

如果您可以同时可靠地增长1和2，那么您就不用担心太多了。 您根据此决定来跟踪：

1. New user registrations新用户注册
2. Pie sales馅饼销售
3. Repeat usage重复使用

You know you can derive a few more interesting metrics just based off of user registration and pie sale data, but for now you’re cool with these big 3 high level metrics. Now how do you actually make these?

您知道您可以仅根据用户注册和饼图销售数据得出一些更有趣的指标，但是现在您对这三大高级指标很感兴趣。 现在，您实际上是如何制作这些的？

At this point a lot of people head to Google Analytics, Mixpanel, or some other 3rd party event analytics provider. While I love these products, and I love event analytics, I also think that this is part of the reason why early stage startups punt on analytics. To set these up the right way means engineering time spent on something orthogonal to developing your core product.

此时，很多人前往Google Analytics(分析)，Mixpanel或其他一些第三方事件分析提供商。 虽然我喜欢这些产品，也喜欢事件分析，但我也认为这是早期初创公司偏爱分析的部分原因。 以正确的方式进行设置意味着在与核心产品开发正交的工作上花费了工程时间。

So before you make a case for the engineering team to spend a cycle instrumenting user signups and pie sales, consider this: these metrics are probably already in your application database. In other words, if you’re building a product to delivery pie to users and you don’t have a database table or collection to store your users or the pies they’ve ordered . . . well then a lack of analytics is not your biggest concern.

因此，在为工程团队辩护花一个周期来测试用户注册和饼图销售之前，请考虑以下因素： 这些指标可能已经存在于您的应用程序数据库中。 换句话说，如果您要构建一个产品以将馅饼交付给用户，并且没有数据库表或集合来存储用户或他们订购的饼。 。 。 那么，缺乏分析并不是您最大的担忧。

Remember, a piece of software is basically made up of data and logic to operate on that data. Many don’t realize that data in your application can actually be used for analytics as well. So feel free to put your event analytics instrumentation in the backlog and let’s see how much we can get done with just your application database.

请记住，一个软件基本上是由数据和对该数据进行操作的逻辑组成的。 许多人没有意识到应用程序中的数据实际上也可以用于分析。 因此，随时将您的事件分析工具放入待办事项中，让我们看看仅使用您的应用程序数据库就可以完成多少工作。

Now how do you get these metrics out?

现在如何获取这些指标？

元数据库：与数据库一起使用的分析工具 (Metabase: An analytics tool that works with your database)

There are a lot of ways to fetch information from a database, but there is only one easiest way, and this post is about easy ways.

有很多方法可以从数据库中获取信息，但是只有一种最简单的方法，本文是关于简单的方法的。

My favorite tool that I recommend for any company I advise is Metabase. Metabase is the fastest, easiest way to share data and analytics inside your company. It’s super simple to install or deploy, works with almost all databases, and best of all is open source and 100% free — so you should definitely test it out first before you go with some of the paid options out there.

对于任何我建议的公司，我最喜欢的工具是Metabase 。 Metabase是在公司内部共享数据和分析的最快，最简单的方法。 它非常容易安装或部署，几乎可以与所有数据库一起使用，并且最好的是开源和100％免费的-因此，在使用某些付费选件之前，请务必先进行测试。

Full disclosure: I work at expa, where Metabase was started, and I am an advisor to the company. I have also, in just the past year, advised 8 different technology startups on data and analytics and in each case have recommended Metabase for them. They all continue to use it.

完全公开：我在expa处工作，该处是Metabase的起点，我是公司的顾问。 在过去的一年中，我还为8家不同的技术初创公司提供了数据和分析方面的建议，并分别为他们推荐了Metabase。 他们都继续使用它。

安装/部署 (Installation/Deployment)

If you are just in evaluation mode, I would recommend downloading Metabase’s mac app. Follow their setup guide, and you’re ready to create some metrics. However, deploying Metabase either on Heroku or AWS Elastic Beanstalk (best) is highly recommended, as you’ll get a persistent application that’s hosted in the cloud and your whole team can use it.

如果您只是处于评估模式，建议您下载Metabase的mac应用程序 。 按照他们的设置指南 ，您准备创建一些指标。 但是，强烈建议在Heroku或AWS Elastic Beanstalk (最佳)上部署Metabase，因为您将获得一个持久性应用程序托管在云中，并且整个团队都可以使用它。

For a full guide on the deployment process, check out my video tutorial. Metabase’s documentation is pretty comprehensive, as well. If you’re a non-technical person, you may have to shoulder-tap an engineer, especially if your application database is in a VPC on AWS.

有关部署过程的完整指南， 请查看我的视频教程 。 Metabase的文档也很全面。 如果您不是技术人员，则可能需要与工程师接轨，特别是如果您的应用程序数据库位于AWS的VPC中。

On that note, it’s also a good idea to create a read replica of your application database and plug that into Metabase. That way you can ensure any heavy duty or hanging queries won’t affect your users.

关于这一点，创建应用程序数据库的只读副本并将其插入到Metabase中也是一个好主意。 这样，您可以确保任何繁重的任务或挂起的查询都不会影响您的用户。

Once you get Metabase deployed, sign up and add your database credentials. Next, invite your team members so they can get in on the fun.

一旦部署了Metabase，请注册并添加数据库凭据。 接下来，邀请您的团队成员一起玩耍。

创建指标 (Creating your metrics)

Believe it or not, the rest is pretty easy. The first thing you’ll want to do is build your metrics. In Metabase parlance, these are “Questions.” If you’re the pie business, and you have a reasonably organized schema, you should be able to get your key metrics with just a few clicks. No SQL is required, but of course if you like SQL that option is available.

信不信由你，其余的事情都很简单。 您要做的第一件事就是建立指标。 用Metabase的话来说，这些就是“问题”。 如果您是馅饼企业，并且拥有合理组织的架构，那么只需单击几下就能获得关键指标。 不需要SQL，但是如果您喜欢SQL，那么当然可以使用该选项。

So build your top metrics, and see if any other interesting ones pop into your mind. Although you can find hundreds of smart people who will tell you to never make a pie chart, I won’t hate on you for making a pie chart based on pie popularity. If metabase is the meta database, it’s only right to make your pie chart meta.

因此，建立您的首要指标，然后看看是否有其他有趣的指标突然出现。 尽管您可以找到数百名聪明的人，他们会告诉您不要制作饼图 ，但我不会讨厌您根据饼图的受欢迎程度制作饼图。 如果配置数据库是元数据库，则使饼图成为元数据是唯一的权利。

画龙点睛 (Finishing touches)

Next you’ll want to setup a daily stats email. I don’t know what it is about them, but everyone loves daily stats emails. Metabase calls these “Pulses,” and even lets you use Slack if you’re too cool for email. Add the Questions you want to send out, pick a time and cadence (it doesn’t have to be daily but that tends to be the most helpful) and a list of recipients or Slack channel and you’re done.

接下来，您需要设置每日统计电子邮件。 我不知道它们的含义，但是每个人都喜欢每日统计电子邮件。 Metabase称这些为“脉冲”，如果您太喜欢电子邮件了，它甚至可以让您使用Slack。 添加您要发送的问题，选择时间和节奏(不一定是每天，但这往往是最有用的)，并列出收件人或Slack频道，就可以了。

Lastly, everyone loves to see pretty dashboards up on the monitors in the office. Don’t keep them in suspense. Making a dashboard is also quite straightforward. Pick some Questions and organize them as best as your design sensibilities allow you. Load it up on an external monitor you’ve got up in the office, then full-screen it.

最后，每个人都喜欢在办公室的显示器上看到漂亮的仪表板。 不要让他们悬念。 制作仪表板也非常简单。 选择一些问题，并根据您的设计敏感性尽可能地组织它们。 将其加载到您在办公室里安装的外部显示器上，然后全屏显示。

回顾 (Recap)

You just set up a pretty solid analytics infrastructure for your startup in about an hour. Now your whole team can explore your application database, receive nightly emails, and view a company-wide dashboard. Even better, this setup should last you for quite some time — at least 6 months unless you start experiencing crazy growth (in which case, no complaining).

您只需在大约一个小时内为启动建立一个非常可靠的分析基础架构。 现在，您的整个团队可以浏览您的应用程序数据库，每晚接收电子邮件，并查看公司范围内的仪表板。 更好的是，此设置应该可以持续一段时间-至少6个月，除非您开始经历疯狂的增长(在这种情况下，不要抱怨)。

Ready to try it out? Skeptical of my 1-hour guarantee? Check out my YouTube tutorials, part 1 and part 2, where I’ll walk you through everything you need to know.

准备尝试吗？ 怀疑我的1小时保修？ 查阅我的YouTube教程( 第1部分和第2部分) ，其中将带您逐步了解所有需要了解的内容。

翻译自: https://www.freecodecamp.org/news/how-to-bootstrap-your-analytics-in-1-hour-cb3a549b4780/

引导分区 pbr 数据分析