Before we dissect the nature of analytical excellence, let’s start with a quick summary of three common misconceptions about analytics from Part 1:

在剖析卓越分析的本质之前，让我们从第1部分中对分析的三种常见误解开始快速总结：

1. Analytics is statistics. (No.)

   分析是统计。 (没有。)

2. Analytics is data journalism / marketing / storytelling. (No.)

   分析是数据新闻/市场营销/故事讲述。 (没有。)

3. Analytics is decision-making. (No!)

   分析是决策。 (没有！)

误解一：分析与统计 (Misconception #1: Analytics versus statistics)

While the tools and equations they use are similar, analysts and statisticians are trained to do very different jobs:

尽管它们使用的工具和方程式相似，但分析人员和统计学家却受过训练，可以做非常不同的工作：

• Analytics helps you form hypotheses, improving the quality of your questions.

  Analytics(分析)可帮助您形成 假设 ，提高问题的质量。

• Statistics helps you test hypotheses, improving the quality of your answers.

  统计信息可帮助您检验假设，从而提高答案的质量。

If you’d like to learn more about these professions, check out my article “Can analysts and statisticians get along?”

如果您想了解有关这些专业的更多信息，请查看我的文章 “ 分析师和统计学家可以相处吗？ ”

误解2：分析与新闻/营销 (Misconception #2: Analytics versus journalism/marketing)

Analytics is not marketing. The difference is that analytics is about expanding the decision-maker’s perspective while marketing is about narrowing it.

分析不是营销。 不同之处在于，分析是在扩大决策者的视野，而营销是在缩小视野。

Similarly, data journalism is about capturing the interest of many people in a small way, while analytics is about serving the needs of a few people in a big way. The analyst serves their decision-maker(s) first and foremost.

同样，数据新闻学是要以较小的方式吸引许多人的兴趣，而分析学是要以较大的方式满足少数人的需求。 分析师首先为他们的决策者服务。

误解三：分析与决策 (Misconception #3: Analytics versus decision-making)

If I’m your analyst, I’m not here to choose for you (even though I might have more domain expertise than you). You’d have to promote me to decision-maker for that to be an ethical thing to do.

如果我是您的分析师，那么我不是来这里为您选择的(即使我可能比您拥有更多的领域专业知识)。 您必须将我提升为决策者，这是一件道德的事。

If you want someone to work as an analyst-decision-maker hybrid, understand that you’re asking for two roles rolled into one and assign that responsibility explicitly.

如果您希望某人担任分析师与决策者的混合体，请理解您要将两个角色合并为一个，并明确分配该职责。

To learn more about misconceptions #2 and #3, scoot back to Part 1. In this article, we’ll pick up where we left off and talk about analytical excellence.

要了解有关误解＃2和＃3的更多信息，请回溯至第1部分 。 在本文中，我们将从上次中断的地方继续讨论卓越的分析。

是什么让分析师出色？ (What makes an analyst excellent?)

In Data Science’s Most Misunderstood Hero, I describe the 3 excellences in data science. An analyst’s excellence is speed.

在数据科学的“最容易被误解的英雄”一书中 ，我描述了数据科学领域的三项卓越成就。 分析师的卓越之处在于速度。

Analysts look up facts and produce inspiration for you, while trying to waste as little of their own time (and yours!) in the process. To get the best time-to-inspiration payoff, they must master many different forms of speed, including:

分析师查找事实并为您提供灵感 ，同时在此过程中尝试浪费自己(或您自己！)的时间。 为了获得最佳的灵感产生时间，他们必须掌握许多不同形式的速度，包括：

• Speed of getting data that’s promising and relevant. (Domain knowledge.)

  获得有前途且相关的数据的速度。 ( 领域知识。 )

• Speed of getting data ready for manipulation. (Software skills.)

  为操作准备数据的速度。 ( 软件技能。 )

• Speed of getting data summarized. (Mathematical skills.)

  汇总数据的速度。 ( 数学技能。 )

• Speed of getting data summaries into their own brains. (Data visualization skills.)

  使数据摘要进入他们自己的大脑的速度。 ( 数据可视化技能。 )

• Speed of getting data summaries into stakeholders’ brains. (Communication skills.)

  使数据摘要进入利益相关者头脑的速度。 ( 沟通技巧。 )

• Speed of getting the decision-maker inspired. (Business acumen.)

  激发决策者灵感的速度。 ( 业务敏锐度。 )

That last point is plenty nuanced (and also the most important one on the list), so let me spell it out for you.

最后一点很细微(也是列表中最重要的一点)，所以让我为您讲清楚。

Beautifully visualized and effectively communicated trivia are a waste of your time. Exciting findings which turn out to be misinterpretations are a waste of your time. Meticulous forays into garbage data sources are a waste of your time. Irrelevant anecdotes are a waste of your time. Anything an analyst brings you that you don’t find worth your time… is a waste of your time.

精美可视化和有效沟通的琐事浪费您的时间。 令人兴奋的发现被误解了，这是在浪费您的时间。 大量尝试进入垃圾数据源会浪费您的时间。 无关的轶事浪费您的时间。 分析师给您带来的任何发现，都是您不值得花费的时间……是在浪费时间。

The analytics game is all about optimizing inspiration-per-minute.

分析游戏的全部目的在于优化 每分钟的灵感。

Analysts will waste your time — that’s part of exploration — so the analytics game is all about wasting as little of it as possible. In other words, optimizing inspiration-per-minute (of their time and yours, subject to some exchange rate related to how valuable each of you is to your organization).

分析师会浪费您的时间-这是探索的一部分-因此，分析游戏只不过是在浪费尽可能少的时间。 换句话说，优化每分钟的灵感 (根据他们的时间和您自己的时间，取决于与每个人对您的组织的价值有关的汇率)。

Don’t be fooled by a simplistic interpretation of speed. A sloppy analyst who keeps falling for shiny nonsense “insights” will only slow everyone down in the long run.

不要被简单的速度解释所愚弄。 一个草率的分析员，总是对闪亮的废话“见解”感到迷惑，从长远来看只会使每个人放慢脚步。

评估分析师绩效 (Assessing analyst performance)

For those who love performance assessments, be warned that you can’t use inspiration-per-minute to measure your analysts.

对于那些热衷绩效评估的人，请注意，您不能使用每分钟的灵感来衡量您的分析师。

That’s because the maximum amount of inspiration (as defined subjectively by the decision-maker) that can be extracted varies from dataset to dataset. But you could assess their skills (not job performance) by letting them loose on a benchmark dataset whose contents you are already well-acquainted with.

这是因为可以提取的最大灵感量(由决策者主观定义)在数据集之间有所不同。 但是，您可以通过让他们松散已经很熟悉其内容的基准数据集来评估他们的技能 (而不是工作绩效)。

As an analogy, if you ask two analysts to extract inspiration from a foreign language textbook, the better (faster) analyst for the job might be the native speaker of that language. You could assess their relative skill by measuring the speed with which they comprehend a passage you wrote in that language.

打个比方，如果您要求两位分析师从一本外语教科书中汲取灵感，那么工作的更好(更快)分析师可能是该语言的母语使用者。 您可以通过测量他们理解您使用该语言撰写的文章的速度来评估他们的相对技能 。

If you’re not keen to create a standardized analytics obstacle course yourself, you might like to look into byteboard.dev. Byteboard is a startup revolutionizing tech interviews and they’ve recently launched a skills assessment for data analytics. It uses real-world scenarios plus a nifty interface to measure competence at tasks like data exploration, data extraction, quantitative communication, and business analysis. Sure, they intended it as a way to help you interview new candidates, but there’s no reason you couldn’t also use it to speed-test your incumbent analysts.

如果您不希望自己创建标准化的分析障碍课程，则可以考虑使用byteboard.dev 。 Byteboard是一家革命性的初创公司，彻底改变了技术面试的面貌 ，他们最近启动了数据分析技能评估。 它使用真实的场景以及一个漂亮的界面来衡量诸如数据探索，数据提取，定量通信和业务分析等任务的能力。 当然，他们的意图是帮助您面试新候选人的一种方式，但是没有理由您也不能使用它来快速测试在职分析师。

Skill doesn’t guarantee impact. That’s up to your data.

技能不能保证影响。 这取决于您的数据。

But once you’ve assessed skills, remember that skill doesn’t guarantee impact. That’s up to your data. To go back to the earlier analogy, if you point both analysts at a mysterious textbook you’ve never opened, you can’t hold them accountable for inspiration-per-minute they find because the book might be filled with rubbish. If that’s the case — no matter their level of fluency! — neither one will find any inspiration to bring back to you… and that’s not their fault. Having a textbook doesn’t mean you’ll learn something useful. The same goes for datasets; their quality and relevance matters just as much.

但是，一旦您评估了技能，请记住该技能并不能保证一定会产生影响。 这取决于您的数据。 回到以前的类比，如果您将两位分析师指向从未打开过的一本神秘教科书，您将无法使他们对每分钟发现的灵感负责，因为这本书可能充满了垃圾。 如果是这样，无论他们的流利程度如何！ -没有人会发现任何灵感可以带回您……这不是他们的错。 拥有教科书并不意味着您会学到有用的东西。 数据集也是如此。 它们的质量和相关性同样重要。

Textbooks are a great analogy for datasets, so a couple of additional things to bear in mind about both datasets and textbooks are:

教科书是数据集的一个很好的类比，因此有关数据集和教科书的两点要记住的是：

• One decision-maker’s garbage could be another’s treasure. Like textbooks, datasets are only useful to you if they cover a topic you want to learn about. (I’ve written about that here.)

  一个决策者的垃圾可能是另一个人的财富。 像教科书一样，数据集仅在涵盖了您要学习的主题时才对您有用。 (我已经在这里写过。)

• If it has a human author, it is subjective. Like textbooks, datasets have human authors whose biases can rub off on the contents. (I’ve written about data and bias here and here.)

  如果它有人类作者，那是主观的。 像教科书一样，数据集也有人类作者，他们的偏见可以消除内容。 (我在这里写过关于数据和偏见的文章 和这里 。)

永远不要因为数据中没有的内容而惩罚分析师 (Never punish analysts for what isn’t in the data)

Decision-makers, think of your analyst as a new sensory organ you’ve just evolved: a new kind of eye that allows you to perceive information that you would otherwise have been blind to.

决策者将您的分析师视为您刚刚进化的一种新的感觉器官：一种新型的眼睛，可让您感知原本会视而不见的信息。

If you direct your new eyes at something that wasn’t worth seeing, you wouldn’t gouge them out for it, right?

如果您将新的目光投向了不值得一看的事物，那么您就不会为此而掏腰包，对吗？

Similarly, if analysts find nothing valuable in a dataset you asked them to examine for you, don’t punish them. Keeping them around is an investment in being able to see in new ways. If you don’t like what they’re looking at, direct them towards a scene with more potential.

同样，如果分析师在数据集中发现没有有价值的东西，而您要求他们为您检查，则不要惩罚他们。 保持它们的周围状态是对以新方式进行观看的一种投资。 如果您不喜欢他们在看什么，请将他们引向更有潜力的场景。

Analytics is the difference between seeing where you’re going and flying blind. Unless you’re covered in bubble-wrap and going nowhere, keen senses are worth investing in.

分析是看到您要去的地方和盲目飞行之间的区别。 除非您无所事事，否则明智的投资值得投资。

谢谢阅读！ 喜欢作者吗？ (Thanks for reading! Liked the author?)

If you’re keen to read more of my writing, most of the links in this article take you to my other musings. Can’t choose? Try this one:

如果您希望我的作品，那么本文中的大多数链接都将带您进入我的其他想法。 无法选择？ 试试这个：

揭露 (Disclosure)

I’m not entirely unbiased when it comes to Byteboard’s analytics speed test since I helped design it. I do hope you’ll like it.

自从我帮助设计了Byteboard的分析速度测试以来，我并不是没有偏见。 我希望你会喜欢。