數據分析師 需求分析師

重點 (Top highlight)

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質之前，讓我們從第1部分中對分析的三種常見誤解開始快速總結：

Analytics is statistics. (No.)

分析是統計。 (沒有。)

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

分析是數據新聞/市場營銷/故事講述。 (沒有。)

Analytics is decision-making. (No!)

分析是決策。 (沒有！)

誤解一：分析與統計 (Misconception #1: Analytics versus statistics)

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

盡管它們使用的工具和方程式相似，但分析人員和統計學家卻受過訓練，可以做非常不同的工作：

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

  Analytics(分析)可幫助您形成 假設 ，提高問題的質量。

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

  統計信息可幫助您檢驗假設，從而提高答案的質量。

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

如果您想了解有關這些專業的更多信息，請查看我的文章 “ 分析師和統計學家可以相處嗎？ ”

誤解2：分析與新聞/營銷 (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.

分析不是營銷。 不同之處在于，分析是在擴大決策者的視野，而營銷是在縮小視野。

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.

同樣，數據新聞學是要以較小的方式吸引許多人的興趣，而分析學是要以較大的方式滿足少數人的需求。 分析師首先為他們的決策者服務。

誤解三：分析與決策 (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.

如果我是您的分析師，那么我不是來這里為您選擇的(即使我可能比您擁有更多的領域專業知識)。 您必須將我提升為決策者，這是一件道德的事。

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.

如果您希望某人擔任分析師與決策者的混合體，請理解您要將兩個角色合并為一個，并明確分配該職責。

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.

要了解有關誤解＃2和＃3的更多信息，請回溯至第1部分 。 在本文中，我們將從上次中斷的地方繼續討論卓越的分析。

是什么讓分析師出色？ (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.

在數據科學的“最容易被誤解的英雄”一書中 ，我描述了數據科學領域的三項卓越成就。 分析師的卓越之處在于速度。

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:

分析師查找事實并為您提供靈感 ，同時在此過程中嘗試浪費自己(或您自己！)的時間。 為了獲得最佳的靈感產生時間，他們必須掌握許多不同形式的速度，包括：

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

  獲得有前途且相關的數據的速度。 ( 領域知識。 )

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

  為操作準備數據的速度。 ( 軟件技能。 )

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

  匯總數據的速度。 ( 數學技能。 )

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

  使數據摘要進入他們自己的大腦的速度。 ( 數據可視化技能。 )

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

  使數據摘要進入利益相關者頭腦的速度。 ( 溝通技巧。 )

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

  激發決策者靈感的速度。 ( 業務敏銳度。 )

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

最后一點很細微(也是列表中最重要的一點)，所以讓我為您講清楚。

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.

精美可視化和有效溝通的瑣事浪費您的時間。 令人興奮的發現被誤解了，這是在浪費您的時間。 大量嘗試進入垃圾數據源會浪費您的時間。 無關的軼事浪費您的時間。 分析師給您帶來的任何發現，都是您不值得花費的時間……是在浪費時間。

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

分析游戲的全部目的在于優化 每分鐘的靈感。

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).

分析師會浪費您的時間-這是探索的一部分-因此，分析游戲只不過是在浪費盡可能少的時間。 換句話說，優化每分鐘的靈感 (根據他們的時間和您自己的時間，取決于與每個人對您的組織的價值有關的匯率)。

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.

不要被簡單的速度解釋所愚弄。 一個草率的分析員，總是對閃亮的廢話“見解”感到迷惑，從長遠來看只會使每個人放慢腳步。

評估分析師績效 (Assessing analyst performance)

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

對于那些熱衷績效評估的人，請注意，您不能使用每分鐘的靈感來衡量您的分析師。

SOURCESOURCE

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.

這是因為可以提取的最大靈感量(由決策者主觀定義)在數據集之間有所不同。 但是，您可以通過讓他們松散已經很熟悉其內容的基準數據集來評估他們的技能 (而不是工作績效)。

Wherein the bowl of peas is the benchmark dataset.其中豌豆碗是基準數據集。

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.

打個比方，如果您要求兩位分析師從一本外語教科書中汲取靈感，那么工作的更好(更快)分析師可能是該語言的母語使用者。 您可以通過測量他們理解您使用該語言撰寫的文章的速度來評估他們的相對技能 。

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.

如果您不希望自己創建標準化的分析障礙課程，則可以考慮使用byteboard.dev 。 Byteboard是一家革命性的初創公司，徹底改變了技術面試的面貌 ，他們最近啟動了數據分析技能評估。 它使用真實的場景以及一個漂亮的界面來衡量諸如數據探索，數據提取，定量通信和業務分析等任務的能力。 當然，他們的意圖是幫助您面試新候選人的一種方式，但是沒有理由您也不能使用它來快速測試在職分析師。

SOURCESOURCE

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

技能不能保證影響。 這取決于您的數據。

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，一旦您評估了技能，請記住該技能并不能保證一定會產生影響。 這取決于您的數據。 回到以前的類比，如果您將兩位分析師指向從未打開過的一本神秘教科書，您將無法使他們對每分鐘發現的靈感負責，因為這本書可能充滿了垃圾。 如果是這樣，無論他們的流利程度如何！ -沒有人會發現任何靈感可以帶回您……這不是他們的錯。 擁有教科書并不意味著您會學到有用的東西。 數據集也是如此。 它們的質量和相關性同樣重要。

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

教科書是數據集的一個很好的類比，因此有關數據集和教科書的兩點要記住的是：

• 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.)

  一個決策者的垃圾可能是另一個人的財富。 像教科書一樣，數據集僅在涵蓋了您要學習的主題時才對您有用。 (我已經在這里寫過。)

• 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.)

  如果它有人類作者，那是主觀的。 像教科書一樣，數據集也有人類作者，他們的偏見可以消除內容。 (我在這里寫過關于數據和偏見的文章 和這里 。)

永遠不要因為數據中沒有的內容而懲罰分析師 (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.

決策者將您的分析師視為您剛剛進化的一種新的感覺器官：一種新型的眼睛，可讓您感知原本會視而不見的信息。

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

如果您將新的目光投向了不值得一看的事物，那么您就不會為此而掏腰包，對嗎？

SOURCESOURCE

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.

同樣，如果分析師在數據集中發現沒有有價值的東西，而您要求他們為您檢查，則不要懲罰他們。 保持它們的周圍狀態是對以新方式進行觀看的一種投資。 如果您不喜歡他們在看什么，請將他們引向更有潛力的場景。

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飛行之間的區別。 除非您無所事事，否則明智的投資值得投資。

謝謝閱讀！ 喜歡作者嗎？ (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，那么本文中的大多數鏈接都將帶您進入我的其他想法。 無法選擇？ 試試這個：

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.

自從我幫助設計了Byteboard的分析速度測試以來，我并不是沒有偏見。 我希望你會喜歡。

SOURCESOURCE

翻譯自: https://towardsdatascience.com/what-makes-a-data-analyst-excellent-17ee4651c6db

數據分析師 需求分析師

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