數(shù)據(jù)分析師 需求分析師

重點(diǎn) (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(zhì)之前，讓我們從第1部分中對(duì)分析的三種常見(jiàn)誤解開(kāi)始快速總結(jié)：

Analytics is statistics. (No.)

分析是統(tǒng)計(jì)。 (沒(méi)有。)

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

分析是數(shù)據(jù)新聞/市場(chǎng)營(yíng)銷/故事講述。 (沒(méi)有。)

Analytics is decision-making. (No!)

分析是決策。 (沒(méi)有！)

誤解一：分析與統(tǒng)計(jì) (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(tǒng)計(jì)學(xué)家卻受過(guò)訓(xùn)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(shè) ，提高問(wèn)題的質(zhì)量。

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

  統(tǒng)計(jì)信息可幫助您檢驗(yàn)假設(shè)，從而提高答案的質(zhì)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(guān)這些專業(yè)的更多信息，請(qǐng)查看我的文章 “ 分析師和統(tǒng)計(jì)學(xué)家可以相處嗎？ ”

誤解2：分析與新聞/營(yíng)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(yíng)銷。 不同之處在于，分析是在擴(kuò)大決策者的視野，而營(yíng)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(shù)據(jù)新聞學(xué)是要以較小的方式吸引許多人的興趣，而分析學(xué)是要以較大的方式滿足少數(shù)人的需求。 分析師首先為他們的決策者服務(wù)。

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(lái)這里為您選擇的(即使我可能比您擁有更多的領(lǐng)域?qū)I(yè)知識(shí))。 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(dān)任分析師與決策者的混合體，請(qǐng)理解您要將兩個(gè)角色合并為一個(gè)，并明確分配該職責(zé)。

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(guān)誤解＃2和＃3的更多信息，請(qǐng)回溯至第1部分 。 在本文中，我們將從上次中斷的地方繼續(xù)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(shù)據(jù)科學(xué)的“最容易被誤解的英雄”一書中 ，我描述了數(shù)據(jù)科學(xué)領(lǐng)域的三項(xiàng)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(shí)并為您提供靈感 ，同時(shí)在此過(guò)程中嘗試?yán)速M(fèi)自己(或您自己！)的時(shí)間。 為了獲得最佳的靈感產(chǎn)生時(shí)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(guān)的數(shù)據(jù)的速度。 ( 領(lǐng)域知識(shí)。 )

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

  為操作準(zhǔn)備數(shù)據(jù)的速度。 ( 軟件技能。 )

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

  匯總數(shù)據(jù)的速度。 ( 數(shù)學(xué)技能。 )

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

  使數(shù)據(jù)摘要進(jìn)入他們自己的大腦的速度。 ( 數(shù)據(jù)可視化技能。 )

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

  使數(shù)據(jù)摘要進(jìn)入利益相關(guān)者頭腦的速度。 ( 溝通技巧。 )

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

  激發(fā)決策者靈感的速度。 ( 業(yè)務(wù)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(diǎn)很細(xì)微(也是列表中最重要的一點(diǎn))，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(fèi)您的時(shí)間。 令人興奮的發(fā)現(xiàn)被誤解了，這是在浪費(fèi)您的時(shí)間。 大量嘗試進(jìn)入垃圾數(shù)據(jù)源會(huì)浪費(fèi)您的時(shí)間。 無(wú)關(guān)的軼事浪費(fèi)您的時(shí)間。 分析師給您帶來(lái)的任何發(fā)現(xiàn)，都是您不值得花費(fèi)的時(shí)間……是在浪費(fèi)時(shí)間。

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

分析游戲的全部目的在于優(yōu)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(huì)浪費(fèi)您的時(shí)間-這是探索的一部分-因此，分析游戲只不過(guò)是在浪費(fèi)盡可能少的時(shí)間。 換句話說(shuō)，優(yōu)化每分鐘的靈感 (根據(jù)他們的時(shí)間和您自己的時(shí)間，取決于與每個(gè)人對(duì)您的組織的價(jià)值有關(guān)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(jiǎn)單的速度解釋所愚弄。 一個(gè)草率的分析員，總是對(duì)閃亮的廢話“見(jiàn)解”感到迷惑，從長(zhǎng)遠(yuǎn)來(lái)看只會(huì)使每個(gè)人放慢腳步。

評(píng)估分析師績(jī)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(duì)于那些熱衷績(jī)效評(píng)估的人，請(qǐng)注意，您不能使用每分鐘的靈感來(lái)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?yàn)榭梢蕴崛〉淖畲箪`感量(由決策者主觀定義)在數(shù)據(jù)集之間有所不同。 但是，您可以通過(guò)讓他們松散已經(jīng)很熟悉其內(nèi)容的基準(zhǔn)數(shù)據(jù)集來(lái)評(píng)估他們的技能 (而不是工作績(jī)效)。

Wherein the bowl of peas is the benchmark dataset.其中豌豆碗是基準(zhǔn)數(shù)據(jù)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(gè)比方，如果您要求兩位分析師從一本外語(yǔ)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(yǔ)言的母語(yǔ)使用者。 您可以通過(guò)測(cè)量他們理解您使用該語(yǔ)言撰寫的文章的速度來(lái)評(píng)估他們的相對(duì)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(chuàng)建標(biāo)準(zhǔn)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(chuàng)公司，徹底改變了技術(shù)面試的面貌 ，他們最近啟動(dòng)了數(shù)據(jù)分析技能評(píng)估。 它使用真實(shí)的場(chǎng)景以及一個(gè)漂亮的界面來(lái)衡量諸如數(shù)據(jù)探索，數(shù)據(jù)提取，定量通信和業(yè)務(wù)分析等任務(wù)的能力。 當(dāng)然，他們的意圖是幫助您面試新候選人的一種方式，但是沒(méi)有理由您也不能使用它來(lái)快速測(cè)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(shù)據(jù)。

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(píng)估了技能，請(qǐng)記住該技能并不能保證一定會(huì)產(chǎn)生影響。 這取決于您的數(shù)據(jù)。 回到以前的類比，如果您將兩位分析師指向從未打開(kāi)過(guò)的一本神秘教科書，您將無(wú)法使他們對(duì)每分鐘發(fā)現(xiàn)的靈感負(fù)責(zé)，因?yàn)檫@本書可能充滿了垃圾。 如果是這樣，無(wú)論他們的流利程度如何！ -沒(méi)有人會(huì)發(fā)現(xiàn)任何靈感可以帶回您……這不是他們的錯(cuò)。 擁有教科書并不意味著您會(huì)學(xué)到有用的東西。 數(shù)據(jù)集也是如此。 它們的質(zhì)量和相關(guān)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(shù)據(jù)集的一個(gè)很好的類比，因此有關(guān)數(shù)據(jù)集和教科書的兩點(diǎn)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(gè)決策者的垃圾可能是另一個(gè)人的財(cái)富。 像教科書一樣，數(shù)據(jù)集僅在涵蓋了您要學(xué)習(xí)的主題時(shí)才對(duì)您有用。 (我已經(jīng)在這里寫過(guò)。)

• 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(shù)據(jù)集也有人類作者，他們的偏見(jiàn)可以消除內(nèi)容。 (我在這里寫過(guò)關(guān)于數(shù)據(jù)和偏見(jiàn)的文章 和這里 。)

永遠(yuǎn)不要因?yàn)閿?shù)據(jù)中沒(méi)有的內(nèi)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(jìn)化的一種新的感覺(jué)器官：一種新型的眼睛，可讓您感知原本會(huì)視而不見(jiàn)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(huì)為此而掏腰包，對(duì)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(shù)據(jù)集中發(fā)現(xiàn)沒(méi)有有價(jià)值的東西，而您要求他們?yōu)槟鷻z查，則不要懲罰他們。 保持它們的周圍狀態(tài)是對(duì)以新方式進(jìn)行觀看的一種投資。 如果您不喜歡他們?cè)诳词裁?#xff0c;請(qǐng)將他們引向更有潛力的場(chǎng)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(qū)別。 除非您無(wú)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(shù)鏈接都將帶您進(jìn)入我的其他想法。 無(wú)法選擇？ 試試這個(gè)：

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?guī)椭O(shè)計(jì)了Byteboard的分析速度測(cè)試以來(lái)，我并不是沒(méi)有偏見(jiàn)。 我希望你會(huì)喜歡。

SOURCESOURCE

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

數(shù)據(jù)分析師 需求分析師

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