Metric Design for Data Scientists and Business Leaders
What’s the hardest part of metric design?
In order to make gooddata-driven decisions，您需要3件事：
- 基于精心设计的决策标准metrics。
- 收集的能力data这些指标将基于。
- Statisticsskills to calculate those metrics and interpret the results underuncertainty。
需求＃2和＃3已写过很多（包括me），但是需求＃1呢？
现在data收集比以往任何时候都更容易，许多领导人感到压力要拖动每次会议。不幸的是，在喂养疯狂的情况下，其中许多人没有给公制设计它值得的。在那些愿意付出努力的人中，大多数人都在努力的过程中弥补了这一点，就好像它是全新的。
不是。
心理学 - 思想和行为的科学研究 -一个多世纪以固执的脚趾在试图衡量尚未正确定义的模糊数量的危险中，该领域已经学到了一些固体金块，这些矿物质是商业领导者和data scientists设计指标时借钱是明智的。
If you’re not convinced that metric design is hard, grab a pen and paper. I challenge you to write down a definition of幸福那是so ironclad that no one could take issue with your way of measuring it…
Tricky, right? Now try it with some other abstract nouns people throw around daily, like “memory” and “intelligence” and “love” and “attention” and so on. It’s damned near miraculous that any of us understand ourselves, let alone one another.
And yet, this is exactly the first hurdle that psychology researchers must clear in order to make scientific progress. In order to study mental processes, they must create precise and measurable proxies — metrics — to work with. So, how do psychologists and other social scientists think about metric design?
像心理学家一样思考
您如何严格，科学地研究无法轻易定义的概念？概念喜欢注意力,satisfaction， 和creativity? The answer is… you don’t! Instead, you操作。For the purposes of this example, let’s suppose you’re interested in measuringuser happiness。
什么是操作？
什么是操作？我写了一篇介绍文章这里对您来说，但结果是，当您运作时，您首先对自己说“I am never going to measure happiness and I’ve made my peace with that.”Philosophers have been at this for thousands of years, so it’s not like you’re suddenly going to come up with a single definition that satisfies每个人。
接下来，您将概念的可衡量本质提炼成代理。
永远记住，您实际上并没有衡量幸福。或内存。或注意。或智力。或任何其他诗意的fuzzword，无论您听起来多么宏伟。
现在we’re okay with the fact that we’ll never measure happiness and its friends, it’s time to ask ourselves why we even considered that word in the first place. What is it about this concept — in its fuzzy form — that seems relevant and pertinent to the decision we want to make? What concrete (and obtainable!) information would lead us to prefer一个行动对另一个行动? (Metric design is much easier when you haveactionsin mind before you begin. If possible, think about potential decisions before attempting to design a metric.)
Then we distill the core idea that we are after to create a measurable proxy — a metric that captures this core essence we care about.
在命名之前定义您的指标。
And now comes the fun part! We’re allowed to name our metric anything we like: “blorktibork” or “user happiness” or “X” or whatever.
对于我们被语言警察逮捕没有意义的原因是，无论我们如何努力设计它，我们的代理人都会*not*be the Platonic form of user happiness.
While it may suitourneeds, it’s important to remember that our metric is unlikely to fit每个人else’s needs too。That’s why it would be silly to lock horns in a useless debates about whether our metric does or does not capture True Happiness. It doesn’t. If you’re desperate for some kind of One Metric To Rule Them All, there’s a迪士尼为你的歌。
Any metric we create is simply a proxy that suits our own needs (and possibly no one else’s). It’s our personal means to a personal end: making an informed decision or summarizing a concept so we don’t have to write a whole paragraph every time we mention it. We can get along just fine without involving the language police in either one.
The hard part
到目前为止，一切都很好。您只需确定您的决定所需的信息，然后找出一种以有意义的需求的方式汇总该信息的方法（TA-DA，，那是your metric), and then name it whatever you like. Right? Right, but…
Thereis这一切最困难的部分。继续进行next installmentto find out what it is…
有关指标设计的视频
If you’re keen to learn more, watch lessons039–047 from my Making Friends with Machine Learning course. They’re all short videos of a couple of minutes long. Start here and continue in the attached playlist:
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