We live in a world that values answers. We were taught in school to learn how to answer questions in exams, we were conditioned to go to work knowing that we need to have the answers and our society, by and large, focuses on finding the solutions rather than figuring out if we are asking the right questions. Just like most people who have been through the traditional education system and started working in a corporate job, I was trained to have the answers, I was taught that my contribution and value lie in my ability to solve problems by knowing the right answers. While I do think that problem solving and the ability to find the right answers is a valuable skill to have, I would like to shed some light on the importance of the skill that precedes it, the skill of asking the right questions.

我们生活在重视答案的世界中。 我们在学校里被教导要学习如何在考试中回答问题，我们有条件去上班，知道我们需要得到答案，并且我们的社会大体上都专注于寻找解决方案，而不是弄清楚我们是否在提问正确的问题。 就像大多数经历过传统教育体系并开始从事公司工作的人一样，我也受过训练以获得答案，我被教导我的贡献和价值在于我通过了解正确答案来解决问题的能力。 虽然我确实认为解决问题和找到正确答案的能力是一项宝贵的技能，但我想阐明一些在其之前的技能，提出正确问题的技能的重要性。

Questions and answers are by definition linked together but they are a very different skillset. Seeking answers is a process of elimination through research and experimentation, trying to piece together different information and narrow things down to a solution. But asking questions is a process of expansion through critical thinking and imagination. It is understandable why as a society we don’t value the cost of asking the right questions because in some way the more questions we ask, the more work we need to do and the further away we are from finishing what we need to do. This creates a systemic problem that favours short term patches over long term solutions.

根据定义，问题和答案链接在一起，但是它们是非常不同的技能。 寻求答案是一个通过研究和实验消除的过程，它试图将不同的信息组合在一起，然后将事情缩小为解决方案。 但是，提出问题是通过批判性思维和想象力扩展的过程。 可以理解的是，为什么作为一个社会，我们不珍惜提出正确问题的成本，因为在某种程度上，我们提出的问题越多，我们需要做的工作就越多，而我们离完成需要做的工作就越远。 这就产生了一个系统性问题，与长期解决方案相比，它更倾向于短期补丁。

这对数据科学家意味着什么？ (What does that mean for a data scientist?)

This problem exists everywhere, but it is especially prominent in a solution-orientated field like data science and engineering, where the professions are built on top of solving difficult problems. Data scientists are prone to jumping right into solution mode before we even fully understand the problem because we think we have the answers. If there is a conversion problem, we can do personalised targeting. If there is a retention problem, we can build a churn prediction model. While those might all be valid solutions, they might just be addressing the symptoms and not the root cause.

这个问题无处不在，但是在诸如数据科学和工程等以解决方案为导向的领域中尤为突出，在该领域中，专业是建立在解决难题上的。 数据科学家倾向于完全进入解决方案模式，甚至在我们完全理解问题之前，因为我们认为我们已经找到了答案。 如果存在转化问题，我们可以进行个性化定位。 如果存在保留问题，我们可以建立客户流失预测模型。 尽管这些可能都是有效的解决方案，但它们可能只是解决症状而不是根本原因。

不问正确问题的代价 (The cost of not asking the right questions)

It’s probably natural to think that a solution is a solution even if it only solves the symptoms. Especially in a world that favours immediate actions and quick fixes, we never seem to have the time to dig deeper. I am not going to argue that for every single issue that comes up, you need to deep dive into whether you are asking the right questions, but I will stand by the fact that if we are asking the right questions, more often than not, we won’t be in situations fixing problems that shouldn’t come up in the first place.

即使解决方案只能解决症状，也很自然地认为解决方案就是解决方案。 尤其是在一个主张立即采取行动和快速解决问题的世界中，我们似乎从来没有时间去深入研究。 我不会争辩说，对于每一个出现的问题，您都需要深入研究自己是否提出正确的问题，但是我会坚持这样一个事实，即如果我们提出正确的问题，通常是，我们将不会解决最初不应该出现的问题。

In the context of data science, the cost often comes in two main ways, wasted resources and unintended consequences. The first one is relatively straight forward, if we don’t ask the right questions, we end up wasting time and effort building a solution that is not fit for purpose. The second one is more nefarious because if we are optimising a system based on a wrong metric, we could be systemically worsening the situation. For example, if we treat a churn problem as an isolated incident and focus on improving retention without asking the question of why people are churning, we might miss problems that go back to customer acquisition, through to user experience and engagement before it becomes a churn problem.

在数据科学的背景下，成本通常来自两种主要方式，即浪费资源和意外后果。 第一个是相对简单的，如果我们不提出正确的问题，我们最终会浪费时间和精力来构建不适合目标的解决方案。 第二个则更为危险，因为如果我们基于错误的指标优化系统，则可能会导致系统恶化。 例如，如果我们将客户流失问题视为一个孤立的事件，并专注于提高保留率，而又不问人们为什么会流失的问题，那么我们可能会错过一些问题，这些问题可以追溯到客户获取，用户体验和参与度，直到客户流失问题。

Every system is perfectly designed to get the results it gets

每个系统都经过精心设计，以获取所获得的结果

There is value in having the right answers for every question that comes up, but the cost of not asking the right questions is a bit more subtle and long term. If we always prioritise and value quick fixes, we are subconsciously encouraging problems to occur. We often recognise people who come in and fix the problems, but most of the time we don’t recognise the people who asked the right questions when it comes to designing that system in the first place. So the real hidden cost of not asking the right questions is a society or an organisation that nurtures people who focus on the short term rather than the long term. We will never be able to escape from problems and the need of having answers, but what we want to strive for is the ability to prevent that from happening as much as possible by asking the right questions upfront.

为每个出现的问题都提供正确的答案是有价值的，但是不提出正确的问题的代价会更加微妙和长期。 如果我们始终优先考虑并重视快速解决方案，那么我们在潜意识中鼓励问题的发生。 我们通常会认识到那些会解决问题的人，但是大多数时候，我们一开始并不了解那些在设计系统时提出正确问题的人。 因此，不提出正确问题的真正隐藏成本是一个社会或组织，要培养注重短期而不是长期的人。 我们将永远无法摆脱问题和获得答案的需要，但我们要努力做到的是通过预先提出正确的问题来尽可能避免这种情况发生的能力。

如何提出正确的问题？ (How to ask the right questions?)

In business, people often turn towards technical resources with a technical problem. It makes sense right? However, what happens when we are sick? Let’s say you feel pain and you go to the doctor and all you got was painkillers because that’s your symptoms. You would feely a little short-changed because the doctor didn’t diagnose you properly and try to understand why the pain exists in the first place. The difference here is our presumed knowledge, when we think we know what the problem is, we have a closed mind looking for a specific answer, but when we don’t know what the problem is, we have an opened mind hoping others will help figure out the problem. Here are a few things that can help us combat the tendencies to jump into solutions before we even understand the question.

在业务中，人们经常将技术问题转向技术资源。 有道理吧？ 但是，当我们生病时会发生什么？ 假设您感到疼痛，然后去看医生，而您得到的只是止痛药，因为那是您的症状。 您可能会感到有点变化，因为医生没有正确诊断您，并试图首先了解为什么会出现疼痛。 此处的区别是我们的假定知识，当我们认为知道问题所在时，我们会闭口寻找一个具体的答案，但是当我们不知道问题出在哪里时，我们会持开放的态度，希望其他人会有所帮助找出问题所在。 这里有几件事可以帮助我们克服在解决问题之前就跳入解决方案的趋势。

当心你的假设 (Beware of your assumptions)

Before we even get to ask the questions, we come to the table with a set of assumptions. Assumptions help us move forward quicker and provide the crucial context to the issues at hand and they are powerful. However, they are also dangerous as people often assume things that are not necessarily true and most of the people get to very different conclusions because they are basing their thinking off a different set of assumptions. Starting from facts and data is a good way to keep assumptions from being skewed, but we must learn to be as unbiased as possible during that process. Aligning assumptions with reality gets us to the actual starting point where a logical discussion can be had and the right questions can be asked.

在开始提出问题之前，我们先提出一组假设。 假设可以帮助我们更快地前进，并为眼前的问题提供关键的背景，并且它们具有强大的作用。 但是，它们也很危险，因为人们经常会认为不一定是正确的事情，并且大多数人会得出截然不同的结论，因为他们基于不同的假设来思考。 从事实和数据开始是防止假设歪斜的好方法，但是在此过程中我们必须学会保持公正。 使假设与现实保持一致可以使我们到达可以进行逻辑讨论并可以提出正确问题的实际起点。

问为什么我们要这样做？ (Ask why are we doing this?)

It is tempting to provide an answer to an immediate question because we know it but once we switch to solution mode, it is easy to get tunnel-visioned and lose track of what we are doing. So while it seems trivial, we should be asking ourselves why we are doing what we are doing every so often. It’s important to keep an open mind and be honest with ourselves. There will be times when we invested weeks or even months into something only to realise that it shouldn’t have been done in the first place. A timely critical assessment of why we are doing this can help bring us back on track and focus on the right questions.

提供一个即时问题的答案是很诱人的，因为我们知道它，但是一旦我们切换到解决方案模式，就很容易获得隧道规划的视野，并且无法跟踪我们的工作。 因此，尽管看起来很琐碎，但我们应该自问为什么我们经常这样做。 保持开放的态度并对自己诚实是很重要的。 有时候，我们花了数周甚至数月的时间来投资某件事，只是意识到这本来不应该做的。 及时对我们为什么这样做进行批判性评估，可以帮助我们回到正轨，并专注于正确的问题。

激励解决方案诊断 (Incentivise diagnostics over solutions)

To tackle this issue long term, we need to set up our environment to incentivise diagnostics as much if not more than solutions. We will always be conditioned to focus on coming up with answers if we don’t fundamentally change how we place value on good questions and good answers. While this might sound far fetched, it is something that we can all contribute towards. As tempting as it is to ask your colleagues or friends for a solution, ask them what they think the problem is and maybe we will gain a new perspective and reframe our situation differently.

为了长期解决此问题，我们需要建立环境以激发诊断能力，甚至不仅仅限于解决方案。 如果我们不从根本上改变我们如何重视好问题和好答案的价值，我们将始终有条件专注于提出答案。 尽管这听起来有些牵强，但我们所有人都可以为此作出贡献。 向您的同事或朋友寻求解决方案很诱人，问他们他们认为问题是什么，也许我们将获得一个新的观点并以不同的方式重新构造我们的处境。

现在怎么办？ (What now?)

We face problems every day and those are great opportunities for us to practice asking the right questions. It is unintuitive and at times it might even feel frustrating to take a step back and think if we are asking the right questions. However, we must consider the consequences of not asking the right questions because at best we will be getting the right answers to the wrong questions and that should not an acceptable outcome for any of us.

我们每天都面临问题，这些都是我们练习提出正确问题的绝佳机会。 这是不直观的，有时退后一步来思考我们是否提出正确的问题甚至会感到沮丧。 但是，我们必须考虑不提出正确问题的后果，因为充其量我们只会为错误问题获得正确答案，这对我们任何人都不应该接受。

If you have enjoyed my content and share my view on the topic, sign up for more at https://jchoi.solutions/subscribe

如果您喜欢我的内容并分享了对该主题的看法，请在 https://jchoi.solutions/subscribe上 注册以获取更多信息。