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.

我們生活在重視答案的世界中。 我們在學校里被教導要學習如何在考試中回答問題，我們有條件去上班，知道我們需要得到答案，并且我們的社會大體上都專注于尋找解決方案，而不是弄清楚我們是否在提問正確的問題。 就像大多數經歷過傳統教育體系并開始從事公司工作的人一樣，我也受過訓練以獲得答案，我被教導我的貢獻和價值在于我通過了解正確答案來解決問題的能力。 雖然我確實認為解決問題和找到正確答案的能力是一項寶貴的技能，但我想闡明一些在其之前的技能，提出正確問題的技能的重要性。

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.

根據定義，問題和答案鏈接在一起，但是它們是非常不同的技能。 尋求答案是一個通過研究和實驗消除的過程，它試圖將不同的信息組合在一起，然后將事情縮小為解決方案。 但是，提出問題是通過批判性思維和想象力擴展的過程。 可以理解的是，為什么作為一個社會，我們不珍惜提出正確問題的成本，因為在某種程度上，我們提出的問題越多，我們需要做的工作就越多，而我們離完成需要做的工作就越遠。 這就產生了一個系統性問題，與長期解決方案相比，它更傾向于短期補丁。

這對數據科學家意味著什么？ (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.

這個問題無處不在，但是在諸如數據科學和工程等以解決方案為導向的領域中尤為突出，在該領域中，專業是建立在解決難題上的。 數據科學家傾向于完全進入解決方案模式，甚至在我們完全理解問題之前，因為我們認為我們已經找到了答案。 如果存在轉化問題，我們可以進行個性化定位。 如果存在保留問題，我們可以建立客戶流失預測模型。 盡管這些可能都是有效的解決方案，但它們可能只是解決癥狀而不是根本原因。

不問正確問題的代價 (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.

即使解決方案只能解決癥狀，也很自然地認為解決方案就是解決方案。 尤其是在一個主張立即采取行動和快速解決問題的世界中，我們似乎從來沒有時間去深入研究。 我不會爭辯說，對于每一個出現的問題，您都需要深入研究自己是否提出正確的問題，但是我會堅持這樣一個事實，即如果我們提出正確的問題，通常是，我們將不會解決最初不應該出現的問題。

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.

在數據科學的背景下，成本通常來自兩種主要方式，即浪費資源和意外后果。 第一個是相對簡單的，如果我們不提出正確的問題，我們最終會浪費時間和精力來構建不適合目標的解決方案。 第二個則更為危險，因為如果我們基于錯誤的指標優化系統，則可能會導致系統惡化。 例如，如果我們將客戶流失問題視為一個孤立的事件，并專注于提高保留率，而又不問人們為什么會流失的問題，那么我們可能會錯過一些問題，這些問題可以追溯到客戶獲取，用戶體驗和參與度，直到客戶流失問題。

Every system is perfectly designed to get the results it gets

每個系統都經過精心設計，以獲取所獲得的結果

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.

為每個出現的問題都提供正確的答案是有價值的，但是不提出正確的問題的代價會更加微妙和長期。 如果我們始終優先考慮并重視快速解決方案，那么我們在潛意識中鼓勵問題的發生。 我們通常會認識到那些會解決問題的人，但是大多數時候，我們一開始并不了解那些在設計系統時提出正確問題的人。 因此，不提出正確問題的真正隱藏成本是一個社會或組織，要培養注重短期而不是長期的人。 我們將永遠無法擺脫問題和獲得答案的需要，但我們要努力做到的是通過預先提出正確的問題來盡可能避免這種情況發生的能力。

如何提出正確的問題？ (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.

在業務中，人們經常將技術問題轉向技術資源。 有道理吧？ 但是，當我們生病時會發生什么？ 假設您感到疼痛，然后去看醫生，而您得到的只是止痛藥，因為那是您的癥狀。 您可能會感到有點變化，因為醫生沒有正確診斷您，并試圖首先了解為什么會出現疼痛。 此處的區別是我們的假定知識，當我們認為知道問題所在時，我們會閉口尋找一個具體的答案，但是當我們不知道問題出在哪里時，我們會持開放的態度，希望其他人會有所幫助找出問題所在。 這里有幾件事可以幫助我們克服在解決問題之前就跳入解決方案的趨勢。

當心你的假設 (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.

在開始提出問題之前，我們先提出一組假設。 假設可以幫助我們更快地前進，并為眼前的問題提供關鍵的背景，并且它們具有強大的作用。 但是，它們也很危險，因為人們經常會認為不一定是正確的事情，并且大多數人會得出截然不同的結論，因為他們基于不同的假設來思考。 從事實和數據開始是防止假設歪斜的好方法，但是在此過程中我們必須學會保持公正。 使假設與現實保持一致可以使我們到達可以進行邏輯討論并可以提出正確問題的實際起點。

問為什么我們要這樣做？ (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.

提供一個即時問題的答案是很誘人的，因為我們知道它，但是一旦我們切換到解決方案模式，就很容易獲得隧道規劃的視野，并且無法跟蹤我們的工作。 因此，盡管看起來很瑣碎，但我們應該自問為什么我們經常這樣做。 保持開放的態度并對自己誠實是很重要的。 有時候，我們花了數周甚至數月的時間來投資某件事，只是意識到這本來不應該做的。 及時對我們為什么這樣做進行批判性評估，可以幫助我們回到正軌，并專注于正確的問題。

激勵解決方案診斷 (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.

為了長期解決此問題，我們需要建立環境以激發診斷能力，甚至不僅僅限于解決方案。 如果我們不從根本上改變我們如何重視好問題和好答案的價值，我們將始終有條件專注于提出答案。 盡管這聽起來有些牽強，但我們所有人都可以為此作出貢獻。 向您的同事或朋友尋求解決方案很誘人，問他們他們認為問題是什么，也許我們將獲得一個新的觀點并以不同的方式重新構造我們的處境。

現在怎么辦？ (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.

我們每天都面臨問題，這些都是我們練習提出正確問題的絕佳機會。 這是不直觀的，有時退后一步來思考我們是否提出正確的問題甚至會感到沮喪。 但是，我們必須考慮不提出正確問題的后果，因為充其量我們只會為錯誤問題獲得正確答案，這對我們任何人都不應該接受。

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