Build bridges, keep the maths under your hat and focus on serving.

架起橋梁，將數學放在腦海中，并專注于服務。

通過協作而不是通過孤立的孤島來交付出色的數據工作。 (Deliver great data work through collaboration not through comfortable silos.)

“Talent wins games, but teamwork and intelligence win championships.” Michael Jordan

“人才贏得比賽，但團隊合作和智慧贏得冠軍。” 邁克爾·喬丹

The best data projects and analyses I have worked on have one common denominator: variety.

我從事過的最好的數據項目和分析有一個共同點： 多樣性。

A variety of analytics tools, a variety of insight or a variety of people. (Even better if you have the opportunity to mix and match all the above).

各種分析工具，各種見識或各種人員 。 (如果您有機會混合以上所有內容，那就更好了)。

I will leave out number 1 and 2 for another story and focus on number 3 for this story: variety of people.

我將在第一個故事和第二個故事中省去一些，而在這個故事中，我們將重點放在第3個：不同的人。

One of my most enjoyable and impactful data and analytics pieces of work included:

我最有趣，最有影響力的數據和分析工作之一包括：

• Hypothesis generation to start with.
  假設生成開始于。
• Actual relevant analysis linked to the hypothesis as main course.
  與假設相關的實際相關分析為主要過程。
• A clear executable road map for dessert.
  一份清晰的可執行甜品路線圖。

All of these required solid analytics work but it could not have been delivered with the same strength had content, design, optimisation, digital and market specialists not chipped in.

所有這些都需要扎實的分析工作，但如果沒有內容，設計，優化，數字和市場專家的參與，就不可能以同樣的實力交付。

Why is that?

這是為什么？

Because collaboration is how an OK deliverable becomes rich, great and impactful.

因為協作是OK交付物如何變得豐富，強大和有影響力的方式。

When you consider what it takes to deliver a good analysis, it’s hard to imagine how one individual can do it all without running the risk of producing something that is half relevant, bland or even worse, not actionable…

當您考慮提供良好的分析所需的成本時，很難想象一個人可以如何做到這一點而又不會冒產生一半相關，平淡甚至更糟，不可操作的風險的風險……

Think about it. How many analyses have you delivered that were totally relevant, ground breaking even (and actionable)? I bet you can count them on the fingers of your hand(s), ok, I’ll give you the (s). I know I can!

想一想。 您提供了多少分析是完全相關的，具有突破性的(甚至是可行的)？ 我敢打賭，您可以將它們放在您的手指上，好的，我會給您的。 我知道我可以！

That’s because great analytics work takes time and more than just one analyst’s input for it to resonate within organisations.

這是因為出色的分析工作需要時間，而且要使組織內部產生共鳴，不僅僅需要一位分析師的投入。

The good news is, people are more willing to collaborate than you might think. The bad news is, sometimes, it’s Data departments themselves who don’t see the point in collaboration and think they have all the answers just because they have access to the Data. WRONG.

好消息是，人們比您想象的更愿意合作。 壞消息是，有時是數據部門自己看不到協作的重點，他們以為自己可以訪問數據就是所有答案。 錯誤。

Why is it wrong?

為什么錯了？

One of Data & Analytics’s raison d’être is to drive change and change doesn’t happen single-handedly.

Data＆Analytics的存在理由之一是推動變革，而變革并非單槍匹馬。

旨在服務于組織，而不是您的自我。 (Aim at serving the organisation, not your ego.)

Remember that unfortunately, just understanding basic and complex maths won’t get you very far, at least in the world of business that is. Personally, I get highly stimulated intellectually when breaking down complex maths formulas to fully understand them. I have always functioned that way. Some maths teachers loved me for it and others hated me for it but I always felt better for it!

請記住，不幸的是，僅僅了解基礎數學和復雜數學并不會幫助您，至少在當前的商業環境中。 就個人而言，當分解復雜的數學公式以完全理解它們時，我在智力上受到了極大的刺激。 我一直都這樣運作。 一些數學老師為此而愛我，另一些數學老師卻為此而恨我，但我總是為此感到更好！

However, I realise that’s weird. Even for someone that works in the analytics world. And worst of all, I know no one cares, well hardly anyone does in organisations…

但是，我意識到這很奇怪。 即使對于在分析界工作的人。 最糟糕的是，我知道沒有人在乎，在組織中幾乎沒人在乎……

So, if you want to shine, what will be truly worth your time is to be able to translate those formulas in another language: the language of business.

因此，如果您想發光，那么真正值得您花費的時間就是能夠將這些公式轉換為另一種語言：商務語言。

So yes, work hard at maths but know that it can’t be consumed in its raw form by the organisation, it needs transforming a little before it can shine and make you shine as well.

因此，是的，請努力學習數學，但要知道組織不能以原始形式使用它，它需要進行一些轉換才能使其發光并讓您也發光。

另一個建議：少即是多 (Another word of advice: less is more)

At the end of the day, as a data practitioner, whether analyst, scientist or anything in between, your job is to influence and convince people to act on your findings. So, do yourself a favour and make it easy for your audience to process your findings.

歸根結底，作為數據從業者，無論是分析師，科學家還是兩者之間的任何事物，您的工作都是影響并說服人們對您的發現采取行動。 因此，請幫自己一個忙，并使聽眾容易處理您的發現。

An effective way of achieving this is to, once you are done with a piece of analysis or a dashboard, ask yourself: what can I remove as opposed to what can I add? This is actually the difficult bit. We can get so precious with our data and analytics work sometimes that we want to show everything we have looked at. But there is no need. In fact, it is highly recommended not to do this as you are running the risk of diluting your key messages and overwhelming your audience. That’s how you end up with a “Thanks, that was interesting” as opposed to a “Wow, where do I sign?!” type message from your audience at the end.

完成此工作的一種有效方法是，一旦完成了一項分析或一個儀表板，便問自己： 相對于我可以添加哪些內容，我可以刪除哪些內容？ 這實際上是困難的一點。 有時，我們的數據和分析工作會變得如此珍貴，以至于我們希望展示我們所研究的一切。 但是沒有必要。 實際上，強烈建議您不要這樣做，因為這樣可能會稀釋關鍵信息并壓倒觀眾。 這樣，您最終會得到“謝謝，那很有趣”，而不是“哇，我在哪里簽名？！” 最后輸入聽眾的信息。

See yourself as a service provider first and you will become an asset.

首先將自己視為服務提供商，您將成為資產。

See yourself as an asset first and you will become a commodity!

首先將自己視為資產，您將成為商品！

最后，繼續游戲-永遠 (Finally, up your game — Always)

Sounds obvious, right?

聽起來很明顯，對不對？

However, that’s not easy to make this happen on a practical level when just delivering good data work can sometimes already be a challenge. However, with more and more people becoming data fluent, you don’t have a choice but to constantly try and differentiate. Oh and don’t wait for your employer to send you on the latest data course. By the time this happens, it will be too late anyway. So just take charge.

但是，要使這種情況發生在實際水平上并不容易，因為僅提供良好的數據工作有時已經是一個挑戰。 但是，隨著越來越多的人使用流利的數據，您別無選擇，只能不斷地嘗試和區分。 哦，不要等您的雇主將最新的數據課程發送給您。 到這種情況發生時，還是太晚了。 因此，只需負責。

The good news is: the world of data is constantly evolving and there are so many different areas one can gradually specialise in: data visualisation, machine learning, marketing analytics, you name it. Just make sure you name it before someone else does!

好消息是：數據世界在不斷發展，可以逐步專注于許多不同領域：數據可視化，機器學習，市場分析等。 只要確保先命名就可以了！

In summary, if you want to shine as a data practitioner, you should:

總之，如果您想成為一名數據從業者，您應該：

• Seek input from various pockets of the organisation.
  尋求組織各方面的投入。
• Ask yourself how you can serve the organisation better.
  問問自己如何更好地為組織服務。
• Constantly invest in yourself to sharpen your game.
  不斷投資自己，以提高您的游戲水平。

所以繼續發光！ (So go on and shine!)