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

架起桥梁，将数学放在脑海中，并专注于服务。

通过协作而不是通过孤立的孤岛来交付出色的数据工作。 (Deliver great data work through collaboration not through comfortable silos.)

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

“人才赢得比赛，但团队合作和智慧赢得冠军。” 迈克尔·乔丹

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

我从事过的最好的数据项目和分析有一个共同点： 多样性。

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).

各种分析工具，各种见识或各种人员 。 (如果您有机会混合以上所有内容，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.

我将在第一个故事和第二个故事中省去一些，而在这个故事中，我们将重点放在第3个：不同的人。

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

我最有趣，最有影响力的数据和分析工作之一包括：

• Hypothesis generation to start with.
  假设生成开始于。
• Actual relevant analysis linked to the hypothesis as main course.
  与假设相关的实际相关分析为主要过程。
• A clear executable road map for dessert.
  一份清晰的可执行甜品路线图。

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.

所有这些都需要扎实的分析工作，但如果没有内容，设计，优化，数字和市场专家的参与，就不可能以同样的实力交付。

Why is that?

这是为什么？

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

因为协作是OK交付物如何变得丰富，强大和有影响力的方式。

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…

当您考虑提供良好的分析所需的成本时，很难想象一个人可以如何做到这一点而又不会冒产生一半相关，平淡甚至更糟，不可操作的风险的风险……

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关的，具有突破性的(甚至是可行的)？ 我敢打赌，您可以将它们放在您的手指上，好的，我会给您的。 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.

这是因为出色的分析工作需要时间，而且要使组织内部产生共鸣，不仅仅需要一位分析师的投入。

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，人们比您想象的更愿意合作。 坏消息是，有时是数据部门自己看不到协作的重点，他们以为自己可以访问数据就是所有答案。 错误。

Why is it wrong?

为什么错了？

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动变革，而变革并非单枪匹马。

旨在服务于组织，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!

请记住，不幸的是，仅仅了解基础数学和复杂数学并不会帮助您，至少在当前的商业环境中。 就个人而言，当分解复杂的数学公式以完全理解它们时，我在智力上受到了极大的刺激。 我一直都这样运作。 一些数学老师为此而爱我，另一些数学老师却为此而恨我，但我总是为此感到更好！

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，我意识到这很奇怪。 即使对于在分析界工作的人。 最糟糕的是，我知道没有人在乎，在组织中几乎没人在乎……

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，如果您想发光，那么真正值得您花费的时间就是能够将这些公式转换为另一种语言：商务语言。

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，请努力学习数学，但要知道组织不能以原始形式使用它，它需要进行一些转换才能使其发光并让您也发光。

另一个建议：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.

归根结底，作为数据从业者，无论是分析师，科学家还是两者之间的任何事物，您的工作都是影响并说服人们对您的发现采取行动。 因此，请帮自己一个忙，并使听众容易处理您的发现。

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.

完成此工作的一种有效方法是，一旦完成了一项分析或一个仪表板，便问自己： 相对于我可以添加哪些内容，我可以删除哪些内容？ 这实际上是困难的一点。 有时，我们的数据和分析工作会变得如此珍贵，以至于我们希望展示我们所研究的一切。 但是没有必要。 实际上，强烈建议您不要这样做，因为这样可能会稀释关键信息并压倒观众。 这样，您最终会得到“谢谢，那很有趣”，而不是“哇，我在哪里签名？！” 最后输入听众的信息。

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

首先将自己视为服务提供商，您将成为资产。

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

首先将自己视为资产，您将成为商品！

最后，继续游戏-永远 (Finally, up your game — Always)

Sounds obvious, right?

听起来很明显，对不对？

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，要使这种情况发生在实际水平上并不容易，因为仅提供良好的数据工作有时已经是一个挑战。 但是，随着越来越多的人使用流利的数据，您别无选择，只能不断地尝试和区分。 哦，不要等您的雇主将最新的数据课程发送给您。 到这种情况发生时，还是太晚了。 因此，只需负责。

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：数据世界在不断发展，可以逐步专注于许多不同领域：数据可视化，机器学习，市场分析等。 只要确保先命名就可以了！

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

总之，如果您想成为一名数据从业者，您应该：

• Seek input from various pockets of the organisation.
  寻求组织各方面的投入。
• Ask yourself how you can serve the organisation better.
  问问自己如何更好地为组织服务。
• Constantly invest in yourself to sharpen your game.
  不断投资自己，以提高您的游戏水平。

所以继续发光！ (So go on and shine!)