storytelling with data podcast: #97 delivering business value with data, with Shachar Meir

比如如果是电子商务，那就是销量乘以平均价格再乘以利润率。

So if it's e commerce, for example, it's probably units sold times times average cost times margin.

如果这些是组成部分，再减去退货之类的，对吧？

And if these are the components minus returns maybe, or something like that, right?

你可以把这些因素加进去，但当你有了这个概念框架后，就可以构建一个仪表板，直观地展示总收入。

Like you can, you can add into that, but then when you have this conceptually out there, you can build a dashboard that basically shows you the top line revenue.

然后是销量、利润率、价格，并将每一个进一步拆解成其组成部分。

Then the number of units sold the, the margins, the price, and then break each one of them down into their components.

价格由什么构成？

So what is price made of?

利润率由什么构成？

What is margin made of?

对吧？

Right?

利润率主要由成本、供应商和供应链组成。

So margin is made of like your cost and then like suppliers and the supply chain.

你可以开始把所有东西列出来，但当你查看仪表板时，它会清晰地展示你业务的各个组成部分。

You can, you can start listing everything out, but then what it does is when you look at the dashboard, it basically shows you the components of your business.

然后你可以深入探究每一个部分。

And then you can drill down into each and every one of them.

但你的业务利益相关者是理解这种结构的。

But there's a, there's a structure that your business stakeholders understand.

它不是一堆毫无关联、没人能懂的随机小部件的随意堆砌。

It's not just a random assortment and collection of, of widgets that have no relationship and nobody understands.

对吧？

Right?

我认为关键就在于构建这种结构。

And I think that's the key, like trying to create a structure.

好的。

Okay.

所以如果你已经梳理出了业务结构，有人来找你要求一份或一个仪表板，但内容与这个结构不一致，你会拒绝他们吗？

So if you map out the business and somebody comes to you asking for a report or a dashboard that doesn't align with that, do you turn them away?

你会鼓励他们重新思考为什么需要这些信息吗？

Do you encourage them to rethink why they need that information?

对于那些不太符合这个框架的请求，你会怎么做？

Or what do you do for the requests that don't quite fit into that formula?

好问题。

Great question.

很多请求其实并不符合这个框架。

And a lot of requests will not fit into that formula.

我也认为，很多数据团队的第一反应就是制作一个仪表板，但这个仪表板往往只被使用一两次，之后就不再相关了。

I also think that a lot of data teams, immediate response is building a dashboard and the dashboard is being used once or twice, and then it's not relevant anymore.

所以，一个真正的仪表板必须具有持久性，对吧？

So for that to be a dashboard, it has to have longevity, right?

它需要成为人们今后很长一段时间内都会查看的内容。

It needs to be something that people are gonna look at from now on for a very long time.

否则，投入这么多工作却毫无意义，对吧？

Otherwise it's just too much work for nothing, right?

所以如果有人来找我问一个问题，我觉得是临时性的，我就会直接跑一个查询。

So if somebody would come to me and ask me a question and I feel like it's ad hoc, I would just run a query.

我会把报告以电子表格或其他形式发给他们。

I'll send them the reports in spreadsheet or whatever.

没必要搞得花里胡哨的。

Like it doesn't have to be fancy.

我会提供数据。

I'll provide the data.

我会帮助他们。

I'll help them.

我不会特意为这种需求去搭建一个仪表盘。

I'm not gonna go out of my way and build dashboard for that.

当他们第三次或第四次问同一个问题时，我就会开始问自己：这里有没有什么规律？

When they ask me the same question for the third or fourth time, I'll start asking myself, okay, is there a pattern here?

顺便说一下，这个人可能是一个产品经理在问一个问题，而业务部门的人从另一个角度问了类似的问题，还有其他人也是如此。

And by the way, this one person might be asking one question, a product manager and the person from the business is asking similar questions from a different angle and then somebody else.

关键是不要为每个人单独搭建一个仪表板，而是要理解：他们其实都是从不同角度观察同一个大问题，现在我要为他们整理出一个结构。

The trick is not to build one dashboard for each and every one of them, but to understand, okay, they're all looking at this one big thing from different angles and now I'm gonna structure it for them.

因为归根结底，业务部门里有不同的操作人员在问关于同一事物的不同问题。

Because in the end of the day, the business, you have different operators asking different questions about the same things.

但本质上还是同一个业务。

It's still the same business.

是的。

Yeah.

所以要寻找共同点，打造能够满足众多用户需求的产品。

So look for common denominators to build products that can meet a lot of audiences' needs.

确保你不是为每一件事都搭建仪表板，而是要思考这是一次性请求，还是需要更强大工具支持的持续性需求。

Make sure that you aren't building a dashboard for everything, but thinking about whether it's a one off request versus an ongoing request that needs more robust tooling behind it.

引入新仪表板的标准应该非常高。

The bar for introducing a new dashboard should be very, very high.

如果你要搭建一个仪表板，就必须能合理解释其必要性。

If you're going to build a dashboard, you need to be able to justify that.

而且你需要能够解释清楚，为什么它不适合纳入你已有的任何一个仪表板中。

And you need to be able to explain why does it not fit into one of the existing dashboards that you already have.

我想我们之前聊过激励的问题，对吧？

And I think we talked about incentives, right?

在文化层面。

In culture.

我认为这也是导致这个问题的原因之一：在许多组织中，如果你去修改现有的仪表板，你的工作可能会显得无形，这么说你能理解吧？因为人们不认为这是在做事。

I think this is also something that's causing this, that in a lot of organizations, if you go and make changes to existing dashboards, then your work might seem invisible in a way, if that makes sense, because people don't perceive that as doing work.

但如果你新建了一个仪表板，你就可以说：看，我做了这个，对吧？

Whereas if you build a new dashboard, you can say, oh, I've built this, right?

而且你会因为创建而得到奖励。

And you get rewarded for building.

我不认为人们应该因为创建仪表板而得到奖励。

I don't think people should be rewarded for building dashboards.

我认为仪表板的数量应该非常有限。

I think that the number of dashboards should be very limited.

我认为，人们应该因为找到巧妙的方法将内容融入现有仪表板、增强它们并构建服务于业务的仪表板生态系统而获得奖励。

And I think that people should be rewarded for finding clever ways of adding things into existing dashboards and enhancing them and creating dashboards ecosystem that works for the business.

是的。

Yeah.

所以，衡量标准不应该是本季度开发了多少个仪表板，而应该是对业务成果的影响，或在支持不同方面的决策上所起的作用，这些本质上都回归到你之前提到的数学公式。

So it's less about measuring your value by X number of dashboards developed this quarter versus impact on business outcomes or in supporting decisions in different aspects that really essentially get down to that math equation that you talked about earlier.

所以，重点不在于生产了多少，而在于你推动了哪些成果，对吧？

So it's not about just the producing bit, but more about the outcomes you're driving, right?

对。

Yeah.

我认为，在许多公司里，人们是通过所开发仪表板的数量来衡量影响力的，对吧？

And I think that in many companies, people measure the impact by weight of how many dashboards they produced, right?

他们把这些仪表板放在天平上，说：好吧，我做了六个，而且都是大项目。

They put them on a scale and they're like, okay, I produced six and they're big ones.

所以我应该为此得到奖励。

So I should be rewarded for that.

而这恰恰相反。

And it's like the opposite.

我认为，这更容易统计、量化和证明，但未必能真正推动你希望实现的成果。

Well, I think it's something that's easier to count or easier to quantify and justify, but not necessarily moving the needle where you want it to be.

所以我们一直在讨论你与公司开展的自上而下的工作，你有这四个支柱：技术、人员、流程和文化。

So we've been talking about your top down work with companies and you have these four pillars, tech, people, processes, culture.

我们之前谈到了技术。

And so we talked about tech.

这并不是指特定的一组工具，而是要认真思考如何使用这些工具。

It's not a certain set of tools, but really being thoughtful about how you use those tools.

在与人合作时，关键在于明确目标，了解你的受众和流程。

When you're working with people, it's really about getting clarity on what the goals are and understanding your audience and processes.

我们刚才谈到了不要为了做仪表板而做仪表板，而是要真正通过理解不同团队可能提出的问题来提升效率，而不是总想着尽量减少仪表板的数量。

We just talked about being thoughtful around building dashboards for dashboard's sake, but really making sure you're gaining efficiencies by understanding questions different groups may be having and not always trying to minimize the number of dashboards you're making.

我们还没怎么谈到文化。

We haven't touched much on culture.

你刚才谈到了激励措施，但你多次提到了信任。

You just talked about incentives, but you've mentioned a couple of times trust.

信任是我在很多工作坊中听到的一个话题，人们会提供信息，但其他人并不一定认同结果，因为他们不信任数据，或者不信任数据是如何整合的。

And trust is something that I've heard in a lot of workshops where folks provide information and then people don't necessarily agree with the outcomes because they don't trust the data or trust how it was brought together.

在这种情况下，人们需要做些什么？

What do folks in this situation need to do?

这对很多数据专业人士来说都是一个大话题，因为我觉得这其中涉及非常个人化的东西。

This is a big topic for a lot of data professionals, because I think that there's something very personal about it.

他们有一种，我几乎称之为数据心理学的东西，因为我曾遇到一位CEO，他创办了一家初创公司，公司大概已经运营了七八年。

They had a, I almost call it the psychology of data because I had a CEO that had built a startup and the company was probably about seven or eight years old.

你可以想象，这位CEO已经以这种方式运作了很长时间。

So you can imagine that this CEO had been operating in that way for a very long time.

为他辩护的是，他有着非常敏锐的商业直觉。

In his defense, he had very strong business sense.

他真的能感受到业务的脉搏，知道正在发生什么。

Like he would actually feel the business and he would know what's happening.

每次我向他展示与他的直觉和商业判断相符的数据时，他都会说：‘太好了。’

And it was a very interesting dynamic that every time I presented him data that matched his instincts and his business sense, he would say, Oh, that's great.

非常感谢你跟我分享这些。

Thank you so much for sharing this with me.

但当我提供与他的商业判断相矛盾的数据时，他就会说：‘我觉得你的数据不对。’

And when I share data that didn't, contradicted his business sense, he would say, Oh, I don't think your data is right.

你可能需要再核对一下。

You may want to check that.

我认为这是一个大问题，对吧？

And I think that's a big problem, right?

作为数据从业者，我们也必须承认，并非每个业务人员都想要数据，也不是每个人都想要真相。

And I think we also have, as data people, we have to acknowledge the fact that not every business operator wants data and not everybody wants the truth.

有些人并不在意他们是如何管理的，就像一些组织中的人，他们管理的是表象，或者遵循不同的标准。

Some people don't care about the fact they manage like some people in organizations, they manage perception or they operate by different standards.

尽管我们热爱数据，但也必须明白，有时候数据并不是最重要的，公司需要优化的是其他方面。

And we also, as much as we love our data, we also have to, we have to understand that sometimes that's not the most important thing and companies need to optimize for different things.

数据人员对此并不总是有清醒的认识。

And data people are not always sensible about that.

他们认为我们总是需要数据。

And they think we always need data.

不，我们不需要。

No, we don't.

有时候，其他事情更重要。

Sometimes there are other things that are more important.

所以，我想借此机会说明，在考虑到这些背景的情况下，你所能做的最好的事情之一就是找到组织中的支持者——高管赞助人。

So I just wanted to say that now with this, with this context in mind, I think that one of the best things you can do is find your sponsor, executive sponsor in the organization.

理想情况下，那是首席执行官。

Ideally that's the CEO.

但正如我所说，如果首席执行官来自不同的背景，他们对数据并不熟悉。

But as I said, if the CEO is coming from a different background, they're not very data savvy.

有时候，首席运营官、首席财务官、首席技术官或首席产品官也可以。

Sometimes the COO or the CFO or the CTO or the chief product.

有时候你可能有不同的赞助人，那就找一个最资深、关心数据且你能与之合作的人。

Like sometimes you have a different sponsor, find the most senior person who cares about data that you can work with.

他们可能不在你的直接汇报线路上，但他们很可能是你这段旅程中最强大、最好的盟友。

They may not be like in your line of command in your chain of command, but they will probably be your strongest and best ally in this journey.

利用他们来建立围绕数据的责任流程，比如业务评审、指标评审之类的。

Use them to build processes that create accountability around data like business reviews, metric reviews, things like that.

建立一个定期机制，每隔一周你去CEO或COO的办公室，向他们以及业务相关人员展示指标，然后让业务方解释各种变动。

Create a cadence where every other week you go to the CEO office or the COO office and and and people like you present the metrics and then the people around the business need to explain different movements.

比如，如果客户支持工单数量上升，客户支持团队就必须能解释原因。

So if there's an uptick in customer support tickets, customer support need to be able to explain why it happens.

这可能是因为产品变更导致客户使用时遇到很多障碍。

Now it could happen because of a product change that's causing a lot of friction to customers, to users.

于是他们更频繁地寻求支持，但必须有人能解释这一点，也必须有人对此负责并加以解决。

And then they use support more, but somebody needs to explain that and somebody needs to be on the hook to fixing that.

拥有一个高管赞助人能帮助你做到这一点。

Having an executive sponsor helps you do that.

所以这是第一点。

So that's number one.

第二点是在各个团队中找到你的支持者，对吧？

The second thing is find your champions in the different groups, right?

我曾经有一个客户，那位产品副总裁、产品总监以及产品管理团队经理都对数据不太了解。

So I had a client where the VP product, the director of product and the product management group manager, they were very non data savvy.

他们其实并不关心这些，但我发现有一位产品专员非常擅长数据。

They didn't really care about that, but I could find one product manager that was actually very, very good.

他非常喜欢自己的仪表盘。

They love their dashboards.

他热爱自己的指标和数据。

They love their metrics and data.

于是我决定要和这位同事紧密合作。

So I was like, okay, I'm going to work very closely with this guy.

他是我最好的伙伴，因为他会帮助我从内部推动变革。

That's my best friend because they're going to help me make the change from within.

在其他人的团队和文化中推动变革有时非常困难。

It's sometimes very difficult to make changes in other people's teams and cultures.

但当你有一个内部盟友可以帮助你推动变革时，情况就会好很多。

But when you have an agent inside who can help you make the change, that's a lot better.

找到你的支持者。

Find your champions.

然后你需要一点一点地建立信任。

And then you have to build trust little by little.

所以不要试图一次性改变一切。

So don't try to boil the ocean all at once.

试着选择一些你可以做的小事，比如每周报告几个指标，解释它们，或者每周参加一次高管会议，花十分钟汇报一下数据更新，诸如此类。

Try to choose small things that you can do, like report a few, a few metrics once a week, explain them, maybe join the C level board meetings once a week for ten minutes to give them a metrics update or things like that.

从小处着手，激发好奇心，给予奖励，并持续从中发展。

Start small, create the curiosity, reward that and keep growing from there.

这些都是非常好的建议。

Those are all such great tips.

从小处着手是我们常常忽略的事情。

Starting small is something I think we forget about.

我们总想一次性做太多事，但其实有一个变革管理模型强调的是循序渐进的小步骤，庆祝每一个小胜利，然后继续前进。

We try to do too much at once, but there is a change management model where it's little small steps, celebrate those wins and move on.

我也非常认同合作的理念，找到一位高管赞助人，找到你的支持者。

I also love the idea of partnering, getting an executive sponsor, finding your champion.

真正要做的不是想着如何与组织对抗，而是思考如何与他们合作，理解他们一开始可能并不以数据为导向或数据驱动。

So really working with instead of thinking about working against the organization, how can you work with them and thinking about how it might not be data driven or data focused at the beginning.

你必须从人们当前所处的位置出发，然后逐步在此基础上建立起来。

You have to meet people where they are and and then slowly build upon that.

我还要分享一件事，我认为数据从业者做得还不够多。

I will also share something that I think not not enough data professionals do.

在Meta，我曾经安排了一次与广告业务副总裁的会议。

At Meta, I once set up a meeting with the VP of ads.

我知道他非常懂数据，他叫罗布·戈德曼。

I knew he was very data savvy, Rob Goldman.

我安排了一次和他的会议，然后进入了会场。

I set up a meeting with him and I came into the meeting.

这是一次三十分钟的一对一会议。

It was a thirty minutes one to one.

我说，我知道你非常懂数据。

I said, I know you're very data savvy.

你能给我展示一下你是怎么看你的仪表盘的吗？

Can you show me how you look at your dashboards?

他问：你什么意思？

He was like, what do you mean?

我说，你不是经常看仪表盘吗？

And I said, you look at dashboards, right?

你能给我演示一下你是怎么做的吗？

Can you show me how you do it?

他回答：好啊，没问题。

He was like, yeah, sure.

他给我展示了他有一个名为‘重要仪表板’的书签文件夹，里面包含了大约二十到三十个不同的仪表板。

And he showed me, he had a bookmark folder called important dashboards with like 20 or 30 different dashboards.

然后他开始一个接一个地逐一查看它们。

And he started going over them one by one.

我当时说，好吧，你是在哪里找到这些的？

I was like, okay, where did you find them?

你是怎么得到它们的？

Like, how did you get them?

我当时说，我不知道。

I was like, I don't know.

偶尔我会问一个问题，有人发给我一个仪表板的链接，我会说，哦，这个仪表板真不错。

Every once in a while I ask a question, somebody sends me a link to a dashboard and I'm like, oh, that's a cool dashboard.

所以我把它保存下来，并且会回顾其中一些。

So I keep it and I review some of them.

其中有两三个是我最喜欢的。

And there's these two or three that I like the most.

其他的我会偶尔扫一眼。

And the other ones I'll skim them every once in a while.

我想，好吧，接下来数据发现肯定是个问题。

I'm like, okay, so data discovery is definitely a problem next.

然后我们开始查看一些仪表板，他说，这个指标在上升。

And then we start looking at the, some of the dashboards and he's like, okay, this metric is going up.

这个在下降。

This one's going down.

我不明白这个。

I don't understand that.

比如，如果这个上升了，我预期它们应该朝相同方向变化。

Like I expect if this one goes up, I expect them to go in the same direction.

所以我问，你打算怎么处理这个问题？

So I'm like, What are you gonna do about that?

这是个很好的问题。

So that's a great question.

我恰好知道这个产品的负责人是这个人。

I happen to know that the director for this product is this person.

我上周和他聊过。

I spoke with them last week.

所以我现在要给他发条消息，附上一个截图，问他为什么会这样。

So I'm gonna send them a message now with a screenshot and I'm gonna ask them why is this happening?

我想，为什么不把每个小部件的负责人姓名直接标出来呢？这样大家就知道该找谁了，诸如此类的做法。

I'm like, okay, so why don't we just put the name of the accountable person next to every widget so that people can know who to reach out to and these kind of things.

只有当你坐在用户和利益相关者旁边，观察他们如何使用产品时，才能产生这样的想法。

You only get these ideas when you sit next to your users, when next to your stakeholders and you look at how they use the product.

我认为，太多数据专业人士忽略了这一步。

And I think that's too many data professionals miss that step.

实际上，他们只是搭建了仪表板，扔过去就完事了，关掉Jira工单就结束了。

Actually, they build the dashboard, they throw it over the fence, they close the Jira ticket and they're done.

当人们谈论数据产品时，我认为正是这种缺失的思维模式。

And I think that's when people talk about data products, I think that's exactly the kind of mindset that is missing.

把它当成一个产品来想，对吧？

Think of it like a product, right?

我的用户是谁？

Who are my users?

用户有哪些不同的类型？

What are the different archetypes of users?

他们需要什么？

What do they need?

我的产品中有哪些痛点？

What friction points do I have in my product?

我该如何随着时间推移让产品对他们更好？

Like how do I make the product better for them over time?

不要只是做一个仪表板工厂，不断生产新的仪表板然后就抛之脑后。

Not just being a dashboard factory and producing yet another dashboard and forgetting about it.

是的，我很喜欢这个观点。

Yeah, I love that.

这意味着要考虑到最终用户及其使用体验，思考他们会如何使用它，并以这个目标为导向来推动工作，而不是仅仅为了完成清单上又一个仪表板的任务。

It's thinking about the end user and their experience and what they're going to be doing with it and getting with that end in mind to drive it rather than checking another dashboard off your list, I guess.

我曾经和一位数据工程师开过一次会，我问他：‘好吧，给我看看你正在做什么。’

I had a meeting with one of my data engineers at some point and I asked them, okay, show me what you're working on.

他说：‘我在做一个仪表板。’

And they said, oh, I'm building this dashboard.

我说：‘好的，那你为什么要开发这个？’

And I said, okay, why are you building this?

他回答：‘因为我的产品经理让我做的。’

And they said, because my PM has asked me to.

我就问：‘那他们打算怎么用它呢？’

And I'm like, okay, so how are they going to use it?

他回答：‘呃，我不知道。’

And they were like, oh, I don't know.

我只是按他们要求的去做而已。

I'm just building what they're asking me for.

我认为，这是每个数据从业者可能拥有的最糟糕的心态。

And that's, I think the worst mindset that every data professional can have.

这正是我在德克萨斯州演讲中谈到的内容，也就是我的‘百万美元数据从业者’演讲：如果你只是做一个顺从者，一个只接收业务需求并完成任务的人，那么你的工作现在面临很高风险。

And this is part of what I talked about in my talk in Texas, my $1,000,000 data professional talk, which is if you're going to be a person, if you're going to be an order taker and somebody who just gets orders from the business and and fulfills these orders, then your job is at high risk right now.

你需要有主见。

You need to be opinionated.

你需要创造价值。

You need to add value.

你不能只是因为别人要求你就去构建东西。

You cannot just be a person who just builds things because people ask them to.

这远远不够。

That's not gonna be good enough.

是的。

No.

而且我认为，随着工具、人工智能和其他方面的不断进步，如果你无法提供比仅仅创建功能模块更多的价值，那么你的职业前景令人担忧。

And I think even more important as we have these more advancements in tooling and AI and other aspects, if you can't bring more value than just creating widgets, it is concerning for your career.

所以我们现在转向你与个人合作的更多工作内容。

So we're shifting now into more of the work that you do with individuals.

你一开始提到，你是从上至下与企业合作，关注我们一直在讨论的这些不同方面和支柱，但你也花了很多时间与个人打交道。

So you talked about at the beginning how you work top down with corporations, looking at these different aspects and pillars that we've been talking about, but you also spend a lot of time with individuals.

也就是说，从下至上地帮助个人推动他们的职业发展。

So working bottoms up and helping individuals help further their career.

你提到了那个‘百万美元数据专业人士’的演讲，这个标题很有争议性。

And you mentioned this talk, million dollar data professional, which is a provocative title.

我非常想了解更多关于这个演讲的内容。

So I would love to learn more about that.

我也知道你每周都开设一门课程，旨在帮助人们在数据工作中产生更大的影响，并为他们提供工具。

I also know that you have a weekly course where you're trying to help folks make more of an impact within their data work and you're giving folks tools.

你能大致谈谈这些主题、目标人群以及你看到的成效吗？

You wanna talk at a high level these topics and who it's aimed at and the benefits that you're seeing come out of this?

正如我所说，我一直在成长，过去二十年来我一直带领团队。

As I said, I've I've been growing and I I've always had teams for the past twenty years.

我一直都在培养和指导各个层级的数据专业人士，从实习生到高级高管。

So I've been growing and mentoring people and data professionals all the way from interns to senior executives.

我注意到许多常见的模式和原因，解释了为什么人们在职业生涯中停滞不前、未能取得应有影响力。

I've noticed a lot of common patterns and reasons why people get stuck in their career while they fail, why they are not achieving the impact that they can, or they should achieve and that sort of stuff.

这些模式非常普遍。

There are a lot of common patterns.

其中之一是与领导层合作，学习如何谈论工作、讲故事、建立联盟，以及理解业务等其他方面。

One is working with leadership, way they talk about the work and storytelling and building alliances and all of that, understanding the business and a bunch of other things.

我设计了一个为期五周的邮件形式的迷你课程，来解决这些问题。

I designed a short five week email based mini course to address these topics.

每周你会收到一封邮件，包含三样东西：一个概念或对数据专业人士为何陷入困境的解释、一项你需要完成的练习，以及一个帮助你完成练习的资源。

So every week you get an email with three things, a concept or an explanation of one of the ideas for why data professionals get stuck and exercise that you need to do and a resource that helps you perform this exercise.

它可以是工作表、框架，或者类似的东西。

It can be a worksheet or framework or something like that.

这样做的目的是推动人们走出舒适区，促使他们去做一些平时不常做的事情。

And the purpose of that is to push people out of their comfort zone and to push them to do something that they're not used to do.

这个课程的成功几乎是 guaranteed 的，因为这些正是数据专业人士陷入困境的常见原因。

And the success of that is pretty much guaranteed because these are a lot of the very common reasons for why data professionals get stuck.

我通过多年来在团队中使用这些方法，以及那些完成迷你课程的人，了解到它确实帮助他们突破了瓶颈，持续成长，因为他们突然拥有了理解业务的新工具、思考工作的新方式、发现影响力的新途径等等。

I know from both from my teams over the years that I've used the same methods with and also with people who've done the mini course that it's really helping them get unblocked and continue growing because all of a sudden they have new tools to understand the business, different way to think about the work, a different way to find impact and all of that.

这个迷你课程目前是免费的。

That's on the mini course it's free for now.

访问 Careerunblock.shacharmeir.com，欢迎随时查看。

Careerunblock.shacharmeir.com and feel free to check it out.

你可以在我的领英主页上找到它。

You can find it on my LinkedIn page.

在某个地方有链接，或者在我的 YouTube 频道上。

There's a link somewhere or my YouTube.

某个地方有链接。

There's a link somewhere.

所以我强烈推荐你去试试。

So I would highly recommend that.

我认为在某些情况下，这些是人们已经知道的事情。

And I think that in some cases these are things that people already know.

他们只是需要一点额外的推动力，走出舒适区，开始去做这些事情。

They just need that extra push to get them out of the comfort zone and to start doing these things.

所以这是一个小问题。

So that's a mini question.

《百万美元数据专业人士》是我曾在德克萨斯州日常生活中做过的一场演讲。

The $1,000,000 data professional is a talk that I've given in day to day Texas.

它在我的YouTube频道上可以找到。

It's available on my YouTube.

我录了一个现场版本的演讲。

There's a live recording I did of the session.

这个想法的背后是。

The idea behind that.

所以当林恩邀请我来演讲时，我开始思考我要讲些什么？

So when Lynn asked me to come and speak, started thinking about what am I going to talk about?

我最近一直在反思我所参与的许多辅导对话，以及我与数据领导者和普通人的大量交流。

And I've been reflecting on a lot of the mentoring conversations I've had recently and a lot of conversations I'm having with data leaders and with people in general.

我认为，当前世界充满了不确定性和困惑，这源于许多因素，但本质上是由三方面原因造成的。

And I think that there's a lot of uncertainty and confusion right now in the world resulting from a lot of things, but essentially from three things.

第一是总体的宏观经济状况和地缘政治状态。

One is the general macroeconomic state, geopolitical state.

如今世界充满了不稳定性。

There's a lot of instability right now in the world.

在宏观经济方面，许多公司正在裁员，这使得就业市场更加拥挤。

And macroeconomics companies are letting go a lot of people that's jamming the job market.

这影响了方方面面。

It's impacting everything.

第二点是，我认为我们工作的方式在疫情后发生了变化：我们曾经历过全球大流行，所有人都在家办公，远程工作被视为未来，但如今公司却基本要求我们每周五天回办公室，不再允许远程工作。

The second thing is I think that the way we do our work changes post COVID and we went from global pandemic and everybody can work from home and remote work is the future and all of that to our company is basically telling us come back to the office five days a week, no more remote working.

我们不再信任员工，诸如此类。

We don't trust employees anymore and all of that.

还有裁员，以及人们对雇主与员工之间关系的态度发生了巨大变化，我觉得雇主与员工之间的契约已经破裂了。

And then layoffs and a lot of changes in the attitude towards, and I feel like the contract between employers and their employees is broken.

是的。

Right.

过去，他们说我们会招聘最优秀的人才，我们会给你报酬，照顾好你，你只要尽力工作就行。

And if in the past they said, we'll hire the best talent, we'll pay you, we'll take care of you and you just do your best work.

但现在感觉已经不是这样了。

Right now it doesn't really feel like that.

感觉有什么东西正在改变。

It feels like something is changing.

我认为这把很多人推到了崩溃的边缘。

And I think that that's putting a lot of people over the edge.

最后一件事当然是人工智能，它正在改变并颠覆我们的工作方式。

And the last thing is of course, AI, which is changing and disruption disrupting the way we work.

所以现在充满了不确定性。

So there is a lot of uncertainty right now.

我反思了这一点，意识到我今天能给你们做的最好的分享就是这个。

I've reflected on that and I realized that the best talk I can give you this one.

我给它起了个吸引眼球的标题，因为我觉得现在很多人都需要思考这个问题，尤其是对于数据从业者来说，这可能是我们一生中最大的机遇，对吧？

Gave it a clickbait title because I think that this is something that a lot of people need to think about right now, because especially for data professionals, this is the opportunity of our lifetime, right?

这是一个巨大的威胁，但同时也是千载难逢的机遇。

It's a big threat, but it's also the opportunity of a lifetime.

一些拥有正确心态、能以正确方式应对当前局势的数据从业者，他们的职业生涯将一飞冲天。

Some data professionals with the right mindset who will approach the current situation in the right way will skyrocket and their career will explode.

而对于许多只是被动执行任务、仅仅因为别人要求就制作仪表板，或不断重复生产另一个仪表板的人来说，

And for many others who are just order takers and they just build dashboards because people ask them to, or they produce yet another dashboard yet another dashboard.

情况就像是，人们不再提交JIRA工单，而是直接向支持AI的BI工具发出指令，自己创建所需的仪表板，因为这些岗位的价值已经不复存在，他们的工作将被取代。

It's kind of like instead of JIRA ticket, people will just give direct prompts to whatever BI tool that's AI enabled BI tool and create their own dashboards because the value is just not there and their jobs will be eliminated.

所以我谈到了这些变化。

So I talked about these changes.

我谈到了不确定性，并解释了为什么对数据从业者而言，这是一生中最大的机遇——因为正如我们感到困惑和不安，我们的高管们也同样感到同样的困惑与不安。

I talked about the uncertainty and I explained why for data professionals, this is the biggest opportunity of a lifetime, because just like we are confused and we feel this uncertainty, Our executives feel the same confusion and uncertainty as well.

所以，如果你想想一家大公司的首席执行官，他们需要应对这些变化。

So if you think of a CEO of a big company, they need to navigate these changes.

他们需要应对这种错失恐惧症。

They need to navigate this FOMO.

他们需要做出重大的战略决策。

They need to make very big strategic decisions.

我是应该筹集更多资金继续扩张？

Do I want to raise more money and keep growing?

还是应该保持精简，专注于核心？

Or do I want to stay lean and focus?

我是应该全力投入人工智能，还是应该全力投入我的核心业务？

Do I want to double down on AI or do I want to double down on my core business?

我该如何应对那些在三周内用AI代码复制我整个产品的竞争对手？

Do I want to, how do I deal with all the competitors who vibe code my entire product in three weeks?

面对这种情况，我该怎么办？

And what do I do with that?

而且他们需要应对许多重大问题。

And there are a lot of big questions that they need to navigate.

他们也感到困惑，而关键在于，哪些数据能带来清晰的方向。

They're also confused and guess what data is something that creates clarity.

所以，如果你是一位数据专业人士，能够理解所需的信息，你就可以自己找出答案。

So if you're a data professional who can understand what is needed, you can figure it out on your own.

你是自我驱动的。

You're self directed.

你能与业务人员和技术人员沟通，如果你是这样的人，那么你的成长空间目前几乎是无限的。

You can talk to business operators and technical operators, and you are this kind of person then like your growth is virtually unlimited right now.

这就是我的看法。

That's the way I see it.

这听起来非常鼓舞人心且令人向往。

That sounds very inspiring and aspirational.

我非常喜欢。

I love it.

我觉得你触及到了一些非常重要的东西。

I think you touch on something really important.

关键在于你如何表达，结合业务和你的受众，这才是引人入胜的叙事核心。

It's how you frame it, considering the business, your audience, and that's really the core of compelling storytelling.

我想知道你能否分享几个关键的成长时刻，看看叙事是如何推动你个人或你所合作的那些人的职业发展的，以便让大家对现实中的情况有个具体的认识。

And I'm curious if you could share a couple of pivotal growth moments where storytelling has accelerated either your career personally, or those individuals that you're working with, maybe to give folks a sense of what that looks like in the real world.

当然可以。

Sure.

我这里有好几个故事。

So I have a bunch of stories here.

当然，我之前已经提到过公司模型了。

Obviously, I've talked about company formulas.

过去我多次做过这类事情，每次都能让公司和业务方第一次看到一个真正有意义的、整合了整个业务的仪表盘，这总是具有变革性的。

This is something that I've done quite a few times in the past, and that's always been transformational for companies and for businesses seeing their entire business in one dashboard for the first time that actually makes sense.

我曾为不同的产品、客户和公司工作过，但每次都是一样的：与很多人交谈，理解业务的全貌，问他们：‘你能跟我讲讲，这个业务到底是怎么运作的吗？’

So I've worked on different products, different clients, different companies, and it's always been the same thing, talking to a bunch of people, understanding what the business looks like, asking them, Hey, can you please talk to me and explain to me, how does this business work?

我一直都在收集拼图的各个部分。

And I've been, Hey, I've been getting a lot of pieces of the puzzle.

你会听到很多不同的观点和视角，然后开始把这些信息整合起来，再回过头去跟这些人说：我觉得这个业务就是这样运作的。

And you get a lot of different perspectives and point of views, and then starting to formulate that and then presenting back to these people and saying, I think that the business works like that.

那实际上就是一段话，里面包含一堆方程式，解释了这个业务是如何运作的。

It was literally like one paragraph with a bunch of equations that explain how the business works.

我问他们：这个解释对你来说合理吗？

And I asked them, does that make sense to you?

我有没有遗漏什么？

Am I missing anything?

他们说：没有，这太完美了。

And they're like, no, that's perfect.

然后我就基于这个搭建了仪表盘。

And then I went on and built the dashboard on top of that.

所以，这是一个例子。

So that's, that's one example.

这关乎于框架构建和故事叙述，因为正如我所说，他们第一次看到了一个真正反映其整个业务行为的连贯仪表盘。

And it's about framing and storytelling because again, as I said, for the first time, they see a coherent dashboard that really behaves like their entire business.

我可以分享的第二个例子，是我在Meta工作时参与的一个产品。

The second example I can share is from a product I worked on at Meta.

我与一个产品团队合作。

I worked with a product team.

他们在开发一种衡量解决方案，对吧？

They were building a measurement solution, right?

他们试图衡量广告中的某个指标，比如广告主在广告中的某个结果。

So they were trying to measure something in ads, like one of the outcomes for advertisers in ads.

但他们的衡量存在误差。

And they had errors in the measurement.

出现误差的原因是，这个衡量是估算出来的。

The reason they had errors is because this measurement was estimated.

它并不是基于真实数据。

It wasn't based on real data.

这是一个他们试图估算结果的估计值，而且非常嘈杂。

It was an estimation that they were trying to come up with for the outcome and it was very noisy.

于是这个团队，大概由二十到三十名工程师、数据科学家、数据工程师和产品经理组成。

So the team, basically a team of probably, I don't know, twenty, thirty engineers and data scientists and data engineers and product managers.

他们走上了减少测量误差和噪声的道路，这完全合理，因为他们正在构建测量系统。

They went on this path of trying to reduce the amount of error, the amount of noise in the measurement, which makes absolute sense because they're trying to build measurement.

这个测量结果是有噪声的。

The measurement is noisy.

所以他们并没有走这条纠正错误的路。

So they weren't on this path of correcting that.

而我介入后，提出了两个问题。

And I came in and I asked two questions.

我说：各位，我知道你们在努力减少误差。

I said, people, I know you're trying to reduce the error.

我们能先回答这两个问题吗？

Can we answer these two questions?

我们能做到多好？

How good can we get?

就我们现有的信号而言，我们能达到的绝对最佳水平是什么？

Like what's the absolute best we can get with the signals that we have?

因为这是一个估算，我们是基于某种信号的。

Because it's an estimation, we're based on a certain signal.

我们能做到多好？

How good can we get?

多好才算足够？

And how much is good enough?

因为即使我们能做到的最好结果，对广告主来说仍然不够好，他们也不会使用这个产品。

Because if the best we can do is still not good enough for the advertisers and they're not going to use this product.

好吧，他们可能会使用，但如果说结果不准确，就会把责任推给我们。

Well, they're going to use it, but say it's incorrect and blame us for something.

那投入这么多时间真的值得吗？

Then is it actually worth investing all this time?

如果我们现在花两年时间把产品优化到极致，但结果还是不够好，那我们还不如现在就停下。

And if we spend now two years getting it to as good as we get, as good as we can, and it's still not good enough, then we might as well stop now.

我们其实不必非得做这件事。

We don't have to do it.

没有人能回答这两个问题，但这种‘我们能做得多好’和‘多好才算足够’的提问方式，确实推动了这场讨论。

And nobody could answer these two questions, but this kind of framing of how good can we get and how much is good enough really forced this discussion.

我最后举的一个例子可能不是关于数据的，而是关于如何向高管讲故事、说服他们，比如贝宝和易趣分拆的事。

And the last example I'll give maybe is, is it's not necessarily around data, but it is around storytelling with executives and convincing people is around PayPal and eBay split.

贝宝曾经是易趣旗下的一家公司。

So PayPal used to be an eBay company.

它曾经是易趣的一部分。

It used to be part of eBay.

它们使用相同的服务器、相同的数据仓库，所有东西都完全共享，这两家公司实际上是紧密相连的。

And it was the same servers, same data warehouse, the same everything like these companies were literally connected.

但后来做出了一个决定：两家公司必须分拆，成为两个独立的实体，这是一件非常重大的事情。

And then at some point there was a decision that the companies need to split and to become two independent companies, which is a very big thing.

你想想看，这简直一团糟，而且时间也不多。

You think about it, it's a mess and there wasn't a lot of time to do it.

我们必须从数据仓库中退出来。

And we had to get out of the data warehouse.

所以我们得建立一个新的数据仓库，把我们的数据迁移到那里，并把所有流程都转移到新系统上。

So we had to set up a new data warehouse and move our data there and all of our processes there.

由于技术原因，很难并行运行相同任务并比较结果，因为如果你在不同时间点获取不同的数据集，就无法保证序列一致，也无法保证输入数据完全相同。

For very technical reasons, it was very difficult to run the same jobs in parallel and compare the results because if you get different data sets at different points in time and all of that, you cannot guarantee the sequence and that the input data will be the same.

这实在太困难了。

It was just too difficult.

所以他们说，考虑到我们所剩的时间，这根本做不到。

So they said, given the time that we have, it's just not something that we can do.

所以我们只会迁移所有数据。

So we'll migrate all the data.

然后在某个时候，我们会直接把所有流程切换到新的数据仓库上，不再使用旧的。

And then at some point we'll just move all processes to start working on the new data warehouse instead of the old one.

所以基本上就是把所有任务迁移到新的数据仓库。

So basically just move all the jobs into the new data warehouse.

项目经理们反映他们感到很不安，对这个计划不满意。

And the project managers basically escalated that they felt bad about like they didn't feel good about plan.

他们说：‘那为什么我们不能并行运行所有任务呢？’

They said, oh, but why can we not just run everything in parallel?

他们就是不明白。

They they just didn't get it.

这件事一直上报到了首席风险官那里，他是风险部门的成员。

It got escalated all the way to the chief risk officer that was part of the risk org.

这些任务实际上是在防止损失，涉及大量资金。

These jobs were basically preventing losses, a lot of money.

这是一件非常严重的事情。

And it was it was a very severe thing.

于是他们说：‘好吧，你明天要和这位首席风险官、这位项目经理以及其他几位副总裁开个会。’

So they said, okay, you have a meeting tomorrow with this chief risk officer and this project manager and a bunch of other VPs.

你必须向首席风险官解释为什么我们不能这样做。

You have to explain to the chief risk officer why we cannot do that.

所以我一直在思考该如何解释这一点，因为我只有三十分钟。

So I was thinking about how I'm going to explain that because I have thirty minutes.

首席风险官本身就已经对这个情况感到压力很大。

The chief risk officer was stressed out about the situation anyway.

我们必须在很短的时间内完成这次拆分。

We have to pull off this split at a very short time period.

现在我们说不能并行运行这些任务。

Now we're saying we cannot run jobs in parallel.

这并不是一个理想的局面。

It's not a great situation.

长话短说，我最终决定采用风险评估框架，制作一张风险评估表，列出迁移过程中所有潜在风险及其发生的可能性和严重程度。

To cut a long story short, I basically decided to go with the risk assessment framework with the risk assessment table where you list out all the different risks with the migration and then the likelihood and the severity of each and every one of them.

我只是展示了我们所面临的所有不同风险。

And I just showed all the different risks that we're looking at.

我说，好吧，并行运行任务，对吧？

And I said, okay, running jobs in parallel, right?

我们试图解决的风险是什么？

What is the risk that we're trying to address?

我们想要验证的是，相同的软件、相同的数据库软件，完全相同的版本、完全相同的补丁，以及完全相同的输入数据，是否会产生相同的输出。

We're trying to check that the same software, the same database software, the exact same version with the exact same patches and everything with the same input data will produce the same output.

风险在于它不会。

The risk is it won't.

相同的软件、数据库软件，使用相同的数据，却产生不同的输出。

So the exact same software database software using the same data, producing a different output.

我说，这种情况的严重性非常高，因为它会把一切都搞砸。

I said the severity of that is very, very high because it's going to screw everything up.

对我来说，这种情况发生的可能性极低。

The likelihood of that for me is extremely low.

我是说，我不认为这种事情会发生，对吧？

Like, I don't believe that's a thing, right?

这是同样的软件。

It's the same software.

它应该是非常确定性的。

It's supposed to be very deterministic.

你应该能够用相同的输入数据运行相同的查询，并且每次都能得到相同的结果。

You should be able to run the same query with the same input data and get the same result every single time.

所以我觉得这根本不是什么大风险，对吧？

So I just don't think it's a big risk, right?

这种情况发生的可能性为零。

The likelihood of that is nothing.

我们还有其他很多更严重、对我们来说更重要的风险需要处理。

And we have a bunch of other risks that we're dealing with that are much more severe and much more important to us.

我们优先处理那些风险。

And we prioritize that.

首席风险官看了之后，十分钟内就明白了。

The chief risk officer looked at that and within ten minutes, he was like, okay, I got it.

明白。

Understand.

没有其他问题了。

No further questions.

哦，很好。

Oh, good.

我明白了。

I understand.

所以我认为这里的故事是，这三个例子——公司公式、我给他们的两个问题以及风险缓解——都很有意思。

So I think the story here is, and the interesting thing about all these three examples, company formula, the two questions I gave them and the risk mitigation.

我选择这些例子的原因是，它们都关乎以一种非常简单易懂的方式组织信息，让目标受众能立即理解、产生共鸣并采取行动。

Reason I chose these examples is because it's all about structuring the information in a way that is very easy and digestible for your target audience and something that they can immediately click with and understand and take action on.

我认为所有这些的核心都是与合适的人合作，提出明智的问题以获得理解。

I think at the core of all of those, it was about partnering with the right people, asking smart questions to gain an understanding.

这样你才能以让他们感到合理且产生共鸣的方式来阐述问题。

So then you could frame things in the context that's going to make sense and resonate with them.

这些例子分享给我们的听众非常棒。

So very great examples to share with our listeners.

谢谢。

Thank you.

如果现在有听众正觉得自己卡住了。

If someone is listening right now and thinking, I'm stuck.

我不知道该如何做这些事情。

I don't know how to do these things.

他们这周能采取的一个小步骤是什么，来推动进展？

Is there one small step they could do this week to move forward?

是的。

Yes.

首先，参加一下职业突破迷你课程，看看我的其他YouTube内容，因为我经常谈论这些话题。

First of all, take the career- Unblocking mini course, check out some of my other YouTube content because I talk about these things.

第一步。

Number one.

第二点，试着去了解业务，花大量时间真正理解你的业务利益相关者，了解他们的工作。

Second thing, try to understand the business and spend a lot of time really understanding your business stakeholders, understanding their job.

他们试图实现什么？

What are they trying to achieve?

他们优化的是什么？

What are they optimizing for?

对他们来说，成功意味着什么？

What does success mean for them?

然后开始思考，你如何利用数据来帮助他们？

And then start thinking, how can you use your data to help them?

我在公开演讲时经常问一个问题：那些去办公室上班、在办公室工作的人，你和谁一起吃午餐？

I often ask when I give public talks, I ask people who go to the office, who work from an office, who do you have lunch with?

但他们中的很多人只是和数据团队的成员一起吃午餐，对吧？

And many of them just have lunch with the data team members, right?

再问问他们，你上一次和销售或市场部门的人一起吃午餐是什么时候？

And ask them, when was the last time you had lunch with someone from sales or marketing?

你上一次要求跟随客户支持人员，全天聆听客户支持电话是什么时候？

When was the last time you asked to shadow somebody from customer support and spend the day listening to customer support calls?

你上一次要求参与销售对话，了解他们面对的问题是什么时候？

When was the last time you asked to join sales conversations and see what they deal with?

或者干脆带上你的笔记本电脑。

Or just take your laptop.

不要坐在你的团队旁边，而是去市场部找一个空位，坐在他们旁边，静静聆听他们的对话。

And instead of sitting next to your team, just find an empty desk in the marketing department, sit next to them and just listen to the conversations they're having.

我认为人们做得还不够。

And I think that people just don't do it enough.

他们只困在自己的孤岛和泡泡里，做着自己的事，寄希望于好运，但希望不是策略。

They they're just in their own silo, their own bubble and do their own thing, hoping for the best and hope is not strategy.

所以我想说的是，去你的客户成功团队、客户支持团队，请求旁听电话，花一整天时间去做这件事。

So what I would say is go to your customer success teams, customer support team, ask to listen to calls, spend the whole day doing that.

这会让你大开眼界。

It will blow your mind.

如果你不使用自己的产品，很多人其实也不在用这些产品。

If you're not using your product, a lot of people they use, they work in companies.

他们根本就没在用这些产品。

They're not even using the products.

对吧？

Right?

当我还在PayPal工作时，我尝试用PayPal在线支付所有开销。

So when I worked at PayPal, I tried to pay for everything online using PayPal.

对吧？

Right?

我发现了各种bug、奇怪的客户体验、繁琐的使用流程、混乱的文案等问题。

And I found bugs, bugs, and I found weird customer experiences and weird journeys and strange messaging and things like that.

我没有只是抱怨。

I didn't just complain about it.

我四处打听，想弄清楚这家15000人的公司里，到底是谁负责这个功能。

I went around and I tried to figure out who in this company, 15,000 people company, who is the person responsible for this feature?

我和他们交流，提供了反馈，并问他们：为什么我们要这样设计？

And I talked to them and I gave them the feedback and I asked, why did we build it like that?

只有走出舒适区，主动参与业务，才是让你保持相关性并突破职业瓶颈的唯一方式。

And that's the only way, like getting out of your comfort zone and engaging with the business is the only way you can make yourself relevant and you can unblock your career.

我的课程正是为了帮助你做到这一点而设计的。

And my course is basically designed to help you do that.

太棒的建议了。

Such fantastic tips.

我喜欢你最后的总结。

I love where you ended.

首先，成长总是让人不舒服，但这种有益的不适感最终会带来回报和好处。

First off, growth is uncomfortable, but productive discomfort hopefully will pay off and benefit you.

如果你想对业务产生影响，该如何更好地理解业务呢？

If you wanna make a business impact, how can you understand the business better?

使用你们的产品。

Use your product.

如果你想真正成为组织中的合作伙伴，不妨主动联系他人，聊一聊，一起喝杯咖啡或吃顿午餐，以获得更深入的了解。

And if you wanna true be a true partner within your organization, think about reaching out, having a conversation, a quick cup of coffee, or a lunch with somebody just to gain more understanding.

这些都是非常实用的好建议，人们可以尝试去实施。

So all of these are fantastic practical tips people can look to implement.

非常感谢你分享这些见解。

So thank you so much for sharing those.

很遗憾，我们今天的时间就到这里了。

Unfortunately, that's all the time we have today.

我想衷心感谢Shachar的到来，以及这场精彩的对话。

So I'd just like to say a big thank you to Shachar for being here and for this lovely conversation.

希望收听的朋友们也喜欢这期节目。

I hope those listening also enjoyed the show.

那么，我们下期再见，再见。

So until next time, goodbye.

谢谢。

Thank you.