构建商业智能与财务规划分析间的高效合作关系

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这里是数据轨道出品的《FPNA今日谈》。

From data rails, this is FPNA today.

欢迎收听《FPNA今日谈》，我是主持人格伦·霍珀。在这期分为两部分的蓝图式访谈中，我将对话Wasabi科技的BI与FP&A总监。Wasabi是一家与行业巨头竞争的超大规模云存储公司。随着全球业务快速扩张和复杂的市场进入模式，Wasabi的财务与分析团队持续面临着提供快速、准确、可执行洞察的压力。

Welcome to FPNA today. I'm your host, Glenn Hopper. In this two part blueprint style episode, I'm talking to the directors of BI and FP and A at Wasabi Technologies. Wasabi is a hyperscale cloud storage company competing with the biggest players in the market. With a rapidly expanding global footprint and a complex go to market model, Wasabi's finance and analytics teams are under constant pressure to deliver fast, accurate, and actionable insights.

这正是我们今天要探讨的主题。马科斯·本托与大卫·苏特将分享他们的团队如何跨越职能界限，作为统一洞察引擎将原始数据转化为实时财务智能。马科斯是Wasabi商业智能总监，戴夫则主管FP&A。从信任构建到工具应用，从预测分析到初稿拟定，他们将揭秘构建BI与FP&A高效合作关系的核心要素。两位，欢迎来到节目。

And that's what we're gonna talk about today. Marcos Bento and David Suter share how their teams work together, not just across functions, but as a single insight engine to turn raw data into real time financial intelligence. Marcos is director of business intelligence at Wasabi, and Dave is director of and A. From trust and tooling to forecasting and first drafts, they break down what it really takes to build a high functioning partnership between BI and FP and A. Gentlemen, welcome to the show.

非常感谢你，科莱特。

Thank you very much, Colette.

是的。我想这是首次在非大型网络研讨会背景下，邀请两位嘉宾进行计划中的两期节目录制。这算是个实验，但节目前与你们交流时，我意识到我们有太多精彩内容值得探讨，完全值得分成两期。老听众都知道，我们一直在讨论这类话题。

Yeah. I think this is the first time that I've had two guests with a planned two part episode that wasn't part of a big webinar. So this is kind of an experiment, but I thought, you know, talking to you guys before the show, I realized we're gonna have so much great stuff to talk about that I really think there's two episodes worth here. And I love this conversation. Regular listeners know we talk about it all the all the time.

所以能请你们来探讨这种合作关系，我认为会非常有趣。让我们从Wasabi公司FP&A与BI的关系开始吧。戴夫，能否请你先谈谈，在你看来这种协作如此高效的关键是什么？

And so having you guys to talk about that partnership, I think, is gonna be a lot of fun. So I guess let's start with the relationship between FP and A and BI at Wasabi. And I guess, Dave, if you can kick us off here, from your perspective, what makes this collaboration work so well in your case?

好的。在FP&A部门，我们是数据的重度使用者。需要BI团队帮忙清理海量杂乱数据——这些数据驱动着我们多场景的预测分析。我非常珍视这种合作关系。

Yeah. Well, in FP and A, we're big consumers of data. So we rely on BI team to provide to to to make clean a lot of big messy data. I mean, that drives our forecast across multiple scenarios. I really value that relationship.

成功要素？信任与沟通。我们两个团队都秉持务实的问题解决方式，以客观冷静的态度应对挑战。大家追求的是正确答案，而非个人功劳。

You know, and what makes it work? You know, trust, communication. I think both our teams have a real pragmatic approach to problem solving, you know, in in a real, you know, dispassionate approach to that same problem solving. You know? I think both these teams really wanna get the right answer rather than some, you know, pride of ownership.

我发现马科斯和他的团队特别善于接纳反馈与新视角。我们之间有大量双向交流、持续对话和充分沟通。所以...

I I think I found Marcos' team Marcos and his teams to be, you know, really receptive to feedback and new ways of looking at things. There's a lot of back and forth, a lot of dialogue, a lot of communication. So

是啊。关于报告所有权这件事很有意思。我是说，在运营端，人们总想占据主导权，比如宣称'我们的配置时间缩短了'之类的成绩。有趣的是，无论在财务计划分析部还是商业智能部，我们都在做报告、分析和预测。这种数据所有权问题，我能预见到可能成为紧张点——各方都想掌控数据和报告权。

Yeah. And that that ownership of the reports, it's funny. I mean, you know, on the operations side, people wanna take ownership of the, you know, our provisioning time is down or whatever they've, you know, whatever they've done there. And it's interesting as as both in FP and A and in BI, you know, we're reporting and analyzing and forecasting. And that ownership, I could that could be I could see that being a tension point of, you know, trying to own the data and and the reporting.

马科斯，我想请教你，你们与财务计划分析部合作时，他们虽是客户，但你们肯定也在追踪自己的指标，还有其他客户。你们是如何建立两个团队间的信任与协调的？这是需要刻意坐下来商谈的事，还是在你们合作中自然形成的？当时的具体情况是怎样的？

And I'm wondering, Marcos, from your side, as you work with FP and A, they're a customer, but you guys, know I'm sure are also tracking, you know, your own metrics, and you have other customers too. So how do you how do you build that trust and alignment between the two teams? And I guess, is is that something you have to sit down and be intentional about, or does it in in the case of you guys, did it evolve organically? What was the setup there?

好问题，格伦。我认为是刻意为之与自然演进的结合。刻意部分在于我们需要明确沟通'这个方案是否可行'。就像戴维提到的，我们特别欢迎像戴维团队这样的同事主动来反馈说——

Great question, Glenn. I think it's part intentional and part organic. The intentional part is, hey. We need to communicate if this is working or this is not working. To Dave's point, we always love when people like Dave and his team and other folks come to us and say, hey.

'这组数据与预期不符'或'我们原本期待看到不同的结果'。还有'你能从数据中提炼出什么洞察？'这类反馈对我们改进非常重要。

This data is not looking what we were expected to. We were looking we would expect to see something different. Or, hey. What are the insights that you can give us out of that data? That's also very important feedback that we can learn.

自然演进的部分在于，作为远程优先的公司，我们还要面对如何在整天Zoom会议中维系关系。没有茶水间偶遇的机会，要怎么建立那种自然而然的信任感？

It's also the other part of being organic, and we are as as a, remote first company. It there is also another layering that how can you manage and balance being on Zoom calls all day long meeting people. How do you manage these relationships and not having the coffee or water cooler for you to build in the trust that they prefer to.

确实。Zoom会议在这方面很困难，毕竟大家都有视频疲劳症。当你整天开会开到精疲力尽时，却还要保持协作状态。你们有没有发现什么有效方法，能在连轴转的Zoom会议中保持高效协作，特别是处理复杂问题时？

Yeah. Is that in that can be tough on on Zoom calls because it's especially, you know, we all get Zoom fatigue and you're you feel like you're on it all day and you get to a point where you're just tired of of another meeting, but you still have to have that collaboration. Is there anything any insights or or anything you found that that works well to keep that up when you are really just back to back Zoom and working together on on complex issues?

我认为最根本的还是回归企业文化。这里很多人都非常友善好相处。我个人就特别喜欢和戴维及其团队共事——总能从合作中获得乐趣与新知。重点不在于各自的责任范畴，而在于我们如何共同推进。

I think at the most foundational part, it goes back to our culture. I think that a lot of people here are really good, nice people to work with and to be with. I generally like to work with Dave and everyone on his team. It's it's always a pleasure to work with them and hear and learn and collaborate with them. It's not like you have your point of the ownership and the responsibilities and the deliverables, but how we are doing this.

整个协作过程总是令人愉悦，这让解决复杂问题变得很特别。

The journey is always a pleasure, and that makes working on these kinds of problems and issues really special.

戴维，你们承受着交付数据的压力，需要保持这种协作，但商业智能部不止服务你们一个团队。从你的角度，当管理团队并处理自身报表需求时，你们是如何处理与商业智能部的互动关系的？

Yeah. Dave, I mean, you know, you're under pressure to get numbers and you have to have that that collaboration and, you know, you're not BI's only customer. From your perspective and, you know, when you're managing your team and you know you've got your own reporting requirements and needs, how do you handle that that interaction with with the BI team?

嗯，我想接着马科斯说的团队协作补充几句。这种协作确实有自然形成的成分，但也取决于我们招聘的人才类型——就像马科斯说的，如果招到善意、易相处、合作愉快的同事，协作就会顺畅很多。这直接影响到优先级处理。我知道他们压力很大，尽量予以尊重，但说实话，在我们看来数据永远来得不够快。

Yeah. I mean, think well, to to to follow-up a little bit on what Marcus was saying about, you know, the collaboration between our teams, I think it is organic in a sense, but there's also this function of type of people we hire, you know, to Marcos' point, you know, you gotta be if you're if you've hired people that are well intentioned and easy to work with and a pleasure to work with, it makes that collaboration so much better. And that just rolls right into prioritization. I know they're under a lot of pressure. I try to be respectful of that, but, you know, the data can never come fast enough in our mind.

但这需要平衡。明白吗？有时候我只需要一个大致准确的快速答案，这时就需要沟通优先级和紧急性，然后说‘好的，让我看看我们能做什么’。而其他时候则需要更深入，有些项目要花四周时间，但我们必须确保做对。

But it's a balance. You know? I mean, sometimes there are needs where I just need a fast answer that is ballpark close enough, and and, you know, you communicate the priority and the urgency and, okay, let me see what we can do. Other times, it's a little more in-depth. It takes, you know, some projects four weeks, but we gotta get it right.

所以我认为关键在于通过协作努力去理解对方的立场，明白我们面临着高管或董事会的压力。归根结底，这需要与一个能理解双方问题的优秀团队合作。

So I think it's just that collaborative effort to try to understand where they're coming from, understand that we're under demands from executive or a board meeting. I think it just goes back to that collaboration and with a good team that, you know, is understanding of both both sets of problems.

那么你们团队之间的日常互动是怎样的？是基于项目的、定期同步的，还是更随需应变的？具体如何运作？戴夫，这个问题还是由你继续回答，稍后我再请马科斯补充。

So what what does the the day to day interaction look like between your teams? Is it is it project based or recurring syncs or more on demand? How does how does that work? And, Dave, I guess we'll stick with you on this, and I'll I'll I'll go to Marcos.

是的，主要是随需应变。有时候我们会集中精力处理特定项目一段时间，而其他时候我的需求会减少——我相信其他团队也会让他们忙个不停。这种合作节奏会有起伏波动。

Yeah. It's a little on demand. You know, I think there are times when, you know, I we're we're working on a specific project for a specific period of time. And then other times, you know, it kinda my demands kinda fade away, and I'm sure, you know, other teams are keeping them busy. But it kinda ebbs and flows a little bit.

确实有些时候我们会转向其他项目，让这些同事暂时不用应付我们。

There's definitely times when when we're off on on other projects and give these guys a break from us.

你们两位中有人和内部团队进行每日站会吗？

Do either of you do daily stand ups with your with your internal team?

我不做每日站会。我每周和团队有例会，还有每周的一对一会议，除非有项目在进行。但如果有项目的话，是的，每天都会开。

I don't do one I don't do it daily. I do, you know, weekly. I have calls with our team and then, you know, weekly one on ones, but not unless there's a project going on. But when there is a project, yeah, daily.

是的。我们BI团队每周进行两次站会电话。我们有两周一次的交付物冲刺和更大的产品项目，

Yeah. We, the BI team does twice a week stand up calls. We have two week sprints for deliverables and larger product projects,

而且

and

时不时会有附加请求来来去去。

you have the add on request that comes and goes every other time.

戴夫，我们在节目开始前聊过，你显然是BI部门最重要的内部客户之一。你如何看待——或许你在之前讨论协作时已经部分回答了这个问题——BI在支持FP&A（财务规划与分析）中的角色？比如当你汇总数据、报告指标时，若需要等待BI提供数据，你绝不会甩锅说'都怪马科斯团队没给我数字'。这种来回协作需要相互理解。

Dave, we talked before the show when you're obviously one of BI's biggest internal customers. How do you think of, and I and I maybe this you kind of already answered this when when talking about the collaboration earlier. But what do you think about the role of BI in supporting FP and A? Like, if you're rolling something up and you're reporting metrics, but you're you're waiting on them, you're never gonna never gonna throw BI under the bus and be like, well, we Marcos can't give me the numbers. So I mean, that back and forth and you and you have to understand.

我的意思是，你不会直接使用自己都不理解的数字。那么当需要定义新指标或理解调取的数据时——虽然你不总是亲自提取数据——这种协作如何运作？你必须理解并与BI团队合作完成这些工作。

I mean, you're not gonna just, you know, take numbers that you don't understand. So how does that sort of collaborative part work when you're maybe it's defining new metrics or understanding the data that you're pulling? You're not the one going pulling it all the time, but you've gotta understand and and and work with the BI team for that.

确实。在没有BI团队之前，我常开玩笑说'我不相信任何人'，所以我会尽可能获取最细颗粒度的原始数据自己处理。现在能有个值得信赖的合作伙伴实在太棒了——我们完全依赖马科斯团队了。

Yeah. You know, prior to having a BI team, I I I I would always joke, you know, I don't I don't trust anybody. So I want all the data in as granular as I can take it, and then, you know, we'll make sense of it. To not have to do that, to have a partner that we can rely on is fantastic. So, yeah, I we totally now rely on Marcos' team.

我们建立了深厚信任。每次分析数据时，那些BI团队花大量时间钻研、理解并解决的细微差别和边界案例问题，现在都能放心信任这些数据了。正如我说的，我们是重度数据消费者——不断获取更丰富、更优质、更多维度的数据，颗粒度越细，预测就越精准。

There's been a lot of trust built up. Anytime we're looking at data, there's always this issue of all the nuanced and edge cases that these guys have spent so much time digging into and understanding and mitigated. It's really fantastic to be able to trust that data. And then, you know, we're big consumers. Like I said, we're we're constantly pulling more data, better data, different slice and dice because more granular we can get it, the better we can forecast.

过去几年建立的这种信任，其价值无法估量。

So, you know, the the the the trust that we built up over the past few years has been, you know, invaluable.

我想请教两位：在FP&A做预测时，方向正确即可。这让我想到财务与会计的区别——会计没有'方向正确'之说，试算表必须平衡，借贷必须匹配。但预测只要接近并能解释逻辑就行。不过BI领域取决于你做的是描述性还是预测性分析...

I wanna ask both of you about this because I in FP and A, if you're if you're forecasting, you can be directionally correct. And there's I think about the difference between finance and accounting sometimes. There's no directionally correct in accounting that, you know, trial balance has to balance and and debits and credits have to match up and all that. But if you're forecasting, you can as long as you're close and and can explain what you're doing. But think BI I guess it depends if you're doing descriptive or predictive analytics or whatever whatever.

这里似乎存在某种张力。当然所有结论都必须可复现、可解释，但你们处理的行业数据量如此庞大，速度与准确性之间的权衡尤为突出——马科斯，先从你开始：

There's it seems like there can be more of a push. Obviously, you have to be able to repeatable and explainable of all of your answers. But there's there's also that tension and you guys are dealing with so much data with the industry you're in. And we'll talk obviously more a lot more about that. But with that tension between speed and accuracy and Marcos, let's start with you on this.

如何平衡快速交付的需求（正如戴夫刚才提到的）与尽可能精确的要求？因为纠结最后2%或10%的精准度差距很容易陷入僵局。

How do you balance that need for we have to get something fast, and this goes to what Dave was just saying, but we also need to be as as precise as possible here, cause you can get really bogged down trying to close that last, you know, 2% or 10% gap of of making it exact.

格伦，从哲学层面说，经验无法作弊。作为存储服务商，报告客户存储量时，BI团队必须理解这个指标的内涵与外延，包括所有使用场景和边界案例——比如不同数据中心客户如何使用存储？

Right. I think at a philosophical point, Glenn, I think that you cannot cheat experience. So if we are a storage company and we are reporting on how much storage our customers have, we need to understand the BI team needs to understand what does that mean, what does that not mean as well. And that goes to all the use cases and edge cases that whatever that can be. Is that how do you use think about storage from different customers in different data centers?

这些细微差别只能通过经验积累，需要与懂业务的人交流——无论是客户还是数据输入方（工程或销售部门）。这为我们构建正确数据库提供了决策依据。过去两三年，我们已为这家快速发展的SaaS公司奠定了最核心的数据基础架构。

There are all these kinds of nuances. But you can only learn about this if you have the experience, if you're talking with the folks that know about the business. And it's either your customers, but they how the input of the data comes from. So that comes from engineering and from sales, and that gives us context to make the right assumptions when we are building the correct database. I think that over the last two, three years, we built the foundation of what's the essential, what's required for us to run as a organization, as a fast growing software as a service company.

现在下一步是，我们还能用这些数据做什么？这正是有趣之处，它关乎你们的速度与准确性。大约三年前我们做的首次预测，误差有10%。经过改进后，误差从10%降到8%，再到5%。我认为戴夫现在的预测已经足够准确，与我们的计划或预测值相差仅几个百分点。

Now the next step is what else can we do with this data? And that's where this gets interesting, and it goes to your speed and accuracy. At maybe the first forecast that we did three years ago, we were off by 10%. Then we make improvements, and it goes from 10 to eight to five. I think that Dave now has a good enough forecast that we are within a couple percentage points from what our plan or our forecast.

这让我们——可能也是建立信任的方式——在于你们展示的数据始终可靠，并且能解释其背后的逻辑。

So that gives us that's probably how you also build trust is that the data that you're showing is consistently reliable, and you can explain what's underneath that.

戴夫，我在思考速度、准确性以及潜在的偏差问题。如果马科斯提供给你的数据在建筑损耗预测或其他应用上不够精确，可能会放大误差。如果你的交付物没有尽可能接近需求，结果可能会偏离。从你的角度来看——你之前也提到过——过去需要自己收集分析数据，现在有上游团队提供。你如何平衡这一点？是否从理解BI提供的数据及其潜在误差开始？

And, Dave, I think about that that speed and accuracy and the potential for drift. So if if Marcos' data that is delivered to you is not as precise as it as it needs to be in your building or churn forecast or whatever you're building off of it, the it can exaggerate the the error indifference. So you you're your deliverable, if if you don't have it as as close as possible to what you need, could drift from that. So from from your standpoint, and you alluded to it earlier where you you used to have to get all the data and figure it out yourself, but now you have someone upstream who's providing it for you. How do you balance that knowing does it start with understanding what you got from BI and where there where there could be error there?

或者你具体会如何应对？

Or or what how how do you approach it?

是的。我觉得这是经典的'成本-质量-进度'三角关系。三者只能取其二。对我们来说成本相对固定，所以现在是速度与准确性的权衡。

Yeah. I think it's just the classic cost quality schedule trade off. Cost quality schedule, you can only have two. In our case, costs are relatively fixed, so now it's, yeah, speed versus accuracy. Yeah.

如我所言，有时我只需要快速获得大致答案，有时则需要更可靠的答案——当然耗时更长。关于偏差问题，我总在寻找第三方数据验证。我们销售云存储，按每TB美元计价。

Like I said, I think there's times when I just need a ballpark answer quickly. Other times, I need a, you know, a solid answer and understand it's gonna take a little more time. I to to to your drift point though, I'm always looking for, I guess, a third data point to validate is what I'm getting correct. We sell cloud storage. We sell it in dollars per terabyte.

我会从Arcosa团队获取存储量、TB使用量和ARR数据。但用ARR除以存储量得到每TB单价时，如果发现是标价的10倍，就知道有问题。这时要判断是哪个数据出错。如果有ARR数据，我就能预测收入方向是否正确。

So, you know, I'll get data from Arcosa's team around our storage, our terabyte usage, and our ARR usage. But when you divide ARR by storage, you get a dollar per terabyte. And when I'm looking at this and it's, you know, 10 x what our list price is, I know something's wrong. So now which one is wrong? Well, if I have ARR, I can forecast revenue.

我总通过外部数据源验证所见是否合理。如果能回溯6-18个月找到与外部数据趋势吻合的结果，就说明方向正确。这有助于控制偏差。

I can understand if that's directionally correct. And so I know whether the error is in revenue ARR. So it's things like that where I'm always looking for outside data sources to validate is what I'm seeing right. And if I can go back over six, twelve, eighteen months and get something that is directionally correct to these outside sources, I know we're on the right path. So that kinda helps mitigate that drift.

如果确实出现偏差，我们就启动新项目——就像剥洋葱一样层层排查原因。马科斯团队的价值在于他们擅长剖析这些边缘案例，比如指出'你漏算了X/Y/Z因素，这些超出常规分析范围'。这种支持非常关键。

And if it does start to drift, okay, now we have a new project. We got another layer of the onion, we gotta peel apart to gotta figure out why are we drifting. And then, again, I think where Marcos and his team add a ton of value is they've peeled this thing apart and they know these edge cases. And it's like, oh, it's it's oh, you're not factoring in, you know, x, y, or z, and those are, you know, outside of the bounds of this regular view of what you're looking at. So those are those are it's, you know, really helpful to to be able to peel that onion apart.

没错。我试图理解你们思维方式的差异——因为当我们采访FP&A专家时，他们总说'要做好FP&A就要像调查记者'：不断追问原因，挖掘根源，用调查结果构建叙事。BI工作也类似。这就像两个刑侦机构用不同方法解决同一问题。

Yeah. And you guys are both I'm trying to suss out if there's a a difference in mindset because you're both when we talk to FP and A guests all the time, we hear them say, to be really good at FP and A, you have to sort of be an investigative journalist. You're constantly asking why you're trying to get to the the root cause of something and you're using the what you find there to create a narrative and to present the the facts as as you've seen them through your investigation. And and BI is very similar to that. So that's really it's almost like two crime solving agencies both, you know, trying to solve the same problem with different approaches.

但让我思考这个问题的起因，戴夫，是你提到要了解自己的业务。我认为这种领域专业知识——我们不断听到关于这个承诺的讨论——我和马科斯，你可能会觉得好笑，但我们总以为自助式数据会变得非常简单。因为生成式AI，我们将能轻松获取并整合一切。但即便技术真的成熟，比如Snowflake的某个功能开始高效运作，你可以直接与数据对话，人类的价值仍体现在领域专业知识上。我想到或许因为FP&A作为领域比数据科学发展更久，过去你可以坐在财务的象牙塔里，无需了解业务其他部分——因为那时具体细节并不重要。

But I guess what got me thinking about this, Dave, is when you said talking about you you you have to know your business. And I think that that domain expertise, we keep hearing about this promise, and I and Marcos, I think you're gonna laugh at this, but the the idea that we're we're self serve data is gonna be so easy. It's gonna be easy to get all this because generative AI, we're just gonna be able to access everything and pull all this. But the where humans will add value even if it does get, you know you know, if a cortex or whatever in Snowflake really starts to work great and you can chat with your your data, there's still that domain expertise. And I think about maybe because FP and A has been evolving as a field longer than than data science that it used to be you could sort of sit in this ivory tower of finance and not have to know the rest of the business because you were it didn't matter what the cogs were.

你只需呈现数字。但现在FP&A的工作方式要求你必须成为业务伙伴，必须真正理解业务——因为过去整合数据的所有工作现在变得更快，或者你有像马科斯这样的团队协助。但我想知道，如果我要想象你们共同制定新KPI的场景，你们处理问题的方式或涉及的数据范围是否存在差异？

You were just presenting the numbers. But now, the way that FP and A works, you have to have business partnering. You have to truly understand the business because all the work that used to go into putting the data together and all that, that comes a lot quicker or you have a team, like, that's coming from from Marcos that is is helping with that as well. But I'm I'm wondering if you refine down your approach to the metrics in this if I'm trying to picture a meeting where you guys are both together trying to solve for a new KPI or whatever. Is there a difference between how you guys approach problems or the universe of data that you deal with?

我知道刚才说了很多。戴夫，我想先问你——当你们试图挖掘额外利润或探索新领域时，你们会直接基于现有数据行动，还是何时引入BI团队？这样问是否更清晰些？

And I know that I just said a whole lot there. So so David, I'm gonna start with you and I guess it's if you're trying to figure out something, you're trying to squeeze some extra margin out, or you're trying to find some some new area, are you initially gonna do that just through the data that you have? Or or when do you bring BI into that? I don't Is that maybe did that distill it down a little bit more?

这确实棘手。我喜欢你关于调查记者的比喻。我们绝对是问题解决者，但这个问题很复杂。

Boy, that's a tough tough one. I I like your analogy about investigative reporter. Yeah. I mean, I definitely view us as as problem solvers. That's a tough one.

显然我们会依赖BI的历史数据。如果发现与预测趋势不符，那可能是深入调查的线索。但你说得对——我们本质上就是调查记者。

I mean, obviously, we rely on data from BI to give us historicals. If that then looks off trend to a forecast, that might give us a little hint to dig into. But but you're right. It's You're right. It's it's we are investigative reporters here.

格伦，公平地说，我们常基于相同目标协作，这种重叠很频繁。只是视角不同：我需要原始数据及其输出，而戴夫需要的是对输出的解读。

And, Glenn, to be fair, I think that a lot of times we work with the same set of objectives, and that's that's what the overlap happens quite frequently. Maybe with some different lenses, we are looking at, hey. Give me the raw data and what what's the output of that. And for David's, okay. Give me the output of that.

现在的问题是：如何用这组数字解释现象？正是这种互补性让合作顺畅。无论是ARR、存储量、流失率还是净留存率，我们目标一致。制作报告时，我们会共同理解背景和受众需求，确保认知同步。

Now what can I explain with this set of numbers? So that makes the work our collaboration also work because if we're looking at ARR or storage or churn or net retention or whatever the metric is, we have the same set of objectives. We if we are trying to make or create one of these reports, we need to understand what's the context, what's the audience that this is going, and we try our best to be on the same page.

你们都在做预测分析吗？马科斯，我知道你们肯定用机器学习算法。戴夫，你们的团队做预测时用机器学习还是Excel/Python？马科斯，能否先谈谈你们的预测分析类型及其服务对象？

And are you both doing predictive analytics? Or so I'm I'm I know I'm sure, Marcos, when you are, you're using machine learning algorithms and everything. And I'm wondering, Dave, are you if you're doing something predictive, is your team using machine learning, or are you doing you know, are you using Excel or or pie or what what are you guys doing? I guess, Marcos, we'll start with you. If you can tell me about what kind of predictive analytics you're doing in in your approach and and who the customer if you are doing that, who the customer of of that that analytics package is.

好问题。我们首个机器学习模型是预测客户未来6-12个月的存储量。但主要使用者不是戴夫，而是销售团队——这能帮助他们开展更有前瞻性的客户对话。

Great question. So one of our first predictive, or machine learning models is to forecast the amount of storage of a customer in the next six and twelve months. But the primary user of that, it's not Dave. It's sales. So we use that to engage sales into better discussions about the future.

比如告诉销售：'同类客户在Wasabi的存储增长通常处于X/Y/Z区间。如果客户符合某区间，可考虑提供折扣或特殊条款'。这为销售团队创造了可执行的具体策略。

Hey. We're looking at similar customers for the same amount of storage, for the same amount of years with Wasabi. They tend to grow at x, y, or z rates. Now if you're going into this rate, we can probably offer you better discounts. We can probably offer you some discounts or some turds, and that makes some actionable items for the sales teams to act on.

实际上，就在我们谈话的同时，我们正在努力将这个模型整合到FP&A模型中。戴夫负责对整个损益表进行预测。未来我们希望能利用这些机器学习模型来辅助戴夫的工作。

We are actually, as we speak, we are working to try to integrate this model into the FP and A model. So Dave Dave does a forecasting on the entirety of the P and L. At some point, we wanna use some of these machine learning models to feed whatever Dave is using.

戴夫，你们那边情况如何？

And how about on your side, Dave?

是的。我们还有点停留在石器时代。除了手动预测外，我们目前没有做任何预测性的工作。

Yeah. We're we're a little bit still in the stone ages. We're not doing any sort of predictive stuff other than just manual forecasting.

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了解为何超过一千家财务团队使用DataRails揭示公司真实故事。不要取代Excel，拥抱Excel。详情请访问datarails.com。我知道每个人都有年度计划，尤其是在超大规模模式下。试图预测十二个月后的情况简直疯狂。

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但无论您是在看十二个月还是三十六个月，或是按季度重新预测，您都有大量数据可以支持预测。您的预测模型有多复杂？您是否使用了内部和外部的变量？能否简单谈谈您在FP&A方面的预测方法？

But whether you're looking at twelve months or thirty six months or or you're reforecasting at the quarterly basis, you have a lot of data that can feed your forecast. How complex are your forecasting models? And are you using variables that are are using, like, internal and external? And could you tell us a little bit about your approach to to forecasting on the FP and A side?

当然。简单来说，在支出方面相当直接。如果剔除所有资本支出和数据中心相关费用，我们看起来很像软件公司。80%的成本与人员相关，所以主要是人力资源规划。

Sure. I guess, quickly on the expense side, it's fairly straightforward. If you take away all of the CapEx and data center stuff, we look an awful lot like a software company. 80% of those costs are people related. So that's just workforce planning.

收入方面则更复杂些。我们有非常详细的月度队列级别预测，涵盖不同地区、支付类型、渠道和产品。可以快速细化到非常微观，也可以高度汇总进行经典的ARR滚动预测，包含客户流失率、净留存率和订单功能。所以是两者结合，试图找到一个大家都满意的答案。但这确实工作量很大。

On the revenue side, it's a little more complex. We have a very detailed forecast down at the monthly cohort level across different geos, different payment types, across different channels, across different products. So we can get very granular very quickly, or we can roll it up at a very high level and just do a classic roll forward ARR roll forward with churn and net retention and bookings functions in there. So it's a little bit of both, you know, try to triangulate on an answer that we all feel good about. But it's, you know, it's a lot.

相当复杂。

It's complicated.

格伦，公平地说，我认为戴夫低估了他团队的能力。他们在收入预测方面所做的队列级别工作简直令人难以置信。投入的细节之多，显然他和团队非常擅长使用所有高级公式进行Excel建模。但最重要的是结果输出的准确性——即使以我们的增长速度，能做到准确预测也是非常困难的。

And, Glenn, to be fair, I think that Dave is underselling his capabilities and not his teams because the amount of work that went to the revenue forecast at the cohort level is just absolutely incredible. The amount of details that were put into this. And it's obviously, we he and his team is very good at doing Excel modeling using all the advanced formulas, but the most important piece is what's the output of the results. And the accuracy is very good. Even at our growth rates, the ability to do a accurate forecasting is really hard to do.

正如我之前所说，他们刚开始时我们的偏差达到了10%，那时很难理解。现在他能深入分析，按队列、月份或任何他想要细分的方式查看数据，这使得预测节奏更加合理。

And as I said before, when they've just started, we were off by 10%. So it was very hard to understand. Now that he can go deeper and see per cohort, per month, per whatever type he wants to slice and dice, this gets into a better cadence of forecast.

谢谢。是的。谢谢你，莫里斯。我们正在进步。

Thank you. Yeah. Thank you, Morris. We're getting better.

关于机器学习用于收入预测这件事，你们团队是否曾粗略讨论或考虑过？因为我觉得在你们这个行业，你们拥有海量数据，我隐约觉得这可能行得通。不过我也在最近几期节目中提到过——我曾就职于与你们类似的私募股权支持的公司，深知私募人士的作风。他们痴迷模型，总想打造完美的预测工具。有时候我会因为构建模型太有趣而犯‘把地图当实地’的错误，过度专注模型却忘了现实世界的存在。

And is that is machine learning for revenue forecasting, is that something you guys have sort of spitballed or kicked around the idea of because they're be just I I just think with your industry, you have to have so much data that that it I could see that potentially working, but they also and I think I've mentioned it in the past couple episodes. I because I came from a well, similar to where you you guys are within private equity backed companies and, you know, how private equity folks are. They're they love their models, and they want you to build really great models and stuff. So I I have a tendency sometimes because it's so fun to build these things to sort of confuse the map for the terrain, you know, where it's I'm I'm so focused on the model that I forget there's a a real world out there. I don't know.

除非能更快或更精准，否则你们会考虑这个方案吗？我们Data Rails当然热爱Excel，听众中从事数据科学或财务的也常以Excel为起点。但我不确定——你们是否有在合作探索用机器学习做收入预测？

If you're if you're doing this, unless it was quicker or you could get more precise or whatever, I don't know. We obviously, data rails loves Excel and and all of our listeners and even with data science or or finance. Excel, lot of times, is is the first place you start. But I don't know. Is that is that collaborating on a using machine learning for predictive revenue forecasting?

这是你们讨论过的话题吗？

Is that something you guys have talked about?

在我们三人之间，这确实是我们正在努力的事情。我们有一位资深的数据科学家正在优化这个模型。他正在针对几个不同的变量进行微调，不仅从群体层面，还从用户使用时长、计费方式等角度，同时也在分析我们超过10万客户中每一位的行为模式。Excel在这方面表现不错，它能处理百万行数据并支持预测分析。

Between the three of us, this is something that we are actually working on. We have a great senior data scientist working on this model. He's fine tuning the model for a couple of different variables to understand, not just at the cohort level, but in the tenure, the billing methods, but also looking at the behaviors of each individual of one of our more than a 100,000 customers. What Excel is pretty good at said, okay. Give me a million rows, and let's do a forecasting.

现在当你面对十亿行数据时，该如何管理这些数据库呢？这就是数据湖技术介入的领域，其核心理念是为Dave提速，让他和团队能利用这些数据制定更优的决策方案。

Now when you're looking at, hey. You have a billion rows. How do you manage that those databases? Right? So that's where some of these data lakes joining, and the idea is to speed something for Dave so that he and everyone can use this to create better actions out of that.

但这仍是个持续改进的过程。Dave可能会在初版、第二版中发现一些问题，直到某个阶段他会认可：这个版本已经足够解释现象，我们可以推进实施并进一步完善计划。

But it's still a work in process, and we're gonna refine. And Dave is probably gonna see some problems in version one, version two, up until a point where he's saying, okay. I can get away with this. I can explain. Let's now roll this forward and make this plan even better.

确实。我认为这类技术尚处早期阶段。并非要当反对技术进步者，但我对AI能精准预测未来所有事情持保留态度。它在异常检测、差异分析方面会很出色，能标记需要深入调查的异常点。但对于那种'输入所有数据就能自动生成完美收入预测'的能力，我持更谨慎的态度。

Yeah. And I think it's early days for that kind of thing. I'm not I'm not trying to be like a Luddite, but I'm, you know, I'm a little skeptical of AI's ability to just, like, forecast everything into the future. I think it's gonna be great for anomaly detection, variance analysis, you know, looking at where to highlight anomalies that need to be dug into. I'm a little less bullish on its ability to, you know, hey.

单纯把所有数据输进去，就能蹦出个无懈可击的收入预测。

Just, put in all the data and, like, out pops a look proof revenue forecast.

是的。特别是当你需要可解释性时，你不能简单地把数据丢进黑箱然后说这就是它得出的结论。

Yeah. Especially if you have to have that explainability, and you can't just say that through it into the black box and this is what it figured out.

没错，正是如此。

Yeah. Exactly.

但话说回来，我想回应Marcos的观点，当你有海量数据时，机器学习模型确实能发挥作用。Excel有其局限性——无论是崩溃、运行缓慢还是其他问题，试图用Excel处理所有事情本身就非常笨拙。比如做Surima预测这类工作，用Python比用Excel要容易得多，其他预测模型也是如此。但问题在于，有多少人能用Excel做预测？而能用Python构建预测模型并向董事会和管理层解释清楚的非BI人员又有多少？这里存在输出结果、可解释性以及效率提升之间的鸿沟——究竟能优化多少？能节省多少时间？或许最终关键在于，一旦建立模型后，重新预测确实会更快捷。

But that said, I guess, to Marcos' point, with a machine learning model and you have a boatload of data, you can Excel has its limitations and whether it's crashing or slowing down or or whatever, just the cumbersome nature of of trying to use Excel for everything. I think there's a lot of you know, if you're doing Surima forecast or something, it's a lot easier to do that in in Python than it is in Excel or, you know, if you're do you have other sort of forecasting models. So but the problem is how many people can build forecasts in Excel versus the few people who are not on the BI side who could build the forecast in in Python, and then make it explainable to the board, to management, and all that. So there is that sort of chasm between the output, the explainability, and how much better is it gonna be, or how much time is it gonna save? Maybe, you know, old maybe it ultimately comes to that is once you've said it that it it becomes quicker to reforecast and and do that.

既然你提到了——虽然我原本没准备这个问题，但这确实是我非常感兴趣的领域，似乎每期节目我都会谈到。当你提到流量分析和AI的擅长领域时，我们节目确实经常讨论生成式AI。现在很多SaaS工具都在内置生成式AI向导，让你能与数据对话，用现成的ChatGPT或Gemini也能实现某些功能。

You brought it up. I I didn't have this as a as a planned question, but obviously, is an area I'm very interested in and then it seems like I have to bring it up on every episode. But when you mentioned flux analysis and what AI is good at, we do talk a lot on the show about generative AI. And I know a lot of SaaS tools out there are building in their generative AI wizards and genies that you can talk to your data. And there's some stuff you can do with off the shelf ChatGPT or Gemini or or whatever.

或者...我想问问你们两位。Dave，先从你开始，你们部门目前有成功应用生成式AI的案例吗？你的团队是否在使用这类技术？

Or and I'll ask this to both of you. I guess, Dave, we'll start with you. Are you using generative AI successfully in in anything in your department now, or is your team using it for anything?

目前还没有正式应用。如我所说，我们还有点停留在石器时代——依然用Excel，而且乐此不疲。（笑）想让我放弃Excel除非从我冰冷的尸体上掰开手指。

Not in any sort of official capacity right now. Like I said, we're we're we're a little bit in the stone ages. Still using Excel. Still love it. You know, you'll pry down my cold dead hands.

但我们确实触碰到它的天花板了。就像刚才说的，电子表格能承载的行数有限，而我们现在已经逼近这个极限。目前正在迁移到一个具备AI功能的规划工具，希望未来能利用那些炫酷特性。不过还处于早期阶段，效果有待观察。

But we're definitely bumping up against the limitations. You know? You can like like we said, you you can only fit so many rows into a spreadsheet, and we're we're definitely pushing the bounds of that today. So we're in the process now of moving to a planning tool that has some of those, you know, whiz bang AI features that we hope to take advantage of in the future. But like I said, we're we're still early days, so we'll see what pans out.

Marcos你呢？你的团队有相关实践吗？

How about you, Marcos? Is your team doing anything with it?

有的。我们有些零散但有趣的用例。比如有位数据分析师每周五要给高管团队写总结邮件，最初是手动撰写——汇报新增存储量、管道开闭的月度环比数据等。

We are. There are some interesting use cases here and there. One of our data analysts, he writes a letter to or a summary email to the executive team every Friday. Initially, it was by hand. We were writing the summary.

坚持六个月后，他发现每次都要花几小时起草这些报告。现在积累的数据和文本已足够训练模型来自动生成这些内容了。

Hey. This is how much storage we added. This is where the pipeline came and closed and opened month over month. Six months in and drafting these letters that took a couple hours every day, he realized, okay. We can prob we now have enough data and text that we can train a model to do this for us.

所以每周五早上，他点击一个按钮，生成文本。他现在只需在这里或那里编辑几段文字。好的，我需要修改。数字已经在那里了。

So every Friday morning, he clicks now a button, generates the text. He now just edit a couple paragraphs here and there. Okay. I need to change. The numbers are already there.

它能运行是因为它从数据泄漏的正确位置提取数据。现在的问题在于调整和改进信件的撰写方式，或者我们可能想突出显示与每周发送的常规节奏不同的内容。这是一个我们正在研究的有趣用例。还有一些尚未投入生产的环节，我们正与销售运营部门合作，试图解决如何改进和自动化从订单到收款的部分流程。

It it's played because it pulls the data from the correct place in the data leak. Now it's a matter of adjusting and improving how the letter is written, or maybe we wanna highlight something different than the usual cadence that it sent every week. So that's a interesting use case that we are working on. There is some more they're still not in production that we are trying to figure it out with sales operations, how to improve and automate some of the processes on the order to cash.

我知道你们用的是Snowflake。你们是用Cortex来实现这个的吗？

And I know you guys are, use Snowflake. Are you doing this with Cortex?

没错，正是如此。

That's right. That's right.

那么，你觉得Cortex怎么样？我的意思是，我想你们需要挑选适合的应用场景——

So and how are you, how are you finding Cortex? I mean, I guess you you have to pick your use cases where

哦，绝对如此。总体来说，这对我们非常有利。他们在过去六到十二个月里开发并创造了如此多的新功能。他们的路线图对我们计划中的新用例来说显得非常激动人心且有趣。我们刚刚使用了他们的一项功能，通过销售代表在渠道创建过程中与合作伙伴及终端用户的所有互动来预测结果。

Oh, absolutely. It's in general, it's been great for us. The they have developed and created so many new functions in the last six, twelve months. Their road map seems very exciting and interesting for the new use cases that we have in our road map. We were just using one of their functions to predict from the from all the activities that our sales reps do with partners and with end users during the pipeline creation.

哪些是销售代表在未来三、六、十二个月内必须完成或能预测交易达成的最关键活动？我们曾使用Cortex的三个不同模型进行评估排序，现在已选定最青睐的一个模型。接下来我们将逐一深入分析这些活动。

What are the most important activities that our sales reps need to do in or that can predict a closure of a deal in the next three, six, twelve months. We are using we were using three different models from Cortex. We ranked them. We now have one of our favorite models. We're now going to double click on each one of these activities.

这太棒了。我认为你提到的用例正是生成式AI最能体现价值的领域。我能想象到的问题是——如果数据不在Snowflake里，或者你只能用Excel处理，显然会很快超出AI的上下文窗口限制。但当AI与你的数据深度集成时，比如直接查看总账或生产数据等存储在Snowflake中的信息，情况就不同了。Dave，你刚才提到了差异分析和解释说明。

That's super cool. And I think the use cases you mentioned really are that's the area where generative AI is gonna be the most valuable. And I picture the problem is if you don't if your data is not in in Snowflake or if you're you're just trying to work in Excel, obviously, you blow out the context window of the AIs pretty quickly. But when you have AI integrated into your data, if you can look directly at your GL or if you can look directly at your your production data or whatever's in in in Snowflake. And, Dave, you mentioned variance analysis and explanation.

显然，生成式AI能迅速检查月度财务报表，发现异常波动并寻找关联。计算机在识别相关性和差异方面远胜人类。但如果无法连接总账数据，你仍需手动核查。不过回想我担任CFO的日子，每次拿到财务报表时，第一反应总是手动滚动浏览——尽管财务团队已提供注释，但我总想亲眼确认那些差异。

So, obviously, very quickly, GenAI could go look at your monthly financials and see where there's a variance and even look for things. It could find correlations. Computers are much better at finding correlations and and and variances than than people are. But if it can't tie into your GL data, well, you're still gonna have to go dig it in. I do think though that my whole first round I think back to my days as a CFO, and whenever I got the financial statements, the first thing I did was manually scroll through.

我认为结账后的首轮财务报表审查环节，如果能自动提示'营收增长2%而商品成本下降6%'这类信息会很棒，但关键是要能追问'为什么成本没随收入同步变化'。若未与系统对接，仍需人工处理。不过我对这前景很乐观，特别是与Marcos讨论后——现在很多SaaS工具都在原生集成AI，未来它必将深度嵌入各类系统和软件。

Obviously, the f b n a team would have given me notes too, but I wanted to see what those variances are. And I think that whole first level of financial statement review right after it's closed, if you could have it pull and say, hey, you know, revenue was up 2% and cost of goods were down 6%. So that sounds great, but it's gonna ask you the question of why were our cost of goods not keeping up with revenue. So then but if it's not tied in the jail, you still have to go do it. But I do I'm an optimist around this, and I do see a a future and especially with Marcos and I were talking about where we it's integrated into systems or into your software because a lot of the the SaaS tools now are are integrating it out there.

但这正是其价值所在。我认为目前让概率引擎处理日记账或对账，并非其实际应用场景。不过那些总结与分析类功能确实有价值——但对大多数人来说...戴夫，我完全不认为你们在这方面落后，但Wasabi对生成式AI有何宣言或策略？现在到处都在喊‘我们必须用AI改造这个’...

But that's that's where it's gonna be valuable. I think right now, having a a probabilistic engine doing journal entries or reconciliations, it's not really that's that's not the practical use case of it. But, yeah, I think those summaries and and analysis like that, there's a but for most people and and I don't think, Dave, I I don't think at all you guys are are in the stone age with this, but it is what's the sort of manifesto or approach around GenAI at at Wasabi? Or is there a lot of because you hear it everywhere. Like, we have to AI this.

‘必须用AI改造这个’。戴夫，从你开始吧——领导层或公司是否问过‘能用AI处理这个吗’之类的问题？

We have to AI this. And I guess, Dave, start with you. Or is there do you get any questions from leadership or is from the company of, can you AI that or or anything along those lines?

目前还没有，但感觉即将到来。与您观点不同，我认为AI首轮差异分析的能力会非常强大。‘为什么出现偏差？哦，原来是有笔异常交易’——这样就能快速定位问题。

Not yet, but I feel like it's coming. And unlike you, I think I think it's the ability to do that first pass of variance analysis is gonna be huge. Why was this off? Oh, well, there was, like, know, one anomalous transaction. And then, okay, great.

我们可以立即核查异常原因。这将极大加速整个流程。当然最终仍需人工解读，但能直接标出重点排查项已经很棒。压力确实在逼近——我们CFO就是AI拥趸，每月都会发‘我读到这个AI新功能’的邮件给我们。

We can go look at that and figure out what that was. Why you know, it'll just really speed up that whole process. I'm sure there's still gonna be some annual interpretation of what it is, but, you know, to point big red flashing arrows at a couple of different things to go dig into, I think it's gonna be pressure's coming. Our CFO is definitely a fan and, you know, sends us articles, you know, once a month of, like, hey. I read this, you know, cool new thing that it can do.

这股浪潮势不可挡。

So it's it's coming for sure.

我们秉持审慎态度——不会为AI而AI。必须能证明其产出更优。如果任何生成式AI平台能加速流程自动化，在找到杀手级应用后，高管团队定会全力支持。他们不盲目跟风，但也不无故抵制。

And I think we are intentional. It's not applying AI for just for the sake of applying AI. It has to have an outcome. If we can prove that the outcomes are gonna be better, if we can use any GenAI platform out there that can speed up and automates any of our processes, then I think our executive team will be all in if and once we understand a use a killer use case. I don't think they are owing just for the buzz, and they are not against just because.

我们获准探索这些工具——当然未经法务审核不会使用Wasabi数据——但完全可以借此明确未来方向。

I think we are all open to explore and understand, and we have the green light to of these tools. Obviously, not with Wasabi data unless we've vetted with legal and all the processes, but we can definitely use to understand where we need to go next.

好的，我先从AI讲台下来回归正题。感谢各位见解。还没讨论仪表板和常规报告——要确保数据字典统一准确。

Alright. So hang step down off my AI soapbox now when we get back to our regularly scheduled programming. Thanks, guys, for the for the insight on that, though. So we haven't talked about dashboarding and sort of the the standard routine reports that go on. Thinking about trying to have the data cons have your data dictionary and have it consistent and right.

深入之前先确认：你们是否分别负责某些仪表板？FP&A和BI各有分工？在构建过程中，是你们定义指标、调试优化？用户总想多钻取一层数据...

And I guess, do before I I dive too deep, do you guys both create dashboards of of certain types that are there some that FP and A is responsible and some that BI is responsible for? Yep. In that dashboarding, is it you're defining the metrics. You're you're building it out. You're trying to tweak it.

仪表板常引发更多临时报告需求——当出现无法解释的差异时。马科斯，从你开始：我见过太多...说是仪表板，其实包括幻灯片和月报。有时信息过载导致董事会材料变成80多页图表合集。

People always wanna drill down to one level deeper. And a lot of times, what they see in the dashboard drives more ad hoc reports if there's a variance or something that they can explain. I guess each of you and and maybe, Marcos, we'll start with you on this. When you are I I just I've seen so many I'm I'm saying dashboards, but I'm thinking of of slide decks and and the monthly presentation and everything. Sometimes we can get so people want so much information that there's the monthly the board deck is becomes 80 something pages of charts and graphs.

对我来说，当数量多到一定程度时，它们就像你在州际公路上驶过的广告牌，你根本不会注意到。每位经理或董事会成员都有他们固定查看的那张图表，他们知道它在第30页或其他什么地方。所以我想你们两位都在为他人提供报告，因为这是他们的需求。但马科斯，当人们询问他们想追踪的数据和指标时，随着仪表盘上想看到的内容越来越多，范围不断蔓延，你是否有某种简单的建议、指导或构建仪表盘的初始方法？

And it they'd be to me, at that many, they become like billboards that you you drive by on the interstate and you just you're not seeing it or every every man manager or every board member or whoever has the one graph that they go see. They know it's on page 30 or whatever. So I guess you guys are both delivering to others because they they want these reports. But Marcos, what when people are asking about data and metrics they wanna track and you start hearing the scope creep and all the stuff they wanna see on the dashboard, do you have some sort of simple advice or guidance or or way that you structure that dashboard from the beginning?

这是个很好的问题，格伦。我们可能用三集节目都讲不完这个话题。但总的来说，这要回归到我们最初讨论的背景。只有理解人们为何提出这些问题，我们才能创建出可操作且有价值的仪表盘。有时我们会选择回绝并反问：嘿——

It's a great question. And we can probably use three episodes just on this, Glenn. But I would say that in general, it goes back to context, what we initially started. We can only create a dashboard that is actionable and useful if we understand why people are asking for those questions. Sometimes we will go back and push back and say, hey.

从我们商业智能工具里现有的300份报告中，难道真的找不到你需要的吗？我们已经为每个数据源构建了所有可能的视图和切面。通常这是个用户教育问题——告诉他们：你知道这份现成的报告吗？大多数情况下人们会惊呼：这就是我需要的！

From all our, I don't know, 300 reports available reports that we have in our BI tool, can't you really use whatever we already have today? We have built tons of every single potential view and cuts of every single one of the data sources that we have. In general, it's a matter of educating the users and saying, hey. Do you know that you have this report available? In most cases, people are saying, oh, this is exactly what I needed.

我从来不知道我们有这个。这需要商业智能团队持续教育用户当前可用的数据。但正如你所说格伦，每周一我们都要制作200页的幻灯片。与高管团队开会时我们会总结说：出于历史存档需要我们准备了全部200页，但本周重点只需看第2、14和50页——

I never knew that we had. And did this is on the BI team to constantly educate and inform people about what the day what data is available today. But to your point, Glenn, every Monday, we have to develop a 200 slide pay, slide deck. And the when we meet with the executive team, we will just summarize that. We are like, okay.

然后花整整一小时讨论这三个关键点，因为其他内容都在预期范围内。高管团队需要的是从海量数据中识别新动态，分辨噪音与信号。

For historical purposes, we have all these 200 slides for you. But for this week, the important ones are slide two, fourteen, and 50 set. And let's walk you through all of them. And then we spend a full hour just talking about the three most important ones because the others are in plan or whatever we were expecting to. What the executive team needs is what's new, what's noise versus what signal around all this data.

太棒了。这正是你创造价值的地方——

That's great. And that's where I mean, and that's where you add value is

希望如此...希望如此。

I hope. I I hope.

我的意思是，所有基础工作显然需要整合，工程量巨大。但这正是将信息转化为知识的过程——你在提炼精华，让他们不必阅读州际公路上的每块广告牌或每个路标，而是直接指出可行动项，他们也信任你的专业判断。

That's where I mean, there's all the foundational work. Obviously, you've gotta pull it all together, and it's massive. But that's turning an information into knowledge. It it's you're taking it and refining all that so that they don't have to read every billboard that they drive by on the interstate or every street sign. You're telling them this is these are the actionable items and they're trusting you to to pull that out.

我完全理解这点。有些指标可能并非每月都需要，但如果是因果关系的先导指标或滞后指标，保留历史数据很有必要。关键在于穿透噪音，像聚光灯般指出焦点所在。那么戴夫，你呢？虽然处理的数据量可能没那么庞大，但财务KPI的数量恐怕不亚于公司其他部门的总和——

So that's totally get that. And I get that there these are metrics that are important, maybe not every month, but if they're indicative of something, if there's causation, tad tumor, whatever, if they're a leading indicator, or or even if they're lagging indicate what whatever it is, having that information historically is good. But cutting through all that noise and saying, here's, you know, shining the light on and here's here's what your focus is. So and I guess, Dave, what what about you? I know sometimes maybe not dealing with as massive amounts of data, but there are as many financial KPIs as there are probably across the rest of the company.

你通常采取什么方法应对这些？

So what's what's what's your approach with these?

是的。我也有过类似的经历。我曾在一家公司工作，每周都会收到一封发给全公司的电子表格邮件，里面大概有400个指标。每次我都会看，会读。

Yeah. I've had a similar experience. I worked at a company where every week, you know, we get a spreadsheet email to the entire company of I think it was, like, 400 metrics. And and every you know, I I would look at it. I'd read it.

我试着消化这些数据。但每次我向那些在职超过六个月的人提起时，他们总是直接无视。就像在说‘唉，我连看都不想看那东西了’。所以正如你所说，这些数据就像没人看的广告牌，所有的工作成果都没人关注。

I'd try to digest it. And every time I would reference it to anybody who'd been there for more than six months, they were just it was just I they would just dismiss it. Like, ugh, I I can't even look at that thing anymore. And so to your point, though, it just becomes these billboards that, like, just nobody looks at. So all this work that gets done, nobody looks at it.

因此，我对仪表盘的处理方式一直是：必须抵制指标泛滥。实际上，任何部门都应该能将其精简到最多三到五个指标。可以有几个历史回顾指标，或许再加几个前瞻性指标，但最多三到五个就够了。根本不需要更多。当然，偶尔会有特殊情况需要深入分析，因为某些地方出了问题。

So my approach to dashboards has always been you gotta resist that metric bloat. And really, you should be able to any department should be able to distill it down to three to five metrics tops. Couple of historical looking views, maybe a couple of forward looking view metrics, but, like, three to five max. You you don't need anything more. I mean, of course, they'll be, like, ad hoc or we gotta dig in because something went haywire.

但就常规报告和仪表盘而言，你得控制在三到五个指标内。就这样。

But in terms of, like, regular reporting and dashboarding, you gotta keep it to three to five. That's it.

说到这里，我们即将结束与Wasabi Technologies的Marcos Pinto和David Suter的第一部分对话。本期我们探讨了基础内容——FPNA与BI如何协作、建立信任、平衡速度与准确性之间的张力。而在第二部分中，我想更深入探讨那些让这种合作关系生动起来的指标、系统和策略。我们在节目开始前聊过净留存率、数据治理、Wasabi扩展洞察力的方法等等。这些内容就留到第二期吧，非常感谢两位的参与。

With that, we're gonna wrap up part one with Marcos Pinto and David Suter from Wasabi Technologies. We've covered the foundations in this episode, how FPNA and BI collaborate, build trust, navigate the tension between speed and accuracy. And in part two, I wanna dig a little bit more into the metrics, systems, and strategies that bring the partnership to life. And we talked before the show about net retention, data governance, Wasabi's approach to scaling insights, and all that. So let's save those for episode two, and and thank you guys for for being on.