---
title: "Lying with Data"
episode: 7
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Bernardo Alves"
guest_title: "Engagement Manager, LeanScale"
date_published: 2023-05-23
date_modified: 2026-07-22
duration: 00:14:58
word_count: 2552
topics: ["revenue-operations", "forecasting", "sales-leadership"]
canonical_url: https://leanscale-knowledge-hub.netlify.app/podcast/bernardo-alves-lying-with-data/
source: "LeanScale Podcast Knowledge Hub — https://leanscale-knowledge-hub.netlify.app"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# Lying with Data — Full Transcript

> Episode 7 of The LeanScale Podcast, with Bernardo Alves.
> Published May 23, 2023 · 00:14:58 · 2,552 words.
> Machine-transcribed and **not diarized** — speaker attribution is inferred, so verify
> attribution against the audio before quoting a specific person.
> Structured breakdown: https://leanscale-knowledge-hub.netlify.app/podcast/bernardo-alves-lying-with-data/

## 00:00 — Intro: the world is addicted to data

**[0:00]** But your gut reaction to it is, "Where the hell did my 2 million in pipeline go?" Yep. Welcome to The LeanScale Podcast, where we talk about everything RevOps. Thank you for listening. Today we have a public service announcement for all of our LeanScale Podcast listeners. Today we're going to be talking about data, and the world is completely obsessed with data. I may take it one step further and say that the world is completely addicted to data, and I think that addiction comes from the idea that data is the truth. But today we're going to talk about how data can be incredibly misleading and in some cases flat out lying.

## 00:59 — Two flavors of lying with data

**[0:59]** I have Bernardo with me here today to talk about that. Bernardo, when you talk about lying with data, what do you mean? Yeah, absolutely. When it comes to lying with data, you're essentially looking at two different flavors, the ones that are deliberately or maliciously just outright wrong, which in the business world you're not going to run into as much, and you're also going to be looking at the ones in which somebody just legitimately tried to convey something that they thought was fine, but the way that they approached it introduced certain biases that doesn't necessarily make them super relevant.

**[1:33]** You could be telling a wildly different storyline than what you intended to, or what the data even really suggests if you're not really careful about how you approach it. Yeah, and I think I've run into this quite a few times where you're looking at a data set, you're looking at a visualization of something, and automatically it drives some emotion and you start to create some conclusion, but that might not be the full picture. I know today you've brought some examples. I think this is going to be a fun one, because we're going to get to go through some examples of how data can be misleading, categorize a few ways and a few things to look out for

**[2:10]** while you're looking at data, and just find some practical examples of what you should do about it. Yeah, absolutely, and you haven't seen the data set yet, so this will be fresh to you. It is a surprise. And if you're the listener, take a look at the data. I highly recommend listening to this one on LinkedIn rather than Spotify, because there is a visual component to it, but let's just go through some examples. Yeah, where are you starting off with today? I wanted to start off a little bit lighthearted, because this doesn't just apply to business, it's going to apply everywhere. So we'll go with something that's not polarizing at all, sports, right?

## 02:45 — The quarterback test: cherry-picking stats

**[2:45]** Who's the better quarterback? I present you a little bit of data here, we have two quarterbacks. This was from the same game, this is real data. First one threw for six touchdowns, 469 yards, he completed 36 out of 50 attempts with a 90.6 QBR rating, and the other only threw three touchdowns, 318 yards, 21 for 29, 79.8 QBR rating. Who's the better quarterback? Well, just looking at the data, I'd say quarterback one. Yeah, absolutely right, that's the natural inclination, but let's open up and review a little bit more data here. What if I told you that quarterback one got sacked one time, had eight pressures where

**[3:29]** QB two did not, and he committed two turnovers to one? Does that change your perception at all? Maybe it makes it a little bit more even with the turnovers thrown in. Okay, let's keep going. What if I told you that QB two rushed for 120 yards and I was just going to touchdown? Okay, starting to, maybe at least even at this point. Okay, well let's not stop at that, so if you didn't do the math really quickly, you're looking at six touchdowns to four, 447 to 437 total yards, so getting closer, right? What if I told you that the possession minutes weren't exactly even? quarterback one had a lot more plays than quarterback two. Yep, yep.

**[4:16]** And I feel like it's important to note that somebody won this game. Yeah, I know, a completely different picture as you keep going through. Yeah, and lastly, I'll put names to them. You probably have a bias if you watch football at all and who you think is better between these two. At the end of the day, this is just one sample size, right? Season is many more games than just one, so it's really hard to just look at this one set of data and say, this is decisively who's better, but intuitively, when you first look at that data set, probably thought QB one smokes QB two, right?

## 04:59 — Cherry-picking in the business world

**[4:59]** Yeah, and I think that's something, this is a fun example, but I think in the business world, you see this all the time where you cherry pick data points that really just support the claim you're trying to make. And then you leave out information that might go against the claim you're trying to make. So yeah, definitely seen this one before. Yeah, absolutely. So fun out of the way, let's get into business. First one, I wanted to open up with one that I lovingly call a chart crime. And a chart crime is basically something that's designed in a way to evoke a certain emotion.

## 05:34 — Chart crimes: manipulating the axes

**[5:34]** And this one tends to be fairly malicious, if you're committing chart crimes, it's usually deliberate. So we can take a look at this one, and if your gut reaction wasn't, whoa, we have something wrong with our sales price here, there's a couple of things to consider on here. So let me take a look at this one. So this is y axis, average sales price, and then x axis over time. So we're looking at a course of three months. To me, initial gut reaction looks, oh no, average sales price is dramatically decreasing over the course of three months. Yeah, absolutely. And you'd be wrong. So let's open up our secondary data point.

**[6:18]** This is what the actual data looks like. So first, we need a pretty small period of time when the overall trend looks to be fairly healthy. But most importantly, our y axis is all out of whack. It starts at 157 on the bottom end, and it goes to 162k at the top. So we're really looking at a microscopic portion of the data set. It doesn't give the proper sense of scale. And oftentimes, people are going to run into these kinds of things if they're wanting to highlight a specific thing. And it can come off with a completely different sentiment than if you were to look at that chart on the right and go, yeah, that looks pretty good.

## 07:00 — Zoom out: the trend line is actually positive

**[7:00]** You know, could it be better? Probably. But it's trending up. I think what's interesting about this one too, and I don't know if you're meaning to highlight this, but so you opened up the time series to go back further than just a three month window. And if you look at the trend line, it's actually positive. Yeah, absolutely. So you could show up to the board meeting with chart on the left and say, Q4 sucked. Q4 is awful. Our average sales price is dropping like crazy. Or you could show up with the graph on the right and you'd say, hey, our average sales price is actually steadily increasing over time. Absolutely.

## 07:35 — Board reporting is weaving storylines

**[7:35]** And we'll get into it a lot more with things that go to the board because a lot of times board reporting is really just weaving storylines. Sometimes you sandbag a little. Sometimes you over inflate a performance. It's never lying in the traditional sense, but there's embellishment both ways. We have one that you probably run into if you're in the business world, especially sales at all, uh, somebody going around and going, Hey, who's our best rep? And then you look at how much they sold in a given period. So what's your reaction to this one? Yeah. So did maybe Stewart needs to go to president's club and then I don't know if we need to cut

## 08:17 — Who's our best rep? Absolute dollars vs. quota

**[8:17]** Anna or not, but it doesn't seem to be doing too well. Yeah. It looks grim. Doesn't it? Let's do it. How do they perform against their quota? Does that change your perception at all? A little bit. I am assuming that Anna has a smaller quota because maybe she's working in a SMB segment. Maybe Stewart's an enterprise rep, you know, with a, with a larger bag that Stewart's carrying. I still think, you know, bringing in a million versus 225,000 is still impressive, but yeah. And he seems to have performed pretty decently against his quota, right? Uh, look, looks like it looks like he's touching the top of the line there. Yeah. We got another chart from here.

## 09:02 — Normalizing performance to quota

**[9:02]** So, uh, the axes are not set up to the same value. So the quota is actually two and a half million. He's nowhere close to it. Oh, and we can fix that. Looking at this guy, this would be a different way of interpreting the data where you just look at percentages, right? So if you look at overall achievement to quota without taking into account how much you brought in and it looks like a rock star, even though her monetary impact isn't that significant in the grand scheme of things. And here's what it looks like with a normalized, actually accurate performance to quota changes things pretty drastically. So it does. It does.

**[9:43]** And I know you don't, I don't think you have this in this one, but we don't know the context of Stewart's territory. Why is this quota 2.5 million, did he walk in with a $10 million pipeline or is he enabled by partnerships or, um, you know, what's the business purpose to having a quota that size. And yeah, maybe Anna is just crushing it. Yeah, absolutely. Ultimately for the trend for all of these is probably don't have enough answer to our context to answer any of these. Moving onto a little bit more of missing context pipeline, this is total open pipeline between April and May. What's your gut reaction? Yeah.

## 10:26 — Where did my $2M in pipeline go?

**[10:26]** If you walked to me and said, Hey, in April, we had 22.5 million in pipeline. Now we have 20.5 I'd be asking what happened to $2 million? Did we, did we lose it? Did it move? Yeah, those are the right questions. So here's how it breaks down. It's actually a pretty positive trend. You might think that, oh, we went down 2 million. I'm a little bit worried about that. We had a fantastic close for one performance, we close one, two and a half million. That's more than 10% of the pipeline that was open. It's a good reason for pipeline to go down. Yeah. And it's not something people consider, right?

**[11:01]** The more you win, the less pipeline you have open, uh, tends to work that way. So, uh, close loss. We only close lost a million. We replaced more than we closed lost and the things that were in pipeline grew at a rate faster than they decreased. Overall, I think this would be a fantastic month for most businesses, but your gut reaction to it is where the hell did my 2 million in pipeline go? Yep. And then lastly, just tying it all back together and bringing in that business context that you talked about here, we have what some might look at and go, this is a pretty good chart.

## 11:37 — Waterfall charts and missing business context

**[11:37]** Personally, I love waterfall charts and I feel like they tell a lot of the story. You might look at this chart and feel like you understand what's going on in the business out of the ones that we've had so far, this is far and away the best example of something that's well structured. Yeah. So if I'm looking at this, uh, is that previous quarter sales? That is quarter sales. Yeah. Okay. So North America did 250 K less, um, than what they did and Mia did 175 K more. Okay. Yeah. I mean, my gut is just taking a look at the green ones and saying a Mia and a pack are doing better. And then the other ones struggled this quarter compared to last. Yeah.

**[12:17]** One of the things that we're missing and waterfall charts while fantastic. Do you have this issue is relative length or size. So without being able to know what that previous benchmark was, it's hard to tell what the real story. So APEC's really small region that didn't close a lot last year or last quarter. Uh, but most importantly, business contacts, right? So if I were to come in here and highlight that North America had a really, really good previous quarter where they had an anomalous booking, uh, if we discount that this would have been their historical high, it would change your opinion about it.

**[12:57]** Uh, if I told you that partnerships had a deal that they were going to close through channel and that they gave it to a Mia direct, it changes how you perceive their relative performance. If I told you that we stopped working out of it, uh, Latin and it's just not a region, you're not so worried about going down 75 K anymore, right? Uh, and you can keep layering these on and on and on seasonality, right? Um, there are so many ways to layer more context into the data that if you don't know anything and you're just looking at the data itself, you're going to unintentionally draw the wrong conclusion or if you're doing presenting it, lie about it.

## 13:38 — Data is objective until you put an eye on it

**[13:38]** So that's when it comes down to the line with data. It's just a matter of ethically and doing your due diligence, paint a storyline that makes sense because human beings are prone to jumping to conclusions. And if you don't leave something that is truly what's in there, you're going to lead somebody astray. Data is objective as soon as you put an eye on it, you're looking at a bias. No, I think that's good. I don't think anybody, um, thinks about it that clearly. So Bernardo, this was a ton of fun. Um, so what I learned, Hey, look out for cherry picking data. So make sure you have a better holistic view. Take a look at the axes.

## 14:18 — Recap: cherry-picking, axes, time series, context

**[14:18]** Are they aligned to the visualization and what you're expecting to see? Take a look at the time series that you're looking at. And then most importantly, take a look at the context within the data that you're presenting. What are we trying to tie back to actual business outcomes, actual operating plans that are out in the, in the field right now, what's actually happening that that state is trying to describe and how is it supporting the story that you're trying to tell? So Bernardo, thanks again. Appreciate it. Um, I think we'll refresh this one and for those listening. Thank you. Awesome. Thank you guys.