Lying with Data — Bernardo Alves | LeanScale Podcast Ep. 7

Executive Summary

The one-paragraph brief, extended

Why this conversation matters — and who should spend the hour.

This is a 'public service announcement' episode about the thing every revenue org quietly worships: data. Anthony Enrico opens by arguing the world isn't just obsessed with data but addicted to it — an addiction rooted in the belief that data is the truth. Bernardo Alves, the show's data-literacy voice, joins to dismantle that belief live and unrehearsed (Anthony hasn't seen the data set before recording), showing how the exact same numbers can be framed to tell completely opposite stories. The thesis: data is objective only until a human looks at it, and after that, every choice about how to present it introduces bias — so a careless or motivated presenter can lie without ever fabricating a single number.

The middle is a clinic of worked examples. Bernardo separates two flavors of lying with data — the rare, deliberately malicious kind and the common, unintentional kind where good intentions plus sloppy framing mislead. Then he runs the reps through it: a quarterback stat line that looks like a blowout until you add rushing yards, sacks, pressures, turnovers, and possession time; a 'chart crime' whose truncated y-axis (157k to 162k) makes a healthy uptrend look like a collapse in average sales price; a 'best rep' ranking that inverts the moment you normalize raw dollars to quota attainment; a $2M pipeline 'drop' that is actually a great month because a $2.5M deal closed; and a regional waterfall chart that hides relative size, one-time anomalies, and channel decisions.

The payoff is a practical checklist and an ethical stance. Anthony distills four checks — watch for cherry-picking, inspect the axes, widen the time series, and layer in real business context — and Bernardo lands the point that board reporting is really just weaving storylines, embellished both ways (sandbagging and over-inflating), so honest presentation is a discipline of due diligence, not just design. For RevOps and analytics leaders, sales managers evaluating reps, and any executive building or reading a board deck, it's a foundational data-literacy episode: the numbers won't protect you from a wrong conclusion, but the right questions will.

Key Takeaways

9 things worth stealing

The load-bearing ideas, each with the business implication and who should care.

Data is objective only until a human looks at it

The world treats data as the truth, but Bernardo's core claim is that the moment someone frames, crops, or presents a data set, bias enters. Nobody has to fabricate a number to mislead — the selection and presentation of real numbers is enough to lead an audience astray.

Why it matters: Treat every chart as an argument, not a fact. Ask what was chosen, what was left out, and what emotion the presentation is engineered to produce before you trust the conclusion.

RevOps LeadersRevenue ExecutivesFounders

There are two flavors of lying with data — mostly the unintentional kind

One flavor is deliberately or maliciously wrong (rare in business); the other is someone who genuinely tried to convey something reasonable but introduced biases in how they approached it. The second is far more common and far more dangerous because no one is on guard against it.

Why it matters: Assume most misleading charts are honest mistakes, and build review habits that catch bias regardless of intent — not just fraud detection, but framing detection.

RevOps LeadersRevenue Executives

Cherry-picking: the story is in what you leave out

The quarterback example looks like a blowout on the headline stats, but adding rushing yards, sacks, pressures, turnovers, and possession time closes six touchdowns to four and 447 total yards to 437. In business, people routinely surface the data points that support a claim and omit the ones that undercut it.

Why it matters: Before accepting a metric, demand the counter-metrics. A single flattering number in isolation is a prompt to ask for the rest of the box score.

Sales LeadersRevOps LeadersRevenue Executives

Chart crimes: the axes are where charts lie

Bernardo's 'chart crime' — a visualization designed to evoke an emotion — most often abuses the axis. A y-axis that runs only 157k to 162k turns a healthy, slightly-up average sales price into what looks like a dramatic collapse because you're staring at a microscopic slice with no sense of scale.

Why it matters: Always read the axis range and zero point first. The same data on a full-scale axis often flips 'Q4 sucked' into 'we're steadily trending up.'

RevOps LeadersRevenue ExecutivesMarketing Leaders

Widen the time series before you draw a conclusion

The declining-looking sales price chart was a three-month window; opening the series back further showed the actual trend line was positive. Short windows manufacture drama that a longer view dissolves.

Why it matters: Default to the longest relevant time frame. A three-month dip inside a two-year climb is a very different story than a genuine reversal.

RevOps LeadersSales Leaders

Absolute dollars vs. quota attainment can invert your 'best rep'

Ranked by raw bookings, Stewart (about $1M) looks like the star and Anna ($225K) looks like a cut candidate. But Stewart's quota was $2.5M, so he's nowhere near it, while normalized to quota Anna looks like a rock star. The axis mismatch and the missing quota flip the entire judgment.

Why it matters: Never evaluate reps on absolute dollars alone. Normalize to quota, put both series on the same scale, and factor in territory, segment, and how the quota was set before ranking anyone.

Sales LeadersRevenue ExecutivesRevOps Leaders

Pipeline going down can be a healthy sign

Open pipeline fell from $22.5M in April to $20.5M in May, which triggers the gut-panic 'where did $2M go?' But a $2.5M deal closed-won — more than 10% of open pipeline — only $1M closed-lost, replacements outpaced losses, and existing deals grew. It was a great month. The more you win, the less pipeline you have open.

Why it matters: Don't read a pipeline decline as a problem without the movement behind it. Decompose the waterfall — closed-won, closed-lost, created, expanded — before reacting to the net number.

RevOps LeadersRevenue ExecutivesSales Leaders

Missing business context is the biggest trap — even in a 'good' chart

A regional waterfall chart is the best-structured example in the episode and still misleads: it hides relative size (a tiny APAC region), one-time anomalies (an anomalous North America booking last quarter), and channel decisions (a partnerships deal handed to EMEA direct, or a region you stopped working). Layer in seasonality and the story changes again.

Why it matters: The chart is never the whole truth. Interrogate territory history, one-off events, channel movement, and seasonality before you let a clean visual set your opinion.

RevOps LeadersRevenue ExecutivesSales Leaders

Board reporting is storytelling — do it ethically

Bernardo is candid that board reporting is 'really just weaving storylines': sometimes you sandbag, sometimes you over-inflate, and there's embellishment both ways. It's rarely lying in the traditional sense, but human beings jump to conclusions, so the presenter's job is due diligence — paint a storyline that's genuinely supported by what's in the data.

Why it matters: Own the narrative responsibility. If you present data, you're choosing what people believe; leaving out what's truly there to serve a story is how you lead people astray.

Revenue ExecutivesFoundersRevOps Leaders

Frameworks Discussed

3 named models

Every framework Jimmy names, defined and time-stamped.

The Two Flavors of Lying with Data

00:59

Misleading data comes in two forms: the deliberately or maliciously wrong (rare in business), and the unintentional kind, where someone tried to convey something reasonable but introduced biases in how they approached it.

The distinction matters because the common, unintentional flavor slips past everyone — no one is looking for bias in a good-faith chart. Guarding against it requires framing-detection habits, not just fraud detection.

The Chart Crime

05:34

A chart crime is a visualization designed to evoke a certain emotion — most often by manipulating the axes (a truncated y-axis, mismatched scales) so real data tells a dramatically different story than it should.

Bernardo's marquee example: an average-sales-price chart with a y-axis running only 157k to 162k makes a healthy uptrend look like a collapse. The antidote is to check the axis range, zero point, and scale before reacting.

The Four Data-Literacy Checks

14:18

Anthony's closing checklist for reading any chart honestly: (1) look for cherry-picking and get a holistic view, (2) inspect the axes for alignment and scale, (3) widen the time series, and (4) layer in real business context tied to operating plans and outcomes.

The four checks turn every example in the episode into a repeatable habit: they force the counter-metrics, expose axis crimes, dissolve short-window drama, and connect the visual back to what's actually happening in the field.

Best Quotes

12 lines worth clipping

Pulled verbatim. Copy or share any of them.

“The world is completely obsessed with data. I may take it one step further and say the world is completely addicted to data — and I think that addiction comes from the idea that data is the truth.”

Anthony Enrico 00:00

“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 you're not going to run into as much, and the ones where somebody legitimately tried to convey something they thought was fine, but the way they approached it introduced certain biases.”

Bernardo Alves 00:59

“You could be telling a wildly different storyline than what you intended to — or than what the data even really suggests — if you're not really careful about how you approach it.”

Bernardo Alves 01:33

“In the business world you see this all the time, where you cherry-pick data points that 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.”

Anthony Enrico 04:59

“I wanted to open up with one that I lovingly call a chart crime. A chart crime is basically something that's designed in a way to evoke a certain emotion.”

Bernardo Alves 05:34

“You could show up to the board meeting with the chart on the left and say Q4 sucked, our average sales price is dropping like crazy — or you could show up with the graph on the right and say our average sales price is actually steadily increasing over time.”

Anthony Enrico 07:00

“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.”

Bernardo Alves 07:35

“If you walked up to me and said, hey, in April we had 22.5 million in pipeline and now we have 20.5, I'd be asking what happened to two million dollars? Did we lose it? Did it move?”

Anthony Enrico 10:26

“The more you win, the less pipeline you have open — it tends to work that way.”

Bernardo Alves 11:01

“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 presenting it, lie about it.”

Bernardo Alves 12:57

“Human beings are prone to jumping to conclusions, and if you don't leave in 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.”

Bernardo Alves 13:38

“Look out for cherry-picking, take a look at the axes, take a look at the time series you're looking at, and most importantly take a look at the context within the data you're presenting.”

Anthony Enrico 14:18

Practical Advice

What should you actually do?

The playbook, split by the seat you sit in.

RevOps Leaders

Standardize your charts before anyone reads them: full-scale axes with an honest zero point, consistent scales across compared series, and the longest relevant time window as the default view.
When you present a metric, hand over the counter-metrics too — the QB box score, not just the touchdown count — so leaders can't accidentally draw the wrong conclusion.
Build a waterfall for any 'the number went down' moment (closed-won, closed-lost, created, expanded) so pipeline movement is decomposed instead of panic-read.

Sales Leaders

Never rank reps on absolute bookings alone. Normalize to quota, factor in segment and territory, and ask how the quota itself was set before deciding who's a star and who's at risk.
Watch for 'best rep' questions that assume a single number tells the story — a $1M rep at 40% of a $2.5M quota may be behind a $225K rep who's crushing a smaller bag.

Revenue Executives

Treat board reporting as storytelling with a fiduciary duty: sandbagging and over-inflating are both embellishment — paint a storyline the data genuinely supports.
Interrogate business context behind clean visuals — one-time anomalies, channel handoffs, regions you exited, seasonality — before letting a chart set the room's opinion.

Marketing Leaders

Audit your own dashboards for chart crimes: a truncated axis or a cherry-picked window can make a campaign look like a win or a disaster that a full-scale, long-range view would contradict.
Pair every headline metric with the scale and time frame it lives in, so an executive skimming the slide reads the trend you actually have.

Operations Takeaways

By function

The same conversation, filtered for RevOps, pipeline/marketing ops, and customer ops.

Revenue Operations

Data is an argument, not a fact. Every chart reflects framing choices; the moment a human presents data, bias enters. Read charts as claims to be interrogated.
Axes first. The most common way charts mislead is the axis — truncated ranges and mismatched scales. Check range, zero point, and scale before reacting.
Widen the window. Short time series manufacture drama. Default to the longest relevant window before drawing a trend conclusion.
Decompose the waterfall. A falling net number (pipeline, ASP) needs its movement broken out — closed-won, closed-lost, created, expanded — before it's read as good or bad.
Context beats the chart. Relative size, one-time anomalies, channel handoffs, and seasonality live outside the visual. Layer them in or you'll draw the wrong conclusion.

Pipeline & Marketing Ops

Down pipeline can be healthy. The more you win, the less open pipeline you carry; a decline driven by closed-won is a good month, not a problem.
Read the movement, not the net. Break the change into closed-won, closed-lost, newly created, and expanded before reacting to a top-line pipeline number.
Normalize rep performance. Evaluate sellers on quota attainment and territory context, not absolute bookings, or you'll promote and cut the wrong people.

Metrics Mentioned

The numbers, with context

$157K–$162K

Chart-crime y-axis range

A truncated y-axis over a three-month window made a healthy, slightly-rising average sales price look like a dramatic collapse — the episode's canonical 'chart crime.'

~$1M against a $2.5M quota

Rep bookings vs. quota (Stewart)

Ranked on absolute dollars, Stewart looks like the top rep; normalized to quota he's nowhere near attainment — the axis and missing quota flip the judgment.

$225K

Rep bookings (Anna)

A small absolute number that looks like a cut candidate until you view attainment to a smaller quota, where she looks like a rock star.

$22.5M (Apr) → $20.5M (May)

Open pipeline change

The $2M 'drop' triggers panic but was actually a strong month: a $2.5M deal closed-won (>10% of open pipeline), only $1M closed-lost, and existing deals grew.

6 TD / 469 yds vs. 3 TD / 318 yds

Quarterback headline stats

The lopsided-looking box score evens out to roughly 6 TD to 4 and 447 to 437 total yards once rushing yards, sacks, pressures, turnovers, and possession time are added.

Entities

Companies, people & tools mentioned

Auto-extracted and linked into the knowledge graph.

Companies

People

Bernardo Alvesguest

Engagement manager at LeanScale; came up through customer success into operations and business intelligence, focused on attribution and usage-based tracking.

Engagement Manager, LeanScaleProfile →

Anthony Enricohost

Co-founder of LeanScale and host of The LeanScale Podcast.

Co-FounderProfile →

Tools & software

LinkedInSocial Platform

Bernardo recommends watching this episode on LinkedIn rather than Spotify because the data examples have a visual component you need to see the charts to follow.

Website ↗

Related LeanScale Services

Where this shows up in the work

Fractional RevOps

Embedded, field-facing RevOps leadership and execution — the operator model Jimmy describes, delivered as a service.

Honest, decision-grade reporting — axes, time windows, pipeline waterfalls, and rep-performance normalization — is core field RevOps work LeanScale owns.

Forecasting & ARR Definition

Define a raise-worthy ARR and build a reliable consumption forecast across finance, data science, and go-to-market.

The episode's pipeline-movement and board-reporting discipline is the foundation of a defensible forecast that survives scrutiny.

Frequently Asked Questions

Straight answers

Generated from the conversation, marked up for search and AI extraction.

What does 'lying with data' mean?

Lying with data is presenting real, accurate numbers in a way that leads people to a wrong conclusion — no fabrication required. It comes in two flavors: the rare, deliberately malicious kind, and the far more common unintentional kind, where good-faith framing choices (which metrics to show, how to scale the axis, what time window to use) introduce bias. As Bernardo Alves puts it, data is objective only until a human looks at it — after that you're looking at a bias.

What is a 'chart crime'?

A chart crime is a visualization deliberately designed to evoke a certain emotion, usually by manipulating the axes. The classic example is a truncated y-axis: an average-sales-price chart scaled from 157k to 162k makes a healthy, slightly-rising metric look like a dramatic collapse, because the viewer is staring at a microscopic slice with no sense of true scale. The fix is to check the axis range, zero point, and scale before reacting.

How can a truncated y-axis mislead you?

A truncated y-axis zooms into a narrow band of values so small movements look enormous. A metric that rose gently from about 157k to 162k appears to crash when the axis only spans that range, but the same data on a full-scale axis reads as a steady, healthy uptrend. Always read the axis bounds first; if the scale doesn't start near zero or doesn't match a compared series, treat the visual's emotional impact with suspicion.

Why can a decline in open pipeline actually be a good thing?

Open pipeline falls when deals leave it — and closing deals is the goal. In the episode, pipeline dropped from $22.5M to $20.5M, but a $2.5M deal closed-won (over 10% of open pipeline), only $1M closed-lost, replacements outpaced losses, and existing deals grew. That's a strong month. The lesson: the more you win, the less pipeline you carry, so decompose the movement (closed-won, closed-lost, created, expanded) before reading a net decline as bad news.

How should you compare sales rep performance fairly?

Never rank reps on absolute bookings alone. In the example, Stewart booked about $1M and Anna $225K, so Stewart looks like the star — until you learn his quota was $2.5M, leaving him far from attainment, while Anna is crushing a smaller quota. Normalize to quota, put both series on the same scale, and factor in territory, segment, and how each quota was set before deciding who's performing.

What are the key checks to avoid being misled by a chart or dashboard?

Anthony's four-check framework: (1) look for cherry-picking and insist on a holistic view including the counter-metrics; (2) inspect the axes for alignment, scale, and an honest zero point; (3) widen the time series so a short window doesn't manufacture false drama; and (4) layer in real business context — territory history, one-time anomalies, channel handoffs, and seasonality — tied back to actual operating plans and outcomes.

Is it unethical to shape a story in board reporting?

Not inherently. Bernardo describes board reporting as 'weaving storylines' where teams sometimes sandbag and sometimes over-inflate — embellishment that isn't lying in the traditional sense. The ethical line is due diligence: because humans jump to conclusions, the presenter's responsibility is to paint a storyline the data genuinely supports and not to leave out what's truly there. Shaping a narrative is fine; omitting context that would change the conclusion is how you lead people astray.

Timeline

Jump through the conversation

Click any chapter to open it in the transcript.

00:00Intro: the world is addicted to data 00:59Two flavors of lying with data 02:45The quarterback test: cherry-picking stats 04:59Cherry-picking in the business world 05:34Chart crimes: manipulating the axes 07:00Zoom out: the trend line is actually positive 07:35Board reporting is weaving storylines 08:17Who's our best rep? Absolute dollars vs. quota 09:02Normalizing performance to quota 10:26Where did my $2M in pipeline go? 11:37Waterfall charts and missing business context 13:38Data is objective until you put an eye on it 14:18Recap: cherry-picking, axes, time series, context

Full Transcript

The whole conversation

Broken into chapters, searchable, verbatim from the audio. Speakers inferred (not diarized).

Transcript as markdown ↓ Whole episode as markdown ↓

00:00Intro: 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.

00:59Two 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?

02:45The 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?

04:59Cherry-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.

05:34Chart 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.

07:00Zoom 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.

07:35Board 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

08:17Who'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.

09:02Normalizing 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.

10:26Where 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.

11:37Waterfall 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.

13:38Data 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.

14:18Recap: 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.