Season 2: Episode #21 | Facepalm Moments in Analytics and What They Teach Us

Speaker A: Foreign.

Speaker B: Welcome back to the Data Crunch podcast. I'm Vadim from the OVOX team. And today's episode is going to be a little different, a little messier, a little funnier, and very real because we're talking about the top data analyst mistakes, the ones that actually happen and not theory, not best practices. Real facepalm worthy stories from the trenches. And joining me is someone who's seen more dashboards than most people see movies, Helen, our head of customer success here at ox.

Speaker A: Hi, Vadim. Yeah, I'm really looking forward to this one. Uh, I'd like to keep it light, uh, but to be honest, behind every facepalm moment is the real lesson. So hopefully our listeners take away a few laughs, uh, and a few reminders. Because the truth is, mistakes happen. And in data, they will happen. The important thing is learning from them and ideally not repeating them on a loop. Or even better, learn from someone else stakes before they, uh, become your own. So today I'll be sharing a few real life stories from, um, our clients, our team, and yeah, maybe some from my own experience.

Speaker B: Yeah, exactly, I agree. So if you've ever broken a dashboard, crashed a warehouse, or copy pasted the wrong forecast into a broad deck, you're in a good company for sure. And hey, if you want a fewer fire drills and more confidence in your analytics, we've got your back@, uh, ox.com. just book a free demo and let's clean things up together. And don't forget to subscribe to our YouTube channel to get more episodes like this. All right, Helen, let's get into it. What's our first story?

Speaker A: For starters, I want to share you a story that I call SQL that crashed the warehouse. Uh, picture this. Um, an analyst writes select asterisk on a 50 billion row table, joins without filters and hits run the warehouse, locks up, latency spikes, other jobs, fails, panic. Uh, and the kicker, they didn't even need all those columns. They just didn't want to scroll.

Speaker B: Oof. Yeah, that's something. Uh, I call I'll clean it up later Approach. This is why warehouse costs. Go through the roof and your Friday turns into a fire drill. Also defaulting select asterisk or select star somebody calls it should be considered a minor crime, I think.

Speaker A: Yeah, couldn't agree more. So, um, here is a quick reminder list. Simple, but, you know, like battle tested. Do not use select asterisk in production queries. Or at the very least, check how much data you're about to process. Um, apply filters before running. Uh, joins on massive Tables especially. Um, specify a limit, data set or date range first. And set some processing limits for users or projects if your platform allows it. Um, yeah, some of these tips, you know, they might sound too obvious, but that's exactly the point. Um, ask yourself, honestly, do you just know these things or you actually follow them every single time?

Speaker B: For sure, yeah. Um. All right, next up is what I like to call the dashboard that lied. Here is one what we've all seen. Two teams, two dashboards, both showing roas return on ad spend, but with slightly different definitions. CMO pulls up both dashboards during the meeting. One says ROAS is 12. The other one says it's 0.3. Silence. Eyebrow raises, Budget panic.

Speaker A: Yeah, you know, I've actually been on that call or like that. Uh, and you hear, wait, ah, so why the rose is so different? Uh, well, what's your formula? Same as yours should be, so. And now it's shut down between marketing and bi.

Speaker B: You know, this is why defining metrics once, documenting them and reusing them across all tools is so important. Because in the end, the real problem isn't the metric. It's the trust.

Speaker A: Totally. So, two simple tips to keep your dashboard honest. Put your metric definition somewhere other humans can read them. And, you know, sticky notes on your laptop, they don't count. And sync with other teams agreeing definitions. Uh, do quick cross checks before the actual meeting and not during it. Trust me, future will thank you.

Speaker B: Yeah, for sure. Um, so speaking of dashboards and what happens after you share them, let's talk about the story, what I call copy paste chaos. You build a dashboard. It works, it's solid, everyone loves it. Uh, then someone copies it, tweaks a few filters, adds, joins, maybe changes the formula here and there, but leaves the original data prep untouched.

Speaker A: Yeah, and the next thing you know, the numbers are off, stakeholders are confused, and guess who gets the slack pin. Yep. You. Because, hey, you made the original one, right?

Speaker B: Yeah, that's right. That's the moment your soul leaves your body.

Speaker A: Yeah, this is too real. Um, and once you create, uh, version one, you become an unofficial support rep for every version that follows. You hear, we just reused your dashboard. Cool. But now it's like misreporting conversions and attributing everything to not set. And, um, suddenly, ah, you are the proud parent of a dashboard monster you didn't raise.

Speaker B: Yeah, yeah, put that on a mug, a hoodie, and the onboarding checklist. But since seriously, copying dashboards isn't bad. Right. It's how teams move faster and avoid reinventing the wheel. The key is do it consciously. Here are a few easy habits that, uh, make all the difference. One, label your copies. Add sandbox or draft so it's clear what's official and what's work in progress. And the second one, Check against the original once in a while just to make sure your numbers haven't drafted into a parallel universe.

Speaker A: Yeah, you don't need to be afraid of dashboarding or reusing data marts. It's like using the recipes. If you change the ingredients, taste test before serving them to the execs.

Speaker B: Yes, totally agree. All right, the next story is called when you trusted the business user, you get a data set from the client, you build the dashboard with this data, everything looks great. Then a few weeks later, the CFO opens it and finds missing revenue, messed up costs, and a bunch of rows labeled test.

Speaker A: Yeah, been there. Uh, over time, source tables change, um, columns get added, formats shifts and data gaps sneak in. And the dashboard, it doesn't magically adapt. So still, the first thing we hear is your dashboard is broken, not maybe something changed on our side, you know?

Speaker B: Yeah, yeah, uh, I've seen that. According to our very unofficial stats, only about 8% of clients actually go back and check their data before blaming the report.

Speaker A: So the advice is super simple. Even if the data was clean before, always revalidate when something looks off and start with the source. Even if they swear nothing changed. No, especially when they swear nothing changed.

Speaker B: Yes, totally agree. Because we checked it once doesn't mean it'll stay correct for forever. Okay, ready for a mistake so common it should have its own holiday. The Vlookup of doom. That's what I call it. You use Vlookup, but forget the false parameter so it matches the closest value. Suddenly, 10 customers have the wrong account manager.

Speaker A: Yeah, this one hurts because it feels, uh, like it's working. No red flags, no obvious issues, just broken assignments and silent chaos. So, tip of the day, either switch to Excel Hub or stop pretending spreadsheets are a reliable place to joins. Um, all right, so finally, um, one of my personal favorites, when you modeled to heart, um, you get task build an attribution model, and somewhere in the brief it says, let's make it machine learning based. That's what everyone is doing now. So you go all in. Um, 17 weightings. Ah. Um, four macros, two sleepless nights, and a sprinkle of machine learning. Magic. It's smart, it's sophisticated. And then the team keeps using the last node directly anyway.

Speaker B: Yeah, and what we've seen time and time again is this. It's not about how complex the model is under the hood. What really matters is how the team actually works with the output.

Speaker A: Exactly. Exactly. So if you've got that kind of request, try offering two versions, one more advanced, one more simple. Then just see which one gets used in the weekly marketing meeting. That will tell you everything.

Speaker B: But hey, if the team does consciously choose your masterpiece over last click, that's your sign to ask, uh, for a raise. Because it means you don't just build brilliant models, you actually know how to to implement and explain them. And that's a rare superpower, I should tell you.

Speaker A: Absolutely. So, like, here is the bottom line. Mistakes happen and the key is building systems and cultures that help catch them early or avoid them next time. And if you want fewer messes, better tools, or just a second pair of eyes, we are here for that. Aavox helps teams move from fragile spreadsheets and fire drills to confident, clear reporting at scale.

Speaker B: Exactly. If you want more trust and less troubleshooting, check us out@ahawocs.com we'd love to help you clean up your analytics and maybe avoid your own Vlookup of Doom. Thank you, Helen for sharing your stories. Thank you guys for listening. Share this episode with a fellow analyst who needs a laugh. And don't forget to subscribe to our YouTube channel to get more valuable content like this. We'll see you guys next time on the Data Crunch Podcast.