00:00:05.78 [Announcer]: Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

00:00:14.91 [Michael Helbling]: Hey everybody, welcome to the Analytics Power Hour. This is episode 291. Who knows what evil lurks in the heart of men? The Shadow knows. Moest of our listeners probably don’t know that callback to the extremely famous radio drama The Shadow, but what they probably will recognize is the work that data and analytics people do that lurks in the shadows of our day to day. That’s not really the job description. It usually doesn’t get recognized, but you do it anyway. Maybe some days you feel more like a janitor cleaning up ugly data or a therapist listening to stakeholders’ frustrations or some sort of data marketer just trying to sell your wares internally. I think we should talk about it. Let me introduce my co-hosts. Moee Kisss. How you going?

00:01:03.93 [Moe Kiss]: I’m going great. Thanks for checking in.

00:01:06.77 [Michael Helbling]: Have you ever heard of The Shadow? The radio show, The Shadow?

00:01:10.88 [Moe Kiss]: It was like from the early… No, but I’m deeply familiar with the sentiment.

00:01:14.45 [Michael Helbling]: Oh, okay. Yeah, yeah. And Val Kroll, welcome. Thank you. Bye, everyone. Go Bears. Yeah. And… Hey, it was close. Tim Wilson, probably the only person that got.

00:01:36.40 [Michael Helbling]: I was going to ask you if you remember. I think first up, maybe let’s talk about what kinds of shadow work have you found yourself getting into in your career? Like what are some of the categories or the types of things you’ve gotten into? And then as we sort of get into that discussion, maybe figure out if we thought it was necessary or not or whether it was good or not. So who wants to start us off with some of the stuff you’ve run into?

00:02:16.22 [Moe Kiss]: Oh, I mean, the one that starts with a capital A, admin. And I think this is potentially more on the internal side. I’m going to be curious to hear reflections. But I feel like there ends up being a lot of cadences in a business. And I think I’ve gotten to a point now where I kind of see it. And I’m like, if as a data team, you start to pick up, I don’t know if admin’s the right word or project management or heckling people to be like, you need to fill out this spreadsheet. Have you done this bit of this deck? All of that. And some people might think that that’s fair. But in a space where you have admin support and folks who are meant to have that as part of their role, feel like I see data, people end up having to fill that gap a lot just to keep momentum moving forward. And it’s almost like once you assume responsibility for it, it’s almost impossible to ever roll it back.

00:03:22.03 [Tim Wilson]: I’ve thought, I mean, there’s one specific part of that. There’s like the input, I need to do admin to get stuff. And then when you first said admin, I was thinking like, user governance, like, oh, somebody needs access to whatever. I feel like there’s an admin part that I think is good for the analyst when An analysis is delivered or something is delivered that is supposed to lead to a decision and an action that for a long time, I’ve felt that the analyst does kind of need to own that because it’s pretty easy for somebody to say, yeah, that’s awesome, but they don’t really necessarily have an incentive, direct incentive to take the action as was prescribed. as an accountability mechanism for the analysts to say, oh, I’m going to be here because I know how to set recurring reminders. I’m going to set a reminder to come back and say, hey, you said that was great. In the next release, you were going to do X or Y. Did you do it? I don’t think that’s admin though.

00:04:32.28 [Moe Kiss]: That’s not admin. What’s the word? That’s checking back to be like, if we said there was going to be some outcome, did we achieve that outcome? I would see that almost as being accountable for measurement and making sure that we hit the success bar and making sure that other people in the business are accountable. I think it’s more when you’re like, I know, Tim, you’re going to have strong views on this. But when you think of monthly reports and cadences like that, and it ends up being about getting people to fill out their section, not, hey, I’m doing the data bit and I’m going to partner with my stakeholder on the commentary or whatever it is, it’s like heckling and following up people and making sure people have done their bit. because ultimately like a data person might be responsible for making sure the reports are not or whatever. I think there’s a difference between like ownership and making sure you’re accountable and like Following up people to make sure they do their job. Oh, this is going to be like a trigger point, Tim.

00:05:37.58 [Michael Helbling]: Well, it’s interesting because I’ve definitely found in my career mode where we would go to the business and we would have like a recommendation or insight from the data, which was all part of our job. A couple weeks later, we’d be in a meeting with the IT department to explain what we wanted to change on the website as a result of that. We’re riding shotgun with the project now. It’s like, wait a second, when do we stop doing the analysis and start being the project managers for the implementation of this? That was when I was like, wait a second, what job do I actually have here? Because you’re kind of like, I’m not now not doing data analytics. I’m now running sort of like an integration task force, if you will. So I don’t know if that’s more like in the line of what you’re talking about.

00:06:22.30 [Moe Kiss]: It’s such a fine line though, right?

00:06:25.18 [Michael Helbling]: Because if you want to see your insight go live, you know, yeah.

00:06:30.08 [Moe Kiss]: And it’s something that I do worry sometimes like data folks are like, here, I’ve got a recommendation. I’m going to throw it over the fence. It’s your choice if you do it. And like not taking ownership. I think part of being a strategic partner is taking ownership and being like, I’ve made this recommendation. We’ve agreed on it. I like, I want to see it forward. And I’m, I’m part of this. I’m accountable to it too, because I’ve made this recommendation. So it is such a fine line between picking up too much of the behind the scenes stuff and what you actually need to do to like see the project or recommendation move forward from a business perspective.

00:07:05.72 [Tim Wilson]: Some of it gets down to just recognizing that if it’s kind of Michael, to your example, it’s when everybody agrees that should happen. I mean, that’s kind of like business 101. If it’s like, well, everybody agrees, but no one actually assigned, there was no ownership assigned. If you can do that in the moment, then a lot of times it’s like, well, who should be doing this? If I wait and we haven’t got it, then it shouldn’t be the analyst. But if everybody leaves and the analyst is saying, well, nobody’s gonna do it unless I step up and do it, That’s a little bit of a shame on the organization, shame on the analyst, but there is that part of like the full life cycle is, does need to go all the way through. So what is the next milestone? Who’s going to do what by when? And then looking at that person and being like, are they going to do it or is somebody going to need to babysit them? Which isn’t, I mean, that’s kind of a reality of business as much as the analyst role, I guess.

00:07:59.75 [Val Kroll]: As you were talking through the admin stuff, Moe, I think the consultancy equivalent of some of the admin work is, can you send me that thing that you told me you were going to send me? Can you send me that thing? Or can I have access to that? Especially if it’s like I need one of your other partners or other agencies to send me or give me access to something. The number of times, like, top of a call, like, okay, moving around a lot. Did you get approval for that one thing? Are we good to move forward with that thing? Which is, like, a lot less connected to meaningful stuff.

00:08:36.49 [Michael Helbling]: Explaining another agency’s data analytics to the client. That’s some shadow work right there. I’ll even just say, listen, I don’t think you want to pay me to explain this to you, so let’s find a different way to do it. Not that I don’t want to help you, but I’ve had many experiences where they’re like, okay, we got this from this. Maybe it’s a different agency that runs a specific program for them, like media or SEO or something, and they’re pulling their own reports. They’re like, how did they get these numbers? And I’m like, okay, so now you need me to go reverse engineer how they pulled these numbers together. And it’s like, oh boy.

00:09:26.22 [Tim Wilson]: That’s not a bad part of shadow work, getting poorly documented, regardless of where it comes from. Somebody wants me to take it forward. The first thing I have to do is basically replicate what was done so that I know what I’m carrying forward, which is just not… Some of that can be addressed by documentation, but that’s like this. There can be this expectation like, well, here’s the number and it links to this dashboard so surely you know everything you need to know. You’re at the starting line. It’s like, well, no, no, I’m still actually back in the locker room trying to get ready to come out to the starting line.

00:10:04.00 [Michael Helbling]: That’s a good point. And getting coordinated so that everyone’s kind of using the same data and everyone trusts the data that’s being presented, whether it’s internal or external, goes to that sort of like, that work in preparation I think is very much a part of what I consider like a data and analytics role to be doing. But sometimes it falls in your lap in a weird way, maybe.

00:10:29.36 [Moe Kiss]: Okay. And I think the thing that comes to mind is the word alignment. So like not all shadow work is shit. Some shadow work is actually very valuable. It’s just the fact that the business doesn’t understand like how consuming it is or how important it is. And alignment I think is one of those things where it really is often about like this business unit thinks this or this client thinks this and this area thinks this and like making sure that everyone is speaking the same language, whether it’s about the metric definition, whether it’s about the outcome of the work or like that alignment pace, I think is incredibly important. But I don’t think it’s always something that the business understands that it’s such a big part of a data practitioners role.

00:11:14.50 [Tim Wilson]: I second that. I mean, I think even the alignment, what is it we ran this campaign? What was it supposed to do? And then the fact that the analysts are like, well, I need to be in the meeting up front like that. We need to make sure everybody’s on the same page of what we’re trying to accomplish. It’s not run it. And then the analyst gets involved because the data now exists so they can pull it and they can provide the answers. that upfront, which I mean, some would say I co-created a consultancy that is geared a lot more around trying to get multiple parties on the same page, so that the analytics work or the experimentation work can be productive and successful is a huge part.

00:12:01.60 [Val Kroll]: I’ll third that motion on sometimes the shadow work is really important to move forward when we first started talking about this topic, the first thing that came up for me and granted I do have very much of a recency experimentation bend. is the culture of experimentation work, how that’s become more prominent, especially on LinkedIn in the zeitgeist about how to be successful with experimentation. But if you think how many other roles around a business have to make space for everything that they’re supposed to be doing after the job description was approved and you were hired. It really is all about like building consensus and getting people excited and a little dose of education, a little dose of this is why you should care about what I do kind of a stuff.

00:12:49.38 [Tim Wilson]: And I feel like sometimes that’s a little… The culture of finance, the culture of accounting. Famous.

00:12:56.70 [Val Kroll]: Famous for going around to get people on board. Well, maybe during budgeting season, but just to go back to the point that it’s not that it’s not important, but it’s usually not the first thing you think of when you’re like, oh yeah, I lead an experimentation team inside of an organization. It’s not that it’s not important, but it’s usually not the first thing that comes to mind.

00:13:17.46 [Tim Wilson]: It does seem like maybe to bridge from that to explaining the realities of the data, which kind of takes two angles. there’s always going to be a presumption that the data is cleaner, more accessible, less ambiguous, which is like, no, our data is a company. It is always wildly more complicated than any kind of new person to it thinks it is. And then there’s the other part of that that is what the data can and can’t deliver. Like the data is the objective truth. So there’s a data fluency component where it does sometimes feel like in analytics, and maybe this is the grass is always greener on the other side. If you’re talking about finance, somebody’s in a financial analyst, somebody would expect that they’re an expert around finance and they can go to them and defer to their expertise. I feel like in marketing and product and digital analytics, sometimes it’s like There’s not a presumption of knowledge of complexity. The shadow work is building trust, building the relationship, walking them at the appropriate pace through why a diff and diff is not appropriate in this situation. educating of the business partners that does feel like it’s a proportionally heavier lift than many other roles. Does that count as shadow work?

00:14:57.94 [Michael Helbling]: Tim, when someone asks, which channel has the highest ROI adjusted for LTV, how long does that take you?

00:15:06.30 [Tim Wilson]: Pull GA4, then export to Excel, write SQL for BigQuery, find my LTV formula. I don’t know, let’s say about three hours in a couple of existential crises. At least two.

00:15:20.99 [Michael Helbling]: This is why ask-wise full-stack approach works. Ask in plain English, prism orchestrates across your stack and applies your saved calculations.

00:15:30.06 [Tim Wilson]: So I’m not manually stitching together five tools like some kind of data Frankenstein?

00:15:35.83 [Michael Helbling]: Nope, everything’s traceable, not a black box. DataState secure, semantic layer, generated code, runs locally. It’s all set up for you.

00:15:46.98 [Tim Wilson]: So for a product with a name that makes you think of a title or asking why, repeatedly, this is pretty sophisticated.

00:15:54.65 [Michael Helbling]: I’m not sure making fun of our sponsor’s name is the move here, Tim. Wait, I did say pretty sophisticated. That’s a compliment. All right, fair enough. Well, go to ask-y.ai, that’s ask-y.ai, and use code APH for priority beta access. Join the rise of the AI analyst.

00:16:19.41 [Val Kroll]: 100%. Yeah, I think so. Or even some of that same concept, the explanation to like backend developers, like you were talking about the business partner audience, but that was one, I think we were talking about this a little bit too much. I know that you have scars, but the story that comes to mind for me, when I worked at the American Medical Association, we were working off of the free version of GA at the time, and we had just gotten an analytics canvas license. to overcome the sampling. So it would like hit like every 30 minutes or every hour or whatever it was so that we could extract air quotes, all the data. And I remember it like there was some, some backend developers were like, Oh, perfect. Now we can just, you can just give us all of your GA data every night and we’ll just throw it into the membership cube. And I was like, it doesn’t work like that. Also, like, what do you mean everything? Like, do you even, but like so many conversations, conversations that got escalated, my boss had to pull me into it. And it was like, you guys, This is not like, I don’t, maybe this is on me at this point for not being able to explain this, but this is a little bit of a nightmare. But the other thing is that membership cube, the ID, the key was the membership ID. And I was like, do you think that the only people who visit our website are members and that they’re authenticating at least once every 30 days? Like you are off your rocker, but it was like, at least, at least three months of my life spent on that topic, if not longer.

00:17:45.90 [Michael Helbling]: And a lot of times shadow work is just cleaning up or trying to clean up a data warehouse you inherited from a previous team or something like that. You know, you walk into an Oregon, they’re like, oh, we want to do this, this and this amazing thing. And you’re like, well, the snowflake instance we have is not going to do any of that till we really clean up a bunch of it.

00:18:05.27 [Moe Kiss]: And you’re helps.

00:18:07.76 [Michael Helbling]: Yeah.

00:18:08.24 [Moe Kiss]: Is this like one of those times where you read my exact life situation that is going on right now around to a huge rebuild of our entire data warehouse for a very specific, like very similar reason, right? Like the data wasn’t structured in a way that we can answer the business questions of today. And so, and I think the thing that’s so hard about projects like this is they’re often huge and very time intensive and unlock the heap of value, but people don’t see the value until like months.

00:18:36.97 [Michael Helbling]: Yes, it’s a long time and it’s hard to go pitch those because it’s not very sexy or very exciting to be like it’s not doing anything but setting up a potential for a future as opposed to delivering a business result. It’s so much nicer to go in and say, hey, here’s this analysis where we can make $100 million more this year if we do X, Y, and Z versus, hey, we need to spend a bunch of money redoing stuff we already have because it’s not doing this, this, and this. Eventually, you can write the business case to show where the value will come from, but man, it’s an uphill battle. I don’t know if that’s shadow work exactly.

00:19:15.27 [Tim Wilson]: I think there’s often, I mean, I will see that example and raise it one with wait for a year. I lived this scenario many times, but the most horrifying one, I think, traumatic one, working with a large pharma company that was using Adobe Analytics, and they said, we’re going to get everything into a Azure you know, data store of some sort. And so many requests, they’d say, oh, we don’t have that yet, but it’s all going in. And they were just locked into these backend developers said, we’re going to take the Adobe’s horribly weird and never really thought through, gotta take the Viz high and Viz low, like stitching like messy, messy, messy data feed data. And they were saying, we’re just gonna pump it in at that raw level. And then we’ll just kind of write SQL queries that people can use. I’m like, the SQL query just to answer how many users came to this page is kind of a beast. But we couldn’t get an audience with them because they were just convinced, which seems very common with developers. I feel like it’s maybe less of an issue if you’re taking an event-driven product analytics perspective, but anytime you’re going to something where you’ve got this de-duping sessionization, developers think of event. They don’t think of the need for stuff to be deduplicated by something. So this idea that, well, we’ll just pump all the raw data in, and then you’ll be set. You’ll just have to write SQL, which then becomes a case of needing to maintain SQL libraries, I think. I don’t know whether, Moee, you’re like, that really doesn’t happen if you’ve done it right, or whether you’re thinking, yeah, no, that happens. Oh, or yeah.

00:21:09.32 [Michael Helbling]: Well, I mean, there’s tools that help with that, like, you know, um, dbt or data form or stuff like that that helps you kind of maintain your sequel and repositories and use it effectively.

00:21:19.79 [Tim Wilson]: But sometimes that’s to me, you’re like, you’ve gone with this, like, let’s Let’s get the full ocean, and then we’re going to add layers on top of it.

00:21:30.99 [Michael Helbling]: And then the downstream is the next question that comes from the business user requires yet another SQL query to be written to build out the next reportlet or whatever. So you put yourself in a pretty challenging chain of events just to get answers to data, which AI will totally solve. So don’t worry.

00:21:51.87 [Moe Kiss]: Literally, that’s about to be my comment. I think the biggest challenge right now is that everyone thinks that you can overcome a shitty data architecture with AI, which is just so fucked and hard to manage because you’re literally that’d be broken unless we have the right data architecture. The same way that we’d need to write a bespoke SQL query or you don’t even know where to point the question because of the way we’ve structured the data. That’s the problem that we need to solve. And yeah, it’s not sexy. Like getting the buying is incredibly difficult for this stuff. And it probably is the hardest. I would say one of the hardest parts of my role right now.

00:22:34.80 [Tim Wilson]: So that is deep because the business partners who ultimately want to get value from it, it’s not going to maintain their attention or technical depth, but the analyst is supposed to be engaging with them and serving them. So the analyst becomes the proxy for the business and is now dealing with the backend. And so they become subject matter experts in an area that has Nothing to do with running analyses or validating hypotheses. It’s just they’re living in that middle tier and there’s just no one else. The shadow has to serve it because there is no one. That’s all there is. That’s the best you got.

00:23:20.60 [Moe Kiss]: spot on and then you end up with like one or two people in the air and the business who know one area and no one else can do it because it’s so complex and there are all these like gotchas so even if you’re going to write a bespoke fickle query it has to go through this one or two people because they’re the ones that know those tables know how to to use it well and like that, then you’ve created your own bottleneck, right? And it’s not an intentional thing. I think often the systems were created with the intent to have a lot of flexibility, but then by having flexibility, you don’t have enough standardization and like, yeah, it’s a chicken and egg. But I would say that is one of the hardest shadow tasks for sure.

00:23:59.33 [Tim Wilson]: There does seem like there’s like a macro thought, this whole topic of the show that it’s like the I feel like I’ve worked with analysts who take the attitude, well, that’s, that shouldn’t be my job. So it’s not my job. So I’m not going to do it. And then it kind of falls through the cracks and doesn’t happen.

00:24:20.80 [Michael Helbling]: So on that meta thing, like there’s something to the idea that like some people by personality are going to be more suited to generalist types of roles versus specialist ones or more drawn to them. And so like, I’m definitely much more of a generalist. So when I find myself running further afield of doing the actual data work and the analysis, it doesn’t bug me at all. It’s actually kind of fun to see something different and do something different for a little while. I sometimes will think about, is this really truly serving our purpose? Are we getting done? We need to get done. But generally speaking, doing those tasks, not a big deal. I feel great about it. But I absolutely think there are people who Like that is much more disconcerting to step outside of the role to do those things and less of something that plays to their strengths and much more plays to like the things they definitely do not want to do. And so like that’s the other issue is just sort of like the person kind of matters a little bit to this too.

00:25:19.40 [Val Kroll]: Yeah. And I don’t think, I mean, at least from my personal experience, it hasn’t been like a conscious choice of like, whether I’m going to step outside or, you know, get in someone else’s lane, but it always feels like I’m tugging on a thread of something that in the moment feels necessary for me to understand what I’m analyzing or to understand root cause of like why that had been a problem. I mean, and a lot of times I personally just get fascinated by like, you know, authentication handshakes and like, you know, all the different nuances in that space. But it always ends up feeling like it’s adding to this like mosaic of my understanding, which always feels like it pays dividends in the future too. So I’ve never, I’ve never tried to quiet that voice. Also, I’m just really nosy.

00:26:10.98 [Tim Wilson]: This reminds me of me going overboard on it, where there were webinars in a company that we think we know what webinars. You have a registration and attendance. This was in a business model where it was not that at all, and it was like bonkers how salespeople would sometimes go into an office and sit and watch the webinars, and there were two or three systems involved. The more I pulled on that thread, it definitely was interesting, but it was like, oh, wow, I was looking at this one table of data and interpreting that attendees meant the number of people who attended the webinar, and that was completely wrong. I wound up writing up It was probably a 10 or 12 page document very, very clearly written because there were all these parties in different places and I thought, nobody has put all this together. I have done the most glorious, valuable. This is so useful. I’m pretty sure not even the webinar business owner. read it. I got probably 25% of the information from her, but I was like, oh, she was excited to explain to me the nuances of the complexity, but I kept digging further and further and saying, aha, look what I, the external consultant, has done to really help you understand what’s going on here. And there was kind of no interest. So that was one where I’m like, it was useful for me. It should have been useful downstream. In today’s world now, boy, I’d be throwing that into an LLM somewhere and saying, that’s really helpful data potentially. But I’m pretty sure that document, I was like, I became the domain expert on something that People cared about webinars, they did not care to hear how messy it was to interpret any of the data that was captured.

00:27:57.26 [Moe Kiss]: The thing that’s resonating with me a lot right now, one of the values that I, I do have leadership values, it’s a weird corny thing. But one of them is be unwaveringly useful. Does anyone, pop quiz, anyone remember where that comes from?

00:28:15.39 [Tim Wilson]: I don’t think so.

00:28:16.75 [Moe Kiss]: Oh, from being useful would be… A good friend, Cassie. Yep. I got it. Ding, ding, ding. Yeah. Yeah. She put it in one of her blog articles and it’s always resonated with me. And I’m completely contradicting myself now because at the start I was like, don’t pick up the admin work. But I’m the first person to be like, if someone’s not doing something and I can add value or move something forward, I’ll normally just end up doing it. So like I am, yeah, a walking contradiction. But I do think there is part of that. responsibility of data folk like I tend to get really frustrated when a data person is like, well, that’s not my job. And I’m like, your job is to help the business make better decisions. So if there’s something you can do to be useful to help the business make better decisions, that is your job. Yeah, I don’t know. That’s just the thing that’s bubbling around in my mind at the moment as we’re, I mean, not relevant to Tim’s example, but more broadly about this area of like sometimes it is about getting the domain expertise. Sometimes it is about documenting something that no one in the business has written down. It’s like, sometimes those things are less useful, but a lot of the times are really useful.

00:29:20.48 [Michael Helbling]: just to give some people who might be listening a chance to sort of be like, well, maybe, Moe, I can’t do that thing or I’m not good at that thing. Is it necessarily that you have to go personally be the one in charge of that as much as be part of helping solve it in some way, see that it gets done? So it’s more of like the ownership taking versus the taking on the role and doing it yourself, just so that people who are very specialized or don’t Yeah, because I have a ton of empathy for people who are like, Michael, I just can’t get up in front of people and talk. Cause like I analyze data and that’s what I like to do. And I very stressed out every time I have to go present something. And it’s like, okay, well then has someone else can do that part, but like you just need to make sure you’re a facilitating it up to the moment where it, where it happens. So it doesn’t have to be you necessarily taking on that role. I don’t know if I agree. So don’t pick presenting something then, something else, like managing the project or something like that. But the point being, like, not every person fits every single role. Like, you don’t have to be a polyglot, if you will. Or a polymath.

00:30:27.29 [Tim Wilson]: What that, I mean, if you… Poly PM. First break all the rules, like the precursor to the now discover your strengths, strengths finder, which… But first break all the rules. I’ve always, to that same point, identifying what needs to happen. I think, Moe, that’s the brilliant way to frame it. What is your job? It is not to write SQL. It is not to develop reports. It is not to deliver results. It is to move the organization forward by helping them make decisions. If you say, well, That means that somebody every Tuesday morning needs to reach out to this one person and ask them a question. Like it can be frustrating. It can suck. But you know what? There’s somebody who’s actually super sociable, who loves to ping people or whatever. Like building up that list is kind of, these are the discrete tasks. Not that somebody’s going to love and relish doing every one of them, but it does at a team level. help start to shift around, like, oh, somebody needs to document these database tables, or somebody needs to ask why Guru, they need to know how that tool works really well, figuring out who gravitates to it. I do think there’s, and I think I was cringing similarly with Michael grabbing a random example, there is a fine line between what is a complete analyst need to be able to do and do even if they’re outside of their comfort zone. So it’s, it gets a little squishy. Which of this is shadow work that like somebody’s got to do it, this person gravitates to it. Which of this is going to be a really ineffective handoff because someone just doesn’t, doesn’t want to write sequel. I mean, they’ll use that example. Somebody, I don’t want to, I’m just not the kind of analyst who’s going to learn to write code. It’s like, cool, then you’re not the kind of analyst who’s going to progress particularly far in your career. So, cool. We got it.

00:32:32.88 [Michael Helbling]: Hey, I’ve gotten pretty far. So, you know, no, now you can’t do it. You can’t.

00:32:39.32 [Moe Kiss]: I don’t want to get into team dynamics too much, but I do think a big part of figuring out the shadow work as a team is figuring out who had strengths for different parts of it and we’re making sure people lean in. I know in my previous team, we had a really big gap of, we didn’t really have someone who was really good at the I would say leadership team documenting stuff, pushing it forward, hyper-organized, being like, hey, Moe, these are all the things we have coming up in this time frame. We very intentionally hired someone that was really strong in that space to complement our team. I think that we really need to be thoughtful of what are all those things, especially the shadow work, because if you put someone on something and that’s their strength, it’s so much easier for everyone. They feel like they’re adding value, that the balance feels better. And to be fair, there are some things that no one particularly wants to do, and then it just comes about making sure everyone takes a turn.

00:33:43.61 [Tim Wilson]: Can we hit on that stuff a little bit? And maybe this administrative work, maybe more broadly, because I think that is the danger there. And I do think I’ve seen stuff written that women are much more likely to get screwed on this one, is that this thing needs to happen. And they’re like, oh, well, it’s admin work, like the latent misogyny Maybe not intentional is, well, Moee’s really good at that, but it’s absolute shit work, and she’s not going to speak up. I think there is that the shadow work that needs to be done that has value, that is being done as efficiently as possible, and there can be some gravitate to it. Shadow work that is has to be done. There is value. No one wants to do it and making sure that that doesn’t fall to the passive nice person because because that can spin out where wait now half of your job is unseen shadow work and you can’t advance in your career. Even though everybody’s like, well, this all needs to be done. But good old Jane is, you know, always there for it, you know, but it’s in the shadows. It’s not getting

00:34:59.87 [Michael Helbling]: Yeah. That’s not visible. So this is actually kind of an interesting pivot, Tim, because as you turn into a leader in your space or leading teams and those kinds of things, your job becomes taking the work out of the shadows for some of the exact reasons you just said, because it needs to be recognized. What’s being done, the people doing it need to be recognized. And then who should be doing it, should be much more strategically thought out as opposed to quote, fallen into just because, oh, so-and-so is more agreeable, so they just take it on without fighting too much, which is just a terrible solution to the problem. So anyways, I thought that was a really great point, Tim. And I think that’s sort of the thing that maybe take away is like, when you turn from an individual practitioner or individual contributor into a leader, you know, when you’re just an IC sitting at your desk, you’re like, wow, do all the shadow work. When you’re a leader, you’re like, we need to take the shadow work and expose it to the light.

00:35:55.99 [Tim Wilson]: That sounds hard. That’s why I’m not going to, I’m not striving to be a leader.

00:35:59.48 [Moe Kiss]: I do think though it also is about like recognition. And like one of the things that I would say like, and I’m thinking of this particular person, like I know at the moment their rating would be very good or like they’re like an assessment of their performance, right? Because I value that work. And so I think where the challenge is is like, when there’s that tension where someone’s like picking up a lot of shadow work, that then is not valued or not given the value that it’s deserved. Whereas I see it as like being incredibly essential. And if you do that shit well, like you can unlock a lot for your your team or the business. And so like, I want to make sure that that’s rewarded and reflected. So it there’s a lot of new ones, though, like, obviously, it’s very dependent on specifically like what paths we’re talking about. And yeah, and many factors.

00:36:50.66 [Tim Wilson]: I’d just like to say to all of Moee’s team who’s listening to this podcast, she’s talking about you.

00:36:54.83 [Moe Kiss]: She values you.

00:36:56.86 [Tim Wilson]: Oh, wow. She gave us the name off Mike and it was your name. So good job.

00:37:00.83 [Moe Kiss]: Stop it. You were so cruel.

00:37:03.66 [Val Kroll]: The other thing that this is making me think about is that when any in-house role that I’ve had, I’ve never reported to an analyst. It’s always been, you know, ahead of digital or someone else who it was really hard to message up not only for myself when I was the IC, but then when I grew my team about all the things that takes like, I’ll say, do you think we just sit there and like convey your belt? Just like analyze, analyze. Like that’s so not all that the job is, right? So there’s a lot more. education in that scenario, whereas I was thinking about your comment, Michael, like with the elevation of analysts and to those leadership roles that there’s a lot more visibility and line of sight. So I agree with you on the accountability we’re going to put on any listener to bring that work out of the shadows and acknowledge and like what you were talking about both. So that’s a really good point.

00:37:55.11 [Michael Helbling]: I think what we’re finding out is that the work has value. Whether we should be doing it or not as analytics people isn’t necessarily all the story. Sometimes you should go back and say, workflow-wise, the solution should be to take this group and pull them into this piece of work. rearrange it and come up with a strategy. My early example, Tim, you pointed out, we exposed basically an organizational workflow flaw when we came up with an insight and then had to go drive the insight through the org. What we exposed was no one had thought about, hey, what if we have an optimization, we want to make a reality? How does that get done in our company? Well, somebody should have probably thought about that, and so that was the work that had to be done was to figure out and create a machine that would take care of that. But it’s the same thing with all the rest of it. It’s sort of like, okay, well, what are the parts that need to move into the right places to get it done? Not necessarily you, the data analyst should do it, but that it gets done because it is valuable work at the end of the day, especially if it’s actually driving impact or decision making in the organization using data, which is sort of like the thing that makes me smile anytime I get a chance to be part of something like that.

00:39:13.85 [Moe Kiss]: Can we talk about data quality? We have not touched on that at all.

00:39:17.96 [Michael Helbling]: It’s usually pretty good. Yeah. I mean, just kind of automatically. Yeah. What do you mean? What was there to talk about? So I’m pretty sure.

00:39:25.89 [Moe Kiss]: I think it’s going.

00:39:26.37 [Michael Helbling]: I think it’s going.

00:39:27.19 [Moe Kiss]: Oh my God, stop. Everyone stop triggering me.

00:39:30.04 [Michael Helbling]: Sorry. Sorry, well.

00:39:33.50 [Moe Kiss]: Just come on. I think the one that I’m specifically comes to mind is Bend sent from a media agency. And I just get so frustrated or from a finance team.

00:39:56.95 [Michael Helbling]: Talk about the highly formatted Excel files you might be receiving.

00:40:03.09 [Tim Wilson]: In wide format when they should be in a long format.

00:40:08.30 [Moe Kiss]: Of course. I’m glad you could all laugh about it. I am not at the laughing stage.

00:40:14.39 [Charles Barkley]: Sorry, well, this is probably a whole episode we need to do on stuff like this.

00:40:20.95 [Moe Kiss]: But it just, I think what’s so fucking hard is that your stakeholder will be like, especially the one that owns the relationship with the media agency. I didn’t get it. They sent a spreadsheet over on Moenday. Like, you’ve got the data. What’s the problem? Like, why is it going to take you a week? And you’re like, Do you know that every single city that they run media in is in a completely different format and we then need to sense check it with our record? No, that is a huge lift. And fuck. Anyway, and then you’ve got some very senior, brilliant data scientist that is spending their time basically QAing data. It’s really frustrating.

00:41:08.94 [Tim Wilson]: That is one of those cases where that’s another shadow that the analysts can fall into where they’re the bridge between the data creation. That data may be created out of some contractual necessity, but doesn’t have any real incentive or stake outside of what’s in an agreement. It’s like, oh, we’ll send you data. We’ll send you data. Check the box. And this is going after media agencies pretty hard, that a lot of times they don’t really under, they’re like, whatever the platforms, you know, trade desk spits this data out or runs into our data warehouse and we’ll just give you a feed. And the analyst is the one who winds up having to explain their data to them. So it’s like another version of that. That particularly is another version of what you were talking about earlier, Michael, where you have to be like, Yeah, how can this possibly be zeros across here? It’s like, wait a minute, I’m now having to reach out to… Everybody seems to assume that it’s coming in fine, but I have to set up time to go three levels deep with some partner to get them to agree that it’s actually a problem or explain to me why it’s not a problem.

00:42:27.06 [Michael Helbling]: I’m literally in a situation like that right now. I ran into a situation just this past week where a company is like, yeah, we’re pretty sure the quality of the data in this system is great, and so I get my hands on it and immediately see three things I’m pretty sure making their data quality really bad. And so you’re literally starting out with sort of like, okay, well, our first conversation is gonna be, guess what? The data you thought was really good? Not good. And there’s a number of fixes we’re gonna need to do before we even start on the things we wanna get further along. And it’s frustrating but real, right? So it’s sort of like, yeah. And then the other one that gets me sometimes is sort of like alerts and notifications, anomaly detection and those kinds of things. That is a part of data, but it’s not really what an analyst does necessarily.

00:43:15.77 [Tim Wilson]: Well, the analyst gets blamed if the data all of a sudden it’s found that something wasn’t there for weeks. They’re like, what were you doing as an analyst? How did you not notice?

00:43:24.44 [Michael Helbling]: Raise your hand if you’re the only one that’s had your own secret dashboard so you don’t get caught up in one of those things. So you have advanced warning of something that’s happening.

00:43:34.23 [Moe Kiss]: I think anomalies is part of our job, but you will keep saying analyst, and I think of data practitioners, whether it’s a data analyst, analytics engineer, data scientist. For example, if there is something in our B2B pipeline that breaks our leads coming through that is absolutely data quality and normally detection and I would expect an analytics engineer to go in and solve that. Absolutely. When we’re doing at the complete other end of like a metric goes up, a metric goes down, that sort of stuff, again, I would expect a data person to go in and kind of debug that. It might, they might not be responsible for the complete like up level, you know, challenge of why that thing is or isn’t working anymore. But like, I would expect someone to be pretty across that if we saw like a number tank or something like that or a number skyrocket.

00:44:28.59 [Tim Wilson]: But, but that’s the, I mean, the way you just framed it, not to, I mean, you’re just speaking off the cuff that a, There is a perception that, yeah, yeah, yeah, they need to catch if a number of tanks are a number of skyrockets. In practice, every time I’ve had a system where it’s like trying to tune where, like there’s not a threshold, then there will be platforms out there that say, look, you can set this at a 95% threshold, set up 100 alerts. I’m like, cool, I’ll get on average five alerts a day.

00:44:58.94 [Moe Kiss]: I’m not necessarily expecting a data person to catch them all. I think that’s a really hard thing. It’s so difficult, right? Because if you have a stakeholder who comes to you and is like, hey, this number declined and you’re the data person who’s like, what? I had no idea. That’s shit. It’s hard for trust. But at the same token, expecting a data person to be able to be ahead of the game on every anomaly is also not an expectation I have. But I would I would basically be like, okay, something has gone wrong here. I’m going to reach out to my stakeholders. I’m responsible for letting them know. I’m responsible for letting them know what we’re doing to investigate, how we’re going to solve it, keep them updated. That, absolutely, I do think is a data person’s role.

00:45:47.72 [Tim Wilson]: I still have the alert turned on for a certain tax preparation company that you and I worked on years ago and like January 12th, their home page was down from Seattle because I just never turned it off. But that was one where they were having sporadic Issues and it was like somebody should be monitoring this and I can go set something up And I had to set up on like my personal account and I just never turned it off.

00:46:12.89 [Michael Helbling]: So literally Michael knows the brand I know the brand it was down for about 35 minutes Yeah You need to do some account access cleanup that’s some shadow work that a lot of consultants have to do Get yourself off of those old GA accounts or Adobe accounts that you’ve been on for years and years that you no longer work with.

00:46:36.08 [Tim Wilson]: No, this was using Site 24 by 7. I was doing like a ping tracker that I set up, so I had set it up. So a third-party tool.

00:46:47.24 [Michael Helbling]: You’re doing third-party data collection. I was using a third-party tool. And they’re probably like, why is our website getting crawled by this website?

00:46:55.54 [Tim Wilson]: But that was, they were sometimes saying like, the tool is down. And I’m like, no, like why is this anomaly in the data? Cause your fucking site went down. Like, that’s not a, cause I think I set up a ping for the footer as well. Cause based on where they had the tagging track, but I think it started with them saying your digital analytics, your web analytics data is bad. And I was like, yeah, that’s weird. What’s going on? It’s like, well, no, the whole site went down.

00:47:21.07 [Moe Kiss]: No, I didn’t once find, though I was working somewhere there was like an issue that I couldn’t figure out like why this number had gone weird or whatever. And then like a month after I left, I figured out why. And it was like completely tangential. I was just working on something different. And I did reach out to let them know. I was like, Hey, this is probably what this was. You should fix it. Here is how to fix it. You’re welcome. I’m not a shit human. I want everyone to have the best data they can.

00:47:48.07 [Tim Wilson]: I also get the, it’s backup. So every time I’ve seen it, it’s come back up quickly. So there hasn’t been a point.

00:47:54.94 [Val Kroll]: Okay. So before Michael wraps, cause you got that look in your eyes. I would love to hear. love to hear people’s thoughts on shadow work, not shadow work for like data fluency, data literacy. We’ll call it, we’ll call it, cause data literacy programs I think are one of the more common ways people talk about it because it is like a whole category of work. Yeah. I like data fluency. I think it’s less obnoxious than data literacy.

00:48:24.33 [Michael Helbling]: Everybody can read and write.

00:48:25.89 [Val Kroll]: Yes. So is it shadow work, not shadow work? I think it’s shadow work, but I think it’s important shadow work.

00:48:31.99 [Michael Helbling]: Yeah, I think it goes back to that sort of like what do you need to do to help the organization take a step forward with data, make decisions, use the data, be effective with the data. And a lot of times that’s building up data fluency in an org or helping people build up their data fluency.

00:48:48.85 [Tim Wilson]: But that’s one where if you try to bring it out of the shadows and say, oh, why don’t we just solve this once and for all and send everybody through a data fluency program, pretty ineffective. So it’s the thing that needs to be in the shadows that is a I mean, not that there’s not the opportunity for some of that training. I feel like I’ve been learning how much, I mean, it’s not, it’s the reality of a short attention span that the more you can have like in the moment, like, let me come up with, let me show you this now. Let me explain this little thing now. Let’s talk about, oh, you know what? When you all people say correlation is not causation, this is like the perfect example. Let’s talk about that for five minutes because that’s a trap you’re falling into.

00:49:30.55 [Moe Kiss]: Tim, it actually makes me think about gender bias training and all the research on that, where lots of companies do gender bias training. It doesn’t necessarily result in any differences in behaviour or attitudes or anything, but it’s a tick the box thing. When we start talking about like data fluency or training or education or whatever it is in the data space, I think what happens when we sometimes roll out those programs with really good intent, it’s a tick the box thing. But again, like those in the moment.

00:50:01.93 [Tim Wilson]: That sounds like the sort of observational woman would make, by the way.

00:50:05.21 [Moe Kiss]: We’re going to send Tim back to training. Those in the moment discussions are actually what I think is makes it so hard because it is shadow work because it’s not like I built a program, I’ve shipped it, I’ve ticked it, it’s done. It’s like every time I talk to the stakeholder, I’m trying to help them get a little bit further in how they think and understand data. And that is like you’re never done, you’ve never ticked the box. And so it does have like a very heavy cognitive load, but it’s incredibly important and probably leads to the best outcome I say without a data informed opinion on that at all. Just like that.

00:50:51.84 [Val Kroll]: I’m actually surprised that you guys are all on the same page. I don’t think it’s shadow work at all, whether it’s bite-sized or a big part of it, because even some of the criteria we were talking about using earlier, if your role is to help the business make smarter decisions, like making sure that you’re connecting what you’re finding, what you saw, what you observed, what you validated, what your recommendations are with like what the business can actually be doing with that information. It feels like it’s a, I don’t know, to put it another way, there was a leader who I worked for at UBS who like the four D’s of product development, like the defined design, develop, deploy. He always said there was a fifth like shadow. not actually a fifth one of adoption. Until you understand how people are using that or if this is a data product or whatever, then you’re not done. The work isn’t done when you ship it. The work is done when you understand and create the feedback loops. I feel like it’s very much in the same vein of how to make sure that your work continues. Michael, the work almost similar to what you were saying, creating the processes so that the team knew how to take advantage of those recommendations. I don’t know how I just feel like it’s not Like you’re not done just when the analysis is complete, or it’s not.

00:52:09.76 [Tim Wilson]: Yeah, shadow is optional. Like it has to happen, it’s just not identified as something.

00:52:13.93 [Val Kroll]: It feels like it’s squarely in the court of, I would expect it to be in a job description. Like, that’s what makes me feel like it’s not shadow work.

00:52:21.44 [Michael Helbling]: Again, that’s where I think some of this work should rise up out of shadow work. But again, it’s about recognition. The importance of it, I completely agree. But Tim’s point, I think, was, will you see it in a job description? Probably not. Or if you do, it’ll be run a once-in-a-quarter training and call it done. And we all know that’s not going to be effective. But it’s spending that time, like I’m realizing this episode that like 90% of what I do is shadow work sometimes. It’s so hard to pin down. Michael works the shadows. That or I just don’t do anything. I don’t know. But I remember I had a very specific instance where I had a review and my boss at the time was like, you’re not spending your time the way that it should be spent. And I had to actually walk him through. If I spent the time the way that they wanted me to, it would lose the company money. And I walked him through step by step. If I actually did it the way you said, the company would lose money as a result of the effort. So what you’re telling me is that you would like the company to lose this much revenue by changing what I do day to day, are you sure that’s what you want? And so it was a really interesting conversation because I was able to enunciate exactly where the value lied in each of those things that I was doing. I could show the outputs of those things. But it was a very interesting conversation because it was like, oh yeah. Now, in that case, I had actually prepped that person ahead of time by showing them exactly how I was going to spend my time. They just ignored it and came back with the template.

00:54:01.66 [Moe Kiss]: What was the outcome, though? Like, what was the end of the story? Did you get off to change it? Or did they, like, be like, oh, I see the value of what you’re doing.

00:54:09.14 [Michael Helbling]: I kept going. Yeah. No, I was, uh, that was a role in which firing me probably wasn’t an option. Probably they felt like it in the moment. I sent an email to the head of HR ahead of that meeting being like, I’m about to chew up my boss. Um, But it worked, and I still had a good relationship with that person afterwards. But it was a situation where they were like, oh, OK, well, never mind then. And I just kept going with what I was doing, since it was made sense.

00:54:36.06 [Tim Wilson]: I will claim to the job description that I think when we read job descriptions and say, well, this is looking for a unicorn that’s ridiculous. Or when we read a job description and say, wow, that looks really good, I bet, I’m thinking through some that I’ve seen, the ones that actually have the shadow work articulated as part of the responsibility is collaborating with the business partners to how to ask questions in an informed way. That actually may be, it would be fascinating to look through some job descriptions that when people say this is garbage and say, is any of the shadow work captured? Hey, this one looked, because you’ve had that reaction where you look at one and you’re like, oh, they get it. Like they actually, they’re describing a realistic and practical role. And I bet that that is because there are nuggets of what you’ll be expected to do, include some of the shadow work we’ve talked about.

00:55:30.36 [Moe Kiss]: Okay, but just a push on that I do agree. I think the challenge though is like In my role we write we write the job descriptions for data people data people are writing job descriptions for data people So you can still have a mismatch with the stakeholder of what they think a data person should be doing like so I’m just saying it’s not like bulletproof

00:55:52.79 [Tim Wilson]: Yeah, but I think if that’s recognized, it’s like, hey, we’ve got a bunch of really difficult, unrealistic stakeholder. We should have in the job description that part of this is collaborating with, not you don’t say collaborating with assholes, but you’re like, You know, collaborating with, educating, informing, iterating with, so I think he can still be captured.

00:56:18.53 [Val Kroll]: He’s ambiguous in challenging circumstances. That’s right. Yeah, exactly. Oh, yeah, there we go.

00:56:24.88 [Michael Helbling]: Sell starter, able to juggle multiple priorities simultaneously. It’s like, ugh.

00:56:32.90 [Tim Wilson]: Often when the hiring manager isn’t an analyst, then that’s why that job description doesn’t have the shadow work in it. And that does some of the.

00:56:39.96 [Michael Helbling]: And it comes out ringing false. Yeah. Well, some of my shadow work is trying to get the show wrapped up on time. So let’s go to do that.

00:56:49.96 [Tim Wilson]: We got to find somebody who’s good at it.

00:56:51.36 [Michael Helbling]: All right. Let’s hand that off to somebody else. All right. Well, listen, Moe and Val and Tim, thank you so much. This is, I think, a really interesting topic. And I appreciate your insights on the show. A lot of work is really important, but doesn’t necessarily get recognized for what it is. And I think that’s sort of where this discussion took us today. So thank you for that. You know, as you’re listening, I imagine you’re thinking some of the thoughts yourself. We’d love to hear from you and you can reach out to us. You can reach out to us on LinkedIn or the Measure Slack chat group or through email at contact at analyticshour.io. And if you’re listening to this on Apple podcasts or Spotify or whatever platform you listen to it, give us a review or a rating or a comment. We’d love to see it, love to hear it, love to hear from you. And of course, a couple other things. We’re not doing less calls, but a couple of things where you can find us coming up this year. is at a couple of few conferences and actually coming up really quickly. So, I know Tim and Val, you all will be at the Datatune conference in Nashville. Is that right? You want to talk about it?

00:58:09.09 [Tim Wilson]: Yeah. It’s a little, it’s a Friday is workshops and Saturday, it’s a Conference, it’s a pretty low cost, low three-figures conference all day. It looks kind of not measure campy from an unconference perspective, but from a enthusiasm and critical people, a lot of people, critical mass of people showing up pretty interesting topics.

00:58:32.32 [Michael Helbling]: What are the dates?

00:58:33.02 [Tim Wilson]: Oh, that would be important. Yeah.

00:58:37.65 [Michael Helbling]: I’m here for you.

00:58:38.81 [Tim Wilson]: I’m here for you.

00:58:40.11 [Michael Helbling]: Talk about shadow work.

00:58:43.88 [Tim Wilson]: What is it?

00:58:46.78 [Michael Helbling]: That’s awesome. And then, of course, Measure Camp New York will be in March 28th in New York City. It’s officially in New York City, not New Jersey this year.

00:58:56.14 [Val Kroll]: Very exciting. Very exciting stuff.

00:58:58.06 [Michael Helbling]: Yeah, it’s going to be a great… Measure Camp is always a great time. Obviously, Val’s super involved with Measure Camp Chicago. Moe with Measure Camp Sydney. Tim with Measure Camp Columbus. Me with not being involved with Measure Camp in any official capacity, but I love going to them. And I think right now Tim and I are planning to be at that one, and that’s March 28th in New York City. And then finally, April 28th and 29th, the whole Analytics Power Hour, or a lot of the Analytics Power Hour folks will be at the Marketing Analytics Summit in Santa Barbara, California, which sunshine on the West Coast. Hello. Get there. We love to.

00:59:35.50 [Tim Wilson]: We got some exciting plans for that. Stay tuned to future episodes.

00:59:39.92 [Michael Helbling]: What’s the drink that you have in Santa Barbara? What’s like a good cocktail for that?

00:59:45.15 [Tim Wilson]: I’m sure it’s some fruity California liberal.

00:59:48.22 [Michael Helbling]: Wine. Exactly. Wine. White wine or rosé on the beach or in the sunshine.

00:59:54.24 [Moe Kiss]: Love this.

00:59:54.70 [Michael Helbling]: Love this from me. I don’t know. I’m terrible at picking out drinks. All right. That’s the show. We’re excited to have brought it to you. And I think I speak for all my co-hosts when I say, no matter whether the work is in the shadows or way out in the open, keep analyzing.

01:00:15.26 [Announcer]: Thanks for listening. Let’s keep the conversation going with your comments, suggestions, and questions on Twitter at @analyticshour on the web at analyticshour.io, our LinkedIn group, and the Measure Chat Slack group. Music for the podcast by Josh Crowhurst.

01:00:33.22 [Charles Barkley]: Those smart guys wanted to fit in, so they made up a term called analytics. Analytics don’t work.

01:00:39.83 [Charles Barkley]: Do the analytics say go for it, no matter who’s going for it? So if you and I were on the field, the analytics say go for it. It’s the stupidest, laziest, lamest thing I’ve ever heard for reasoning in competition.

01:00:53.06 [Michael Helbling]: Lacy Fusion Productions. Lacy Fusion.

01:00:58.51 [Tim Wilson]: That’s our production studio’s sister organization on Southern Hemisphere covering Lacy Fusion Media.

01:01:05.83 [Michael Helbling]: 4th floor productions, Lacyfusion Media.

01:01:08.74 [Val Kroll]: Known for expanding into Australia.

01:01:12.26 [Michael Helbling]: Ken Riverside. And the Lacyfusion Media. Present a 4th floor production.

01:01:21.68 [Val Kroll]: Okay, well screw your green bars. You sound like you’re in this building with a paper cup and a string. All right, love you.

01:01:43.82 [Michael Helbling]: Your temperature, Matt.

01:01:50.70 [Moe Kiss]: She is so cute.

01:01:52.76 [Michael Helbling]: I know. It’s ridiculous.

01:01:55.49 [Moe Kiss]: So cute.

01:02:02.79 [Tim Wilson]: Rock flag and who knows what insights lurk in the tables of our databases. The shadow analyst knows.

01:02:15.79 [Michael Helbling]: Nice. That’s actually pretty close.

01:02:20.54 [Moe Kiss]: I’m like, Damn, you got the voice.

01:02:23.81 [Tim Wilson]: That’s got something.