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[Announcer]: Welcome to the Analytics Power Hour.

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[Announcer]: Analytics topics covered conversationally and sometimes with explicit language.

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[Michael Helbling]: Hey everybody, welcome to the Analytics Power Hour.

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[Michael Helbling]: This is episode 291.

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[Michael Helbling]: Who knows what evil lurks in the heart of men?

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[Michael Helbling]: The Shadow knows.

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[Michael Helbling]: 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.

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[Michael Helbling]: That's not really the job description.

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[Michael Helbling]: It usually doesn't get recognized, but you do it anyway.

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[Michael Helbling]: 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.

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[Michael Helbling]: I think we should talk about it.

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[Michael Helbling]: Let me introduce my co-hosts.

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[Michael Helbling]: Moee Kisss.

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[Michael Helbling]: How you going?

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[Moe Kiss]: I'm going great.

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[Moe Kiss]: Thanks for checking in.

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[Michael Helbling]: Have you ever heard of The Shadow?

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[Michael Helbling]: The radio show, The Shadow?

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[Moe Kiss]: It was like from the early... No, but I'm deeply familiar with the sentiment.

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[Michael Helbling]: Oh, okay.

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[Michael Helbling]: Yeah, yeah.

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[Michael Helbling]: And Val Kroll, welcome.

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[Michael Helbling]: Thank you.

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[Michael Helbling]: Bye, everyone.

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[Michael Helbling]: Go Bears.

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[Michael Helbling]: Yeah.

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[Michael Helbling]: And...

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[Michael Helbling]: Hey, it was close.

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[Michael Helbling]: Tim Wilson, probably the only person that got.

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[Michael Helbling]: I remember sitting around.

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[Michael Helbling]: I was going to ask you if you remember.

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I was going

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[Michael Helbling]: I think first up, maybe let's talk about what kinds of shadow work have you found yourself getting into in your career?

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[Michael Helbling]: Like what are some of the categories or the types of things you've gotten into?

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[Michael Helbling]: 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.

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[Michael Helbling]: So who wants to start us off with some of the stuff you've run into?

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[Moe Kiss]: Oh, I mean, the one that starts with a capital A, admin.

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[Moe Kiss]: And I think this is potentially more on the internal side.

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[Moe Kiss]: I'm going to be curious to hear reflections.

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[Moe Kiss]: But I feel like there ends up being a lot of cadences in a business.

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[Moe Kiss]: And I think I've gotten to a point now where I kind of see it.

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[Moe Kiss]: 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.

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[Moe Kiss]: Have you done this bit of this deck?

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[Moe Kiss]: All of that.

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[Moe Kiss]: And some people might think that that's fair.

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[Moe Kiss]: But in a space where you have admin support and folks who are meant to have that as part of their role,

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[Moe Kiss]: feel like I see data, people end up having to fill that gap a lot just to keep momentum moving forward.

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[Moe Kiss]: And it's almost like once you assume responsibility for it, it's almost impossible to ever roll it back.

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[Tim Wilson]: I've thought, I mean, there's one specific part of that.

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[Tim Wilson]: There's like the input, I need to do admin to get stuff.

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[Tim Wilson]: And then when you first said admin, I was thinking like,

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[Tim Wilson]: user governance, like, oh, somebody needs access to whatever.

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[Tim Wilson]: I feel like there's an admin part that I think is good for the analyst when

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[Tim Wilson]: 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.

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[Tim Wilson]: as an accountability mechanism for the analysts to say, oh, I'm going to be here because I know how to set recurring reminders.

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[Tim Wilson]: I'm going to set a reminder to come back and say, hey, you said that was great.

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[Tim Wilson]: In the next release, you were going to do X or Y. Did you do it?

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[Tim Wilson]: I don't think that's admin though.

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[Moe Kiss]: That's not admin.

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[Moe Kiss]: What's the word?

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[Moe Kiss]: That's checking back to be like, if we said there was going to be some outcome, did we achieve that outcome?

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[Moe Kiss]: 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.

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[Moe Kiss]: I think it's more when you're like,

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[Moe Kiss]: I know, Tim, you're going to have strong views on this.

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[Moe Kiss]: 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.

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[Moe Kiss]: because ultimately like a data person might be responsible for making sure the reports are not or whatever.

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[Moe Kiss]: I think there's a difference between like ownership and making sure you're accountable and like

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[Moe Kiss]: Following up people to make sure they do their job.

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[Moe Kiss]: Oh, this is going to be like a trigger point, Tim.

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[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.

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[Michael Helbling]: 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.

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[Michael Helbling]: We're riding shotgun with the project now.

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[Michael Helbling]: It's like, wait a second, when do we stop doing the analysis and start being the project managers for the implementation of this?

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[Michael Helbling]: That was when I was like, wait a second, what job do I actually have here?

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[Michael Helbling]: Because you're kind of like, I'm not now not doing data analytics.

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[Michael Helbling]: I'm now running sort of like an integration task force, if you will.

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[Michael Helbling]: So I don't know if that's more like in the line of what you're talking about.

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[Moe Kiss]: It's such a fine line though, right?

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

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[Moe Kiss]: And it's something that I do worry sometimes like data folks are like, here, I've got a recommendation.

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[Moe Kiss]: I'm going to throw it over the fence.

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[Moe Kiss]: It's your choice if you do it.

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[Moe Kiss]: And like not taking ownership.

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[Moe Kiss]: I think part of being a strategic partner is taking ownership and being like, I've made this recommendation.

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[Moe Kiss]: We've agreed on it.

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[Moe Kiss]: I like, I want to see it forward.

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[Moe Kiss]: And I'm, I'm part of this.

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[Moe Kiss]: I'm accountable to it too, because I've made this recommendation.

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[Moe Kiss]: 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.

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[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.

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[Tim Wilson]: I mean, that's kind of like business 101.

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[Tim Wilson]: If it's like, well, everybody agrees, but no one actually assigned, there was no ownership assigned.

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[Tim Wilson]: If you can do that in the moment, then a lot of times it's like, well, who should be doing this?

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[Tim Wilson]: If I wait and we haven't got it, then it shouldn't be the analyst.

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[Tim Wilson]: But if everybody leaves and the analyst is saying, well, nobody's gonna do it unless I step up and do it,

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[Tim Wilson]: 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.

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[Tim Wilson]: So what is the next milestone?

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[Tim Wilson]: Who's going to do what by when?

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[Tim Wilson]: And then looking at that person and being like, are they going to do it or is somebody going to need to babysit them?

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[Tim Wilson]: Which isn't, I mean, that's kind of a reality of business as much as the analyst role, I guess.

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[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?

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[Val Kroll]: Can you send me that thing?

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[Val Kroll]: Or can I have access to that?

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[Val Kroll]: Especially if it's like I need one of your other partners or other agencies to send me or give me access to something.

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[Val Kroll]: The number of times, like, top of a call, like, okay, moving around a lot.

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[Val Kroll]: Did you get approval for that one thing?

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[Val Kroll]: Are we good to move forward with that thing?

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[Val Kroll]: Which is, like, a lot less connected to meaningful stuff.

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[Michael Helbling]: Explaining another agency's data analytics to the client.

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[Michael Helbling]: That's some shadow work right there.

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[Michael Helbling]: 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.

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[Michael Helbling]: Not that I don't want to help you, but

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[Michael Helbling]: I've had many experiences where they're like, okay, we got this from this.

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[Michael Helbling]: 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.

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[Michael Helbling]: They're like, how did they get these numbers?

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[Michael Helbling]: And I'm like, okay, so now you need me to go reverse engineer how they pulled these numbers together.

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[Michael Helbling]: And it's like, oh boy.

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[Tim Wilson]: That's not a bad part of shadow work, getting poorly documented, regardless of where it comes from.

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[Tim Wilson]: Somebody wants me to take it forward.

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[Tim Wilson]: 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.

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[Tim Wilson]: 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.

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[Tim Wilson]: You're at the starting line.

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[Tim Wilson]: 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.

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[Michael Helbling]: That's a good point.

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[Michael Helbling]: 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.

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[Michael Helbling]: But sometimes it falls in your lap in a weird way, maybe.

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[Moe Kiss]: Okay.

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[Moe Kiss]: And I think the thing that comes to mind is the word alignment.

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[Moe Kiss]: So like not all shadow work is shit.

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[Moe Kiss]: Some shadow work is actually very valuable.

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[Moe Kiss]: It's just the fact that the business doesn't understand like how consuming it is or how important it is.

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[Moe Kiss]: 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

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[Moe Kiss]: the metric definition, whether it's about the outcome of the work or like that alignment pace, I think is incredibly important.

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[Moe Kiss]: 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.

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[Tim Wilson]: I second that.

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[Tim Wilson]: I mean, I think even the alignment, what is it we ran this campaign?

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[Tim Wilson]: What was it supposed to do?

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[Tim Wilson]: And then the fact that the analysts are like, well, I need to be in the meeting up front like that.

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[Tim Wilson]: We need to make sure everybody's on the same page of what we're trying to accomplish.

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[Tim Wilson]: It's not run it.

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[Tim Wilson]: And then the analyst gets involved because the data now exists so they can pull it and they can provide the answers.

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[Tim Wilson]: 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.

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[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.

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[Val Kroll]: 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.

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[Val Kroll]: 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.

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[Val Kroll]: 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.

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[Tim Wilson]: And I feel like sometimes that's a little... The culture of finance, the culture of accounting.

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[Tim Wilson]: Famous.

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[Val Kroll]: Famous for going around to get people on board.

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[Val Kroll]: 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.

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[Val Kroll]: It's not that it's not important, but it's usually not the first thing that comes to mind.

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[Tim Wilson]: It does seem like maybe to bridge from that to explaining the realities of the data, which kind of takes two angles.

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[Tim Wilson]: 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.

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[Tim Wilson]: It is always wildly more complicated than any kind of new person to it thinks it is.

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[Tim Wilson]: And then there's the other part of that that is what the data can and can't deliver.

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[Tim Wilson]: Like the data is the objective truth.

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[Tim Wilson]: So there's a data

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[Tim Wilson]: fluency component where it does sometimes feel like in analytics, and maybe this is the grass is always greener on the other side.

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[Tim Wilson]: 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.

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[Tim Wilson]: I feel like in marketing and product and digital analytics, sometimes it's like

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[Tim Wilson]: There's not a presumption of knowledge of complexity.

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[Tim Wilson]: 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.

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[Tim Wilson]: educating of the business partners that does feel like it's a proportionally heavier lift than many other roles.

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[Tim Wilson]: Does that count as shadow work?

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

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[Tim Wilson]: Pull GA4, then export to Excel, write SQL for BigQuery, find my LTV formula.

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[Tim Wilson]: I don't know, let's say about three hours in a couple of existential crises.

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[Tim Wilson]: At least two.

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[Michael Helbling]: This is why ask-wise full-stack approach works.

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[Michael Helbling]: Ask in plain English, prism orchestrates across your stack and applies your saved calculations.

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

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[Michael Helbling]: Nope, everything's traceable, not a black box.

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[Michael Helbling]: DataState secure, semantic layer, generated code, runs locally.

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[Michael Helbling]: It's all set up for you.

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

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[Michael Helbling]: I'm not sure making fun of our sponsor's name is the move here, Tim.

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[Michael Helbling]: Wait, I did say pretty sophisticated.

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[Michael Helbling]: That's a compliment.

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[Michael Helbling]: All right, fair enough.

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[Michael Helbling]: Well, go to ask-y.ai, that's ask-y.ai, and use code APH for priority beta access.

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[Michael Helbling]: Join the rise of the AI analyst.

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[Val Kroll]: 100%.

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[Val Kroll]: Yeah, I think so.

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[Val Kroll]: 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.

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[Val Kroll]: 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.

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[Val Kroll]: to overcome the sampling.

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[Val Kroll]: 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.

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[Val Kroll]: And I remember it like there was some, some backend developers were like, Oh, perfect.

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[Val Kroll]: 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.

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[Val Kroll]: And I was like, it doesn't work like that.

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[Val Kroll]: Also, like, what do you mean everything?

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[Val Kroll]: Like, do you even, but like so many conversations, conversations that got escalated, my boss had to pull me into it.

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[Val Kroll]: And it was like, you guys,

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[Val Kroll]: 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.

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[Val Kroll]: But the other thing is that membership cube, the ID, the key was the membership ID.

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[Val Kroll]: 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?

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[Val Kroll]: 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.

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[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.

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[Michael Helbling]: You know, you walk into an Oregon, they're like, oh, we want to do this, this and this amazing thing.

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[Michael Helbling]: 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.

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[Moe Kiss]: And you're helps.

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[Michael Helbling]: Yeah.

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[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?

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[Moe Kiss]: Like the data wasn't structured in a way that we can answer the business questions of today.

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[Moe Kiss]: 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.

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[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.

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[Michael Helbling]: 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.

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[Michael Helbling]: Eventually, you can write the business case to show where the value will come from, but man, it's an uphill battle.

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[Michael Helbling]: I don't know if that's shadow work exactly.

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[Tim Wilson]: I think there's often, I mean, I will see that example and raise it one with wait for a year.

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[Tim Wilson]: 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

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[Tim Wilson]: you know, data store of some sort.

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[Tim Wilson]: And so many requests, they'd say, oh, we don't have that yet, but it's all going in.

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[Tim Wilson]: And they were just locked into these backend developers said, we're going to take the Adobe's horribly

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[Tim Wilson]: weird and never really thought through, gotta take the Viz high and Viz low, like stitching like messy, messy, messy data feed data.

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[Tim Wilson]: And they were saying, we're just gonna pump it in at that raw level.

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[Tim Wilson]: And then we'll just kind of write SQL queries that people can use.

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[Tim Wilson]: I'm like, the SQL query just to answer how many users came to this page is kind of a beast.

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[Tim Wilson]: But we couldn't get an audience with them because they were just convinced, which seems very common with

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[Tim Wilson]: developers.

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[Tim Wilson]: 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.

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[Tim Wilson]: They don't think of

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[Tim Wilson]: the need for stuff to be deduplicated by something.

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[Tim Wilson]: So this idea that, well, we'll just pump all the raw data in, and then you'll be set.

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[Tim Wilson]: You'll just have to write SQL, which then becomes a case of needing to maintain SQL libraries, I think.

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[Tim Wilson]: 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.

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[Tim Wilson]: Oh, or yeah.

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[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.

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[Tim Wilson]: But sometimes that's to me, you're like, you've gone with this, like, let's

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[Tim Wilson]: Let's get the full ocean, and then we're going to add layers on top of it.

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[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.

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[Michael Helbling]: So you put yourself in a pretty challenging chain of events just to get answers to data, which AI will totally solve.

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[Michael Helbling]: So don't worry.

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[Moe Kiss]: Literally, that's about to be my comment.

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[Moe Kiss]: 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.

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[Moe Kiss]: 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.

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[Moe Kiss]: That's the problem that we need to solve.

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[Moe Kiss]: And yeah, it's not sexy.

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[Moe Kiss]: Like getting the buying is incredibly difficult for this stuff.

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[Moe Kiss]: And it probably is the hardest.

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[Moe Kiss]: I would say one of the hardest parts of my role right now.

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[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.

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[Tim Wilson]: So the analyst becomes the proxy for the business and is now dealing with the backend.

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[Tim Wilson]: And so they become subject matter experts in an area that has

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[Tim Wilson]: Nothing to do with running analyses or validating hypotheses.

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[Tim Wilson]: It's just they're living in that middle tier and there's just no one else.

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[Tim Wilson]: The shadow has to serve it because there is no one.

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[Tim Wilson]: That's all there is.

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[Tim Wilson]: That's the best you got.

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[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

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[Moe Kiss]: to use it well and like that, then you've created your own bottleneck, right?

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[Moe Kiss]: And it's not an intentional thing.

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[Moe Kiss]: 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.

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[Moe Kiss]: But I would say that is one of the hardest shadow tasks for sure.

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[Tim Wilson]: There does seem like there's like a macro thought, this whole topic of the show that it's like the

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[Tim Wilson]: I feel like I've worked with analysts who take the attitude, well, that's, that shouldn't be my job.

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[Tim Wilson]: So it's not my job.

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[Tim Wilson]: So I'm not going to do it.

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[Tim Wilson]: And then it kind of falls through the cracks and doesn't happen.

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[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.

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[Michael Helbling]: And so like, I'm definitely much more of a generalist.

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[Michael Helbling]: So when I find myself running further afield of doing the actual data work and the analysis, it doesn't bug me at all.

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[Michael Helbling]: It's actually kind of fun to see something different and do something different for a little while.

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[Michael Helbling]: I sometimes will think about, is this really truly serving our purpose?

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[Michael Helbling]: Are we getting done?

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[Michael Helbling]: We need to get done.

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[Michael Helbling]: But generally speaking, doing those tasks, not a big deal.

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[Michael Helbling]: I feel great about it.

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[Michael Helbling]: But I absolutely think there are people who

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[Michael Helbling]: 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.

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[Michael Helbling]: And so like that's the other issue is just sort of like the person kind of matters a little bit to this too.

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[Val Kroll]: Yeah.

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[Val Kroll]: 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

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[Val Kroll]: understand what I'm analyzing or to understand root cause of like why that had been a problem.

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[Val Kroll]: 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.

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[Val Kroll]: 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.

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[Val Kroll]: So I've never, I've never tried to quiet that voice.

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[Val Kroll]: Also, I'm just really nosy.

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[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.

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[Tim Wilson]: You have a registration and attendance.

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[Tim Wilson]: This was in a business model where it was not that at all, and it was like

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[Tim Wilson]: bonkers how salespeople would sometimes go into an office and sit and watch the webinars, and there were two or three systems involved.

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[Tim Wilson]: 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.

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[Tim Wilson]: I wound up writing up

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[Tim Wilson]: 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.

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[Tim Wilson]: I have done the most glorious, valuable.

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[Tim Wilson]: This is so useful.

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[Tim Wilson]: I'm pretty sure not even the webinar business owner.

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[Tim Wilson]: read it.

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[Tim Wilson]: 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,

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[Tim Wilson]: has done to really help you understand what's going on here.

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[Tim Wilson]: And there was kind of no interest.

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[Tim Wilson]: So that was one where I'm like, it was useful for me.

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[Tim Wilson]: It should have been useful downstream.

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[Tim Wilson]: In today's world now, boy, I'd be throwing that into an LLM somewhere and saying, that's really helpful data potentially.

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[Tim Wilson]: But I'm pretty sure that document, I was like, I became the domain expert on something that

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[Tim Wilson]: People cared about webinars, they did not care to hear how messy it was to interpret any of the data that was captured.

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[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.

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[Moe Kiss]: But one of them is be unwaveringly useful.

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[Moe Kiss]: Does anyone, pop quiz, anyone remember where that comes from?

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[Tim Wilson]: I don't think so.

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[Moe Kiss]: Oh, from being useful would be... A good friend, Cassie.

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[Moe Kiss]: Yep.

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[Moe Kiss]: I got it.

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[Moe Kiss]: Ding, ding, ding.

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[Moe Kiss]: Yeah.

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[Moe Kiss]: Yeah.

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[Moe Kiss]: She put it in one of her blog articles and it's always resonated with me.

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[Moe Kiss]: And I'm completely contradicting myself now because at the start I was like, don't pick up the admin work.

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[Moe Kiss]: 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.

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[Moe Kiss]: So like I am, yeah, a walking contradiction.

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[Moe Kiss]: But I do think there is part of that.

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[Moe Kiss]: responsibility of data folk like I tend to get really frustrated when a data person is like, well, that's not my job.

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[Moe Kiss]: And I'm like, your job is to help the business make better decisions.

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[Moe Kiss]: So if there's something you can do to be useful to help the business make better decisions, that is your job.

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[Moe Kiss]: Yeah, I don't know.

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[Moe Kiss]: 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.

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[Moe Kiss]: Sometimes it is about documenting something that no one in the business has written down.

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[Moe Kiss]: It's like, sometimes those things are less useful, but a lot of the times are really useful.

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[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.

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[Michael Helbling]: 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?

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[Michael Helbling]: 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

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[Michael Helbling]: 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.

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[Michael Helbling]: Cause like I analyze data and that's what I like to do.

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[Michael Helbling]: And I very stressed out every time I have to go present something.

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[Michael Helbling]: 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.

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[Michael Helbling]: So it doesn't have to be you necessarily taking on that role.

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[Michael Helbling]: I don't know if I agree.

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[Michael Helbling]: So don't pick presenting something then, something else, like managing the project or something like that.

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[Michael Helbling]: But the point being, like, not every person fits every single role.

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[Michael Helbling]: Like, you don't have to be a polyglot, if you will.

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[Michael Helbling]: Or a polymath.

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[Tim Wilson]: What that, I mean, if you... Poly PM.

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[Tim Wilson]: First break all the rules, like the precursor to the now discover your strengths, strengths finder, which... But first break all the rules.

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[Tim Wilson]: I've always, to that same point, identifying what needs to happen.

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[Tim Wilson]: I think, Moe, that's the brilliant way to frame it.

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[Tim Wilson]: What is your job?

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[Tim Wilson]: It is not to write SQL.

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[Tim Wilson]: It is not to develop reports.

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[Tim Wilson]: It is not to deliver results.

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[Tim Wilson]: It is to move the organization forward by helping them make decisions.

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[Tim Wilson]: If you say, well,

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[Tim Wilson]: That means that somebody every Tuesday morning needs to reach out to this one person and ask them a question.

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[Tim Wilson]: Like it can be frustrating.

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[Tim Wilson]: It can suck.

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[Tim Wilson]: But you know what?

366
00:31:12,493 --> 00:31:17,441
[Tim Wilson]: There's somebody who's actually super sociable, who loves to ping people or whatever.

367
00:31:18,242 --> 00:31:22,408
[Tim Wilson]: Like building up that list is kind of, these are the discrete tasks.

368
00:31:22,428 --> 00:31:28,718
[Tim Wilson]: Not that somebody's going to love and relish doing every one of them, but it does at a team level.

369
00:31:28,698 --> 00:31:44,322
[Tim Wilson]: 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.

370
00:31:44,903 --> 00:31:55,600
[Tim Wilson]: 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

371
00:31:55,580 --> 00:32:00,907
[Tim Wilson]: need to be able to do and do even if they're outside of their comfort zone.

372
00:32:02,008 --> 00:32:03,470
[Tim Wilson]: So it's, it gets a little squishy.

373
00:32:03,530 --> 00:32:06,915
[Tim Wilson]: Which of this is shadow work that like somebody's got to do it, this person gravitates to it.

374
00:32:06,995 --> 00:32:17,449
[Tim Wilson]: Which of this is going to be a really ineffective handoff because someone just doesn't, doesn't want to write sequel.

375
00:32:17,469 --> 00:32:18,610
[Tim Wilson]: I mean, they'll use that example.

376
00:32:18,630 --> 00:32:24,658
[Tim Wilson]: Somebody, I don't want to, I'm just not the kind of analyst who's going to learn to write

377
00:32:24,638 --> 00:32:25,180
[Tim Wilson]: code.

378
00:32:25,300 --> 00:32:30,154
[Tim Wilson]: It's like, cool, then you're not the kind of analyst who's going to progress particularly far in your career.

379
00:32:30,394 --> 00:32:31,457
[Tim Wilson]: So, cool.

380
00:32:31,698 --> 00:32:32,119
[Tim Wilson]: We got it.

381
00:32:32,881 --> 00:32:33,965
[Michael Helbling]: Hey, I've gotten pretty far.

382
00:32:34,225 --> 00:32:37,434
[Michael Helbling]: So, you know, no, now you can't do it.

383
00:32:37,816 --> 00:32:38,698
[Michael Helbling]: You can't.

384
00:32:39,320 --> 00:32:52,405
[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.

385
00:32:52,565 --> 00:33:00,079
[Moe Kiss]: 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

386
00:33:01,358 --> 00:33:11,615
[Moe Kiss]: 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.

387
00:33:12,356 --> 00:33:18,286
[Moe Kiss]: We very intentionally hired someone that was really strong in that space to complement our team.

388
00:33:18,887 --> 00:33:22,793
[Moe Kiss]: I think that we really need to be thoughtful of what are all those

389
00:33:22,773 --> 00:33:32,411
[Moe Kiss]: things, especially the shadow work, because if you put someone on something and that's their strength, it's so much easier for everyone.

390
00:33:32,432 --> 00:33:36,259
[Moe Kiss]: They feel like they're adding value, that the balance feels better.

391
00:33:36,920 --> 00:33:43,633
[Moe Kiss]: 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.

392
00:33:43,613 --> 00:33:45,836
[Tim Wilson]: Can we hit on that stuff a little bit?

393
00:33:46,156 --> 00:33:54,267
[Tim Wilson]: And maybe this administrative work, maybe more broadly, because I think that is the danger there.

394
00:33:54,487 --> 00:34:03,338
[Tim Wilson]: 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.

395
00:34:03,478 --> 00:34:10,127
[Tim Wilson]: And they're like, oh, well, it's admin work, like the latent misogyny

396
00:34:10,107 --> 00:34:21,909
[Tim Wilson]: 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.

397
00:34:22,069 --> 00:34:31,407
[Tim Wilson]: 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.

398
00:34:31,387 --> 00:34:34,370
[Tim Wilson]: Shadow work that is has to be done.

399
00:34:34,410 --> 00:34:35,851
[Tim Wilson]: There is value.

400
00:34:35,911 --> 00:34:49,843
[Tim Wilson]: 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.

401
00:34:49,903 --> 00:34:52,085
[Tim Wilson]: Even though everybody's like, well, this all needs to be done.

402
00:34:52,145 --> 00:34:58,591
[Tim Wilson]: But good old Jane is, you know, always there for it, you know, but it's in the shadows.

403
00:34:58,751 --> 00:34:59,892
[Tim Wilson]: It's not getting

404
00:34:59,872 --> 00:35:00,253
[Michael Helbling]: Yeah.

405
00:35:00,493 --> 00:35:01,354
[Michael Helbling]: That's not visible.

406
00:35:01,695 --> 00:35:19,721
[Michael Helbling]: 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.

407
00:35:19,761 --> 00:35:22,004
[Michael Helbling]: What's being done, the people doing it need to be recognized.

408
00:35:22,064 --> 00:35:26,250
[Michael Helbling]: And then who should be doing it, should be much more strategically thought out as opposed to

409
00:35:26,230 --> 00:35:35,038
[Michael Helbling]: 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.

410
00:35:36,059 --> 00:35:38,382
[Michael Helbling]: So anyways, I thought that was a really great point, Tim.

411
00:35:38,422 --> 00:35:50,593
[Michael Helbling]: 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.

412
00:35:50,753 --> 00:35:54,617
[Michael Helbling]: When you're a leader, you're like, we need to take the shadow work and expose it to the light.

413
00:35:55,998 --> 00:35:56,759
[Tim Wilson]: That sounds hard.

414
00:35:56,879 --> 00:35:59,463
[Tim Wilson]: That's why I'm not going to, I'm not striving to be a leader.

415
00:35:59,483 --> 00:36:03,750
[Moe Kiss]: I do think though it also is about like recognition.

416
00:36:03,790 --> 00:36:17,111
[Moe Kiss]: 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?

417
00:36:17,271 --> 00:36:19,735
[Moe Kiss]: Because I value that work.

418
00:36:19,755 --> 00:36:22,579
[Moe Kiss]: And so I think where the challenge is is like,

419
00:36:22,559 --> 00:36:31,027
[Moe Kiss]: 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.

420
00:36:31,487 --> 00:36:34,029
[Moe Kiss]: Whereas I see it as like being incredibly essential.

421
00:36:34,150 --> 00:36:39,394
[Moe Kiss]: And if you do that shit well, like you can unlock a lot for your your team or the business.

422
00:36:40,015 --> 00:36:42,637
[Moe Kiss]: And so like, I want to make sure that that's rewarded and reflected.

423
00:36:42,677 --> 00:36:49,083
[Moe Kiss]: 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.

424
00:36:49,143 --> 00:36:49,984
[Moe Kiss]: And yeah,

425
00:36:49,964 --> 00:36:50,646
[Moe Kiss]: and many factors.

426
00:36:50,666 --> 00:36:54,819
[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.

427
00:36:54,839 --> 00:36:56,845
[Moe Kiss]: She values you.

428
00:36:56,865 --> 00:36:57,266
[Tim Wilson]: Oh, wow.

429
00:36:57,507 --> 00:36:59,633
[Tim Wilson]: She gave us the name off Mike and it was your name.

430
00:36:59,834 --> 00:37:00,717
[Tim Wilson]: So good job.

431
00:37:00,837 --> 00:37:01,258
[Moe Kiss]: Stop it.

432
00:37:01,278 --> 00:37:02,843
[Moe Kiss]: You were so cruel.

433
00:37:03,667 --> 00:37:10,833
[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.

434
00:37:11,234 --> 00:37:25,266
[Val Kroll]: 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?

435
00:37:25,366 --> 00:37:27,488
[Val Kroll]: Just like analyze, analyze.

436
00:37:27,508 --> 00:37:31,231
[Val Kroll]: Like that's so not all that the job is, right?

437
00:37:31,832 --> 00:37:33,353
[Val Kroll]: So there's a lot more.

438
00:37:33,333 --> 00:37:42,365
[Val Kroll]: 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.

439
00:37:42,385 --> 00:37:51,878
[Val Kroll]: 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.

440
00:37:51,898 --> 00:37:53,620
[Val Kroll]: So that's a really good point.

441
00:37:55,118 --> 00:37:59,025
[Michael Helbling]: I think what we're finding out is that the work has value.

442
00:38:00,347 --> 00:38:06,898
[Michael Helbling]: Whether we should be doing it or not as analytics people isn't necessarily all the story.

443
00:38:06,938 --> 00:38:15,372
[Michael Helbling]: 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.

444
00:38:15,352 --> 00:38:18,097
[Michael Helbling]: rearrange it and come up with a strategy.

445
00:38:18,718 --> 00:38:31,540
[Michael Helbling]: 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.

446
00:38:31,841 --> 00:38:37,731
[Michael Helbling]: What we exposed was no one had thought about, hey, what if we have an optimization, we want to make a reality?

447
00:38:37,751 --> 00:38:39,975
[Michael Helbling]: How does that get done in our company?

448
00:38:39,955 --> 00:38:48,227
[Michael Helbling]: 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.

449
00:38:48,748 --> 00:38:50,570
[Michael Helbling]: But it's the same thing with all the rest of it.

450
00:38:50,630 --> 00:38:55,638
[Michael Helbling]: It's sort of like, okay, well, what are the parts that need to move into the right places to get it done?

451
00:38:55,658 --> 00:38:59,483
[Michael Helbling]: Not necessarily you, the data analyst should do it, but

452
00:38:59,463 --> 00:39:13,658
[Michael Helbling]: 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.

453
00:39:13,857 --> 00:39:15,079
[Moe Kiss]: Can we talk about data quality?

454
00:39:15,499 --> 00:39:17,943
[Moe Kiss]: We have not touched on that at all.

455
00:39:17,963 --> 00:39:19,064
[Michael Helbling]: It's usually pretty good.

456
00:39:19,084 --> 00:39:19,185
[Michael Helbling]: Yeah.

457
00:39:19,645 --> 00:39:21,147
[Michael Helbling]: I mean, just kind of automatically.

458
00:39:21,808 --> 00:39:22,149
[Michael Helbling]: Yeah.

459
00:39:22,209 --> 00:39:22,630
[Michael Helbling]: What do you mean?

460
00:39:22,750 --> 00:39:23,591
[Michael Helbling]: What was there to talk about?

461
00:39:23,831 --> 00:39:25,874
[Michael Helbling]: So I'm pretty sure.

462
00:39:25,894 --> 00:39:26,355
[Moe Kiss]: I think it's going.

463
00:39:26,375 --> 00:39:27,036
[Michael Helbling]: I think it's going.

464
00:39:27,196 --> 00:39:28,278
[Moe Kiss]: Oh my God, stop.

465
00:39:28,298 --> 00:39:29,940
[Moe Kiss]: Everyone stop triggering me.

466
00:39:30,040 --> 00:39:30,541
[Michael Helbling]: Sorry.

467
00:39:30,661 --> 00:39:33,465
[Michael Helbling]: Sorry, well.

468
00:39:33,505 --> 00:39:34,987
[Moe Kiss]: Just come on.

469
00:39:36,409 --> 00:39:42,598
[Moe Kiss]: I think the one that I'm specifically comes to mind is

470
00:39:43,186 --> 00:39:46,654
[Moe Kiss]: Bend sent from a media agency.

471
00:39:50,021 --> 00:39:56,816
[Moe Kiss]: And I just get so frustrated or from a finance team.

472
00:39:56,956 --> 00:40:02,629
[Michael Helbling]: Talk about the highly formatted Excel files you might be receiving.

473
00:40:03,098 --> 00:40:07,446
[Tim Wilson]: In wide format when they should be in a long format.

474
00:40:08,307 --> 00:40:09,610
[Moe Kiss]: Of course.

475
00:40:09,630 --> 00:40:11,874
[Moe Kiss]: I'm glad you could all laugh about it.

476
00:40:12,134 --> 00:40:14,318
[Moe Kiss]: I am not at the laughing stage.

477
00:40:14,398 --> 00:40:19,808
[Charles Barkley]: Sorry, well, this is probably a whole episode we need to do on stuff like this.

478
00:40:20,952 --> 00:40:30,990
[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.

479
00:40:31,290 --> 00:40:32,172
[Moe Kiss]: I didn't get it.

480
00:40:32,452 --> 00:40:34,356
[Moe Kiss]: They sent a spreadsheet over on Moenday.

481
00:40:34,376 --> 00:40:35,758
[Moe Kiss]: Like, you've got the data.

482
00:40:35,798 --> 00:40:36,319
[Moe Kiss]: What's the problem?

483
00:40:36,359 --> 00:40:37,541
[Moe Kiss]: Like, why is it going to take you a week?

484
00:40:37,581 --> 00:40:38,623
[Moe Kiss]: And you're like,

485
00:40:38,603 --> 00:40:49,675
[Moe Kiss]: 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?

486
00:40:50,435 --> 00:40:54,219
[Moe Kiss]: No, that is a huge lift.

487
00:40:55,180 --> 00:40:56,922
[Moe Kiss]: And fuck.

488
00:40:57,663 --> 00:41:06,172
[Moe Kiss]: Anyway, and then you've got some very senior, brilliant data scientist that is spending their time basically QAing data.

489
00:41:06,692 --> 00:41:08,374
[Moe Kiss]: It's really frustrating.

490
00:41:08,945 --> 00:41:20,598
[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.

491
00:41:20,638 --> 00:41:33,252
[Tim Wilson]: 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.

492
00:41:33,272 --> 00:41:34,273
[Tim Wilson]: It's like, oh, we'll send you data.

493
00:41:34,293 --> 00:41:34,994
[Tim Wilson]: We'll send you data.

494
00:41:35,074 --> 00:41:37,517
[Tim Wilson]: Check the box.

495
00:41:37,497 --> 00:41:51,293
[Tim Wilson]: 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.

496
00:41:51,994 --> 00:41:56,119
[Tim Wilson]: And the analyst is the one who winds up having to explain their data to them.

497
00:41:56,139 --> 00:41:57,781
[Tim Wilson]: So it's like another version of that.

498
00:41:57,961 --> 00:42:02,627
[Tim Wilson]: That particularly is another version of what you were talking about earlier, Michael, where you have to be like,

499
00:42:02,607 --> 00:42:07,113
[Tim Wilson]: Yeah, how can this possibly be zeros across here?

500
00:42:07,153 --> 00:42:21,612
[Tim Wilson]: 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

501
00:42:21,592 --> 00:42:25,760
[Tim Wilson]: to agree that it's actually a problem or explain to me why it's not a problem.

502
00:42:27,063 --> 00:42:29,788
[Michael Helbling]: I'm literally in a situation like that right now.

503
00:42:30,189 --> 00:42:42,553
[Michael Helbling]: 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.

504
00:42:42,533 --> 00:42:49,000
[Michael Helbling]: And so you're literally starting out with sort of like, okay, well, our first conversation is gonna be, guess what?

505
00:42:49,080 --> 00:42:50,361
[Michael Helbling]: The data you thought was really good?

506
00:42:50,962 --> 00:42:51,503
[Michael Helbling]: Not good.

507
00:42:51,823 --> 00:42:56,909
[Michael Helbling]: 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.

508
00:42:57,409 --> 00:42:59,992
[Michael Helbling]: And it's frustrating but real, right?

509
00:43:00,072 --> 00:43:01,253
[Michael Helbling]: So it's sort of like, yeah.

510
00:43:02,134 --> 00:43:11,224
[Michael Helbling]: And then the other one that gets me sometimes is sort of like alerts and notifications, anomaly detection and those kinds of things.

511
00:43:11,204 --> 00:43:15,731
[Michael Helbling]: That is a part of data, but it's not really what an analyst does necessarily.

512
00:43:15,771 --> 00:43:21,639
[Tim Wilson]: Well, the analyst gets blamed if the data all of a sudden it's found that something wasn't there for weeks.

513
00:43:21,659 --> 00:43:23,382
[Tim Wilson]: They're like, what were you doing as an analyst?

514
00:43:23,402 --> 00:43:24,424
[Tim Wilson]: How did you not notice?

515
00:43:24,444 --> 00:43:31,955
[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.

516
00:43:31,995 --> 00:43:34,258
[Michael Helbling]: So you have advanced warning of something that's happening.

517
00:43:34,238 --> 00:43:46,889
[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.

518
00:43:46,949 --> 00:43:52,382
[Moe Kiss]: For example, if there is something in our B2B pipeline that breaks

519
00:43:52,362 --> 00:44:01,679
[Moe Kiss]: 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.

520
00:44:01,840 --> 00:44:02,361
[Moe Kiss]: Absolutely.

521
00:44:02,401 --> 00:44:13,982
[Moe Kiss]: 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.

522
00:44:13,962 --> 00:44:22,192
[Moe Kiss]: 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.

523
00:44:22,272 --> 00:44:28,219
[Moe Kiss]: 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.

524
00:44:28,599 --> 00:44:36,088
[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,

525
00:44:36,068 --> 00:44:41,073
[Tim Wilson]: There is a perception that, yeah, yeah, yeah, they need to catch if a number of tanks are a number of skyrockets.

526
00:44:41,093 --> 00:44:54,725
[Tim Wilson]: 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.

527
00:44:54,745 --> 00:44:58,869
[Tim Wilson]: I'm like, cool, I'll get on average five alerts a day.

528
00:44:58,949 --> 00:45:03,112
[Moe Kiss]: I'm not necessarily expecting a data person to catch them all.

529
00:45:03,953 --> 00:45:06,075
[Moe Kiss]: I think that's a really hard thing.

530
00:45:06,055 --> 00:45:07,938
[Moe Kiss]: It's so difficult, right?

531
00:45:07,958 --> 00:45:13,448
[Moe Kiss]: 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?

532
00:45:13,528 --> 00:45:14,169
[Moe Kiss]: I had no idea.

533
00:45:14,389 --> 00:45:14,950
[Moe Kiss]: That's shit.

534
00:45:15,030 --> 00:45:15,872
[Moe Kiss]: It's hard for trust.

535
00:45:16,333 --> 00:45:24,607
[Moe Kiss]: 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.

536
00:45:25,128 --> 00:45:26,410
[Moe Kiss]: But I would

537
00:45:26,390 --> 00:45:30,698
[Moe Kiss]: I would basically be like, okay, something has gone wrong here.

538
00:45:30,739 --> 00:45:32,923
[Moe Kiss]: I'm going to reach out to my stakeholders.

539
00:45:32,983 --> 00:45:34,546
[Moe Kiss]: I'm responsible for letting them know.

540
00:45:34,566 --> 00:45:40,558
[Moe Kiss]: I'm responsible for letting them know what we're doing to investigate, how we're going to solve it, keep them updated.

541
00:45:41,039 --> 00:45:46,710
[Moe Kiss]: That, absolutely, I do think is a data person's role.

542
00:45:47,720 --> 00:45:59,511
[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.

543
00:45:59,531 --> 00:46:03,822
[Tim Wilson]: But that was one where they were having sporadic

544
00:46:03,802 --> 00:46:12,854
[Tim Wilson]: 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.

545
00:46:12,894 --> 00:46:29,235
[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

546
00:46:29,215 --> 00:46:36,068
[Michael Helbling]: 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.

547
00:46:36,088 --> 00:46:37,530
[Tim Wilson]: No, this was using Site 24 by 7.

548
00:46:37,550 --> 00:46:43,301
[Tim Wilson]: I was doing like a ping tracker that I set up, so I had set it up.

549
00:46:44,323 --> 00:46:47,028
[Tim Wilson]: So a third-party tool.

550
00:46:47,248 --> 00:46:49,372
[Michael Helbling]: You're doing third-party data collection.

551
00:46:49,753 --> 00:46:50,835
[Michael Helbling]: I was using a third-party tool.

552
00:46:50,815 --> 00:46:55,520
[Michael Helbling]: And they're probably like, why is our website getting crawled by this website?

553
00:46:55,540 --> 00:46:58,384
[Tim Wilson]: But that was, they were sometimes saying like, the tool is down.

554
00:46:58,424 --> 00:47:00,967
[Tim Wilson]: And I'm like, no, like why is this anomaly in the data?

555
00:47:00,987 --> 00:47:02,468
[Tim Wilson]: Cause your fucking site went down.

556
00:47:02,588 --> 00:47:07,494
[Tim Wilson]: Like, that's not a, cause I think I set up a ping for the footer as well.

557
00:47:07,514 --> 00:47:15,303
[Tim Wilson]: 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.

558
00:47:15,343 --> 00:47:17,105
[Tim Wilson]: And I was like, yeah, that's weird.

559
00:47:17,145 --> 00:47:17,866
[Tim Wilson]: What's going on?

560
00:47:17,906 --> 00:47:20,569
[Tim Wilson]: It's like, well, no, the whole site went down.

561
00:47:21,072 --> 00:47:29,224
[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.

562
00:47:29,825 --> 00:47:32,870
[Moe Kiss]: And then like a month after I left, I figured out why.

563
00:47:32,910 --> 00:47:34,813
[Moe Kiss]: And it was like completely tangential.

564
00:47:34,833 --> 00:47:36,054
[Moe Kiss]: I was just working on something different.

565
00:47:36,375 --> 00:47:37,637
[Moe Kiss]: And I did reach out to let them know.

566
00:47:37,657 --> 00:47:40,661
[Moe Kiss]: I was like, Hey, this is probably what this was.

567
00:47:40,721 --> 00:47:41,683
[Moe Kiss]: You should fix it.

568
00:47:41,803 --> 00:47:42,865
[Moe Kiss]: Here is how to fix it.

569
00:47:42,945 --> 00:47:43,526
[Moe Kiss]: You're welcome.

570
00:47:43,866 --> 00:47:45,509
[Moe Kiss]: I'm not a shit human.

571
00:47:45,549 --> 00:47:47,772
[Moe Kiss]: I want everyone to have the best data they can.

572
00:47:48,072 --> 00:47:49,575
[Tim Wilson]: I also get the, it's backup.

573
00:47:49,635 --> 00:47:51,840
[Tim Wilson]: So every time I've seen it, it's come back up quickly.

574
00:47:52,281 --> 00:47:54,506
[Tim Wilson]: So there hasn't been a point.

575
00:47:54,947 --> 00:47:55,187
[Val Kroll]: Okay.

576
00:47:55,207 --> 00:47:58,555
[Val Kroll]: So before Michael wraps, cause you got that look in your eyes.

577
00:47:59,116 --> 00:48:01,120
[Val Kroll]: I would love to hear.

578
00:48:02,500 --> 00:48:09,490
[Val Kroll]: love to hear people's thoughts on shadow work, not shadow work for like data fluency, data literacy.

579
00:48:09,991 --> 00:48:18,964
[Val Kroll]: 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.

580
00:48:19,485 --> 00:48:19,785
[Val Kroll]: Yeah.

581
00:48:19,805 --> 00:48:21,047
[Val Kroll]: I like data fluency.

582
00:48:21,107 --> 00:48:24,012
[Val Kroll]: I think it's less obnoxious than data literacy.

583
00:48:24,332 --> 00:48:25,634
[Michael Helbling]: Everybody can read and write.

584
00:48:25,894 --> 00:48:26,115
[Val Kroll]: Yes.

585
00:48:26,575 --> 00:48:28,298
[Val Kroll]: So is it shadow work, not shadow work?

586
00:48:28,839 --> 00:48:31,883
[Val Kroll]: I think it's shadow work, but I think it's important shadow work.

587
00:48:31,998 --> 00:48:42,527
[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.

588
00:48:42,988 --> 00:48:48,613
[Michael Helbling]: And a lot of times that's building up data fluency in an org or helping people build up their data fluency.

589
00:48:48,853 --> 00:48:58,182
[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.

590
00:48:58,382 --> 00:49:02,005
[Tim Wilson]: So it's the thing that needs to be in the shadows that is a

591
00:49:01,985 --> 00:49:06,051
[Tim Wilson]: I mean, not that there's not the opportunity for some of that training.

592
00:49:06,091 --> 00:49:18,469
[Tim Wilson]: 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.

593
00:49:18,730 --> 00:49:20,252
[Tim Wilson]: Let me explain this little thing now.

594
00:49:20,292 --> 00:49:22,035
[Tim Wilson]: Let's talk about, oh, you know what?

595
00:49:22,335 --> 00:49:25,880
[Tim Wilson]: When you all people say correlation is not causation, this is like the perfect example.

596
00:49:25,920 --> 00:49:30,507
[Tim Wilson]: Let's talk about that for five minutes because that's a trap you're falling into.

597
00:49:30,554 --> 00:49:39,029
[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.

598
00:49:39,430 --> 00:49:47,044
[Moe Kiss]: It doesn't necessarily result in any differences in behaviour or attitudes or anything, but it's a tick the box thing.

599
00:49:47,024 --> 00:49:59,047
[Moe Kiss]: 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.

600
00:49:59,628 --> 00:50:01,912
[Moe Kiss]: But again, like those in the moment.

601
00:50:01,932 --> 00:50:04,898
[Tim Wilson]: That sounds like the sort of observational woman would make, by the way.

602
00:50:05,215 --> 00:50:14,713
[Moe Kiss]: We're going to send Tim back to training.

603
00:50:15,274 --> 00:50:21,807
[Moe Kiss]: Those in the moment discussions are actually what I think is

604
00:50:21,787 --> 00:50:27,713
[Moe Kiss]: 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.

605
00:50:27,853 --> 00:50:35,801
[Moe Kiss]: 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.

606
00:50:36,181 --> 00:50:39,965
[Moe Kiss]: And that is like you're never done, you've never ticked the box.

607
00:50:40,065 --> 00:50:49,834
[Moe Kiss]: 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.

608
00:50:50,575 --> 00:50:51,456
[Moe Kiss]: Just like that.

609
00:50:51,841 --> 00:50:55,529
[Val Kroll]: I'm actually surprised that you guys are all on the same page.

610
00:50:55,549 --> 00:51:09,478
[Val Kroll]: 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,

611
00:51:10,353 --> 00:51:21,066
[Val Kroll]: 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.

612
00:51:21,086 --> 00:51:32,479
[Val Kroll]: 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.

613
00:51:32,560 --> 00:51:35,483
[Val Kroll]: He always said there was a fifth like shadow.

614
00:51:35,463 --> 00:51:38,166
[Val Kroll]: not actually a fifth one of adoption.

615
00:51:38,646 --> 00:51:46,755
[Val Kroll]: Until you understand how people are using that or if this is a data product or whatever, then you're not done.

616
00:51:46,995 --> 00:51:48,397
[Val Kroll]: The work isn't done when you ship it.

617
00:51:48,637 --> 00:51:51,440
[Val Kroll]: The work is done when you understand and create the feedback loops.

618
00:51:52,081 --> 00:51:56,666
[Val Kroll]: I feel like it's very much in the same vein of how to make sure that your work continues.

619
00:51:56,906 --> 00:52:03,333
[Val Kroll]: 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.

620
00:52:03,373 --> 00:52:05,115
[Val Kroll]: I don't know how I just feel like it's not

621
00:52:05,534 --> 00:52:09,743
[Val Kroll]: Like you're not done just when the analysis is complete, or it's not.

622
00:52:09,763 --> 00:52:11,246
[Tim Wilson]: Yeah, shadow is optional.

623
00:52:11,406 --> 00:52:13,911
[Tim Wilson]: Like it has to happen, it's just not identified as something.

624
00:52:13,931 --> 00:52:17,819
[Val Kroll]: It feels like it's squarely in the court of, I would expect it to be in a job description.

625
00:52:18,361 --> 00:52:21,467
[Val Kroll]: Like, that's what makes me feel like it's not shadow work.

626
00:52:21,447 --> 00:52:26,315
[Michael Helbling]: Again, that's where I think some of this work should rise up out of shadow work.

627
00:52:26,335 --> 00:52:28,058
[Michael Helbling]: But again, it's about recognition.

628
00:52:28,740 --> 00:52:31,564
[Michael Helbling]: The importance of it, I completely agree.

629
00:52:31,625 --> 00:52:36,213
[Michael Helbling]: But Tim's point, I think, was, will you see it in a job description?

630
00:52:36,273 --> 00:52:36,954
[Michael Helbling]: Probably not.

631
00:52:37,094 --> 00:52:41,642
[Michael Helbling]: Or if you do, it'll be run a once-in-a-quarter training and call it done.

632
00:52:41,662 --> 00:52:44,046
[Michael Helbling]: And we all know that's not going to be effective.

633
00:52:44,026 --> 00:52:52,038
[Michael Helbling]: But it's spending that time, like I'm realizing this episode that like 90% of what I do is shadow work sometimes.

634
00:52:53,019 --> 00:52:54,341
[Michael Helbling]: It's so hard to pin down.

635
00:52:54,361 --> 00:52:55,383
[Michael Helbling]: Michael works the shadows.

636
00:52:55,763 --> 00:52:57,185
[Michael Helbling]: That or I just don't do anything.

637
00:52:57,265 --> 00:52:57,706
[Michael Helbling]: I don't know.

638
00:52:58,507 --> 00:53:05,798
[Michael Helbling]: But I remember I had a very specific instance where I had a review and my boss at the time was like,

639
00:53:05,778 --> 00:53:10,706
[Michael Helbling]: you're not spending your time the way that it should be spent.

640
00:53:11,447 --> 00:53:13,711
[Michael Helbling]: And I had to actually walk him through.

641
00:53:13,751 --> 00:53:20,481
[Michael Helbling]: If I spent the time the way that they wanted me to, it would lose the company money.

642
00:53:21,222 --> 00:53:22,865
[Michael Helbling]: And I walked him through step by step.

643
00:53:23,586 --> 00:53:28,213
[Michael Helbling]: If I actually did it the way you said, the company would lose money as a result of the effort.

644
00:53:28,233 --> 00:53:33,642
[Michael Helbling]: So what you're telling me is that you would like the company to lose this much revenue

645
00:53:33,622 --> 00:53:37,628
[Michael Helbling]: by changing what I do day to day, are you sure that's what you want?

646
00:53:38,509 --> 00:53:44,598
[Michael Helbling]: 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.

647
00:53:44,638 --> 00:53:46,641
[Michael Helbling]: I could show the outputs of those things.

648
00:53:46,701 --> 00:53:49,886
[Michael Helbling]: But it was a very interesting conversation because it was like, oh yeah.

649
00:53:50,507 --> 00:53:57,276
[Michael Helbling]: 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.

650
00:53:57,296 --> 00:54:01,182
[Michael Helbling]: They just ignored it and came back with the template.

651
00:54:01,668 --> 00:54:02,830
[Moe Kiss]: What was the outcome, though?

652
00:54:02,910 --> 00:54:04,133
[Moe Kiss]: Like, what was the end of the story?

653
00:54:04,233 --> 00:54:05,655
[Moe Kiss]: Did you get off to change it?

654
00:54:05,676 --> 00:54:08,641
[Moe Kiss]: Or did they, like, be like, oh, I see the value of what you're doing.

655
00:54:09,142 --> 00:54:09,843
[Michael Helbling]: I kept going.

656
00:54:09,883 --> 00:54:10,244
[Michael Helbling]: Yeah.

657
00:54:10,264 --> 00:54:14,472
[Michael Helbling]: No, I was, uh, that was a role in which firing me probably wasn't an option.

658
00:54:14,552 --> 00:54:15,734
[Michael Helbling]: Probably they felt like it in the moment.

659
00:54:16,556 --> 00:54:20,784
[Michael Helbling]: I sent an email to the head of HR ahead of that meeting being like, I'm about to chew up my boss.

660
00:54:21,626 --> 00:54:22,427
[Michael Helbling]: Um,

661
00:54:22,407 --> 00:54:26,791
[Michael Helbling]: But it worked, and I still had a good relationship with that person afterwards.

662
00:54:26,831 --> 00:54:31,436
[Michael Helbling]: But it was a situation where they were like, oh, OK, well, never mind then.

663
00:54:32,377 --> 00:54:36,041
[Michael Helbling]: And I just kept going with what I was doing, since it was made sense.

664
00:54:36,061 --> 00:54:43,728
[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.

665
00:54:43,848 --> 00:54:47,672
[Tim Wilson]: Or when we read a job description and say, wow, that looks really good,

666
00:54:47,652 --> 00:55:05,070
[Tim Wilson]: 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.

667
00:55:05,050 --> 00:55:13,321
[Tim Wilson]: 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?

668
00:55:13,361 --> 00:55:18,247
[Tim Wilson]: Hey, this one looked, because you've had that reaction where you look at one and you're like, oh, they get it.

669
00:55:18,307 --> 00:55:22,072
[Tim Wilson]: Like they actually, they're describing a realistic and practical role.

670
00:55:22,132 --> 00:55:30,383
[Tim Wilson]: 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.

671
00:55:30,363 --> 00:55:33,727
[Moe Kiss]: Okay, but just a push on that I do agree.

672
00:55:33,747 --> 00:55:52,810
[Moe Kiss]: 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

673
00:55:52,790 --> 00:56:00,223
[Tim Wilson]: Yeah, but I think if that's recognized, it's like, hey, we've got a bunch of really difficult, unrealistic stakeholder.

674
00:56:02,066 --> 00:56:10,140
[Tim Wilson]: 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,

675
00:56:10,120 --> 00:56:18,476
[Tim Wilson]: You know, collaborating with, educating, informing, iterating with, so I think he can still be captured.

676
00:56:18,536 --> 00:56:20,400
[Val Kroll]: He's ambiguous in challenging circumstances.

677
00:56:20,420 --> 00:56:21,321
[Val Kroll]: That's right.

678
00:56:21,422 --> 00:56:22,303
[Val Kroll]: Yeah, exactly.

679
00:56:22,323 --> 00:56:24,728
[Val Kroll]: Oh, yeah, there we go.

680
00:56:24,888 --> 00:56:30,960
[Michael Helbling]: Sell starter, able to juggle multiple priorities simultaneously.

681
00:56:31,020 --> 00:56:32,162
[Michael Helbling]: It's like, ugh.

682
00:56:32,902 --> 00:56:38,656
[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.

683
00:56:38,717 --> 00:56:39,940
[Tim Wilson]: And that does some of the.

684
00:56:39,960 --> 00:56:41,443
[Michael Helbling]: And it comes out ringing false.

685
00:56:41,464 --> 00:56:41,885
[Michael Helbling]: Yeah.

686
00:56:41,905 --> 00:56:45,373
[Michael Helbling]: Well, some of my shadow work is trying to get the show wrapped up on time.

687
00:56:45,434 --> 00:56:49,223
[Michael Helbling]: So let's go to do that.

688
00:56:49,962 --> 00:56:51,344
[Tim Wilson]: We got to find somebody who's good at it.

689
00:56:51,364 --> 00:56:52,725
[Michael Helbling]: All right.

690
00:56:52,785 --> 00:56:56,450
[Michael Helbling]: Let's hand that off to somebody else.

691
00:56:56,931 --> 00:56:57,211
[Michael Helbling]: All right.

692
00:56:57,271 --> 00:57:01,016
[Michael Helbling]: Well, listen, Moe and Val and Tim, thank you so much.

693
00:57:01,156 --> 00:57:04,059
[Michael Helbling]: This is, I think, a really interesting topic.

694
00:57:04,380 --> 00:57:06,462
[Michael Helbling]: And I appreciate your insights on the show.

695
00:57:06,482 --> 00:57:12,870
[Michael Helbling]: A lot of work is really important, but doesn't necessarily get recognized for what it is.

696
00:57:12,910 --> 00:57:15,233
[Michael Helbling]: And I think that's sort of where this discussion took us today.

697
00:57:15,313 --> 00:57:18,417
[Michael Helbling]: So thank you for that.

698
00:57:18,397 --> 00:57:22,905
[Michael Helbling]: You know, as you're listening, I imagine you're thinking some of the thoughts yourself.

699
00:57:22,966 --> 00:57:25,891
[Michael Helbling]: We'd love to hear from you and you can reach out to us.

700
00:57:25,951 --> 00:57:34,367
[Michael Helbling]: You can reach out to us on LinkedIn or the Measure Slack chat group or through email at contact at analyticshour.io.

701
00:57:35,389 --> 00:57:38,194
[Michael Helbling]: And if you're listening to this on

702
00:57:38,174 --> 00:57:44,726
[Michael Helbling]: Apple podcasts or Spotify or whatever platform you listen to it, give us a review or a rating or a comment.

703
00:57:44,847 --> 00:57:46,991
[Michael Helbling]: We'd love to see it, love to hear it, love to hear from you.

704
00:57:47,953 --> 00:57:50,237
[Michael Helbling]: And of course, a couple other things.

705
00:57:50,437 --> 00:57:57,651
[Michael Helbling]: We're not doing less calls, but a couple of things where you can find us coming up this year.

706
00:57:57,631 --> 00:58:02,960
[Michael Helbling]: is at a couple of few conferences and actually coming up really quickly.

707
00:58:03,041 --> 00:58:07,288
[Michael Helbling]: So, I know Tim and Val, you all will be at the Datatune conference in Nashville.

708
00:58:07,308 --> 00:58:07,709
[Michael Helbling]: Is that right?

709
00:58:07,729 --> 00:58:08,470
[Michael Helbling]: You want to talk about it?

710
00:58:09,091 --> 00:58:09,312
[Tim Wilson]: Yeah.

711
00:58:09,492 --> 00:58:15,302
[Tim Wilson]: It's a little, it's a Friday is workshops and Saturday, it's a

712
00:58:15,282 --> 00:58:20,169
[Tim Wilson]: Conference, it's a pretty low cost, low three-figures conference all day.

713
00:58:20,189 --> 00:58:31,624
[Tim Wilson]: 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.

714
00:58:32,325 --> 00:58:32,966
[Michael Helbling]: What are the dates?

715
00:58:33,026 --> 00:58:36,510
[Tim Wilson]: Oh, that would be important.

716
00:58:36,891 --> 00:58:37,632
[Tim Wilson]: Yeah.

717
00:58:37,652 --> 00:58:38,753
[Michael Helbling]: I'm here for you.

718
00:58:38,813 --> 00:58:40,075
[Tim Wilson]: I'm here for you.

719
00:58:40,115 --> 00:58:41,076
[Michael Helbling]: Talk about shadow work.

720
00:58:43,880 --> 00:58:45,222
[Tim Wilson]: What is it?

721
00:58:46,788 --> 00:58:47,349
[Michael Helbling]: That's awesome.

722
00:58:47,389 --> 00:58:53,357
[Michael Helbling]: And then, of course, Measure Camp New York will be in March 28th in New York City.

723
00:58:53,437 --> 00:58:55,881
[Michael Helbling]: It's officially in New York City, not New Jersey this year.

724
00:58:56,141 --> 00:58:57,003
[Val Kroll]: Very exciting.

725
00:58:57,123 --> 00:58:58,044
[Val Kroll]: Very exciting stuff.

726
00:58:58,064 --> 00:59:01,189
[Michael Helbling]: Yeah, it's going to be a great... Measure Camp is always a great time.

727
00:59:01,689 --> 00:59:04,273
[Michael Helbling]: Obviously, Val's super involved with Measure Camp Chicago.

728
00:59:04,333 --> 00:59:05,895
[Michael Helbling]: Moe with Measure Camp Sydney.

729
00:59:05,915 --> 00:59:07,197
[Michael Helbling]: Tim with Measure Camp Columbus.

730
00:59:07,698 --> 00:59:12,745
[Michael Helbling]: Me with not being involved with Measure Camp in any official capacity, but I love going to them.

731
00:59:12,725 --> 00:59:18,454
[Michael Helbling]: And I think right now Tim and I are planning to be at that one, and that's March 28th in New York City.

732
00:59:18,955 --> 00:59:32,015
[Michael Helbling]: 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.

733
00:59:32,475 --> 00:59:32,916
[Michael Helbling]: Hello.

734
00:59:33,757 --> 00:59:34,238
[Michael Helbling]: Get there.

735
00:59:34,499 --> 00:59:35,260
[Michael Helbling]: We love to.

736
00:59:35,500 --> 00:59:38,304
[Tim Wilson]: We got some exciting plans for that.

737
00:59:38,405 --> 00:59:39,947
[Tim Wilson]: Stay tuned to future episodes.

738
00:59:39,927 --> 00:59:42,079
[Michael Helbling]: What's the drink that you have in Santa Barbara?

739
00:59:42,140 --> 00:59:44,453
[Michael Helbling]: What's like a good cocktail for that?

740
00:59:45,158 --> 00:59:47,981
[Tim Wilson]: I'm sure it's some fruity California liberal.

741
00:59:48,222 --> 00:59:48,642
[Michael Helbling]: Wine.

742
00:59:48,682 --> 00:59:49,583
[Michael Helbling]: Exactly.

743
00:59:49,663 --> 00:59:50,304
[Michael Helbling]: Wine.

744
00:59:50,645 --> 00:59:53,909
[Michael Helbling]: White wine or rosé on the beach or in the sunshine.

745
00:59:54,249 --> 00:59:54,469
[Moe Kiss]: Love this.

746
00:59:54,709 --> 00:59:55,470
[Michael Helbling]: Love this from me.

747
00:59:55,490 --> 00:59:55,931
[Michael Helbling]: I don't know.

748
00:59:56,432 --> 00:59:58,134
[Michael Helbling]: I'm terrible at picking out drinks.

749
00:59:58,774 --> 00:59:59,095
[Michael Helbling]: All right.

750
00:59:59,796 --> 01:00:00,637
[Michael Helbling]: That's the show.

751
01:00:00,677 --> 01:00:04,001
[Michael Helbling]: We're excited to have brought it to you.

752
01:00:04,061 --> 01:00:13,512
[Michael Helbling]: 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.

753
01:00:15,263 --> 01:00:16,064
[Announcer]: Thanks for listening.

754
01:00:16,585 --> 01:00:29,020
[Announcer]: 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.

755
01:00:29,721 --> 01:00:32,124
[Announcer]: Music for the podcast by Josh Crowhurst.

756
01:00:33,225 --> 01:00:37,630
[Charles Barkley]: Those smart guys wanted to fit in, so they made up a term called analytics.

757
01:00:37,650 --> 01:00:38,892
[Charles Barkley]: Analytics don't work.

758
01:00:39,833 --> 01:00:42,576
[Charles Barkley]: Do the analytics say go for it, no matter who's going for it?

759
01:00:42,936 --> 01:00:45,780
[Charles Barkley]: So if you and I were on the field, the analytics say go for it.

760
01:00:46,120 --> 01:00:52,087
[Charles Barkley]: It's the stupidest, laziest, lamest thing I've ever heard for reasoning in competition.

761
01:00:53,068 --> 01:00:54,550
[Michael Helbling]: Lacy Fusion Productions.

762
01:00:55,551 --> 01:00:58,494
[Michael Helbling]: Lacy Fusion.

763
01:00:58,514 --> 01:01:04,761
[Tim Wilson]: That's our production studio's sister organization on Southern Hemisphere covering Lacy Fusion Media.

764
01:01:05,838 --> 01:01:08,722
[Michael Helbling]: 4th floor productions, Lacyfusion Media.

765
01:01:08,742 --> 01:01:12,208
[Val Kroll]: Known for expanding into Australia.

766
01:01:12,268 --> 01:01:13,470
[Michael Helbling]: Ken Riverside.

767
01:01:13,510 --> 01:01:16,855
[Michael Helbling]: And the Lacyfusion Media.

768
01:01:17,736 --> 01:01:21,662
[Michael Helbling]: Present a 4th floor production.

769
01:01:21,682 --> 01:01:23,064
[Val Kroll]: Okay, well screw your green bars.

770
01:01:23,425 --> 01:01:31,337
[Val Kroll]: You sound like you're in this building with a paper cup and a string.

771
01:01:32,679 --> 01:01:33,280
[Val Kroll]: All right, love you.

772
01:01:43,823 --> 01:01:50,159
[Michael Helbling]: Your temperature, Matt.

773
01:01:50,701 --> 01:01:52,686
[Moe Kiss]: She is so cute.

774
01:01:52,766 --> 01:01:53,769
[Michael Helbling]: I know.

775
01:01:53,789 --> 01:01:55,152
[Michael Helbling]: It's ridiculous.

776
01:01:55,493 --> 01:01:57,057
[Moe Kiss]: So cute.

777
01:02:02,791 --> 01:02:09,343
[Tim Wilson]: Rock flag and who knows what insights lurk in the tables of our databases.

778
01:02:09,363 --> 01:02:12,449
[Tim Wilson]: The shadow analyst knows.

779
01:02:15,254 --> 01:02:16,296
[Michael Helbing]: Nice.

780
01:02:18,260 --> 01:02:19,683
[Michael Helbling]: That's actually pretty close.

781
01:02:20,544 --> 01:02:22,448
[Moe Kiss]: I'm like, damn, you got the voice.

782
01:02:23,810 --> 01:02:26,335
[Tim Wilson]: That's got something.