#291: The Data Work that Lives in the Shadows

Summary

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The Analytics Power Hour #291: The Data Work that Lives in the Shadows

Release Date: February 17, 2026
Hosts: Michael Helbling, Moe (Mo) Kiss, Tim Wilson, Val Kroll
Main Theme:
Exploring the "shadow work" in analytics—the vital but under-acknowledged tasks data professionals do that fall outside formal job descriptions. The hosts dissect, debate, and affirm the value, frustrations, and nuances of this unseen labor, highlighting its criticality to successful data-driven organizations.

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Episode Overview

• The conversation delves into the hidden, often-overlooked work done by data professionals—the "shadow work" that is essential for organizational effectiveness but rarely recognized or celebrated.
• Topics include the types of shadow work, who does it, its impact, the interplay with personality and team structure, pitfalls around recognition, and strategies to bring invisible labor into the light.

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Key Discussion Points

What Constitutes Shadow Work in Analytics?

00:14–08:45

• Admin/Project Management:
  • Val Kroll opens with "admin" as a major shadow work category—project management, chasing stakeholders for contributions, filling out spreadsheets, and maintaining momentum.
    • "Once you assume responsibility for it, it’s almost impossible to ever roll it back." – Val (02:16)
  • Tim differentiates between necessary accountability and pure admin, advocating that follow-up after recommendations is analyst territory.
    • "There’s an accountability mechanism for the analyst to say, 'hey, did you do it?'" – Tim (03:22)
• Integration with Implementation:
  • Michael shares stories of data teams unintentionally morphing into project managers when following analysis through to implementation.
    • "You start out being the analyst, next thing you know, you’re running the integration task force." – Michael (05:37)
  • Both Mo and Val agree taking ownership can be empowering, but warn about being drawn too far from specialist roles—there’s a balance.

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Explaining and Interpreting Others’ Data

08:45–12:51

• Data teams are often tasked with deciphering, explaining, and documenting data (and reports) originating from other agencies, departments, or predecessors—with little to no handover or documentation.
  • "Now you need me to reverse engineer how they pulled these numbers together. Oh boy." – Michael (08:47)
• This is exacerbated by poor documentation and alignment between business units.
  • "Getting everyone using the same data, everyone trusts the data, whether it’s internal or external… sometimes it falls in your lap in a weird way." – Michael (10:03)
  • "Not all shadow work is shit. Some shadow work is actually very valuable—alignment is one." – Val (10:29)

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Alignment, Consensus Building & Data Culture

11:14–14:57

• Alignment:
  • Data professionals often perform crucial consensus-building work—aligning definitions, clarifying objectives, unifying measurements—that falls outside formal requirements.
  • Tim frames it as “business partners on the same page,” essential yet invisible.
• Experimentation Culture:
  • Mo reflects on the extensive, informal labor in fostering a culture of experimentation—internal advocacy, education, and maintaining enthusiasm—that’s essential but rarely spelled out.

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Educating the Business & Organizational Data Fluency

13:17–15:07

• Analysts frequently build business stakeholders’ data fluency—but this, too, is undervalued and rarely acknowledged as core work.
  • "The shadow work is building trust, building relationships—walking [stakeholders] at the appropriate pace." – Tim (14:57)
• The challenge: the expectation that data is pristinely clean or simple, and that the analyst can answer highly nuanced questions at a glance.

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Technical Debt, Data Cleaning, and Re-engineering

16:19–23:41

• Stories abound of inheriting poor data warehouses, unstructured datasets, or chaotic pipelines, requiring vast cleanup/rebuilding efforts before value can be realized.
  • "You walk into an org, they want to do amazing things, but the Snowflake instance isn’t going to do any of that until we clean it up." – Michael (17:45)
  • "These are often huge and time intensive and unlock a heap of value, but people don't see the value until months later, sometimes a year." – Val (18:08)
• Business users and developers often underestimate the architectural work needed, assuming AI or better queries can fix bad structures.
  • "Everyone thinks you can overcome a shitty data architecture with AI, which is just so fucked and hard to manage." – Val (21:53)

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The Analyst as Organizational Glue—and Bottleneck

23:20–24:20

• Analysts become the cross-functional “glue” bridging business needs, technical delivery, and organizational siloes—but sometimes end up sole domain-holders and accidental bottlenecks.
  • "You end up with one or two people in the business who know one area and no one else can do it... you’ve created your own bottleneck." – Val (23:20)

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Who Should Do the Shadow Work? Personality, Team Fit, and Equity

24:20–36:00

• Generalists vs. Specialists:
  • Michael and Mo reflect on how personality shapes comfort with shadow work; generalists often find it stimulating, while specialists may resent it.
• Value of Curiosity:
  • Mo: "It always ends up feeling like it adds to the mosaic of my understanding, which pays dividends in the future."
• Documentation No One Reads:
  • Tim shares a story about compiling a comprehensive process document that was greatly educational for him, ignored by others.
• Leadership and Recognition:
  • As teams grow, leaders must actively surface shadow work, recognize who does it, and distribute it fairly—avoiding overburdening those who are agreeable or less likely to object (noting this often impacts women disproportionately).
    • "Half of your job is unseen shadow work. And you can’t advance in your career…good old Jane is always there for it, but it’s not visible." – Tim (33:43)
• Team Composition:
  • Teams should mindfully hire to fill gaps in strengths and manage shadow work equitably.

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Pitfalls: Recognition, Job Descriptions, and Team Visibility

36:00–52:21

• Reporting & Recognition:
  • Val: "If you do that shit well, you can unlock a lot for your team or business. I want to make sure that’s rewarded and reflected."
• The 'Conveyor Belt' Fallacy:
  • Mo: "It was always hard to message up...Do you think we just sit there like a conveyor belt, analyze, analyze?"
• When Shadow Work Reveals Workflow Flaws:
  • Michael discusses how shadow work can expose larger organizational process gaps that need system-level fixes.
• Job Descriptions:
  • The group notes that shadow work is often absent from formal job postings—especially when non-analysts do the hiring.

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Data Quality, Hygiene, and The Eternal Struggle

39:13–47:47

• Data Quality as Shadow Work:
  • Cleaning up poorly formatted spreadsheets from agencies or finance is a perennial burden—and business stakeholders often have no idea how significant the lift is.
    • "Every city they run media in is a completely different format. That is a huge lift...very senior data scientists are spending their time basically QAing data." – Val (40:31)
• Alerting, Anomalies & Getting Blamed:
  • The expectation that analysts should have "caught" every spike or drop in metrics is ever-present, but unrealistic given data complexity and noise.
    • "The analyst gets blamed if the data...wasn’t there for weeks...How did you not notice?" – Tim (43:16)
  • Many maintain “secret dashboards” for self-monitoring.

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Data Literacy and Training – Shadow Work or Not?

47:58–56:21

• Discussing whether data fluency/education is shadow work or core work; agreement that it's critically important, ongoing, and rarely done solely through "tick the box" programs.
  • "Every time I talk to a stakeholder, I’m trying to help them get a little bit further in how they think and understand data...heavy cognitive load, but incredibly important." – Val (50:15)
  • Mo: "If your role is to help the business make smarter decisions, making sure you’re connecting findings, observations, recommendations with what the business can actually do...that’s core, not shadow work." (50:51)

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Notable Quotes & Memorable Moments

• On the persistence and importance of shadow work:
  • "Not all shadow work is shit. Some shadow work is actually very valuable. The business just doesn’t realize how necessary it is." – Val (10:29)
• On bottlenecks and documentation:
  • "You end up with one or two people in the business who know one area and no one else can do it...you’ve created your own bottleneck." – Val (23:41)
• On gender dynamics and admin burdens:
  • "I do think I’ve seen stuff written that women are much more likely to get screwed on this one...‘Mo’s really good at that’...it’s absolute shit work and she’s not going to speak up." – Tim (33:43)
• On leadership and surfacing hidden labor:
  • "When you turn from an individual practitioner...into a leader, you need to take the shadow work and expose it to the light." – Michael (35:01)
• On job descriptions:
  • "The ones that actually have the shadow work articulated...describe a realistic and practical role." – Tim (54:35)
• On data quality pain:
  • "Every single city they run media in is in a completely different format...very senior data scientists spending their time QA’ing data. It’s really frustrating." – Val (40:31)

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Important Timestamps

• 00:14–03:22 — Defining shadow work and the admin trap
• 05:37–07:59 — Data teams as accidental project managers
• 10:29–13:17 — Alignment, consensus and business impact
• 17:45–23:59 — Technical debt, data re-engineering, unsung labor
• 24:20–36:00 — Personality fit, team structure, and the equity of invisible work
• 39:13–46:36 — Data quality, anomaly detection and getting blamed
• 47:58–56:21 — Data literacy: is educating the organization shadow work?
• 56:30–60:00 — Wrapping up: organizational recognition, job descriptions, and the value of shining a light on shadow work

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Takeaways & Conclusion

• Shadow work is deeply embedded in analytics, often necessary for real business impact—but seldom visible or celebrated.
• There’s a fine line between being “unwaveringly useful” (Cassie Kozyrkov, via Val, 28:15) and being trapped in menial work that stalls your progression.
• Leaders should illuminate shadow work, recognize and distribute it fairly, and ensure it's reflected in both job descriptions and rewards.
• Team composition, communication, and organization design can help balance necessary shadow work—making the invisible, visible.
• Data literacy/fluency efforts, technical debt cleanup, and process alignment are all examples where shadow work has high leverage—deserving recognition and resourcing as much as front-line analysis.

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Final Thought

Shadow work may live in the margins, but it's essential for turning analytics insights into business action. As Michael concludes:

"A lot of work is really important, but doesn’t necessarily get recognized for what it is. The work has value, and who does it—and how it’s surfaced—matters."

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Transcript

[Mo Kiss]

Foreign.

[Tim Wilson]

Analytics topics covered conversationally and sometimes with explicit language.

[Michael Helbling]

Hey, everybody. Welcome to the Analytics Power Hour. This is episode 291. Who knows what evil lurks in the heart of men? The Shadow knows. Most of our listeners probably don't know that callback to the extremely famous raid 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. It's not really in the job description. It usually doesn't get recognized, but you do it anyway. Maybe some days you feel more like a janitor cleaning up ugly data or a therapist listening to stakeholders frustrations, or some sort of data marketer just trying to sell your wares internally. I think we should talk about it. Let me introduce my co hosts, Mo Kiss. How you going?

[Val Kroll]

I'm going great. Thanks for checking in.

[Michael Helbling]

Have you ever heard of the Shadow? The radio show the Shadow?

[Val Kroll]

It was like, no, but I'm deeply familiar with the sentiment.

[Michael Helbling]

Okay. Yeah, yeah. And Val Kroll, welcome.

[Mo Kiss]

Thank you.

[Val Kroll]

Hi, everyone.

[Michael Helbling]

Go Bears.

[Mo Kiss]

Yeah.

[Michael Helbling]

And hey, it was close. Tim Wilson, probably the only other person around.

[Tim Wilson]

I was gonna ask, listening to the first episod of the Shadow. I'm pretty sure that's not true because.

[Michael Helbling]

I think it came out like 1913 or something like that. So even that's before your time, but it ran for a long time anyways. I'm Michael Helbling. So this is what we want to talk about. So I think first up, maybe let's talk about what kinds of Shadow work have you found yourself getting into in your career? Like, what are some of the categories or the types of things you've gotten into? And then as we sort of get into that discussion, maybe figure out if we thought it was necessary or. Or not, or whether it was good or not. So who wants to start us off with some of the stuff you've run into?

[Val Kroll]

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

[Tim Wilson]

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

[Val Kroll]

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

[Michael Helbling]

Well, it's interesting because I've definitely found in my career mo 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. And then 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. And we're riding shotgun with the project now. And it's like, wait a second, when did we stop being doing the analysis and start being the project managers for the implementation of this? And that was sort of when I was like, wait a second, what job do I actually have here? Because you're kind of like, I'm now not doing data analytics. I'm now running sort of like an integration task force, if you will. So I don't know if that's more like in the line of what you're talking about.

[Val Kroll]

It's such a fine line though, right? Because especially because if you want to.

[Michael Helbling]

See your insight go live.

[Val Kroll]

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

[Tim Wilson]

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

[Mo Kiss]

As you were talking through the admin stuff, Mo, I think the consultancy equivalent of some of the admin work is, can you send me that thing that you told me you were going to send me? Can you send me that thing? Or can I have access to that or what? Especially if it's like I need one of your other partners or other agencies to send me or give me access to something. The number of times, like, top of a call, like, oh, okay, moving right along. Did you get approval for that one thing? Did they sign off on that thing? Are we good to move forward? Which is like a lot less in the consulting space connected to meaningful stuff.

[Michael Helbling]

But explaining another agency's data and analytics to the client, that's.

[Val Kroll]

My stomach.

[Michael Helbling]

That's some shadow work right there. And I'll even just say, like, 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. You know, not that I don't want to help you, but I've had many experiences where they're like, okay, we got this from this. Maybe it's a different agency that runs a specific program for them, like media or SEO or something, and they're pulling their own reports or like, how did they get these numbers? And I'm like, okay, so now you need me to go reverse engineer how they pulled these numbers together. It's like, oh, boy.

[Tim Wilson]

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

[Michael Helbling]

That's a good point. And, 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 is goes to that sort of like that work and preparation I think is very much a part of what I consider like an a data and analytics role to be doing. But sometimes it falls in your lap in a weird way maybe.

[Val Kroll]

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

[Tim Wilson]

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

[Mo Kiss]

Yeah, I'll third that motion on sometimes the shadow work is like really important to move it forward. When we first started talking about this topic, the first thing that came up for me and granted I do have very much of a recency experimentation bend is like the culture of experimentation work, how that's like become more prominent especially on LinkedIn in the zeitgeist about how to be successful with experimentation. But if you think like 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. Right. But it really is all about like building consensus and getting people excited and a little dose of education, little dose of this is why you should care about what I do kind of stuff. And I feel like sometimes that's a little.

[Tim Wilson]

The culture of. The culture of finance, the culture of accountants. You gotta really build a culture going.

[Mo Kiss]

Around to get people on board. Well, maybe during budgeting season, but. But just to go back to the, the point that it's. It's not that it's not important, but it's usually not, you know, the first thing you think of when you're like, oh yeah, I lead an experimentation team inside of an organization. It's not. That's not important, but it's usually not the first thing that comes to mind.

[Tim Wilson]

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

[Michael Helbling]

Tim, when someone asks which channel has the highest ROI adjusted for LTV, how long does that take? You.

[Tim Wilson]

Pull GA4, then export to Excel, write SQL for BigQuery, find my LTV formula. I don't know. I'd say about three hours and a couple of existential crises.

[Michael Helbling]

At least two. This is why askwise full stack approach works. Ask in plain English. Prism orchestrates across your stack and applies your saved calculations.

[Tim Wilson]

So I'm not manually stitching together five tools like some kind of data Frankenstein?

[Michael Helbling]

Nope. Everything's traceable. Not a black box. Data stays secure. Semantic layer generated code runs locally. It's all set up for you.

[Tim Wilson]

So for a product with a name that makes you think of a toddler asking why repeatedly, this is pretty sophisticated.

[Michael Helbling]

I'm not sure making fun of our sponsor's name is the move here, Tim.

[Tim Wilson]

Wait, I did say pretty sophisticated. Sophisticated. That's a compliment.

[Michael Helbling]

All right, fair enough. We'll go to ask Y AI. That's ask Y AI and use code APH for priority beta access. Join. The Rise of the AI Analyst.

[Mo Kiss]

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

[Michael Helbling]

And a lot of times shadow work is just cleaning up or trying to clean up a data warehouse you inherited from a previous team or something like that. You walk into an org and they're like, oh, we want to do this, this and this amazing thing. And you're like, well, the snowflake instance we have is not going to do any of that until we really clean up a bunch of it. And you're helps. Yeah.

[Val Kroll]

Is this like one of those times where you read my exact life situation that is going on right now or how to do a huge huge rebuild of our entire data warehouse for a very specific, like very similar reason. Right. Like the data wasn't structured in a way that we can answer the business questions of today. And so, and I think the thing that's so hard about projects like this is they're often huge and very time intensive and unlock the heap of value, but people don't see the value until like months later, sometimes a year later.

[Michael Helbling]

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. Like it's so much nicer to go in and say, say hey, here's this analysis where we can make $100 million more dollars 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 gonna, it's not doing this, this and this. And eventually you can write the business case to show like where the value will come from. But man, it's an uphill battle. I don't know if that's shadow work.

[Tim Wilson]

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

[Michael Helbling]

Oh, or yeah, well, I mean there's tools that help with that like DBT or Data form or stuff like that that helps you kind of maintain your SQL and repositories and use it effectively.

[Tim Wilson]

But, but sometimes that's. To me you're like, you've gone with this like let's, let's get the full ocean and then we're gonna, then we're gonna add layers on top of it.

[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 report let or whatever. So you put yourself in a pretty challenging chain of events just to get answers to data which AI will totally solve. So no worries.

[Val Kroll]

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

[Tim Wilson]

So that is deep because the business, it's kind of the business partners who ultimately want to get value from it. It's not going to maintain their attention or technical depth, but the analyst is supposed to be engaging with them and serving them. So the analyst becomes the proxy for the business and is now dealing with the back end. And so like they become subject matter experts in an area that has nothing to do with running analyses or validating hypotheses. It's just they're living in that middle tier and there's just no one else. The shadow has to serve it because there is no one. There's that's all there is. That's the best you got.

[Val Kroll]

Spot on. And then you end up with like one or two people in the air in the business who know one area and no one else can do it. It's so complex and there are all these gotchas. So even if you're going to write a bespoke SQL query, it has to go through this one or two people because they're the ones that know those tables, know how to use it well. And like that, then you've created your own bottleneck.

[Mo Kiss]

Right.

[Val Kroll]

And it's not an intentional thing. I think often the systems were created with the intent to have a lot of flexibility, but then by having flexibility, you don't have enough standardization. And like, yeah, it's a chicken and egg. But I would say that is one of the hardest shadow tasks for sure.

[Tim Wilson]

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

[Michael Helbling]

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

[Mo Kiss]

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

[Tim Wilson]

This reminds me of me going overboard on it where there were webinars in a company that, like, we think we know what webinars you have a registration and attendance. This was in a business model where it was not that at all. And it was like bonkers how salespeople would sometimes go into an office and sit and watch the webinars. And there were two or three systems involved. And 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. And I wound up writing up. It was probably a 10 or 12 page document, very, very clearly written because there were all these parties in different places. And I thought, nobody has pulled all this together. I have done the most glorious, valuable. This is so useful. I am pretty sure not even the webinar business owner read it. And I got probably 25% of the information from her. But I was like, oh, this is. She was excited to explain to me the nuances and the complexity, but I kept digging further and further and saying, aha, look what I, the external consultant has done to really help you understand what's going on here. And there was kind of no interest. So that was one where I'm like, it was useful for me. It should have been useful down the stream downstream in today's world. Now, boy, I'd be throwing that into an LLM somewhere and saying, that's really helpful data potentially. But I'm pretty sure that document I was like, I became the domain expert on something that people cared about webinars. They did not care to hear how messy it was to interpret any of the data that was captured.

[Val Kroll]

The thing that's resonating with me a lot right now, one of like the values that I, I do have leadership values. It's a weird, corny thing, but one of them is be unwaveringly useful. Does anyone pop quiz? Anyone Remember where that comes from?

[Michael Helbling]

I don't think so.

[Tim Wilson]

Oh, from being useful would be our good friend Cassie. Yep, I got it. Ding, ding, ding.

[Michael Helbling]

Yeah, yeah.

[Val Kroll]

She put it in one of her blog articles and it's always resonated with me. And I'm completely contradicting myself now because at the start I was like, don't pick up the admin work. But I'm the first person to be like, if someone's not doing something and I can add value or move something forward, I'll. I'll normally just end up doing it. So like, I am.

[Michael Helbling]

Yeah.

[Val Kroll]

A walking contradiction. But I do think there is part of that responsibility of data folk. Like, I tend to get really frustrated. Frustrated when a data person is like, well, that's not my job. And I'm like, your job is to help the business make better decisions. So if there is something you can do to be useful to help the business make better decisions, that is your job. Yeah, I don't know. That's just the thing that's bubbling around in my mind at the moment as we're. I mean, not relevant to Tim's example, but more broadly about this area of like, sometimes it is about getting the domain expertise, sometimes it is about documenting something that no one in the business has written down. It's like, sometimes those things are less useful, but a lot of the times.

[Michael Helbling]

They'Re really useful just to give some people who might be listening a chance to sort of be like, well, maybe Mo, I can't do that thing or I'm not good at that thing. Isn't necessarily that you have to go personally be the one in charge of that as much as be part of helping solve it in some way, see that it gets done. So it's more of like the ownership taking versus the taking on the role and doing it yourself just so that people who are very specialized or don't. Yeah, because I have a ton of empathy for people who are like, Michael. I just can't get up in front of people and talk because I analyze data and that's what I like to do. And I'm very stressed out every time I have to go present something and it's like, okay, well then has someone else can do that part. But like, you just need to make sure you're facilitating it up to the moment where it, where it happens, so it doesn't have to be you necessarily taking on that role. I don't know.

[Val Kroll]

I agree.

[Michael Helbling]

Not. So don't pick presenting something then something else like managing the project or Something like that. But the point being, like, not every person fits every single role. Like, you don't have to be a polyglot, if you will, or a polymath.

[Tim Wilson]

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

[Michael Helbling]

Hey, I've gotten pretty far. So, you know, no, now you can't do it. You can't.

[Val Kroll]

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

[Tim Wilson]

Can we hit on that stuff a little bit? And maybe this administrative work, maybe more broadly, because I think that is the danger there. And I do think I've seen stuff written that women are much more likely to get screwed on. This one is that this thing needs to happen. And they're like, oh, well, it's admin work. The latent misogyny, maybe not intentional, is. Well, Mo's really good at that. I'm like, but it's absolute shit work. And she's not going to speak up. So 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 someone gravitate to it? Shadow work that is has to be done, there is value, no one wants to do it. And making sure that that doesn't fall to the passive nice person, because that can spin out where, wait, now half of your job is unseen shadow work. And you can't advance in your career even though everybody's like, well, this all needs to be done. But good old Jane is always there for it. But it's in the shadows. It's not getting. It's not visible.

[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 or those kinds of things, your job becomes taking the work out of the shadows for some of the exact reasons you just said, because it needs to be recognized, what's being done, the people doing it need to be recognized. And then who should be doing it should be. Be much more strategically thought out as opposed to fallen into just because oh, so and so is more agreeable. So they just take it on without fighting too much, which is Just a terrible solution to the problem. So anyways, I thought that was a really great point, Tim. And I think that's sort of the thing to maybe take away is like when you turn from an individual practitioner or individual contributor into a leader, when you're just an IC sitting at your desk, you're like, wow, do all this shadow work. When you're a leader, you're like, we need to take the shadow work and expose it to the light.

[Tim Wilson]

That sounds hard. That's why I'm not gonna. I'm not striving to be a leader.

[Val Kroll]

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

[Tim Wilson]

I just like to say to all of Mo's team who is listening to this podcast, she's talking about you. She Valuz.

[Mo Kiss]

Wow.

[Tim Wilson]

She gave us the name off mic and it was your name. So good job.

[Mo Kiss]

It.

[Val Kroll]

You were so cruel.

[Mo Kiss]

The other thing that this is making me think about is that when any in house role that I've had, I've never reported to an analyst. It's always been, you know, ahead of digital or someone else who it was really hard to message up not only for myself when I was the ic, but then when I grew my team about all the things it takes. Like, I was like, do you think we just sit there and like conveyor belt, just like analyze, analyze. Like that's so not all that the job is right. So there's a lot more education in that scenario where as I was thinking about your comment, Michael, like, with the elevation of analysts into those leadership roles that there's, there's a lot more visibility in line of sight. So I agree with you on the accountability we're going to put on any Listener to bring that work out of the shadows and acknowledge and like what you were talking about, Mo. So that's, that's a really good point.

[Michael Helbling]

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

[Val Kroll]

Can we talk about data quality? We have not touched on that at all.

[Tim Wilson]

It's usually pretty good. I mean, just kind of automatically. Yeah. What do you mean? What does there talk about?

[Michael Helbling]

So pretty sure.

[Val Kroll]

Oh my God, stop. Everyone stop triggering me.

[Michael Helbling]

Sorry, Sorry. Well.

[Val Kroll]

Just come on. I think the one that I'm specifically comes to mind is bend sent from a media agency. And I just get so frustrated. Or from a finance team.

[Michael Helbling]

Talk about the highly formatted Excel files you might be receiving.

[Tim Wilson]

Oh, in wide format. When they should be in the wrong format.

[Mo Kiss]

Of course.

[Val Kroll]

I'm glad you laugh about it. I am not at the laughing stage. Sorry.

[Tim Wilson]

Well, this is probably a whole episode.

[Michael Helbling]

We need to do on stuff like this.

[Val Kroll]

But it just, it. 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.

[Mo Kiss]

I don't get it.

[Val Kroll]

They sent a spreadsheet over on Monday. Like, you've got the data. What's the problem? Like, why is it going to take you a week. And you're like, do you know that every single like city that they run media in is in a completely different format? And like we then needs to sense check it with our record. Like, no, that is not a, that is a huge lift and fuck. Anyway. And then you've got some very senior brilliant data scientists that is spending their time basically QA ing data. It's really frustrating.

[Tim Wilson]

Well, but 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 and that data may be created out of some contractual necessity, but doesn't have any real incentive or stake outside of what's in an agreement. It's like, oh, we'll send you data, we'll send you data. Check the box. This is going after media agencies pretty hard that a lot of times they don't really enter. They're like, whatever. The platforms trade desk spits this data out, it runs into our data warehouse and we'll just give you a feed. And the analyst is the one who winds up having to explain they're data to them. So it's like another version of that particularly is another version of what you were talking about earlier, Michael, where you have to be like, yeah, how can this possibly be zeros across here? And it's like, wait a minute, I'm now having to reach out to everybody seems to assume that it's coming in fine, but I have to set up time to go three levels deep with some partner to get them to agree that it's actually a problem or explain to me why it's not a problem. Yeah, that's a big one.

[Michael Helbling]

I'm literally in a situation like that right now. I ran into a situation just this past week where a company's 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. So you're literally starting out with sort of like, okay, well our first conversation is going to be, guess what? The data you thought was really good, good, not good. And there's a number of fixes we're going to need to do before we even start on the things we want to get further along on. And it's frustrating but real. Right. So it's sort of like, yeah. And then the other one that gets me sometimes is sort of like alerts and notifications, anomaly detection and those kinds of things because like that is a part of data, but it's not really what an analyst does necessarily.

[Tim Wilson]

Well, the analyst gets blamed if the data, if the data, all of a sudden it's found that something wasn't there for weeks. They're like, what were you doing as an analyst? How did you not notice?

[Michael Helbling]

Raise your hand if you're the only one that's had your own secret dashboard so you don't get caught up in one of those things. So you have advanced warning of something that's happening.

[Val Kroll]

I think anomalies is part of our job. But you all keep saying analyst. And I think of data practitioners where they're, it's a data analyst, analytics engineer, data scientist. So like for example, if there is something in our B2B pipeline that like breaks our leads coming through that is absolutely like quote unquote, data quality, anomaly detection. And I would expect an analytics engineer to like go in and solve that. Absolutely. When we're doing like at the complete other end of like a metric goes up, a metric goes down, that sort of stuff. Like again, I would expect a data person to go in and kind of debug that. They might not be responsible for the complete up level challenge of why that thing is or isn't working anymore. But I would expect someone to be pretty across that. If we saw a number tank or something like that or a number skyrocket.

[Tim Wilson]

But that's the, I mean the way you just framed it not to, I mean you're just speaking off the cuff that there is a perception that.

[Michael Helbling]

Yeah, yeah, yeah.

[Tim Wilson]

They need to catch if a number tanks or a number skyrocket. In practice, every time I've had a system where it's like trying to tune, where like there's, there's not a threshold, then there will be platforms out there say look, you can set this at a 95% threshold, set up a hundred alerts. I'm like, cool, I'll get on average five alerts a day and start ignore.

[Val Kroll]

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

[Tim Wilson]

So, Michael, I still have the alert turned on for a certain tax preparation company that you and I worked on.

[Michael Helbling]

Oh, nice.

[Tim Wilson]

Years ago and like January 12th, their homepage was down from Seattle because I just never turned it off. But that was one where they were having sporadic issues and it was like, somebody should be monitoring this and I can go set something up. And I had to set up on like my personal account and I just never turned it off. So literally, Michael knows the brand, I know the brand. It was down for about 35 minutes.

[Michael Helbling]

You need to do some account access cleanup. That's some shadow work that a lot of consultants have to do. Get yourself yourself off of those old GA accounts or Adobe accounts that you've been on for years and years.

[Tim Wilson]

Site 24 by 7 I was doing like, oh, okay. You know, like ping tracker that I'd set up. So it was, I had set it up and it was third party tool.

[Michael Helbling]

You're doing third party tool. Okay.

[Tim Wilson]

Yeah.

[Michael Helbling]

They're probably like, why is our website getting crawled by this website? But that was.

[Tim Wilson]

They were sometimes saying like the tool was down. And I'm like, no. Oh, like why is this anomaly in the data? Because your fucking site went down. Like that's not a. Because I think I set up a ping for the footer as well because based on where they had the tagging track. But I think it started with them saying your digital analytics, your web analytics data is bad. And I was like, yeah, that's weird. What's going on? It's like, well, no, the whole site went down.

[Val Kroll]

No, I did once find. So I was working somewhere. There was like an issue that I couldn't figure out, like why this number had gone weird or whatever. And then like a month after I left, I figured out why and it was like completely tangential. I was just working on something different and I did reach out to let them know. I was like, hey, this is probably what this was. You should fix it. Here is how to fix it. You're welcome. I'm not a shit human. I want everyone to have the best data they can.

[Tim Wilson]

I also get the it's back up. So every time I've seen it, it's come back up quickly. So there hasn't Been a. A point.

[Mo Kiss]

Okay, so before Michael wraps. Because you got that look in your eye.

[Michael Helbling]

Well, I want to do.

[Mo Kiss]

I would love to hear. Love to hear people's thoughts on shadow work. Not shadow work for, like, data fluency. Data literacy, we'll call it. We'll call it. Because data literacy programs, I think, are one of the more common ways people talk about it, because it is like data culture category of work.

[Val Kroll]

Yeah, I. I like data fluency. I think it's less obnoxious than data.

[Michael Helbling]

Yeah. Everybody can read and write.

[Mo Kiss]

Yes. So is it shadow work? Not shadow work?

[Val Kroll]

I think it's shadow work, but I think it's important. Shadow work.

[Michael Helbling]

Yeah, I think it goes back to that. Sort of like, what do you need to do to help the organization take a step forward with data? Make decisions, use the data, be effective with the data. And a lot of times that's building up data fluency in an org or helping people build up their data fluency.

[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 flow fluency program? Pretty ineffective. So it's. It's a thing that needs to be in the shadows. That is a. I mean, not that there's not the opportunity for some of that training. I feel like I've been learning how much. I mean, it's not a. It's the reality of a short attention span that the more you can have, like, in the moment, like. Let me come up with. Let me show you this. Now. Let me explain this little thing. Now let's talk about. Oh, you know what, you know how people say correlation is not causation? This is like the perfect example. Let's talk about that for five minutes. Because that's a trap you're falling into.

[Val Kroll]

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

[Tim Wilson]

Sounds like the sort of observation a woman would make, by the way.

[Michael Helbling]

Jesus.

[Mo Kiss]

Oh, boy.

[Tim Wilson]

It was right there.

[Michael Helbling]

We're gonna send Tim back to training.

[Val Kroll]

Those in the moment discussions are actually what I think is makes it so hard because it is shadow work. Because it's not like I built a program, I've shipped it, I've ticked it, it's done. It's like every time I talk to the stakeholder I'm trying to help them get a little bit further in how they think and understand data. And that is like you're never done, you've never ticked the box. And so it does have a very heavy cognitive load. But it's incredibly important and probably leads to the best outcome. I say without a data informed opinion on that at all, just my gut.

[Mo Kiss]

So I'm actually surprised that you guys are all on the same pages. I don't think it's shadow work at all. Whether it's like bite sized or a big part of it. Because I mean to even some of the criteria we were talking about using earlier. If your role is to help the business make smarter decisions, like making sure that you're connecting what you're finding, what you saw, what you observed, what you validated, what your recommendations recommendations are with like what the business can actually be doing with that information. It feels like it's a. I don't know. To put it another way, there was a leader who I worked for at UBS who like the four Ds of product development, like the define design, develop, deploy. He always said there was a fifth like Shadow, not actually a fifth one of adoption. And so like until you understand how people are using that or if those, if this is like a data product or whatever, then you, you're not done. Like the work isn't done when you ship it. Like the is done when you understand and create the feedback loops. Right. So I feel like it's very much in the same vein of how to make sure that your work continues. Like Michael, the work almost similar to what you were saying, creating the processes so that the team knew how to take advantage of those recommendations. I don't know, I just feel like it's not like you're not done just when the analysis is complete or shadow work isn't optional.

[Tim Wilson]

Like it has to happen, it's just not.

[Michael Helbling]

Yes, it feels like it's square.

[Mo Kiss]

Clearly in the court of. I would expect it to be in a job description. Like that's what makes it me feel like it's not work.

[Michael Helbling]

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

[Val Kroll]

But what was the outcome, though? Like, what was the end of the story? Did you get asked to change it or did they? I like the value of what you're doing.

[Michael Helbling]

I kept going, yeah, no, I was. That was a role in which firing me probably wasn't an option. Probably they felt like it in the moment. I sent an email to the head of HR ahead of that meeting being like, I'm about to chew up my boss. But it worked. And I still had a good relationship with that person afterwards. But it was, it was a situation where they were like, oh, okay, well, never mind then. And I just kept going with what I was doing. So it made sense.

[Tim Wilson]

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

[Val Kroll]

Okay, but just a push on that. I, I do agree. I think the challenge though is like, in my role, we write. We write the job descriptions for data people. Data people are writing job descriptions for data people. So you can still have a mismatch with the stakeholder of what they think a data person should be doing. Like, so I'm just saying it's not like bulletproof.

[Tim Wilson]

Yeah. I mean, but I think part of the. If that's recognized, it's like, like, hey, we've got a bunch of really difficult, unrealistic, you know, stakeholder. We should have in the. We should have in the job description. That part of this is kind of collaborating with. Not. You don't say collaborating with, but you, you know, you're like, you know, collaborating with, educating, informing, iterating with. So I think still can be in.

[Val Kroll]

Ambiguous and challenging circumstances.

[Michael Helbling]

That's right.

[Mo Kiss]

Yeah, exactly.

[Michael Helbling]

Oh, yeah, exactly. Self starter, able to juggle multiple priorities simultaneously.

[Tim Wilson]

It's like often when the hiring manager isn't an analyst, then that's why the job description doesn't have the shadow work in it. And that does set up some of the.

[Michael Helbling]

And it comes out ringing false. Yeah, well, some of my shadow work is trying to get this show wrapped up on time. So let's go to do that.

[Tim Wilson]

We got to find somebody who's good at it.

[Michael Helbling]

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

[Tim Wilson]

Friday is workshops and Saturday it's conference. It's a pretty low cost, low three figures conference all day. Looks kind of not Measure Campy from an unconferenced perspective, but from a enthusiasm and critical people, a lot of people, critical mass of people showing up. Pretty interesting topics.

[Michael Helbling]

So what are the dates?

[Tim Wilson]

Yeah, they. Oh, that would be important. Yeah, I'm here for 6th and 7th.

[Michael Helbling]

Talk about shadow work.

[Tim Wilson]

When is it?

[Michael Helbling]

That's awesome. And then of course Measure Camp New York will be in March 28th in New York City. It's officially in New York City, not New Jersey this year.

[Mo Kiss]

Very exciting, Very exciting.

[Michael Helbling]

Yeah, it's going to be a great Measure Camp is always a great time. Obviously Val super involved with Measure Camp Chicago, Mo with Measure Camp Sydney, Tim with MeasureCamp Columbus, me with not being involved with Measure Camp in any official capacity. But I love going to them. And I think right now Tim and I planning to be at that one. And that's March 28th in New York City. And then finally April 28th and 29th, the whole analytics Power hour, or a lot of the Analytics Power Hour folks will be at the Marketing Analytics Summit in Santa Barbara, California, which sunshine on the west coast. Hello. Get there. Sign me up too.

[Tim Wilson]

And we got some exciting plans for that. Stay tuned to future episodes.

[Michael Helbling]

What's the drink that you have have in Santa Barbara? What's like a good cocktail for that?

[Tim Wilson]

I'm sure it's some fruity California liberal something wine.

[Michael Helbling]

Exactly. White. A white wine or a rose on the beach or in the sunshine.

[Val Kroll]

Love this. Yeah, love this.

[Michael Helbling]

For me, I don't know. I'm terrible at picking out drinks. All right, that's the show. We're excited to have brought it to you. And I think I speak for all my co hosts when I say no matter whether the work is in the shadows or way out in the open. Keep analyzing. Thanks for listening.

[Tim Wilson]

Let's keep the conversation going with your comments, suggestions and questions on Twitter @nalyticshour.

[Michael Helbling]

On the web at analyticshour.IO, our LinkedIn.

[Tim Wilson]

Group, and the MeasuredChat Slack group.

[Michael Helbling]

Music for the podcast by Josh Crowhurst.

[Tim Wilson]

Those smart guys wanted to fit in, so they made up a term called analytics. Analytics don't work.

[Michael Helbling]

Do the analytics say go for it no matter who's going for it. So if you and I were on the field, the analytics say go for it. It's the stupidest, laziest, lamest thing I've ever heard. For reasoning in competition, Lacy Fusion Productions.

[Tim Wilson]

Lacy Fusion. That's our production studio. Sister sister organization on the Southern hemisphere.

[Michael Helbling]

Covering Lacy Fusion Media. 4th Floor Productions.

[Mo Kiss]

Lacy Fusion Media, known for expanding into Australia and Riverside.

[Michael Helbling]

And the Lacy Fusion Media present a fourth floor production.

[Mo Kiss]

Okay, well, screw your green bars. You sound like you're building with a paper cup and a string.

[Val Kroll]

All right.

[Mo Kiss]

Love you.

[Tim Wilson]

Love you too. Oh, wait.

[Mo Kiss]

Temperature.

[Val Kroll]

Matt, she is so cute.

[Michael Helbling]

I know. It's ridiculous.

[Val Kroll]

Like, so cute.

[Tim Wilson]

Rock flag. And who knows what insights lurk in the tables of our databases. The shadow analyst knows.

[Michael Helbling]

Nice.

[Tim Wilson]

That's actually pretty close.

[Val Kroll]

I'm like, damn, you got the voice.

[Tim Wilson]

Let's got something.