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Are you reading this? If so, then you are literate. But, are you (and are your stakeholders) data literate? What does that even mean? On this episode — recorded in front of a live audience at Marketing Evolution Experience in Las Vegas — the gang tackled the topic. Mid-way through the show, they were delighted to be joined on stage by Gary Angel (unplanned, but due to a series of unfortunate travel and communication mishaps — recording with a live audience is exciting! He is officially over halfway to joining the podcast’s Five-Timers Club)! It was an engaging discussion with some smart questions from the live audience.
As a participatory episode, in addition to having Amy Sample introduce the show and Gary join us on stage, we had some smart perspectives and questions from the audience: Michelle Gosselin, Joe Ebbeler, Jim Sterne, Michele Kiss, and Andy Bickerton.
00:04 Announcer: Welcome to the Digital Analytics Power Hour. Tim, Michael, Moe, and the occasional guest discussing digital analytics issues of the day. Find them on Facebook at facebook.com/analyticshour and their website analyticshour.io. And now, the Digital Analytics Power Hour.
00:27 Amy Sample: Hi everyone. Welcome to the Digital Analytics Power Hour. This is episode 91. I’m Amy Sample, Senior Director of Consumer Insights and Analytics at PBS and a past podcast guest. Whether on purpose or not, you are now part of a podcast recording in front of a live audience at Marketing Evolution Experience in Las Vegas, 2018.
00:50 AS: They drink, they swear, but they care about Analytics and how Analytics gets used by organizations. Let me introduce you to the host, she is the manager of Analytics at The Iconic, hailing from Sydney, Australia. Give it up for Moe Kiss.
01:02 Moe Kiss: Hi.
01:04 AS: He is the Senior Director of Analytics at Search Discovery, he calls Columbus, Ohio home. Let’s hear it for Tim Wilson.
01:13 AS: And he is the Analytics Practice Lead at Search Discovery, from Atlanta, Georgia, it’s Michael Helbling. Okay, let’s start the show.
01:21 Michael Helbling: Yeah. Well, hi everyone. It’s maybe the first time we’ve been introduced by somebody else, which is awesome. Thank you Amy Sample. Alright, one of my favourite movie quotes is from a classic movie called The Inspector General with Danny Kaye, this is from the early days of colorization, some of the SDI people in the room will know that I have movie quotes that are off the hook to me, but not well regarded by others.
01:52 MH: But there’s a section where Danny Kaye’s character is being confronted by someone else and he goes to read this letter and at the end he’s finally like, “Fine, maybe I can’t read and write, but I’m not illiterate.” And I just… That quote kept going through my head thinking about this topic of data literacy. And so, we launch together into how do we sort that out? How do we become data literate in a world where we’re neck deep in data, but dealing with business users and other people who maybe don’t even understand it or don’t quite get the nuances within it?
02:33 S?: Hmm…
02:33 Tim Wilson: Drink. [laughter] Every time, Michael says illiterate, drink. I don’t know.
02:43 MH: Is that gonna be a thing?
02:44 TW: I don’t know.
02:45 MH: Okay.
02:45 TW: Probably not.
02:45 MH: I’m good with it.
02:46 TW: Everybody at home… Everybody driving to work in the morning, your morning commute.
02:48 MH: Yeah.
02:49 MK: Drawing no attention to our inconspicuous activities going on here on stage.
02:54 MH: Yeah. Well, okay, so do you wanna start with defining data literacy? Is that where you’d like to start or…
03:00 MK: Jim’s like the only one who’s had a genuine crack at this and I’m still not sure you got to a final resolution.
03:03 MH: Jim Stern is literally in the room.
03:05 TW: And Jim Stern has had a crack at that.
03:07 MH: He’s had a crack at it as well. But no, what do we think? What is data literacy?
03:13 TW: Well, and I think it kinda came around ’cause we were kicking around this topic and that prompted me to organize some thoughts, and I don’t think I helped the challenge that everybody says, you use the literacy example, that reading literacy seems like it’s an easier thing, can you read and comprehend or not? And data literacy, therefore, it seems, we know what data is, we know what literacy is, so that’s easy, right? But as I started looking for what’s the real definition of data literacy, outside of a really surface level of can I do, or use, or understand data, there’s not much. So, Jim actually has a post on the Marketing Evolution Experience blog, where he treats it as a spectrum, and actually data literate is not at one extreme of the spectrum, that’s kind of on a progression. The way I wound up approaching it, which is still I think probably too simplistic was that there are different dimensions of data literacy. That I was thinking there’s metrics literacy, there back in the day when people said what’s the difference between a visit and a page view? Or how is time on site measured or from web analytics?
04:21 TW: But that goes to any field, even if you say revenue, or lead source. So there’s… Do I understand what the… Do I understand the data that I’m looking at? What that’s actually reflecting in the real world? That’s one type of literacy. I think there’s tools literacy, there are people who are Excel or Tableau or R or Python jockeys who can use the tools to manipulate, and I think that’s fair to put that under a literacy bucket. And then, the other one I came up with, and I called it conceptual literacy, which I don’t think is necessarily the best term, but that’s actually understanding how to think about data, understanding the inherent uncertainty of data. I was in actually a session earlier today that was… I’m gonna botch what it was. It was statistics for the non-data scientist. Is that what it was? And it was interesting, ’cause it was trying to talk through, “Hey, we go from this data, we understand this data,” and then boom, we were all the way into [05:18] ____, and that’s a hell of a leap. So, under conceptual literacy, I throw all the stuff that goes beyond just, can you understand a chart or not? And I think that’s probably still a grossly over-simplified view.
05:34 MH: Yeah. How about you, Moe?
05:36 MK: So, but… Okay, I keep having this thing where every time when someone talks about data literacy, what I come back to is, why do we need to know? Why do we need to know where you are on a spectrum, on some other scale? Is it about better understanding your stakeholder and their understanding of whatever dataset you’re working with? Is it about your own professional development? And I don’t feel like we actually had that nutted out, or maybe I’m just missing the point.
06:03 MH: So I like that. So that… Part of it may be the way you say data versus others. No, I’m just kidding.
06:12 MH: Data, data, data. That’s part of our problem.
06:15 MH: No, but here we are in Las Vegas, and 100 yards away or 400 yards away, it’s hard to measure distances in these enclosed spaces, but there’s all kinds of gambling that we could be doing instead of talking about data literacy. But who’s a gambler here? Anybody, show of hands, knows what they’re doing? I know you know what you’re doing.
06:33 TW: I love it how you do show of hands on an audio format.
06:35 MH: Yeah. On an audio format, it’s one of my gifts. It’s just helping people understand. There’s only a couple of people who consider themselves gambling literate in this room. But I could probably go play blackjack poorly. So I concept… I understand the mechanics of blackjack, but I don’t understand the strategy for playing blackjack well. I don’t know how to… What levers can I pull to move blackjack in my favor, right? So in other words, how do I use data to maneuver a business? So, as an analogy, I like… I’ve been thinking about it, ’cause I’m in Vegas, so I’m thinking about gambling. [chuckle] And that’s kind of the analogy that’s sort of sticking with me is, yeah, we all know what the games are. Like, oh yeah, we should play craps. How do you play craps? I don’t know. There’s this line, you keep two chips behind it, and then there’s numbers out there and you bet some of them, maybe or maybe not.
07:27 TW: But in marketing, it’s basically like you’re being told, “You’re gonna play blackjack,” and so you…
07:34 MH: So, right.
07:34 TW: Have to. And I think that’s…
07:35 MH: If I decided to become a professional gambler after today, do you think I might focus on it just a little more?
07:40 TW: But you would, like you would be drawn to it, right? Whereas in, I think the challenge we go through that… I think part of it is even as analysts and having grown up through the web interfaces and Microsoft Excel and kind of hitting what was a natural plateau, it was very easy for me to say, “Yeah, I’m continuing to improve my capabilities, ’cause I’m getting better and better at Excel.” And totally missing, let’s just call it The Matt Gershoff world of… There’s a much deeper level of understanding and thought and thinking through data, and that’s from somebody who’s full time in the space. But when we started shifting to people who say, “Well, I’m a digital marketer, I’m a SEO person…
08:22 MH: Right. What do we expect of them?
08:23 TW: I need to use the data. So they’re kind of like… They just sort of inherited they’re playing a game of blackjack, and they’re not… If they say, “Yeah, you know what? I know how to… I know what the rules are,” and then that’s… They’re missing something.
08:35 MK: I don’t even think it’s that, I think it’s that there is this… I don’t even wanna say the word bravado, but it’s not like blackjack. I feel like people sometimes admit that they’re not great at that.
08:46 MH: Right, you don’t have to take my analogy so far.
08:46 MK: With data everyone thinks that…
08:49 TW: No, it’s the Dunning-Kruger right there, right? I mean it’s…
08:52 MH: But maybe…
08:52 MK: Everyone thinks that they’re awesome with data. Like I have a person at my company who shall remain nameless, who thinks that they make data-driven decisions, they’re exceptional with data, they understand it. And there are times that you’re trying to explain why they can’t have a metric that’s session based at a user level or something like that, that you’re just like, “But you say you’re a data person, I don’t get this disconnect.”
09:14 MH: Yeah. Or they haven’t understood it. What’s that called?
09:16 TW: Well, the Dunning-Kruger Effect?
09:19 MH: Yes.
09:20 TW: The more or before that it was the illusory superiority. I mean, I do, I think that’s what illusory superiority…
09:26 MH: No, no, say it again. I love that.
09:29 TW: See, how much has he had to drink? But I think that… I do think that’s real that if we asked… If you pulled [09:35] ____ be the classic like [09:37] ____, all marketers think they’re more data literate than the average marketer. And so, if you’re living and Dunning-Kruger is the least… The less literate you are, the more likely you are to think, I know how to read a bar chart. That drives me up the fucking wall when…
09:54 MH: Can I ask a clarifying question?
09:56 TW: Little conflict. We were given speakers notes, do not curse from stage part of the podcast.
10:00 MH: Oh, yeah.
10:01 TW: So this is immovable object meets a unstoppable force.
10:04 MH: Tim Wilson, stop.
10:05 TW: I think we just… Yeah, just hit my profanity.
10:07 MH: That’s fine. I mean, we can always not have us back.
10:14 MH: We’ll edit that out.
10:15 MK: So, clarifying question.
10:18 MH: Okay. So the clarifying question. We’re talking about marketers, but isn’t this illusory competence, whatever thing you just said…
10:25 TW: Illusory superiority.
10:26 MH: Illusory superiority. I love it… The civil… All of that, that’s so nice.
10:33 TW: Is that a literation or…
10:34 MH: Yeah, literation.
10:36 TW: Okay.
10:37 MH: Yeah. Isn’t that true of anyone who approaches data, even as analysts aren’t we kind of afflicted with this as well? So I just wanted to say, it’s not like only marketers have this problem. Or are you saying, because we’re analysts, we’re also just naturally humble before data. So, marketers just have this, or…
10:52 TW: Oh, I…
10:52 MK: No, no, I feel like we are better at picking out the flaws, ’cause we understand the limitations, or we understand that there could be an error with this metric…
11:00 MH: Okay, I just wanted to…
11:00 MK: Because of this issue.
11:02 MH: Get your point of view…
11:02 MK: Oh, no, no.
11:02 MH: Like, do we feel this was sort of like unique to our compatriots or is this something we should all be on the lookout for?
11:09 TW: I mean, the mission… I feel like I’ve been on a mission… Oh, yes! Sorry. When one is presenting against Gary Angel, we bring Gary Angel to the…
11:23 MK: To the podcast.
11:24 MH: That’s right. Ladies and gentleman, please welcome, Gary Angel.
11:29 TW: Where’s the handheld…
11:30 MH: Yeah. Gary, we’ve been warming up the crowd to have you answer a specific question.
11:36 TW: This is perfect.
11:37 Gary Angel: Alright.
11:37 MH: There’s a concept called…
11:39 GA: I didn’t know that. Yeah.
11:40 MH: Yeah. Illusory superiority, and do you believe that as it pertains to data literacy, do you think that affects both marketers and analysts or just marketers as a general rule?
11:53 TW: Let’s reframe it, as do you… I believe that most analyst or marketers think they are…
11:57 GA: I am so fricking lost.
12:00 TW: Okay. The topic is data literacy. For those listening in their car, we just picked up an extra panelist, two times past guest.
12:07 MH: Yeah, and I guess, third guest? I mean, does this count?
12:11 MK: Third time, yes.
12:11 MH: Third time?
12:12 MK: It counts.
12:13 MH: Like Saturday Night Live, fifth time you get a jacket, so…
12:18 GA: Excellent.
12:18 MH: You’ve got that to look forward to. So yeah, that’s what we’re talking about.
12:21 TW: This is perfect, ’cause he kinda came in cold too, so can you define data literacy?
12:28 TW: This is gonna be the definitive definition. Jim Stern and I are both gonna be like, “Oh yeah, that’s right.”
12:32 GA: Having the ability to look at data, understand it, apply it appropriately.
12:38 S?: Well, that’s way more…
12:39 MK: Yeah, it sounds like it makes sense.
12:39 TW: Yeah, that was… I was gonna make a short episode…
12:42 GA: It doesn’t mean anything, but heck, no. I don’t know. That’s what I would define it as. Where were you guys going with it?
12:48 MH: Oh, we had whole frameworks built.
12:51 TW: There were maturity models…
12:53 GA: See, I’m not consulting anymore, I don’t get to do this.
12:55 MH: On Jim’s model, I’m data blessed, I think. No, data #lucky. No. But yeah, I think you said three words that I think match up nicely with your definition Tim, which is understand… Say yours again.
13:13 TW: He can’t…
13:13 GA: Say it again.
13:14 MH: No, dang it. We’ll go back to the tape, but I think there was a good alignment. We’ll go back to the show notes and we’ll pick out where it doesn’t match up. Alright, so we have an excellent opportunity to maybe open the conversation up just a little bit and we’d love to take some questions or comments. Where have you seen data literacy or lack of it impact you or what questions do you have for Gary Angel or the panel?
13:40 MK: I would also really like to hear, why do you think it’s important to understand where your organization or client or stakeholder is at? Because that’s the thing that I’m struggling with is, is it worth investing time to understand and map out where everyone in the business is, so that we can tailor what we do? ‘Cause I think think that’s part of my job as an analyst anyway is understanding where my stakeholders are at. So when we talk about data literacy, I sometimes struggle with the practical, tactical level to the very heavy documentation model, maturity model kind of stuff.
14:12 TW: But do you wanna… You said, meet them where they’re at. Do you wanna meet them where they’re at and then drag them slowly? You think that naturally just happens? ‘Cause if you’re constantly having the same beating your head against the wall, you have to spend the first hour and a half of an hour long meeting just trying to get them to get over some misguided notion, then that’s not efficient. So you do need to either stop engaging them, which is maybe not an option, or say, “How am I also one, recognizing, wow, they really are hung up on time on page.” Like, they cannot get past this.
14:47 MK: But isn’t the educating bit our job? I just feel like that’s our job is to help get them from wherever they’re at to the next bit and sometimes that does mean repeating yourself a bunch of times, because apparently people have to hear things a bunch of times before it sinks in. But I just see that, that’s my job.
15:03 TW: Well, but I think that’s where when we just paint a broad brush of data literate or not, if it’s treated as binary, even to some extent if you’re treating on… That’s where…
15:13 MH: Yeah.
15:13 TW: There’s… It’s not the same answer.
15:15 MH: It’s a spectrum.
15:15 TW: Well, it’s a spectrum but I think it’s a multi-dimensional and it’s a spectrum and it’s what actually matters for what they’re doing right now.
15:21 MK: So straight back to the complex definition.
15:22 MH: Yeah.
15:23 TW: Well, but it’s… But I think, yeah, you may intuitively do it. I think a lot of these discussions we have to try to say what are the angles, should we do an R training for our marketers? Probably not. Should we do a “Here’s how to navigate Google Analytics to answer basic questions?” Maybe so. I’ve sort of loosely assessed that the entire department, marketing department, doesn’t understand it, so whether it’s happening formally or not, I think.
15:49 GA: Well, one thing I’d say is I don’t think that data literacy is at all comparable to real literacy, in the sense that I don’t think you do start with the most basic building blocks. I don’t think you start with ABC and then get people cat. And part of the reason for that is that people aren’t learning to be data literate in a broad sense. They’re not analysts. They’re learning it in the context of their organization. So one of the things I’d say is, you start with the ABCs for their organization, but that may be modestly advanced metrics that you’re delivering to them, but in the context of what they can understand because they understand the business. So, I don’t think it’s a case, and I sometimes saw this, where we were often asked as consultants to deliver very, very simple reports to people that I thought were misleading. And the argument always was, “You gotta start simple.” But I don’t think that’s a good argument. I think you gotta start right and simple and I think that’s a basic distinction that people sometimes forget.
16:44 MH: Oh yeah, round of applause.
16:46 TW: Give it up for Gary Angel.
16:48 GA: Whew!
16:49 MH: Alright, we turned to the audience for questions like three minutes ago and then failed to do so, but now we are. And don’t worry, we’ll wait as long as we need to, because we can just edit out all the silent spots. Oh, perfect. Some people.
17:04 Speaker 7: Hi there.
17:05 MH: Hello.
17:06 S7: I agree that we need to teach the basic. I often did do that with some client, because honestly, sometimes I get into project, I’m consulting as an independent contractor, you’ve got some project manager managing it, an analytic project, they don’t even know anything about analytic, it’s all over the place. They think that you’re gonna come in, install everything in two months, you’re gonna be gone, and everything’s gonna live on its own, so you really just gotta start with the basic, like explain the ABCs, but I also agree with you that in some cases you cannot just wait for them to understand everything to deliver the more advance stuff, which is the right stuff. So I think there’s a little bit of both, where you kinda know… You kinda need to cover some basics, so at least they have the right vision, they understand globally maybe, especially for those who are new at analytic in an organization, but for in other cases you cannot just wait for them to be ready to understand everything.
18:10 TW: And I think are you… ‘Cause you said… You brought up project manager, which makes me think of account manager, a client manager, that there’s also that other challenge is at times the analyst is separated from the business stakeholder with some intermediary. And then it’s like, well, and I guess this goes back to your question, if… I mean, if you have an account manager who just keeps promising that, oh, give us a login to Adobe Analytics and my analyst will find glorious insights. Okay, now I’ve got to educate the account manager or the project manager, and then ultimately I wanna get to educating the business person, which is another challenge.
18:45 S7: Well, in this case, I was more referring to when organization are really [18:49] ____ dealing with Analytics, it often starts as a project and they don’t have anybody, and then you come in and then they have this map they put this roadmap and this timeframe, but nothing makes sense. So in cases like that, I find like having a session of 101 Analytics to start with, it’s a good way to a align the manager, and executive and everything on the vision.
19:15 TW: Week one, tag the site, week two, make a dashboard, week three, automate the generation of insights.
19:20 MH: Week four, massive insights.
19:22 TW: We’ve already come up with a work breakdown structure for you. You’re welcome.
19:27 MK: But see, to me, that does just sound like that is part of the journey that you would go on with a client or a stakeholder to educate them. So that’s why I struggle sometimes when we talk about data literacy, to me I’m like, “That’s just part of what we do in educating about data and getting people that one step deeper to understanding what it is they’re working with and how they’re doing it.”
19:46 S?: Alright.
19:47 S8: Hi. Joe [19:47] ____ first-time question asker, a long-time listener. [laughter] One of the things that I seem to experience on a fairly frequent basis, and I would love to hear it from you guys is, if you have a person that you’re working with who is a key stakeholder who seems to have been highly educated in something that is not at all the approach that’s going to work for them in this new situation that they’re in, how have you guys approached those things where they almost feel like they’re the experts, but yet they’re the experts in what’s not necessarily right for this context?
20:29 MH: The [20:29] ____ old rules.
20:32 TW: I’ve heard the, “Oh, well the person who’s asking this is a data scientist or is a PhD, so they’re really, really smart.” And it’s like, well that’s good, that’s great to know, but it doesn’t necessarily mean that they’re actually… I guess, smart’s kind of a [20:50] ____ way to put it, but it is a challenge ’cause you’re like, “I’m not wanting to tell you that you’re dumb, but I do need to guide you as to why that doesn’t work here.”
21:03 MH: Yeah. I recently, just personally, started working with someone, a guy named Tim Wilson.
21:16 MH: So it is interesting, ’cause we’ve definitely run across that. And I feel like there’s two ways personally that I’ve handled that, one is if they’re open to the conversation than you can have and try to showcase the differences on why this works and why that doesn’t. And sometimes they’re not. And then it just becomes a much more complicated scenario, and honestly, I don’t know the answer to that. It’s just conflict at that point, and then you affect the management.
21:43 TW: So we’re down to Moe or Gary to come up with the answer.
21:43 MK: I was gonna say, I’ve got like two things. To be honest, I’m still trying to figure out the answer to that question myself. So everything I share with you is a test and learn from my own experience which I don’t know yet whether they’re paying off, probably not. So one method… I mean I work at a company where a lot of our executive team came from big consulting companies. There, I would describe them as very data literate, which means that they also wanna see a shit ton of…
22:09 TW: Why would you even assess them? Why would you have to think about that?
22:12 TW: I’m sorry.
22:15 MK: Well…
22:16 TW: You just sort of tossed it out there.
22:22 MK: Yeah. They are very good with data though and very competent, and would sometimes say to me like, “Moe, I want this graph with this exact thing on it answering this exact question, and this is how to do it.” But I still go through… I mean what I do is I say, “Sure, I’ll give you exactly what you want, but I also think this other way is a really good way. Here are the reasons why I think this way is really good.” And I put a of shit ton of research into an appendix, which if they want to go look at, they can look at, but I don’t include it in the actual content, ’cause I do, weirdly, have a CEO that will go read that appendix and educate himself further. And I present that to him, “Pick what you want.” The other tactic, which I’ve yet to try but I’ve seen it, it’s dynamite, and this guy at my company does it, which I’ve mentioned on the show before…
23:08 TW: Different guy from the…
23:09 MK: Different guy.
23:10 TW: Okay.
23:10 MK: Different guy. All anonymous guys.
23:13 TW: Moe has two co-workers here who are literally running through, they’re like, “When is she gonna bring us up?”
23:18 TW: “Is it gonna be positive or negative?”
23:20 MK: But what he does is he goes to everyone that’s in a meeting, ’cause our decision-making really happens in one meeting, and everyone is there, and we go through a memo, yes, insert some kind of wise crack, and we make a decision.
23:33 TW: Long-time fan of the one-page memo.
23:35 MK: Anyway, he goes through to every single stakeholder before the meeting and tries to get as many of them [23:40] ____ on site before the meeting. And so then when you have that one person that’s like, “Well, I would have done it this way,” everyone else is like, “Actually, I kinda think that way works,” ’cause you’ve already addressed all their concerns. So I should probably get better at that one. But anyway…
23:53 S8: I was gonna say two things. One… And it’s funny ’cause the previous question, we used to talk about project managers as people whose job it was to know nothing, and now you’re talking about somebody whose job it is to know too much in a way. Coming to digital is hard for a lot of people who have done traditional analytics. It is quite different. I do think that finding the analogies, finding similar problems in their skillset and then appealing to them and showing them the relationships, if you can find analogies, a lot of times that helps. I also really like Moe’s idea, thereabout, do what they want, but also do what you want and give them a little bit of both.
24:27 MK: I think I learned that from Tim [24:29] ____.
24:30 S8: And I think that’s a really good way to do it. There’s nothing wrong with giving people what they want. And I think that’s often an obligation we have, but it’s a necessary first step for building their trust. And when they get what they want, sometimes they see the limitations in it, and then you can surprise them with something extra too. But it’s a hard challenge for sure.
24:46 TW: Which I think the counter of that is telling them that they’re… Telling that they’re wrong, it never works. And it’s kinda funny sitting up here with the nicest guy in analytics… Well, Michael, Moe who’s just personable…
25:00 MK: I’m just wondering what’s coming.
25:02 TW: Did that, and I think… I don’t think I was even necessarily in analytics before I kinda learned the hard way that just because somebody’s wrong, guess what, telling them they’re wrong, not gonna really help your cause. So, kind of the yes and… And I think that some of the people I admire most in the industry, I sort of see that, that they know how to meet somebody where they are and kind of gently guide them and help them kind of evolve their own thinking. But it’s not a, “What’s the the one thing I do in this one meeting to do that?” It’s like, “Okay, we’re on a journey together that person doesn’t realize they’re on.”
25:42 MK: It’s funny though, because Michele Kiss’ session today was talking about exactly that. Sometimes when you had…
25:48 S?: Wait, you’re not Michele Kiss?
25:49 MK: Oh, jeez, no. But we’ll have photographic evidence this time. [chuckle] She was talking about exactly that where sometimes when you give someone more evidence that they’re wrong, all it does is make them bunker down on their initial belief, and that’s just something that we’re conditioned to do. And so try as you might to be like, “Look, I got all of this evidence, this is totally irrefutable.” They’ll be like, “Actually, I refute it.” So, it doesn’t always work out well.
26:17 TW: Well, but, I mean we’re gonna… Joe’s gonna take us through the rest of the show. ‘Cause there’s the data piece, right? I’m looking at the evidence and there’s all the confirmation bias and all the other kind of biases, and then there’s the process and the approach, which is tougher. And I think maybe that was also kind of where you were coming at and like, “No, we don’t wanna start by listing off 72 KPIs.” Yeah, that’s probably not. We don’t wanna start by doing a big export and building a model off of it. We wanna start somewhere else and that can be tougher. I do believe that’s an insidious thing in our industry that a lot of analysts are not doing things necessarily the most effective way.
27:02 TW: So I’m learning from therapy, it’s not right or wrong, it’s effective or not effective. But you have a huge company that does something, we do the weekly report, and the weekly report gets sent out. Okay, maybe that’s… And the weekly report is 42 slides long and nobody looks at it and it’s not effective. Okay, it’s not good for that company, maybe at some point they’ll figure it out. The problem is, the people at that company leave the company, and they’ve risen to some level, they go somewhere else, they get a position of responsibility, they wanna say, “We’re gonna do something.” So they say, “I want… This is what I had in my old company.” I think that’s a really, really tough thing for us to battle ’cause they’re like, “Look, I came from insert massive brand,” and you wanna believe that the thing you were doing for 15 years that it had always been done for the last 50 years of that company was the most effective thing to do. So now you go to a different company and you say, “That’s what I’m gonna tell my team to do. I’m training them.” And it’s just this vicious, vicious cycle.
27:58 MH: Alright, try as you might to filibuster, we are gonna take more questions.
28:03 Jim Sterne: I have a question from the Twittersphere. Matt Gershoff wants to know, what are you drinking?
28:10 TW: Well, what I have discovered from an extensive analysis, I did experience the 100-degree heat, ’cause I tried the in casino and outside casino, it is hard to get without hopping in a cab, a decent liquor selection. So we’re just drinking Maker’s Mark.
28:25 MK: Except if anyone comes in the room who actually works at Caesar’s, we’re all drinking iced tea.
28:27 TW: Corking fee be damned.
28:29 MH: We’re not drinking anything.
28:30 TW: And where’s the bottle that we’re not drinking from? Is there…
28:35 MK: Oh, of course, it’s next to Krista!
28:37 S?: If anybody… Are there glasses over there if anybody wants to…
28:38 S?: If you’d also like to not partake, do so at your own peril.
28:43 S?: It’s not like [28:44] ____.
28:45 S?: Alright, other questions.
28:46 MK: I’ve got a question. Oh, yeah.
28:48 Michele Kiss: I have a question. So what do you think is more…
28:52 TW: I’m sorry. Your name is, ma’am?
28:52 MK2: Michele Kiss.
28:55 TW: Wait. I thought you were Michele Kiss.
28:57 MK2: Oh, wait. Maybe I’m Moe Kiss.
29:00 MK: They really know how to [29:00] ____ run a joke way over the line.
29:03 S?: People are still confused.
29:05 MK2: That’s true. So what do you think is more dangerous? The data illiterate analyst or stakeholder who is let loose on the data, or the analyst or stakeholder who is approaching the data with an agenda?
29:22 MK: Ooh.
29:25 S?: Ooh.
29:26 MK: I actually feel like the agenda’s worse. The reason that I think the agenda is worse is because it’s gonna bias your results versus when you’re incompetent you can learn stuff where someone can educate you. An agenda, like reshaping an agenda is a tough game. But I’m actually, can we vote and then we’re gonna do a percentage of the audience so that people can kinda feel…
29:47 TW: Can you do a show of hands? My head will explode. So better be by applause.
29:52 MK: Okay, fine, by applause, by applause. Who thinks option one, which was the data illiterate numpty…
30:00 TW: Numpty.
30:00 MK: Yeah.
30:02 S?: We’re gonna have…
30:02 S?: Who represents the bigger threat?
30:04 S?: So we have two, is it… Two options, by applause. Option one is the data illiterate analyst running amok through the data. Option two is the analyst with an agenda. Which one is more harmful? We’re gonna do option one, applause, and option two, applause.
30:24 MK: And I’ve already biased the audience by telling them what I think.
30:27 S?: Yeah but what if [chuckle] you bias something and you still lose? [laughter] So option one, who thinks the data illiterate analyst running amok?
30:37 S?: Yeah. Alright, that’s good.
30:42 S?: Option two, the… One analyst with an agenda. [chuckle]
30:44 S?: The agenda.
30:47 TW: Alright. Oh! Question for the A/V guy: How well was applause picked up in the audience?
30:54 S?: We’ll fill it in later.
30:55 MH: We’ll go with the agenda. So we think basically that was a vote between Facebook and Cambridge Analytica right there.
31:02 TW: Do people who have an agenda consciously have an agenda?
31:06 MH: I think that’s the question. That was kind of the question. I think you can be data illiterate with an agenda. That does exist, right? So maybe that’s the most dangerous. [laughter] I never thought it’s the super…
31:21 S?: It’s definitely a quadrant.
31:23 S?: Yeah. [laughter]
31:25 MH: Alright, we have time maybe for one more, yes.
31:27 S?: I kind of look at data literacy as an art more or less, and I wonder from your guys’ perspective, how do I get my data literacy skills up? [chuckle] From your perspective, how do you just hone your skills? Do you do puzzles?
31:50 MH: So first off, you are arriving at the start, which is to know that there is no destination. [laughter] But it’s… Tim is the one that said it in our show notes, he’s like “Yeah the more you know, the more you learn there is to know.” And I think that’s really what journey we’re all on, and so to your point, I think that makes sense. I think honing those skills is like, yeah, it’s a personal path, but yeah, there’s lots of things I think you can do to do that. People play chess… I read a lot of books. I watch an inordinate amount of YouTube videos.
32:22 TW: I don’t think that helps.
32:23 MH: Yeah probably not.
32:24 MK: It doesn’t help.
32:25 MH: I’m culture literate, or I try to be.
32:29 TW: That’s interesting. It makes me think that I probably felt I was more data literate three years ago than I feel like I am now. [laughter] I don’t know that I’m moving backwards, I think I’m just becoming more aware.
32:39 MH: Yeah.
32:40 MK: So it’s actually really funny that you ask that. I was… I had a team lunch not long ago, and we were talking about this exact concept. So our product manager was asking me and all of the engineers in our team in our UX like, “Who tinkers on the weekend? Whatever it is the field that you work in. Who reads blogs, or if you’re [33:00] ____ build stuff in R… Do you tinker around in your area of work on your weekend or your evenings?” And across the team, there was some really mixed results. Some of us did, some of us never did, some of us did at different peaks and troughs in our life. I think you have to figure out what works for you. And what I’ve worked out, there are times where I’m not in a space to learn, and when I’m away from work, I actually need that time away to be out of work because sometimes that helps me think through a problem even better.
33:31 MK: But then there were also times where I’m really like, “Yeah, I wanna play around with an R script on my Saturday afternoon.” And it actually doesn’t matter whether you fall into the like, “I wanna spend all weekend working on stuff,” or “I don’t want to,” just… You’ve gotta figure out which bit works for you and also learn the stuff that you enjoy. Because I’ve tried to do it where you force yourself to learn a stuff you don’t like, and you just end up miserable and you don’t wanna do it. So whatever it is, whether it’s like stats, or data visualization or automating a report, it really doesn’t matter. It’s like pick the thing that you like and I would suggest getting better at that than trying to guilt yourself into spending time on stuff that you’re not good at anyway.
34:13 S?: Right.
34:14 S?: [34:14] ____ Excel.
34:17 TW: Learn to love it.
34:18 MK: That’s okay.
34:18 GA: I think that’s really good advice. And a couple of things I’d say, one, I think it’s always a trough if you’re spending your weekends looking at data. That’s not a peak, that’s a trough. [laughter] But having said that, a couple things I guess I’d say; one, there is an academic side to data literacy, and it is actually kind a sad how little many analysts know about statistics. And I personally was never academically trained in stats, which I don’t regret, ’cause it is kind of a boring subject. [laughter] But having said that, I think there is some academic work that people could benefit from.
34:50 GA: I would also say that data literacy you described as an art, I think that’s fair. I probably would put it as a craft. You learn it by doing it. You get your hands on the numbers, that’s the only way to do it. And I think actually engaging with data, trying to figure things out and trying to come to conclusions is the only way I’ve ever seen anybody actually get confident at it. I mean the academic side on the stats, that’s useful to know, but it’s not gonna make you actually good at it until you actually start doing stuff. So I’d be about what I said, “Get your hands on the data and do real stuff,” not bad to fill in some academic theory, but no one ever got good at it without getting their hands dirty on the data.
35:27 MK: I completely agree. I think practical stuff is super important.
35:29 MH: Yeah. And read Measuring the Digital World by Gary Angel, that also helps.
35:34 S?: Oh yeah, book plug, book plug.
35:36 MH: Hey, I plug it ’cause it’s true. Alright, so we are out of time, but we have enjoyed so much getting a chance to spend a little bit of time with you. There are people listening right now in the future who may have questions or comments, and we would love to hear from you. So if you wanna get in touch with us you can do that through things like our Facebook page, or the Measure Slack, Lee Isensee in the house…
36:05 MH: And/or Twitter. And we’d love to hear from you. There’s a number of people in the room right now, and we’re so thankful we got to spend this time with you, many of whom we’ve had the chance to meet, but many we haven’t. And we’d love to get a chance to meet you sometime during Marketing Evolution Experience. Enjoy the rest of the show. And remember, I say this confidently for my three co-hosts this time…
36:29 MH: Gary, Moe, and Tim, keep analyzing.
36:35 TW: [36:35] ____ Rock flag and data literacy.
36:46 S1: Thanks for listening. And don’t forget to join the conversation on Facebook, Twitter, or Measure Slack group. We welcome your comments and questions. Visit us on the web at AnalyticsHour.io, Facebook.com/analyticshour, or @analyticshour on Twitter.
37:06 S?: So smart guys wanted to fit in, so they’ve made up a term called analytics. Analytics don’t work.
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