#237: Crossing the Chasm from the Data to Meaningful Outcomes with Kathleen Maley

The backlog of data requests keeps growing. The dashboards are looking like they might collapse under their own weight as they keep getting loaded with more and more data requested by the business. You’re taking in requests from the business as efficiently as you can, but it just never ends, and it doesn’t feel like you’re delivering meaningful business impact. And then you see a Gartner report from a few years back that declares that only 20% of analytical insights deliver business outcomes! Why? WHY?!!! Moe, Julie, and Michael were joined by Kathleen Maley, VP of Analytics at Experian, to chat about the muscle memory of bad habits (analytically speaking), why she tells analysts to never say “Yes” when asked for data (but also why to never say “No,” either), and much, much more!

Links to Articles and Other Resources Mentioned in the Show

Photo by NEOM on Unsplash

Episode Transcript

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0:00:05.8 Announcer: Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

0:00:13.9 Michael Helbling: Hi, everybody. This is the Analytics Power Hour. Welcome to Episode 237. As an analyst, the best jobs are where you can and even are expected to have an impact. And as a consultant, the best engagements are the ones where we can point to the actual business value of the work done. And as a leader in analytics, nothing feels better than when you can present the work of your teams and show how they’ve grown the business in strategic ways. And yet, it is strikingly rare to encounter analysts, consultants, and analytics leaders who can tell these kinds of vibrant stories as part of data and analytics initiatives and programs that they are a part of. Why is that? How do we as an industry so readily miss the boat when it comes to aligning activity to outcomes? Well, we’re gonna talk about it. Let me introduce my co-host, Moe Kiss, Director of Marketing Data at Canva. How are you doing?

0:01:12.8 Moe Kiss: I’m doing great. I am super pumped for this discussion.

0:01:18.0 MH: I mean, this is a perfect way to start 2024. Julie Hoyer, Analytics Manager at Further. Welcome. How are you doing?

0:01:25.7 Julie Hoyer: I am fantastic. Same as Moe, I cannot wait for this conversation today.

0:01:30.4 MH: Me as well. I’m Michael Helbling. I’m the Managing Partner at Stacked Analytics. And here to help us with this conversation, we have a guest. Kathleen Maley is VP of Analytics at Experian. She is also a member of the Analytics Expert Network at the International Institute for Analytics. And in her career, she’s held numerous analytics leadership roles at KeyBank and Bank of America. And today she is our guest. Welcome to the show, Kathleen.

0:01:55.3 Kathleen Maley: Thank you so much. It’s great to be here. I’m also excited. My heart is already going pitter-patter because this is my favourite topic in the world.

0:02:04.7 MK: Well, and it’s great. And Tim, who’s one of our co-hosts, is not recorded with us today, has been really excited for this episode as well. I know this is one that is just near to all of our hearts. But I think it all started for us when we ran across a LinkedIn post that you put up that basically had something to say about only 20% of analytics insights will deliver business outcomes and that 90% of data science projects fail. And just that post and a lot of the comments, I want to talk about that for a second. Just, where did that come from, and then what was your experience in the conversation that followed that?

0:02:44.3 KM: Well, I’ll tell you first where this stat became very real and very personal and very powerful to me. When I quit my job at KeyBank at the very beginning of the pandemic, so that I could move home to be closer to my mother, I was starting a job search at not the best time. Usually, one is advised against beginning a job search at the start of a global pandemic. And so it gave me the opportunity to talk to lots and lots and lots of hiring managers and institutions. What I discovered was that the story I tell when it comes to analytics is very, very different than the story that is often told. And so what were employers hearing? They were hearing something that didn’t align with the stories out in the market that were most common. So they had to make a choice. “Does that mean this is a good story or does that mean this is somebody who is out of touch?” And in fact, most went with, “This sounds different. Different is unsafe. I’m going with what I know, even if I know that 80% or 90% of the time going with what I know fails.”

0:04:04.2 KM: So I started to think about that, like, “Okay, if we continue to go with what we think we know, at the same time there is this obvious stat that we are failing. We have to think unconventionally.” And I started actually writing a little bit about this and talking to people specifically around, what you will hear from me is unconventional books, the status quo, but that’s a good thing because of this stat. What is also very interesting is there will be a moment of acknowledgment and recognition and an immediate conversion back to muscle memory, which is, “Let’s over-index on data, let’s over-index on methods, and talk to me about how well you know SQL and Python.” And as someone being interviewed for a leadership position, being asked how good my SQL is, does not ever make sense. And yet that’s what happens.

0:05:06.1 JH: With you saying that, Kathleen, it makes me think too, even with the conversation that was happening off of your post, it feels similar to what you were saying you were experiencing during that job search. Because I feel like you brought up such great points in your post on LinkedIn talking about how it is. It’s like taking that focus away from not just the technology and the data that’s there, but how to actually use it and put it to use in the proper way. And it’s very much like the people in the business point of view. And it was funny ’cause you got people agreeing with you, I could tell in the beginning they were agreeing with you, but then they went back to muscle memory as you called it, to say, “But what about data quality? What about building better data products?” Things like that. And so I would love your reaction to some of that ’cause I found that so surprising.

0:05:49.8 KM: Yeah. I think it is muscle memory. That’s a huge part of it. I think another piece of it is that’s the easy stuff. That’s the easy stuff. “Oh, we have solutions for data quality. Let’s talk about data quality. We’ve had that conversation before, we can have it 100 more times and it feels good because I know something about that.” It is easy to go back to the things that we have the answers to, but that’s not what brings us value. What brings us value is, “Okay. In spite of the fact that we will never have perfect data quality, how do I make use of what I already have?” It is not acceptable. And again, my experience is singular. I have worked as an analytics leader within institutions for whom analytics was never the product. Analytics was a support function to some other business objective. So I think that’s an important context.

0:06:45.1 KM: But in that context, it is never acceptable for me to go to the president of the bank and say, “I’m sorry, our data quality isn’t good enough for me to answer this question for you.” When he comes to me and says, “Kathleen, what will be the impact of the government shutdown?” I had better figure out a way to answer that question with some reasonable level of confidence, so that he knows what actions he needs to take to protect the bank, or perhaps identify some opportunity where we can meet the needs of our consumers when somebody else isn’t. So it’s never acceptable to say, “Oh, it’s the data quality.” Those are things we have to work on. But we cannot use that as a distraction from really fulfilling our obligation to the organisation that’s paying us everyday.

0:07:37.7 MK: Okay. Just to play devil’s advocate a little bit, which I quite enjoy doing, I’m going to try and interpret what I’m hearing and you can correct me if I’m off on the wrong track, that we shouldn’t be focusing our conversations on the data quality, but making sure we answer the questions effectively with what we have available. But I’m just thinking of this scenario in my head where we don’t bring up things like data quality with leadership, which I don’t know if I’m interpreting that correctly. And then, for example, let’s say there’s a massive problem in our pipeline and everything breaks down, or a key metric is suddenly unavailable. And then the leadership comes to you and is like, “But you’re responsible for managing this pipeline. Why is it broken?”

0:08:18.1 MK: And they don’t have that historical context of like, “Hey, things weren’t working great. We had this huge project underway to rebuild it, or whatever. And it means that we have had impacts on things downstream, which we are fixing.” Do you know what I mean about, there needs to be some sophisticated understanding about what we are working with. And I 100% agree about focusing on answering the questions. But if we don’t also in some part educate them on the, I guess the tools or the technology we have available. And I feel like I’m arguing against myself here, because I don’t agree with what I’m saying. But at the same token I’m like, “There’s a chicken and an egg, and I don’t know how we solve this.”

0:09:01.6 KM: I think… So again, every situation is somewhat unique, but broad brush. I think of a couple of things. First of all, if my leader… If I have to get into a conversation about what is happening with the data, generally, that means I’m not effectively answering the questions that are being asked. Because the truth is, my level of sophistication around what is happening will always outpace them, ’cause that’s what I think about 100% of the time. And so if they begin to ask questions about, “Well, what is happening with the data pipeline?” That means they’re not getting what they need. My obligation, I see, is twofold. Number one, and primary, get those questions answered. My specialty is investigative analytics. So that means my questions range from something that requires 30 minutes to a much, much broader multi-month, multi-year solution, it is across the board. And so part of what I have to do is make sure I’m addressing the need of the moment, the need of the year, while simultaneously thinking longer term.

0:10:15.8 KM: And a lot of that work is invisible to the partners I support. I actually prefer it that way. Now, when does it need to not be invisible? When I need money. That’s fine. I typically try to find my money through business projects, business priorities, nonetheless. I also always, always, always, think about the capacity that I have to give to the business. I do not have this conversation with them, but in my head, the capacity I have to give to the business is 80%. I don’t tell them that they’re getting 80%. They believe they’re getting 100% because they are getting 100% of the capacity I’m allocating to them. 20% has to go to those underlying initiatives. That also means that I need to have very, very good line of sight into, “Where do we need to improve precision? Where do we need to improve data quality? Because it’s not gonna be the same across the board.” Striving for perfect data quality, I think, is an irresponsible use of company resources. Striving for the level of data quality required for any individual use is where I should be focused.

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0:11:29.5 S?: It’s time to step away from the show for a quick word about Piwik PRO. Tim, tell us about it.

0:11:39.3 Tim: Well, Piwik PRO has really exploded in popularity and keeps adding new functionality.

0:11:42.9 MH: They sure have. They’ve got an easy-to-use interface, a full set of features with capabilities like custom reports, enhanced e-commerce tracking, and a customer data platform.

0:11:53.9 Tim: We love running Piwik PRO’s free plan on the podcast website. But they also have a paid plan that adds scale and some additional features.

0:12:01.3 MH: Yeah. Head over to piwik.pro and check them out for yourself. You can get started with their free plan. That’s piwik.pro. And now let’s get back to the show.

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0:12:12.1 JH: So, it sounds like there’s a distinction too, and maybe this plays a little bit into what you were bringing up, Moe. What I’m hearing too, is if we’re talking about important things we need to do with data, data quality is there and it’s underlying, we need to have it, we need to keep working at it. But it sounds like we can’t say, going back to even the statistic in your LinkedIn post, we shouldn’t be measuring our value as the analytics group to the company solely on those things.

0:12:42.2 KM: Oh, for sure.

0:12:43.0 JH: Those are tools and things, but if we’re not doing the answer the question part, Kathleen, that you’re talking about, that’s like, we can’t hang our hats on that as our value. Is that fair?

0:12:52.4 KM: Who cares if we have perfect data quality, if we’re not able to answer effectively any business question? Then all of that work was wasted. It had negative return on investment.

0:13:06.4 MK: And I love, Kathleen, what you said about if we’re focusing on explaining, I guess, the technical pitfalls to our stakeholders, then we’re not really answering the question that they have. And I’m like, “That has just set off this light bulb in my mind… ” And I feel like we have a tendency to do that, of like, “We need to explain how complex this is. They asked me for data and they said it was simple and it wasn’t simple. So I’m gonna tell them how hard it was and all of the caveats that they need to understand because otherwise they’re gonna misinterpret it.” And it’s like, actually all of that is detracting from helping them answer the question they’ve got.

0:13:44.5 KM: Yes. And, it’s not their job to interpret it. I don’t want them to interpret it. That’s why they pay me. I speak data. They don’t. I do the interpretation. They don’t.

0:13:57.4 JH: Preach.

[laughter]

0:14:00.9 JH: Thank you.

0:14:01.0 KM: Yeah.

[laughter]

0:14:01.0 KM: Yeah.

0:14:02.5 JH: Okay. One other thing, I had watched your presentation at Crunch in 2022. And something you said that I… This is making me think of, is you were talking about how you sit between, or you hear a lot of, “Are you one of us or are you one of them?” And I definitely run into that a lot being a consulting analyst. It’s like, “Whose team are you on?” And it’s like, “I’m on both people’s team.” How do you break down that idea of us versus them between analytics and the business? And what do you find the best way to manage that? ‘Cause I feel like it’s never gonna go away. But how do you combat it between those groups and how do you manage it going forward?

0:14:40.3 KM: I think, first of all, personally, I take it as a great compliment, “Are you one of us or one of them?” Because that means that I’m doing a really good job connecting with both sets of partners. I have trust with both, and I’m able to communicate effectively with both. Both feel that I’m on their side. And, as luck would have it, it’s genuine. I am on both of their side. I want the business to be successful. I want the analysts that work with me and for me to feel like they matter, to feel like they have an impact, to feel respect that they’ve earned from the business partners they support. And so it means I have to talk in terms of business with the business. I have to, and I do tell my business partners, “This is the level you should expect from the analysts that work with you. And if you’re not getting that, we are not serving you. Oh, by the way, analyst, this is what you have a right to require from the business partner you support. You are not in a position of servitude. You are a thought partner. That means you have to act like a thought partner, but you also deserve to be treated as a thought partner. And we’re gonna make sure you’ve got the level of skills and the confidence to actually fulfill that role.” And so it has to be both sides.

0:16:06.3 KM: So much of being able to ask for something from someone requires, first of all, a trusting relationship. And in my experience, the best way to build a trusting relationship is through vulnerability. One of the first things I do when I… I’ve done a lot of takeover jobs. “We’ve hired a bunch of analysts, we’re spending a lot of money, we’re not sure what we’re getting for it. We need you to help us.” Super. I love that. That’s the job I love to do. That’s my favourite thing to do. And the first thing I do is tell my analysts, “Your job is not to provide data anymore. That’s not your job. Somebody calls you and ask for data, you’re not allowed to say yes. I don’t care if you have to pretend you’re going through a tunnel and you lose cell phone signal. You are not allowed to say yes.”

[laughter]

0:16:53.6 KM: “If you are panicked, you literally hang up the phone and call me immediately. Here’s my cell phone number.” I say to the partner, “I’m gonna ask my analysts to do something a little bit different. It’s gonna feel different to you. Here’s why I’m doing it. Here is what you might hear from them. It might feel off-putting at first, but bear with me. I’m here. I want you to call me if there’s an issue, I want… ” So I’m letting them know. I’m asking my analyst to do something different. It’s gonna be hard for them. They’re gonna make mistakes. “It’s gonna feel different for you too. So don’t be surprised. If there’s any question, call me immediately.” And this is where it starts to happen. Everybody feels like they’ve got an advocate. Of course, we have to back it up with the goods. We have to back it up with the goods. But being open, being honest, being transparent, building that trust, building that credibility, allowing myself to be vulnerable for the sake of the team and ultimately, in support of success for the business.

0:17:49.3 MK: Okay. I’m not gonna lie, there are like 50 different directions I wanna take this conversation just based on what you said. So that’s a tough choice, ’cause I do have this whole team versus us thing, but instead I’m gonna be like, “Okay.” So you’ve said to your analysts, “People ask you for data, we’re gonna say no.” What happens next in your plan?

0:18:08.0 KM: Perfect. And it’s the opposite, “You’re not allowed to say yes. Somebody asked you for data, you cannot say, ‘Yes, here’s your data.’” Okay. Right. Right. But…

0:18:18.6 MK: No.

[chuckle]

0:18:19.4 KM: You make a good point.

0:18:20.0 JH: No.

0:18:21.4 KM: You also can’t say no. I’m the president of the bank, I just call you, “Junior analyst, I want my data.” “No, I’m not gonna send you data.” How’s that gonna go? Not well. Not well.

[laughter]

0:18:31.5 KM: I’m a business leader. I’ve been asking for data for the last five years, the answer is always yes. And all of a sudden today, the answer is no? I don’t think so. That’s not gonna work. I stumbled upon… Everything I know, I know from my own personal experience and trial and error. My goal as a leader is to prevent my analyst from the same pain that I suffered, or at least to shorten it, to lessen it. I stumbled across on accident a phrase, “Help me understand what you’re trying to achieve so I can better meet your needs.” This is an invitation. This is an invitation. This is exposing, not that you business don’t know what you’re doing or you don’t know what you’re asking for, I’m not telling you that you have to tell me what it’s worth before, I’ll tell you, I’ll give you my time.

0:19:26.2 KM: What I’m exposing is my own lack of knowledge around your business and what you’re trying to do. And that if you compensate, if you help me fill that gap in knowledge, I’m gonna be able to do a better job for you. So it is, it’s sincere. It should be sincere. If it’s not sincere, there’s a different problem to address, but it’s sincere. It is a non… Oh, what’s the word I’m looking for? Hostile or abrasive or competition-based. It’s very welcoming. It’s very open. I was trained, as many of us were, to say, “What are you going to do with it? What is it worth? How do you know you need this?” These are all very off-putting questions.

0:20:11.7 MH: Yeah. Even the why question, like you said, in a business environment, it’s very challenging. Like, “Don’t ask me why, just give me the data I asked for.” You know what I mean?

0:20:21.2 KM: Exactly.

0:20:22.0 MH: So that’s really great.

0:20:23.2 KM: Yeah, [0:20:24.1] ____ it’s personal. Yes. [laughter] Yeah. And so being able to phrase it differently. And that… Stumbling across that, then it was like, “Oh my God, language is so important.” And I just started thinking about language now in everything I do, everywhere, all the time.

0:20:38.0 JH: That makes me think too, if you get into a conversation with a business partner and you ask them that, you open the door to say, “I wanna be a thought partner with you,” and they accept your invitation, how do you then battle… The issue that I always run across is, even if I get them talking and I get the context, I tend to run across the issue where they have a hard deadline and I was brought in too late. And so even if they give me the context, our only option now is for me to pull the data and the number they are hoping to see to make a decision when we both know, maybe I get them to know, but I know that’s not what we wanna do. And I feel like I can never get ahead of that process. I always get brought in too late. So, for you working internally with analysts, how do you help them combat that part?

0:21:23.5 KM: Okay. This answer could be the rest of our time because there are so many different components to it. But let me…

[laughter]

0:21:31.3 KM: Let me just try and give you a taste, of a few different things. And I’m very practical and I’m very pragmatic. It’s like, “Oh, I have a template for that sort of thing.” Okay. So, first of all, getting the context, really inviting the conversation, having the dialogue, for analysts who are listening, don’t ever go to your partner and say, “I want to be a thought partner.” That is subtext. That has to come through as subtext. Many analysts tend to be very black and white. We’re special people. Our brains work slightly differently. That’s not an explicit, we don’t say explicitly, “I wanna be your thought partner.” That’s subtext. That’s another talk for another day, maybe. Nonetheless…

0:22:14.1 MK: I’m glad you said that though.

0:22:14.6 KM: Yes. Right. These are my people. I know. That’s the subtext that comes across. So then they call me and they say, “Hey Kathleen, I need this data.” There’s really two parts to this gathering of context. The first part is really understanding the problem. The second part of this is, “Okay. Do you need an answer in 30 minutes? Do you need an answer in two hours, a week, two months? What are we talking here?” And so I start to understand, okay, if they tell me 30 minutes, I say, “Okay. Well, here’s what I can do in 30 minutes.” If they say, “Well, I have a meeting at the end of the week.” “Okay, well, that probably means you want at least a day in advance because you need to have time to think about it. I cannot get you this answer. This answer would take six months. But what are you really trying to get out of that meeting? Are you just demonstrating that there’s work in flight? Did you promise something to your boss? And now you… And you forgot about it until just now, so you have to give something that’s enough to get us through?” All of that is equally important. So that’s how I deal with it in the moment, is as much dialogue and understanding and empathy because I don’t… It’s not just, “Tell me what you’re asking for, let me give it to you.”

0:23:32.0 KM: It is, “You have a problem, let me see what I can possibly do to help you get through this. And then let’s talk about what’s bigger. I want to help you. I want you to be successful. Let’s figure out how we do that.” Again, it has to be genuine and sincere on my part. Longer term. Well, guess what? An experience like that, that is what builds trust and credibility. So that individual is more likely to come to me earlier in the process the next time. Especially if I say, “By the way, if this comes up again, get in touch with me earlier. Even if we don’t start working on something, at least I can start thinking about it.” So that is a great time. I just did you a favour. Now you’re appreciative, you’re grateful. I’m gonna tap into that emotion by saying, “By the way, next time, get with me earlier, it’ll help us both.” Another is one of my favourite routines, I call it the business strategy review. This is something that I’m very well with… It’s well within the bounds of reasonable to start to ask for once I’ve developed a little bit of credibility with these partners and appreciation.

0:24:40.9 KM: But I ask them to give me a half day, usually after they’ve done their business STRAP planning with their bosses, they’ve already started to think through what their tech investment requests will be for that year. And I say, “Okay, guys, now you get to spend half of your day presenting to me what your business strategy is, what you’re planning on doing, what’s gonna matter this year.” And so I start to identify those big rocks. I can start to ask a few probing questions, “Ah, so I’m hearing this. If I could do something like this for you, would that be helpful?” So I can start to think about what might be coming. And as those things kick off, I’m already there. It’s not one thing, it is a collection of different practices to put in place, each with its own calendar, each with its own cadence, but all of it, all of it, very purposeful. I know exactly why I’m having that meeting. I know exactly what I wanna get out of it. And I usually communicate that explicitly so that we’re all on the same page and we know how these different pieces fit together and why.

0:25:55.1 JH: That was awesome. I love just even the way you talked about like, “For the next time,” the way, the wording you would use “For the next time,” I actually think that’s so big too for consultants. Again, I’m in consulting and I think it’s so powerful to be, for a consultant to be open and a little bit vulnerable to say, “I can’t get you the exact thing you asked for,” which is really uncomfortable in those situations ’cause that’s why they’re coming to you. But if you can say, “I can get you this ’cause I wanna help you, and hey, next time let’s do this a little differently so I can help you more.” I think that is amazing.

0:26:24.0 MK: To be honest, Julie, though, I don’t think that’s… And surprisingly not just a consultant thing. I work totally in-house. I’ve always had a very similar career to Kathleen for the most part. I work in in-house analytics teams where data is not really the product per se. And everything is about trust in relationships because you don’t get in the room, you’re not part of the conversation, unless they see that you’re gonna add value or they understand that you’re gonna add value and help solve problems. So, it’s the exact same thing. And I feel like I’m having this same, not epiphany, but the same reflection on how important word choice is when it comes to little comments like that, or the question that you ask to solicit context. Because like you said, Kathleen, asking why is, or like, “What are you gonna do with it?” “What are you gonna do with it?” is, literally I feel like every analyst has that trained in their brain for some reason. And it’s not the best question because it is very adversarial of like, “Well, I might give you this if I decide that what you’re gonna do with it is good enough.” That’s how I interpret it. It’s quite hostile. And so, yeah, asking better questions is, I feel like that’s gonna be my mantra for 2024.

[laughter]

0:27:38.9 KM: Yes. Yes. I agree.

0:27:41.2 MH: I wanna turn the conversation a little bit ’cause as we’ve been talking, Kathleen, a lot of what you’re talking about requires a pretty strong dose of proactivity across the board. And so, how do you coach or how do you think about how, let’s… As you come in as a director of analytics or VP of analytics, you’re so often faced with massive backlogs and data debt and all these things that put you in a reactive frame, or you’re just trying to keep your head above water when you first start out. How do you carve out that space to start building that proactive mentality that allows you to do some of that future planning, to do some of that structural shift, those kinds of things? ‘Cause that is, it’s heart of this, there’s some real leadership lessons that you’re doing right now in this conversation about vulnerability and demonstrating this proactiveness. And I just wanna pull more of that out of you and hear a little more about how you would jump into situation and start to create the right dynamic as a leader.

0:28:46.8 KM: Yeah. Well, if there is an existing analytics group, there is a backlog that people will never get through. And so part of it is just embracing that reality and going, “Okay. So we’ll never actually get through this.” This is a good thing and this is to be expected. Good analytics begets good analytics. Never in my career have I delivered something and had the partner say, “Oh, thank you very much. We’re all done now.” Never. And part of that is ’cause their business is always changing.

0:29:16.8 MH: Yeah. That’s right.

[laughter]

0:29:19.8 KM: So I see something and go…

0:29:20.7 MH: That’s right.

0:29:21.8 KM: “Oh my gosh, this is amazing. I could do this, this, and this.” A good answer invariably results in more questions than were initially asked. Okay. So that’s…

[overlapping conversation]

0:29:31.0 MH: And more nuanced questions. Yeah.

0:29:33.4 KM: And more nuanced questions. So we can never, ever, ever staff our way to demand. Prioritisation is the only answer. It is the only answer. So what does prioritisation look like? Well, there’s annual prioritisation, this is identifying the big rocks. You guys know that mason jar where you put in the big rocks and the sand finds a way around it, but if you start with your sand jar you’re in big trouble? So in fact, if you wait until your backlog is cleared, you’re done. You’re done. Instead, it is going against every instinct we have as human beings. And taking a very logical approach too. “I’m gonna do my business strategy review. I’m gonna know what that… ” By the way, that’s not usually my first routine. I’m usually working with a partner for a few months before I get to that, three to six months. But this is what it looks like once we’re stood up and we’re ready. Now I’ve got my big rocks, it’s really easy to fit the little pebbles in the sand around it.

0:30:42.1 KM: And so I have to take the time away from those daily requests to get these other pieces stood up. And it feels uncomfortable, but it actually isn’t as disruptive as it feels in the beginning. Usually in advance of a business strategy review, I’ll put in place a portfolio review. So again, because I’ve done a lot of takeover jobs and I’ve adopted existing analytics teams, there’s almost always a shadow analytics team, or several scattered around the organisation. And one of the first things they do is say, “Okay, business partner, my job is not to fight you for territory and demands that all of those resources be pulled into the centralised function. My job is to make sure your analytical needs are being met. And so I will take on the administrative task of managing the portfolio of analytical work being done on your behalf, whether those resources report to me or report to you.”

0:31:42.9 KM: And I stand up a routine where we are talking about, “What are all of the requests?” Probably 100 deep of data requests and reporting requests and requests for models maybe, although it may not be articulated quite that way. And I start to look through that. And I have a lead analyst usually who helps me do this depending on the size of the team, and we just go through what really matters. “What are you trying to answer? Where is this all headed? Okay, great. Now let’s allocate the different resources, whether they report to me, report to you, it doesn’t matter.” And really start to, all of a sudden we’re having different dialogues, we’re figuring out what matters, what doesn’t. I almost never take anything off the list ’cause it doesn’t matter. It could be 500 deep. We’re working on the top five things. So who cares how long that list gets? I don’t have to say no to anything ever. “Oh yeah. Business partner, where does this fall on the list of priorities? Oh, okay. Good.” Top five. Bottom 30. Who cares?

0:32:44.6 MH: It’s a classic case of what got you here won’t get you there. Right? Because so often, people who rise into data leadership have done so because they’re great at delivering against a set of priorities and doing a great job hitting requests. And so they think, “Oh, well, then I’ll just work harder to even hit more things and work harder.” And I find so many people in their early stages of data leadership just burning themselves out in a request pipeline that it can never fulfill.

0:33:16.9 KM: And guess what?

0:33:17.0 MH: And so, I think this is really key. This is really key.

0:33:22.4 KM: And when we take that approach of just burning through the request and getting done as much as possible as quickly as possible, we reinforce the 80% to 90% failure rate. I’m not interested in that.

0:33:34.4 MH: Yeah. Huge.

0:33:35.6 MK: So Kathleen, I’m trying to understand, I’m at a stage of my career where all those relationships with stakeholders, some of them yes, are relationships I directly have, but in many cases it’s a relationship one of my team leads owns. And so I obviously do interface with those stakeholders a lot ’cause they’re senior business partners, but ultimately a team lead is responsible for that and the people that are part of that team. And I’m trying to figure out, everything you say, literally I watched that presentation from Crunch in 2022, it was like, “Oh my God, these are all the answers I need.” But that’s really great for me to understand it and for me to… I can easily change the way I work. I can be interested in a new idea and test something out. But then it comes down to, how do you filter that down to the other people in the team and make sure that they’re on board or… And I can think in the top of my head, straight after this podcast, I know what I wanna do. I wanna email my team leads and be like, “Hey team, I want you to set up these meetings. These are the questions I want you to ask, yadda yadda yadda.” But I feel like there is something that is not going to work there because they are not bought in. So, how do I do that?

0:34:44.9 KM: Yeah. And it may not even be be biased…

0:34:46.7 MK: Sorry. Sorry, Kathleen, to cut you off. But the other context I need to add is, a lot of these people are also a bit burnt out because they have tried some of this stuff and they’re feeling a bit deflated of… So you’re trying to give them the like, “Yeah, let’s try something new. This is gonna solve our thing, our problem.” And they’re like, “We’ve tried all this stuff. It hasn’t worked.” This relationship is not great. Do you know what I mean? You already had this uphill battle. Apologies for interjecting.

0:35:16.4 KM: Not at all. Not at all. I love it. And I think this is really important because this is how a lot of analysts feel, “I wanna love my job.” I want them to love their jobs. I want them to be excited everyday when they come to work. I want them to be happy. I spend more time with my workmates than I do my husband. Right? And so I wanna be having fun at work. Okay. So, what do we do? For your team, it is probably more than buy-in. It’s how. Even if you tell me, “Go and do this thing.” How? How do I do it successfully? I am a former teacher. I did teach high school math for seven…

0:35:57.7 S?: Ooh.

0:35:58.3 KM: Years. I think the same skillset that made me a good teacher are the skills that I use here. The first thing I know is I can tell somebody to do something. They might even be able to tell me in theory how to do it. But actually doing something is a whole different animal. And so I know that, okay, that means we need hands-on-the-job training and practice. I have to practice it. I know what it looks like. I believe in the Socratic method. I’ll give you an example. A guy that worked for me for a while, and he was fantastic and he had the right instincts and I knew he could do the job that we’re basically talking about today, interacting with partners and managing a portfolio of work and all of these sorts of things. And he came to me one day and he said, “I need to have this really uncomfortable conversation with this new leader about something that has been going on for a long time in this business.” And I said, “Okay, great. What do you think is gonna happen?” That was his big idea, “So Kathleen, I’m gonna go talk to this guy.” I was like, “Great. What do you think is gonna happen now that he’s been in the job for literally three days and you go and tell him everything that’s wrong with his team, everything that’s wrong with how things have been working, everything that’s wrong about the decisions that have made up to this point. What do you think is gonna happen?”

0:37:18.7 KM: And he looked at me, he said, “Well, it’s gonna be a disaster.” I said, “Good instincts. What are you gonna do differently?” “I have no idea.” It’s like, “That’s great, that’s perfect. You have the instincts that tell you it’s gonna be a disaster. Let’s talk about what you might try differently.” And I literally sat there and it only took about 15 minutes. And I role-played with him. I said, “Okay. What are you gonna say? Okay, now I’m gonna think about it as if I were in that seat. How would it make me feel? So how can we do this differently? This other person that has been a problem with the old boss, do you think she’s still gonna be a problem?” He said, “No, I think she agrees with me, but she was beat down by the other guy.” And I said, “Aha. Now is your chance to go to her and say, ‘Hey, we have an opportunity here. Let’s come together and let’s take a story about some changes we’ve always wanted to make, let us talk you through. How do you feel about it, boss?’” I said, “Now you’re going as a partner, as opposed to somebody from the outside who’s given him all kinds of problems.” And so literally taking the 15 minutes to talk it through.

0:38:24.4 KM: The second thing is provide safety. And what I mean by that is two things. One, I tell them about, same example. I said, “You’re gonna go have this conversation. You’ve never had this type of conversation before. Expect that something is gonna go sideways. That’s fine. I am planning on something going sideways, you calling me, and then you and I will go together and clean it up. Don’t worry about it. You have to practice using a new set of muscles. You’re going to make those mistakes. Make those mistakes now as quickly as possible so that we can get you to a different level of performance sooner rather than later. Go, go forth and make mistakes. Know that I expect it, and I’m gonna be here to help you clean it up.” In fact, in that case, everything was fine, and he was thrilled. He came back, he’s like, “Kathleen, I can’t believe it.” And I was like, “I can. Why not? Why not?” The second thing…

0:39:23.0 MK: Oh, I love this.

0:39:25.2 KM: It’s brilliant, right? Psychological safety is a thing. But we have to not just say, “You are psychologically safe.” We have to actually say, “This is what it means to be psychologically safe. You’re gonna have… I expect you to make a mistake, and I will be here.” The second thing is, and I don’t usually tell my analysts this part until later, because I want them to have the confidence, and I want them to go into a situation really believing that the change rests on their shoulders. But I do, I prep the way. I go to these new business partners, and I say, “I am asking my analysts to operate in a different way. They are no longer going to say yes when you ask them for data. This is what they’re gonna say. These are the types of questions. The intention is good. And if you ever feel like the intention is not good, please call me immediately so I can rectify it.”

0:40:25.0 KM: Because anytime, we’re still animals constrained by our old brains, right? And when we were cave people, if there was a sound around the corner and we couldn’t see what it was, we instantly go to death. If my husband doesn’t come home one night, I don’t think he’s not having a great time with his friends. I think he’s dead in a ditch somewhere. Our brains are primed for that. So business partners, same thing. “Why is this analyst coming to me and all of a sudden asking me questions? I don’t like this. This is scary. This is different. I’m rejecting it.” That’s the instinct. But when we prepare them for it, all of a sudden, the response can be very different because they’re anticipating it.

0:41:07.1 MK: Hmm. You need to write a book.

0:41:09.8 MH: I love this. Yeah.

0:41:10.8 KM: Funny you should say it. I am writing a book.

[laughter]

0:41:16.4 MK: Whoo!

0:41:17.6 KM: And if procrastination were a measure of success, I would be crushing it.

[laughter]

0:41:25.3 MK: Well, the good news is, anytime you want some anecdotes or real-life scenarios that you can have as examples in your book, you can call me ’cause I’ve got lots of problems.

0:41:36.3 KM: I love it.

[chuckle]

0:41:37.7 MK: I’m right here for you.

0:41:38.5 MH: And I don’t think we’re running out of this issue in analytics anytime soon. So…

0:41:44.1 KM: No.

0:41:45.2 MH: It’ll be timely no matter when.

0:41:45.2 MK: Because we haven’t yet addressed it in a material way.

0:41:49.5 MH: That’s right.

0:41:50.2 KM: I have a couple of things I would really love… And again, this is partly the teacher in me. The teacher has never left me and I hope it never does. I want… Just like when I was a teacher, I wanted all of my students to feel good about what they were doing, and wanted them to feel successful and to learn something and to enjoy coming to class, I want all of my analysts to enjoy coming to work, to enjoy answering these questions, to have good relationships and feel respected by, genuinely respected by their partners. And so we have to teach them what this job is. It is not fair that we have them leave graduate school with some knowledge of the data and some practice with algorithms. And then we say, “Good luck.” It’s like teaching a plumber how to turn a screwdriver and how to hammer a nail and… Plumbers probably aren’t hammering nails, but you get the idea. And then saying, “Now go fix a toilet. I’ve never showed you how to fix a toilet, but I taught you how to use a screwdriver and I taught you how to use a wrench and I taught you how to use a hammer.”

0:42:52.4 KM: Well, that’s what we’re doing to our analysts. I want them to love it. And so I’ve got all this stuff in my head, I’ve gotta get it on paper. I have… My last job, I went to vendors to say, “This is the type of educational program I need.” One of them actually said to me, “Oh, we don’t do that. That’s too hard. We focus on the algorithms and the statistics, because what you’re describing, Kathleen, is too hard.” So I built my own. I’m a teacher. I know how to build a curriculum. I built my own. And now it’s… I’ve got it in my head, I wanna get it on paper, I’ve got my outline, I’ve got all of that, but it really is just about, I want that to be a useful tool, mainly for newer analysts, for emerging leaders, analytics leaders. ‘Cause I think it is missing in the market.

0:43:45.5 MK: Can I selfishly ask you another scenario question?

0:43:48.9 KM: Of course. Please do.

0:43:51.4 MK: Okay. I feel like everyone has had this stakeholder at some point in their career. The ones who love data and often, or in many times they’ve had some relationship where they’ve worked closely with data or whatever. And so they’re like, “Yeah, I’m a data person.” What normally results is request for 50,000 things. They want every metric under the sun. They want every dashboard breakdown you could possibly have. And the asks just keep coming. And, I think the bit that’s really difficult is that the sheer volume of asks becomes unattainable, but then they’re in this position where they don’t even realise that by asking for so much stuff, you’re not actually answering the question. But that’s also because I think they probably think, “Well, if you give me the data, I can answer the question myself.” And I know that, you’re probably gonna say some stuff that you’ve already said earlier on, but… We’ve… I’m just curious what your working style would be. ‘Cause this is very different to a stakeholder that’s giving one data point. This is like the person that wants everything under the sun all the time available at their fingertips.

0:45:07.6 KM: Yes. I love this one. And every situation is unique because every person is unique. So usually my approach is very, very carefully adapted to the individual. Because I think that does matter. And in your case, Moe, like mine, maybe I had eight primary stakeholders. Yes, they all had their leadership teams, but I could… You can manage that many people, with a fair level of customization. The guy who wants all the data wants to be smart. So it’s like, what’s the real motivation here? He wants to feel smart. He wants to feel like an analyst. He wants to be the guy with the answers. He wants to be… He’s probably also… He, I’m assuming, I could be wrong, but I’m assuming, he, uh-huh, yeah, is probably the guy who wants self-serve analytics. And so you build a dashboard and another dashboard and another dashboard, and there’s so many, now we need a summary dashboard for the dashboard, and, and, and, and, and…

0:46:07.5 MK: Yes. Yep.

0:46:08.2 KM: It’s like, it’s insane. It’s insane. So I sometimes will use my feminine wiles in this case.

[laughter]

0:46:17.5 KM: I mean, honestly.

0:46:19.3 MK: [0:46:19.3] ____ Great if it’s a man?

0:46:20.6 KM: You know, yeah…

0:46:21.5 MH: I’m not above it. I can try that. [laughter]

0:46:26.2 KM: If it’s gonna be a speed bump in so many situations, I might as well feel okay using it to my advantage when I can. I’m not above playing to somebody’s ego. And this is a person that, “Let’s make sure you have the right sets of dashboards.” And frankly, I actually, again, genuinely do believe leaders need the right dashboard. I no longer build a dashboard because someone says, “I want all of us to have all access to all the data all the time.” That’s fantastic. Everybody can have access, but who’s the one person on your team who is actually responsible for leveraging this as part of their job function? And do they know it’s their job? And do they know why they’re looking at it and what they’re supposed to do when they see different things? Do they know what they’re supposed to be looking for? So I gotta have at least one person who actually has some purpose to this dashboard. But that’s where what I have found often is, “I need this piece of data. Now I need this one. Now I need this one. Now I need this one.” This is a person who actually has a need. They’re not articulating the need, the more complex question behind. They’re not articulating.

0:47:37.6 KM: They might not even consciously be aware of it. And so what they’re doing is they’re asking for these individual data points and they’re trying to do analysis one data pool at a time. It doesn’t work. So what I might do in that case is never say any of that, but what I might try, and you could try this, say, “Hey, look, there’s so much data requests and we wanna get it all to you, but it will take us so much time to be doing it one at a time. It would be so helpful to me if we could sit down and talk about… ” And again, the personality matters a lot. You gotta figure that out, “Is this over coffee? Is this over lunch? Do you need to have a few lunches before you can have this conversation?” These sort of things. “Help me understand where we’re headed with a lot of this, to the extent you can, because I’d like to be able to accelerate. And if I have an idea of how you are putting this together in your mind, I will probably be able to get more to you faster in a way that’s more consumable.”

0:48:42.8 KM: Well, what you’re really doing is trying to figure out, like, “What’s the endgame, dude? Do you… ” And I use questions like, “Do you have a hypothesis? Is this stemming from a debate you’re having with a certain colleague? Is there a change in business strategy?” I’ll give you one quick example. I had the president of the bank called me and said, “Hey, Kathleen, we’re gonna start doing a lot more acquisition through the digital channel. We are concerned that the quality coming through digital is not the same as what we typically will book through our brick and mortar. We need to start looking at that.” I went to his head of digital acquisition and said, “This is what we’ve been asked to pull together. What do we need to be looking at? What do we need to see?” When it was time to take this back to the president of the bank, I said to my partner, “Okay, I’ll tee us up, I’ll host the call, but then I’ll hand it off to you to talk about what’s in here.” He said, “What do you mean, Kathleen? It’s not my report, it’s your report.”

0:49:41.1 KM: I said, “What do you mean? It’s not my report.” I said, “By the way, your boss doesn’t care at all about the dashboard. He wants to know how you’re gonna run your business differently now that you have this data.” So that’s the… Is there a strategy change? And he’s thinking of it in terms of, “I have to get the data” as opposed to, “I need to start thinking about my business differently.” So, trying to uncover as much of that as possible will eventually lead you to, “Aha, so we need this solution which incorporates many of these one-off data requests.” Investigative analytics cannot happen one data point at a time. And usually when I see this drip, drip, drip for data, there’s a hypothesis, there’s a question, business may not be able to articulate it exactly, they don’t know exactly what’s going on, but they have an idea for the next thing to look at. Then once I see that, “Ah, now my hypothesis changes.” So it may be iterative, but iterative is different than drip, drip, drip. Does that make sense?

0:50:46.4 MK: Yes.

0:50:47.3 MH: This is awesome. All right. Sadly, we have to start to wrap up. This is so good.

0:50:52.1 MK: Noo!

0:50:53.1 MH: Okay. Two things though, as we get wrapped up. First off, Kathleen, will you come back when you get done with the book and talk about it with us on the show?

0:51:00.6 MK: Yes.

0:51:00.7 KM: [0:51:00.7] ____ Yeah, perfect…

0:51:00.9 MH: That’s an invitation right now, for sure. We’ve gotta do that. We’re excited that you’re doing it and we wanna talk about it when you’re ready. That’s gonna happen. That’s for sure, lock it in. Okay. The other thing is, on the show, we love to go around the horn and do a last call, something that might be of interest to our listeners. Kathleen, you’re our guest. Do you have a last call you’d like to share?

0:51:22.8 KM: I do.

0:51:23.9 MH: Or even more than one, in Tim Wilson fashion.

0:51:26.7 KM: That’s the problem. I have never been in my life at a loss for words. But I think I’m gonna…

[laughter]

0:51:35.5 KM: I think I’m gonna go with this one because this is one of my favourites and we don’t always get to talk about it. I’m gonna give you two actually. The first one, my opinion, women are socially engineered for success as analytics leaders. Again, Moe, you and I are mostly in-house. We’re not selling our work. We’re really working on behalf of others to help them be more successful. So what does that mean? That means, my priorities are my business partner’s priorities. I don’t actually have my own priorities. That means that I’m working for the success of someone other than myself. That means I’m not building territory. And these are all things that women tend to be very, very good at. Whether it’s nature or nurture, I prefer to think of it as social engineering. Being able to listen, being able to empathize, being able to really put my own personal interest to the side for the benefit of someone or something else. And I don’t know that those skills are always valued the way they should be for an analytics leader. And I think it’s important to recognise that whether you’re finding that set of skills in a man or a woman, or anywhere on that spectrum, those are the skills that are critical to success for an analytics leader and the success of an analytics program.

0:53:20.6 KM: And if those skills are often found in women, then perhaps we should be searching for those skills where we see concentrations of them. And so, again, I think avoiding the territorialism, avoiding the competition is so, so important to a role like this. And I would love to see us acknowledge that women are particularly good at these things and perhaps focusing on that set of skills during the hiring process would be a very, very good thing for these organisations. The second thing, I don’t know about you, but as many times as I’ve volunteered for Habitat for Humanity, I still really am terrible at hanging drywall and siding and squaring windows and those sorts of things, I prefer instead to volunteer as a statistician. I do my volunteer service through analytics projects for non-profit organisations. That’s something else to consider. For anyone who is interested in giving back, we can actually rely on our primary skillset to give back. We don’t have to badly build houses for Habitat for Humanity. We can do what we do best. Those are my two.

0:54:39.0 MH: Awesome.

0:54:39.8 MK: Helbs… Sorry, Helbs, I just need to interject very quickly.

0:54:44.9 MH: Yeah.

0:54:45.6 MK: Kathleen, I just wanted to say a very big thank you for saying that, because I’ve had a pretty tough week this week. And I know hearing that is something that’s gonna help me go into work even more excited tomorrow and ready to tackle the next tough conversation. And I’m sure there are many people listening, whether they’re women or not, who probably needed to hear that. So thank you for sharing it.

0:55:07.4 KM: My pleasure.

0:55:08.5 MH: Very awesome. All right. Julie, what about you? What’s your last call?

0:55:12.4 JH: Oh gosh, well, it’s hard to follow that up. [chuckle]

0:55:15.3 MH: Well, you don’t have to change Moe’s life like that, but you know, whatever.

[laughter]

0:55:20.9 JH: Well, I will give a little… My last call is a little bit of food for thought, and it’s something, after reading this newsletter from David Epstein in my inbox a couple of weeks back, I’ve just been, it’s really had me thinking, and so I wanted to share it, because I find it really interesting. It starts off scientific, which I always love a little bit of that stuff. And, he’s saying how people’s natural brain chemistry, you have people that have more receptors for dopamine and people with less. And he was talking about how there were these studies then of people getting medication to either increase that or alter that. And so it was crazy because he was saying, depending on the individual and the situation, some people may need help having that increase of dopamine or level of arousal to be successful, and for some people who naturally maybe are already on a higher level, those medications would actually be detrimental, because it’s taking them too high and then they can’t perform as well as they would have.

0:56:17.9 JH: And so in the end of it, he’s talking about how thinking through all of this and these different conversations he had had, made him more attuned to his own, finding his ideal level of stimulation, balancing his nervous excitement or his calmness before certain tasks, whether it’s a big talk or going to an interview or things like that. And it made him pay a lot more attention to what motivates him and best performance rhythms for him. And I… It just spoke to something in me where I haven’t been able to put words to that and just hearing the scientific backup to it, it really helped me start to think of like, “What is my right rhythm? What is my right level of stimulation? Or where’s the point where I tip over and it’s too much and I start to not perform as well?” So yeah, I just… It was a good read and really got me thinking.

0:57:08.3 MH: Nice. Thank you. Awesome. All right. Moe, what about you? What’s your last call?

0:57:15.3 MK: Well, I’m causing lots of trouble today because now I have two because Julie has encouraged me to add to hers. I don’t actually have the link handy, but I will track it down and share it. But I listened to a really interesting interview from Radio New Zealand the other day, and I don’t know whether he’s a professor or a PhD, I don’t know the exact title, but his field of expertise is circadian rhythms. And I listened to it because sleep is something I’m very passionate about, because it’s not something I’m great at. One of the things that was really interesting is, he was sharing similar things about time of day. But in this particular case, he was actually talking about a bunch of research that’s showing that medicine can have different efficacy effects based on time of day taken, and surgery can have different outcomes based on the time of day of surgery. And so one of the real takeaways for me is actually to have your vaccine or your children’s vaccines first thing in the morning because your body can build up immunity better.

0:58:09.2 MK: Those antibodies that need to flare up just do a better job in the morning, so it’s going to perform better. That was a totally tangential one to add on to Julie’s. But the original last call I had, I don’t even know the post now which I first came across on LinkedIn which made me start following this guy. I’m gonna try and take a stab at his name, but it’s Aurélien Vautier. And he is an expert in data viz. And I just started to follow his LinkedIn now. And he’s just publishing a bunch of really great content. And if there’s one ultimate table of charts that I found really great, he has another link that has the best data viz books. But the one that particularly caught my eye, in light of today’s conversation, was that numbers have an important story to tell, they rely on you to give them a clear and convincing voice, which many of you might know comes from Stephen Few. But yeah, he just, he seems to be a really great contributor to the data viz space. So if you’re interested in that, he’s probably worth a follow on LinkedIn.

0:59:12.6 JH: Already looking him up.

0:59:13.9 MK: And over to you, Helbs.

0:59:15.7 MH: Well, you know, it’s been a little over a year since ChatGPT got released, so AI is still on top of mind for everybody. O’Reilly actually did a survey of enterprises about their adoption of AI, which I thought was pretty interesting. And so we’ll share that in the show notes. But it was pretty cool to see where companies think that AI is gonna be useful to them, how they’re using it, some of the ways they’re trying to adopt it and those kinds of things. ‘Cause it’s certainly impacting all of us across various levels of analytics, for sure. So it’s fun. A little bit different note there. All right. Well, as you’ve been listening, you’re probably thinking, “How do I hear more?” or, “I’d like to comment on that,” we would love to hear from you. And the best way to do that is through LinkedIn or the Measure Slack chat group or any other way that you can get ahold of us. You can reach out to us via email at contact@analyticshour.io.

1:00:18.5 MH: And we’d love to hear from you, some thoughts or ideas you have or things you’re trying. So… Yeah. And please do reach out to us. And of course, no show would be complete without a huge thank you to Josh Crowhurst, our producer. Thank you, Josh, for everything you do. And a special honorable mention thank you to Tim for being willing to let us carry this conversation even though this is probably one of the things he’s most passionate about in all the world in terms of analytics. So we thank you, Tim, too for being behind the scenes on this one. All right. Kathleen, what a pleasure. Thank you so much again for coming on the show. It’s been delightful to have you.

1:01:01.0 KM: Well, thank you. And I’m so excited to know you all now. You’re not gonna be able to get away from me. When I make a friend, it sticks.

1:01:09.0 MK: Yay. [chuckle]

1:01:09.7 MH: Awesome. Awesome. Well, and I know, I speak for both of my co-hosts, Julie and Moe, when I say, no matter how well you’re using the data today, you know you’ll be using it better tomorrow, and therefore, keep analyzing.

[music]

1:01:26.8 Announcer: Thanks for listening. Let’s keep the conversation going with your comments, suggestions, and questions on Twitter at @AnalyticsHour, on the web at analyticshour.io, our LinkedIn group, and the Measure Chat Slack group. Music for the podcast by Josh Crowhurst.

[music]

1:01:44.5 Charles Barkley: So smart guys wanted to fit in, so they made up a term called analytics. Analytics don’t work.

1:01:51.4 Kamala Harris: I love Venn diagrams. It’s just something about those three circles and the analysis about where there is the intersection, right?

1:02:00.8 KM: I do think, and we forget this at times, analytics, applied analytics, is still a relatively new discipline. And so it isn’t that there is a group of people or an institution, it’s that there are individuals who have figured this out through trial and error. And we are not yet at critical mass. Business analytics programs still focus on compiling data, running algorithms, and probably building a model of some sort. Fine, good for you. Nothing about analyzing data, nothing about how to connect that to the work a business is doing, and effectively inform a decision with meaningful data.

1:02:49.0 MK: Okay. Now this means…

1:02:50.8 MH: Now we need to stop…

[overlapping conversation]

1:02:50.9 MK: You are gonna have to stay on the podcast.

1:02:52.2 JH: You’re gonna have to say it again.

[laughter]

1:02:54.5 MH: Yeah, that’s so… Just bumperize that and make that the intro to the show. That’s the perfect lead-in right there.

1:03:00.8 S?: Yeah.

[laughter]

1:03:05.3 MH: And of course… What? Julie, why are you laughing? What’s going on?

1:03:05.9 JH: I’m sorry, ’cause I watched Moe’s face read what I was waiting for her to read.

[laughter]

1:03:11.1 MH: Oh. Okay. Don’t… Okay. Fine. All right. We’ll give it a little break. Can you guys just maintain for a couple of minutes?

1:03:21.0 MK: No, I just saw that.

[laughter]

1:03:23.2 JH: The fact that Julie knew… And Tim, both watching knew exactly what I just read and my face go… Anyway, sorry, folks. [laughter]

1:03:32.8 MH: I’m very confused right now. Oh, I see. Okay. Sorry. Okay. Back on track now.

[music]

1:03:41.8 MK: Rock flag and help me understand.

1:03:45.2 JH: That was good.

[laughter]

1:03:47.2 MH: Yeah, that was good.

1:03:48.3 MK: I was really… I was like, “Fuck it, I’m gonna give it my all.” But I’d feel… ‘Cause…

1:03:49.7 MH: Yeah. [1:03:50.7] ____ Get there.

1:03:50.9 MK: Tim, I did you proud, did okay?

1:03:55.2 Tim: Yeah, that was good.

1:03:55.5 JH: So that was…

1:03:56.7 Tim: I didn’t call it out, yeah. So…

1:03:58.1 JH: That’s why I was laughing, Kathleen, to let you in on our inside joke.

1:04:02.1 KM: Okay. Yeah. When I was reading the chat. I thought, “I have no idea why any of that is hilarious,” but now I know.

1:04:12.0 JH: Look at the chat.

[laughter]

1:04:12.4 JH: It was Moe with the rock flag.

[laughter]

1:04:15.5 MK: So Tim normally does it. And so…

1:04:17.5 MH: That’s right.

1:04:18.1 MK: Whenever Tim is not on the show, everyone’s terrified ’cause they’re like, “Oh God, who’s gonna do it in Tim’s place?”

1:04:22.9 KM: Right.

1:04:23.3 MK: And it was not until that very last moment that I saw that I was nominated.

1:04:28.5 MH: It’s right there in the show prep document, Moe.

[laughter]

1:04:32.6 MK: Thanks, Helbs.

[laughter]
[music]

One Response

  1. Ashish says:

    This was wonderful episode. One of the skill analysts needs how to be a diplomat and answer those tricky stakeholder queries.

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