#193: The Modern Data Culture Stack with Prukalpa Sankar

It’s easy to get sucked into the “technology” side of things when it comes to improving the effectiveness and scaling up data teams, but, much to Tim’s dismay, shoring up the people, process, and culture is often just as (if not more) critical. So, sure, we can talk about the modern data stack, but what about the modern data CULTURE stack? Prukalpa Sankar, co-founder of Atlan, joined us for a lively discussion of the topic!

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Episode Transcript


0:00:05.9 Announcer: Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language here are your hosts Moe, Michael, and Tim.

0:00:19.9 Michael Helbling: Hello everyone, welcome to the Analytics Power Hour this is episode 193. Drucker famously said, “Culture eats strategy for breakfast.” I’ve never really understood why breakfast but that’s a different issue but what about that culture within any company there’s kind of two layers if you will, the culture around the work the public-facing version, sort of what we tell the world about us. Like our cool foosball tables and snacks we have in the break room or the gym memberships that we provide to our employees but much more crucial and I think this is the kind Drucker was talking about is the second kind which I call the culture in the work. When people are working together in a team to accomplish a goal how is risk taking treated, how do mistakes get handled, how is success celebrated. In the modern data world we’re inundated with lots of new tools and innovation that are part of a rapidly expanding ecosystem. How does culture keep up? It’s just as important as it was but there’s a lot more going on. Well, we’d like to talk about it let me introduce my two co-hosts Tim Wilson, how are you doing today I know you love to talk about culture.

0:01:34.5 Tim Wilson: All the soft skills.

0:01:35.9 MH: There you go. And Moe Kiss, how are you doing today?

0:01:40.6 Moe Kiss: I’m doing just great.

0:01:43.1 MH: And I know you’re excited about this topic as much as I am. Well, we wanted to bring on a guest someone who could kind of help us deep in this conversation Prukalpa Sankar is the co-founder of Atlan, a modern data collaboration workspace for data teams. She’s also founded SocialCops which was recognized as one of the world’s leading data for good companies by the New York Times as a global visionary. And today she is our guest, welcome to the show Prukalpa.

0:02:11.6 Prukalpa Sankar: Thank you so much for having me.

0:02:17.4 MH: So I know this conversation kind of keyed in off of some of the articles and speaking you’ve been doing over the last year and this has become a topic that you’ve kind of been spearheading I think within the data industry how did that rise to your attention? Is something like to pay close attention to and invest in as something important for your time.

0:02:37.0 PS: Sure yeah so I think I mean some of this is backstory. I’ve been a data practitioner my whole life. Prior to founding Atlan, did a lot of work in the data science for social good space and so we were typically working with organizations like the UN, the World Bank, the Gates Foundation. Trying to see how can we use data science to solve global problems right? Like national health care and poverty alleviation and things like that. And so while on the outside we were dealing with these what seemed like very cool projects like at one point we were processing data for 500 million Indian citizens. We were dealing with data for billions of pixels of satellite imagery a day. So all really like dream projects for a data practitioner right? And so, well that’s what it seemed like on the outside, the reality was that on the inside it was not cool at all. It was a lot of chaos.

0:03:28.9 PS: I feel like as a data leader I’ve dealt with every fire drill that you can potentially ever deal with. I remember there was this like six month period where I don’t think I left office, honestly. I think I just slept in office. I would be woken up every morning with something breaking or some kind of chaos. I’ve had cabinet ministers call me at eight in the morning and say, Prukalpa, the number on this dashboard doesn’t look right. And that’s a really terrible position to be in. I remember waking up and opening up my laptop and really hoping that nothing was wrong and I saw that there was a 2x pipe and so that was there was clearly something wrong but there was nothing I could do at that point.

0:04:08.2 PS: So I said okay I’ll call you back and then I called my project manager and I woke him up and then I called my analyst and then I called my data engineer and it took us eight hours to figure out what went wrong and that was the story of our life. I’ve sat on the top of a terrace and cried once because an analyst quit on me exactly a week before a project was due. And he walked away with all our knowledge about the data, he was the only one who has worked on the project, he was the only one who knew everything about the data. I think you know it’s easy to think about these as problems that just affect agility right. But they’re different things. At that moment I had promised my client that I was going to deliver that project and I was not going to be able to deliver that project without that analyst and that knowledge that just walked out of the door.

0:04:56.1 PS: And at that moment where I couldn’t respond to my client who in this case was the business in a typical data team context, agility broke but trust broke. I had built trust with that customer for a year, year and a half and like right then and there because I wasn’t able to answer why something broke, the trust broke and at some level I think trust broke for me as well I wasn’t hundred percent sure if you know the data pipeline broke or my data engineer was not doing his job. And that’s the unique thing about data teams, data teams are by nature diverse. You need analysts, engineers, scientists, business to come together and collaborate really effectively but that collaboration is really hard because in most other teams everyone is similar. Let’s take a sales rep. Like a sales rep goes up the ladder, everyone knows what the other person is working on, like a sales manager started out as a sales rep that’s not the case with a data team.

0:05:54.7 PS: Data engineer fundamentally different from an analyst, fundamentally different from a scientist, fundamentally different from the business. And so those environments need a lot of collaboration and trust baked into these teams to make these teams successful. And that’s really what I call the data collaboration challenge and I think there’s a lot of talk about tech and tooling that can that can help your teams work better. But I think the bigger thing is as you think about this from a cultural perspective, how do you make it possible for these naturally diverse people to come together and work together in a way that they’re effective and in environments of trust and where they can collaborate to really do their life’s best work.

0:06:35.1 MK: Do you think that there is a lot we can learn from I guess broader technology to do with cross-functional teams like they have I guess maybe been operating this way longer than us. Is that is that something where we can take lessons from or do you feel like this is particularly unique for data?

0:06:56.7 PS: I think it’s definitely unique for data, like there are no real teams, like data teams in any other… Maybe the closest analogy I can think of is like a product team, where you have a product manager and a designer and an engineer and there… But I think the diversity in a data team is definitely unique. But I think there’s a lot we can learn from a bunch of different teams. In fact, I think, in the data space, we all spend a lot of time thinking about data like engineering. So there’s a lot of talk about data software, learnings from software teams. And I think there’s a lot we can learn from software teams. But I actually also think there’s a lot we can learn from other kind of teams.

0:07:39.1 PS: So for example, I spent a lot of time thinking about… In sales there are sales enablement organizations that are responsible for making sales teams productive. What can we learn from like sales enablement teams? What can we learn from product teams? What can we in fact, learn from more traditional teams? Like lean manufacturing is a great example. If you think about the way a data pipeline works, in some ways, it’s actually sort of like the best analogy I can think of is like supply chain operations. And so how can you learn from supply chain operations? So I think there’s a lot, you can learn? And I think the question is, how do you learn the best, these practices from other teams. But remember that data teams are unique. And so approach these cultural practices as experiments, and I think we have to figure out as a broader industry, like the data team is new, the data function is new, career paths, ladders, team structures like these, we’re still honestly creating these in the ecosystem. And so I think a lot of it comes down to can you experiment? And can you build, hopefully, what is the gold standard of how a data team should operate? And just keep that lens in mind as we approach this.

0:08:49.6 TW: So it’s, I think it’s you’re talking and describing the uniqueness, Is it… Part of it seems like, the uniqueness is that, one, there is a super strong independency along the entire… From the raw data all the way through somebody doing something with it. The interdependencies are critical unlike some of those… I think you had, you have an anecdote, one of those waking up and the numbers off and you’re like I don’t know if it’s a problem in the dashboard platform, I don’t know if it’s a problem with the data engineering, I don’t know if it was the ETL I don’t know. And you just see the end result. And you’re having to go through all these people with this expertise in order to figure it out. And as we say, like, what is a data scientist? What is a data engineer.

0:09:35.3 TW: You ask 10 people you get 10 different answers. So that to me does feel like this unique part of what’s making this so challenging. But also how do you find the… How did you go about trying to address that? Knowing that the demand, the data demand, the demands on the team were presumably are always growing and you’re barely getting by keeping things like held together and doesn’t it… Don’t you have to like step back and say, “Oh, we have to take a deep breath and and think about the culture part.” But you have to kind of fend off all of the… I mean you’re trying to rebuild the plane while it’s flying, right?

0:10:20.6 PS: Yeah, absolutely I still remember, we were in this space. This was before… This is how we ended up building Atlan. And it was all like an internal tool we started building for ourselves. But I still remember when we started, we said, we had this like six month phase where it was just like firefighting, firefighting, firefighting. And then that quarter, we hired twice the number, we doubled our team size, and we thought that would solve all our problems.


0:10:46.9 MK: That old chestnut.

0:10:49.8 PS: By end of the quarter, we were actually… I think we actually produced less than we had the quarter before that because we had all these analysts and they weren’t productive ’cause they didn’t know what data should you… What data they should use or what the column names meant, or any of those things. And then so they weren’t productive. And they were sucking on the productivity of our existing analysts who are spending a ton of time trying to enable our exist… Like the newer analysts. And so all in all, net we were actually worse off than we would have been if we’d not hired these analysts. And it wasn’t their fault, like if you think about it, people like to feel productive, no one wants to be in a company for three months and not produce outcomes and… So it was actually kinda frustrating for them too ’cause they weren’t able to produce outcomes.

0:11:38.3 PS: It was frustrating for our older analysts who had all this work and they were like, the reason we hired all these people was because… [chuckle] It was ’cause we wanted to… We wanted to like make life easier for them. And so it was frustrating for them ’cause they were spending all this time enabling people and it was not giving them the kind of… They weren’t… They were still getting pulled back into firefighting and day to day stuff. And this reached a pinnacle for us where one morning, I got an email from our most senior analyst, and he quit. And that was so bad for us, I remember, three hours I’m convincing him, I’m like “Please stay back, we cannot do this without you.” I’m begging him, I’m cajoling him, I did everything I possibly could and I couldn’t convincing him lying.


0:12:24.0 PS: And you know I as leader at that… It was the lowest point I’ve ever been at. I remember me and my co-founder… I remember going to like the top five data analysts in the office and I cried, I just cry for like three hours ’cause I didn’t know what to do. And I woke up the next morning, and I was just like “We can’t be here again. We just can’t be here again. We can’t be in this position again”. And so we basically rallied the team, that was there that was left.


0:12:53.7 PS: Rallied them [chuckle] We brought them into a room and we basically ran this exercise that we… It’s sort of modeled on this thing that Google has, which is called like a design sprint, but basically it’s, you talk about frame problems as opportunities. So you say, “How might we fix this?” And basically came up with everything that was broken in our team. And we said, “Okay, how do we?” And then we started coming up with solutions against each of those. And we created sort of like a manifesto from a values perspective and said, “Okay, what do we want our team to feel and look like?” This is something I encourage a lot of our teams that we work with data engineering to do, which is, what are the values that are actually important to you as a team? And, so two years out, how do we want to be? What are our emotional goals as a team? And we came up with things, the team came up actually. We were like, we don’t want to be hit by the bus syndrome, we don’t want to be in this situation, again, we want to create a resilient team. And diversity and trust was really important. Our team is always gonna to be diverse.

0:14:03.1 PS: So how do we build an ecosystem of trust, high-performance culture, those kinds of things came up from the team, and then those became sort of like our North Stars. And then we were able to sort of go back and work going towards it. This is all three modern data stack going back in our day. So we actually had a way worse than a team today would. In the last five years, there’s been a ton of innovation, lots of tooling in this space. And so, you don’t have, it probably isn’t as hard of the today, as it was, five years ago, but we actively invested in it. So we actually created an entire team that we call, our back then, you all know our data team. And with that data engineering team, and our data engineering teams, whole goal was to make our data team more productive. And our data team was analyst, scientist like that, essentially that those set of personas. And we started working towards building tooling that helps our team become better.

0:14:58.5 PS: The biggest unlock that I think, we had was when we started doing a ton of things around culture. So we actually, actively and I think, this was just also enabled by who we are as an organisation, we were always a culture focused organisation, as a team and company. But, we started investing in things like data enablement. We started investing in things like, how do we move our team from sort of like a data service mindset to a data product mindset? And what does that mean for every single data practitioner? And I think that made a huge difference. In 2 1/2 years, we had improved our team’s productivity by six times. And I’d attribute it to the tooling stack, of course, but also to the culture stack that I think we were able to build over time.

0:15:42.7 TW: This is probably a really dumb question but what do you mean by data enablement?

0:15:46.6 PS: Yeah. So I see this a lot as almost I think, the best analogy I think of, is like sales enablement. So if you think about in a sales org, you have a team that is responsible for making your sales team as productive as possible. And, I think, in other words too, if you think about in the developer world, there’s actually new teams called, developer productivity engineering, that is starting to get set up, to make your developers more productive. And so, we started thinking about this as a function, almost to say, “Can we really start thinking about a team?” It’s a team, that’s focused on making the rest of the team more productive and more collaborative and things like that. And the reason we call it, data enablement.

0:16:33.1 PS: A lot of people talk about the engineering element of it, which is, what typically are, which is what our data engineering team then and maybe you call it data productivity engineering, whatever. There’s engineering element to it, which I think of as ops and automation. But in a data team, there’s also a lot of, almost like cultural enablement that you need to do, inside the org because of the fundamental diversity of the team. So for example, one of our customers We Work, has this awesome person called Emily. And she, if you think about her background, she basically used to be a children’s librarian. And she studied Library Sciences and Information Sciences. And then she went on to work in the We Work design team as an information architect for some time. And now she’s joined the data team. And so she’s a super unique persona, where she can understand user types, she can understand the diversity of users. But she’s also, because she was, sort of a children’s librarian. She’s also, naturally extroverted, she’s created bringing people together and drive a lot of these cultural enablement efforts that it’s gonna to take to truly make data successful.

0:17:43.3 PS: A part of it is your data team itself, which is your analysts and your engineers, and how do you make them work well together, but a good part of it is also, how do you think about the business? And how do you think about having the business work, start to appreciate data and understand truly data? If you think about companies like Airbnb, they’re famous for doing things like the Data University. And so all of that is data enabled. You’re truly enabling the rest of the org to be able to work with and think about data as a first class citizen, and that’s a roll that we started sort of building up inside the org and we had great success with it.

0:18:18.9 MH: Alright, it’s time to step away from the show for a quick word about our sponsor, ObservePoint. We’re a little over a month from being together in person at marketing analytics summit, which we hope to see many of you at. And ObservePoint is a sponsor of that conference. Any guesses as to what that has me thinking about Tim?

0:18:40.4 TW: Whether or not you’ll be able to get a new pair of ObservePoint’s signature yellow socks.

0:18:46.2 MH: Nailed it. But of course, the socks are just marketing and branding.

0:18:51.6 TW: That’s right. The ObservePoint platform itself is a great tool for organisations who care about the integrity of the data collection on their websites. Sticking with the sock-theme, just thinking about complete, reliable, privacy-compliant data makes my, wait for it, toes all tingly. Socks.

0:19:11.2 MK: Oh, God. Did ObservePoint actually approved dad jokes for the spot?

0:19:15.7 MH: Well, pre-approval is overrated. What’s not overrated is having a tireless machine like ObservePoint, constantly monitoring key pages and user pads on your site, for the presence of the correct tags, the values being recorded in those tags and errors that are coming from them. And they alert you in real-time, if it finds a problem.

0:19:39.3 TW: Real-time alerts and tracking and trending audit results over time. You might even say, that ObservePoint, helps automate data governance and data monitoring so that analysts can dive into the data without getting cold feet.

0:19:52.5 MK: Oh Tim, another sock reference. Maybe you should put a sock in it.

0:19:58.0 MH: Oh, boy. I think we should stop now before, Moe socks, Tim to get him to stop.

0:20:05.8 TW: Zing.

0:20:06.9 MH: Okay, I’m sorry, Moe. It was right there. Okay, but if you would like to learn more about ObservePoint’s many capabilities without terrible foot couture puns, you can request a demo over at observepoint.com/analyticspowerhour. Now let’s get back to the show.

0:20:23.2 TW: Socks.

0:20:26.2 MK: The thing that I really love, sorry, I’ve gone quiet because I’ve got like a thousand cogs turning in my head at the moment. Because today, we were working on some long term data goals and someone said something about, like, “Maintain foundational data” and I was like, “That sounds boring as batshit.” Who wants to be in the team that’s maintaining foundational data? That doesn’t sound sexy or fun at all. But the thing I really love about the concept that you’re talking about, about data enablement, and really this idea about how to make other data practitioners more effective, or more productive. There is something really I think motivating about that as kind of the team’s North Star, and it’s also hopefully measurable too. Anyway, sorry, I’m writing notes, ’cause I’m like, “Oh, this is good. This is good.”

0:21:19.7 TW: Well, I thought I was getting a quick little clarification. You said that when you equated it to like a sales enablement team, that makes a lot of sense. But now I have a bunch of questions like… I’m sure the responsibility falls to everyone for that but there’s got to be… Is that the sort of thing that there is somebody who’s got accountability, like Emily. Is she really half of her job being a data enabler? How much of it becomes a person’s responsibility to figure it out? Or a group? How does that work?

0:21:55.8 PS: Yeah. So I think people like Emily, and wherever we’ve seen this in customers, in some ways, I mean, this is not a function that’s really there actually inside an organization. I think we’re starting to see more and more of it in the teams that we’re working with. And I think we are in the very early days of really building out the career path and the function and what else is functioning. I think that’s true for most of the data org, but especially so for thinking about data enablement. But the way I think about it is that, your role is to make everybody more enabled, to be able to use data. This is using tools, but also using data itself, and I think that element of thinking about it in that way, where your role itself is to be an enabler, is I think the true North Star, right?

0:22:43.1 PS: Now I think the projects that you work towards it could actually change quarter on quarter, right? Ideally, once you’ve enabled one part of the organization, they should be self-sufficient, and you should be even more on problem number two and problem number three. But the way I think about it is, you might wanna start out with something as simple as data team service requests. Something I did, like data teams tell us a lot about is that their Slack channels, and then people get a ton of messages about random things. What is ANR? How do we measure this? This number doesn’t look right, can you go fix it? Hey, can you just go pull this data for me, please. It should just take you a couple of seconds now it takes you a couple of seconds, right?


0:23:28.3 PS: Right? And so one way of thinking about is, hey, we actually asked those teams to think about this, which is go look on your Slack channels and look at the service requests you’ve gotten in the last two weeks and categorize them. And you will realize, what are the things your team is spending a ton of time on? And then you can say, “Hey, you know what, let me go automate this.” That’s where you have tooling like Atlan, or you have a tooling… There’s a ton of tooling in this space that you can go in and like think about these specific problems and say, “Okay, a part of it is I go and implement the tool.” A part of it is I implement the best case practice to make sure that making people go away from service. Human beings are used to service. If you can, in anything, if you can call somebody, you will call somebody. It’s just as simple as that, right?

0:24:10.1 MK: Totally.

0:24:11.5 PS: So changing behaviors is also a thing. And so then how do I change the behavior? And then how do I measure against it? And hopefully, you’ve done that, and that’s when you close, right? That’s when the project’s really done. And then you move on to problem number two, right? So for example, problem number two, could basically, be, “Hey, my business users don’t actually understand what metrics actually mean. Nobody knows how we actually define annual recurring revenue and so maybe I have to like go fix that.” And I think that element of just the thought process, where a part is… You’re part consulting, you’re part data practitioner, you’re part culture enabler, people ops, that unique skill set is basically… I mean, it doesn’t need to all be in one person, you can build out a team that can focus on that, but every time we’ve seen that I kid you not, every time we’ve seen data leaders be successful, data leaders have invested in this capability inside their organizations. They might call it very different things but this capability and this persona exists in the teams that are most successful.

0:25:14.4 TW: It has me thinking about some of the data teams, and I’m on the kinda agency side but working with larger clients, and we’ve got teams of analysts who are constantly frustrated by… There are things that suck down their time and it’s kind of a lot of time just sitting around and trying to kinda cobble together, what are the things that we can figure out to do that really kind of are the enablement, but it never gets kind of addressed as, “Let’s systematically figure out what the big persistent blockers that are hitting multiple analysts and figure out a process”. And that’s oversimplifying it, there are… Clearly things that are evolving and getting better, but I don’t know I’m gonna to be dropping this enablement phrase on a few people over the next few weeks.

0:26:05.2 MK: Here is the thing that is in my mind, which I feel like I always come back to because I work at a young “startup”, if you still call it a startup when you’ve 3000 people, probably not. And things move really, really fast, and so yeah. I guess there is, I wouldn’t say like constant firefighting, but we build stuff so quickly and then we just grow so fast that we outgrow what we built a year ago. And so talking to people sometimes I think about some of these more sophisticated or particularly time intensive… It is an investment of time, right? You have to stop doing something else to say, “We’re going to do data enablement,” or whatever it is and. I’m really interested to hear when your team got in that room and decided you were gonna do this, how did you really curve out the time for that, given you were firefighting?

0:27:00.6 PS: It is hard. It is always hard. And so that’s why it needs to be a priority, but you could argue that to be true for every organization, right? Like in sales, typically like 15 years ago, nobody invested in sales enablement. And I’m sure when you’re hiring reps, you want to focus most of your time and energy on the rep going on field and selling. And I think today, for example, if you’re interviewing a VP of sales or a CRO and they don’t tell you that sales enablement is going to be one of their biggest priorities, you shouldn’t hire that person. And so I think a large part of it just comes down to prioritization. And I think that comes down at a couple of different levels. Or literally at the leadership level, at the CEO level, are you actually committed to maintain data work? Or do you actually think that data will be a competitive advantage for you as an organization?

0:27:55.3 PS: Now it has to start there. If you do not believe that as a leader, then there is no point. You will not invest in data. So then don’t do anything. Like that’s when I tell them, “If you don’t believe in that, don’t invest in it because it’s the sexiest thing to do and don’t invest in it because your body is telling you to do it.” You have to fundamentally believe. I personally believe that 20 years from now, organizations that don’t have a very strong data function will be obsolete. 20 years ago, this was not very clear with product organizations. Product roles were just getting starting and people were saying… It wasn’t obvious that we needed a product board. Today, you can say if a company doesn’t have a product board, that company will be obsolete.

0:28:35.7 PS: It has to start at that level, do you believe in it? And then I think it comes down to the data leader to say, “Hey, you know what… ” And I think there’s a serious business ROI here and I think practitioners struggle to communicate this. The biggest challenge, I think most practitioners face inside organizations is, it’s really hard like a person outside. How do you convince a CEO to give you a budget to build a data enablement team when they don’t realize the actual problem, right? So for example, one of the things our customers have started doing, we tell them like, “Go to your Slack, take screenshots. Just take screenshots across Slack of just the last two weeks, the firefighting that’s happened, take screenshots.” And people take these screenshots literally, and they turn into 30, 40 page decks of just screenshots.


0:29:25.8 PS: And they send it to their leadership. And then the leadership… I mean, it’s really funny if a smart CEO is not gonna be like, “Yeah, I wanna hire all these data people, but I want them to spend like 80% of their time doing things that we can actually automate.” Nobody does that. Honestly it’s an element like we as data practitioners have to get better about telling people why we’re not able to achieve the results that they expect us to achieve. And we need to actually make a point that the foundation is important. Enablement is important. So I think that’s problem number one. I think problem number two from there turns into then how do you systematically solve for it? Which is why I’m a very big believer that you cannot have someone do this as a part-time job. The reality of it I think of this as problems of today and problems of tomorrow, right?

0:30:16.7 PS: And the reality all organizations, be it customer success, engineering, product, sales, no matter what problems of today will take priority over problems of tomorrow. It will always happen. And so if you want to solve problems of tomorrow, then you have to say, “Hey, you know what, we cannot solve this problem if we don’t… Six months from now we will die if we don’t solve this.” Then you’re all like… [laughter] And the same person can’t do it. The same human being can’t solve problems of today and tomorrow at the same time.

0:30:50.7 PS: I’m a very big believer of separating out responsibilities. It doesn’t need to be a large investment. Depending on the size of your team, it could even just be like one person. It could be two people. It doesn’t need to be a lot, but I think there need to be people in the organization whose priority it is to ensure that you’re constantly improving the way you work and you’re automating more and more. And I think there are direct results that can improve this. How quickly your ramp time of your analysts improve?

0:31:18.7 MK: Sorry, how quickly your?

0:31:20.9 PS: Your ramp time of analysts.

0:31:21.6 MK: Ramp time. What’s ramp time?

0:31:22.7 PS: Ramp is how long it takes for them to be productive, right? Which is…

0:31:27.7 TW: To ramp up. To get started.

0:31:29.3 MK: Yeah, but how do you measure that?

0:31:31.8 PS: You can actually measure it literally by how long it takes for them to run their first three analytics projects without needing anyone else, without any help. So…

0:31:43.7 TW: I mean, that to me it sounds a little bit like when we talked to JD Long and he was talking about give them a project, let them make a mistake. I could see having some structure as somebody’s ramping up to see how long until they… Or even having that on the plan, like this is the ramp up. Sorry, maybe that was a tenuous connection.

0:32:02.3 PS: So the best sales teams have this today. If you think about sales like a large part of your… Because if you think sales teams, basically, say, “Hey, you know what, my sales team has X amount of time to ramp.” And you measure your leaders and measure your sales enablement team is measured on did the person get to ramp? And then you can always measure it. Did they close their first deal? Did they pass X number of marks? There are ways to measure it. And the reason this is important is in today’s job market, an analyst is spending 18 months on a job and if they’re taking six months to ramp and three months to off board…

0:32:41.3 PS: That’s like you get nine months of productive time for an analyst inside in an organization. And so I think you can start adding measurables to it and you can very quickly… If you save 50% time for every data engineer inside your org, your org is suddenly gonna start being able to do more. And these are low hanging fruits. People don’t invest in them enough, because they don’t realize the amount of impact you can have on your existing team. But I bet you in like three or six months, one data enablement manager will pay for themselves just in terms of the amount of value they’re creating for the rest of the team.

0:33:19.4 TW: I love that. Do you get credit with the quote or did you take this from somewhere that the priorities… And now I’m gonna butcher it. I’m gonna have to go back and listen to get it. You said the problems of today will always take precedent over the problems of tomorrow?

0:33:36.8 MH: Yeah, I like that.

0:33:37.7 TW: I was like, “Oh, yeah. That’s a good way to point out.”

0:33:40.2 MK: That’s brilliant.


0:33:41.6 PS: Yeah, well, that, is probably me. I don’t think I have any quote. Yeah.

0:33:45.9 MH: Okay. Yeah. The tyranny of the urgent, all that kind of stuff, but it’s really well said and I am living this right now in my company. So it’s very current.


0:33:57.9 MH: Alright, it’s time for the quizzical query, the Conductrics Quiz, brought to you by Conductrics. It’s the conundrum that stumps my two co-hosts as they go toe to toe on behalf of our listeners. Will be answering tough questions from Conductrics. Alright, before we get started, a quick word about our sponsor for this segment. Conductrics is a sponsor of the Analytics Power Hour and the Conductrics Quiz. They build industry leading experimentation software for AB-testing, adaptive optimization and predictive targeting. For more information on how Conductrics could help your organization, go visit them at www.conductrics.com. Alright. Tim, are you ready to know who you are competing on behalf of?

0:34:48.8 TW: I am ready.

0:34:49.9 MH: Alright. You are competing for Tracy Newman. It’s Tracy from being a listener. And Moe, you are competing on behalf of Roberto Balestrero-Ortega.

0:35:05.2 MK: Oh, Roberto.

0:35:06.6 MH: Alright. Thank you Roberto for being a listener. Alright, here we go. We’re gonna dive right into the action. So just assume that I have somehow come up with a need to answer a question and I slam open the door and rush into the room and it’s some kind of a…


0:35:25.1 MH: No am just just kidding. We don’t really have that. But I am just pretending I need to know an answer. So when we’re analysing data, we talk about estimating the direct effect of a variable of interest by holding everything else constant. What is the phrase that best describes when we don’t hold the other factors constant and let them change accordingly? Is it, A, Mutatis mutandis? B, Ipso facto? C, A posteriori or posteriori, posteriori? D, Ceteris paribus? Or E, prima facie? Get your Latin books out.

0:36:11.4 TW: I know this one, I am gonna say that and then be dead wrong.


0:36:17.4 TW: But, so, Moe you want first crack?

0:36:20.6 MK: No, take it away.

0:36:21.0 TW: I am going to go with the Ceteris paribus, as the correct answer.

0:36:27.9 MH: Ceteris paribus is the correct answer, or is it not the correct answer.


0:36:32.8 MH: What you didn’t fail to hold constant, Tim was that that is not the correct answer.

0:36:40.4 TW: Damn it.

0:36:40.8 MH: So, guess what? By default.

0:36:41.9 MK: Yes.

0:36:43.7 MH: Roberto you’re a winner. But Let’s talk about the answer really quick. It is A, Mutatis mutandis.

0:36:50.1 TW: Oh my God.

0:36:51.3 MH: It’s sort of the opposite of Ceteris paribus.


0:36:56.5 MH: For example, if we think of regression coefficients as the partial derivatives of the dependent variable with respect to each of the independent variables, Ceteris paribus…

0:37:06.3 TW: Damn it.

0:37:06.8 MH: We can think about the effect of the independent variables or the dependent variable as the total derivative, Mutatis mutandis.

0:37:16.0 TW: Okay, yeah. I just… God damn it. Yeah, okay, well…

0:37:21.0 MH: [0:37:22.4] ____ and all that stuff. All right, so that means Roberto, you are a winner. Moe, great job. Sometimes, just letting the other side mess up is the best strategy.


0:37:33.1 TW: This is screwed up.

0:37:34.3 MH: Well, if they’re connected to each other, it’s like, you know, that’s…

0:37:38.0 TW: Yeah. It was super familiar and I hear it in the context of it. And then, yes. Because you actually put in the Ceteris paribus, which means holding everything else constant, so damn it.

0:37:49.6 MH: That’s right.

0:37:50.2 TW: I got too excited by recognizing that I could pronounce it closer to right than you did.


0:37:57.5 MH: Well, that is an accomplishment, so that puts you in second place on the quiz, Tim. Great job.


0:38:05.7 MH: Alright.

0:38:08.2 TW: I’m sorry, Tracy.

0:38:08.6 MH: That’s no problem. Thanks for listening. Thank you to Conductrics for sponsoring the Conductrics Quiz. And now, let’s get back to the show.

0:38:16.6 MH: So, you know, one thing I wanted to touch on is in some of your writing, you talk about how you’ve leveraged what you call rituals to kind of enable the culture. Could you talk a little bit about that? Because I think that’s one of those things that I really like the idea of it, but I think you put a lot of thought into the structure and how you introduce that within the company that you’re working with. So I’d like to maybe ask you a little bit about how you approach that.

0:38:44.2 PS: Yeah, absolutely. Yeah, I think… So I’ve been a student of culture, I think. And I think we as a company, even at Atlan I think we spend a lot of amount of time thinking about, “What does it take to build like a two bottoms-up culture inside our heads?” But I think the thing that most people miss, is that a lot of people think that culture just happens, right? And the thing is, culture doesn’t just happen. I think you can actually create carefully a culture that you would like to see in the company. And so the way we think about this today, is almost like, there’s values, which is what I talk about. And mind you, I don’t think you can… This is not a leader sitting in a room and saying, “These are the five things I want my culture to look like.” Right? Like the team needs to agree that this is what we want our culture to be and look like, right? And so I would…

0:39:37.8 PS: Going back to that North Star exercise we did with our team. We came together and we said, “Hey, these are the four or five values we want to build in our team, right?” Agility was one, trust was another. Innovation was another really important one for our team. Like, we were like, we want to push the boundary on what data science can do. We don’t want to just become like a business as usual team, right? And in fact, I think that was a really important core, because we were like, “Hey, if we need to be able to innovate, and we need to, like, get away from the day to day and… ” You know, that’s why we need to build agility. And that’s why we need to build like… And so those kinds of things became like, sort of like our core values. I think we came up with like, four core values. And then against each of these, I think a mistake a lot of people make is that they basically come up with these values, and they put it up somewhere. And they’re like, “These are our values.” The problem is that no one remembers the values, right? People remember… Like, how does… How do you start imbibing values? You start imbibing values, because you live it every day. And so I think you can actually start creating rituals in your org to enforce these… Or enforce is the wrong word, right? But like to remind people what it takes to like live these values every day.

0:40:47.2 PS: So to give you an example, agility was one of our values. And then we said, “Okay, how do we get to agility?” And then we came up with like, a bunch of ideas on the ways we can think about this. And one of those was, can we go learn from Scrum? And can we go learn through like the way engineering does agile, and other learnings that we can take to our data team? Now, the things we did there was, we actually did not roll it out to the team. What we did was our entire team read the book, there’s a very awesome book called The Art of Doing Twice the Work in Half the Time, I highly recommend it to anybody. It works, right? So, our entire team read the book. And we came back and we did a book club meeting. And we were like, what are the things we learned from this book?

0:41:33.6 PS: And then, you know, once someone in the team basically said, you know, “Can we experiment with this in our own team?” And so we actually rolled it out as an experiment. And for the, you know, and for the first three months, we basically committed to say, we’re going to do this experiment to see if agile and Scrum is going to work in our team. And we kept in our retros actually talking about not just the retro which Scrum tells you to do, but also the actual process of doing agile and it was working. Right?


0:42:02.9 PS: And then, you know, what we end up realizing was that actually agile was super helpful, in some ways. Our velocity actually went up… I think, our velocity went up at 4x over the first three months. It was amazing. We can create wave plan.

0:42:16.8 MH: Wow.

0:42:16.9 MK: Wow.

0:42:17.0 PS: But we end up finding that wasn’t working in every case. So for example, we end up finding that… So if you think about data work, there’s a part of data work which is very similar to engineering, where you know what you need to get built, and you build it. But there’s a part of data work that’s not. In fact, it’s like research work, because it’s exploratory in nature. So for example, like when we were reading these data science modules, a good part of the initial work was figuring out the methodology that we’re going to work with. That would then go into like, scale, right? And figuring out the methodology, honestly, it’s very hard to tell whether it’s gonna take you like three days, or whether it’s gonna take you three weeks. And so we actually… And so going back to innovation, we didn’t want to start measuring velocity on methodology, right? Because if we measured the velocity [0:43:03.2] ____, then we wouldn’t actually optimize for the right problem, which is like, “Can we do this? Can this be the best work?” And not just can we do the work, right? And so we actually pulled that out of the agile process. So every time someone on our team was working on research-based work, they did not participate in Scrum in those couple of weeks.

0:43:23.9 MK: Oh, wow.

0:43:26.9 TW: Wow.

0:43:28.3 MK: That’s interesting.

0:43:28.6 PS: And so those kinds of things, right? Like, you know, you can… And then that became a great basis for us as a team. And we were able to build out like a way for us to work that helped us improve agility, but also helped us hit innovation, right? And so I can have a bunch of, you know, experiments and [0:43:47.6] ____.

0:43:47.7 TW: I mean, on the ritual front, and as an inherently kind of negative person who likes complaining about stuff, I feel like the data cribbing parties, like those kind of fascinate me. I mean, does that fall under the ritual as well? Like, can you talk about the data cribbing parties because those I find intriguing, especially since there’s like a picture on one of your posts about it, where I’m like, everybody seems awfully happy. So maybe it is just like, you can kvetch if you’ve got pizza and beer, and it’s productive. But I mean, that goes on to the trust and collaboration thing, I think, right?

0:44:20.8 PS: Yeah, yeah, yeah. Well, I also like twittered Himanshu who is our Director of Strategy now at Atlan. He sort of came up with this like his one on ones. He was like, “Oh, like I’m, you know, I’m doing these one on ones with people and like a lot of them are frustrated.” And you know, why are you frustrated or angry at him, right? He’s like, you know, “Hey, you know what, like, you got the data and you have to clean the data.” And then, you know, you realize while you’re cleaning the data, that there’s this, there’s this other thing you need to do.

0:44:47.9 MK: Yes, yes.

0:44:49.2 PS: And then there’s this you need to fix it, and there’s that you need to fix it right?


0:44:53.6 PS: And it’s pretty frustrating, right? Like, I mean, you know, a data job, you know, is… It’s not that, like, it’s not that glamorous on a daily basis, right? Like, there’s actually a ton of pains and frustrations on a daily basis. And so Himanshu had this idea and like, he was like, “You know, we just need to bring this out in the open.” Like, let’s just make this like, let’s talk about this. I think this is life. It’s okay, as a data practitioner, it’s okay, we know what we’re doing. We still believe in the impact of our work, we still believe that, you know, solving this problem is going to solve this like bigger problem, right? We just need to figure out a way where the frustrations don’t bog us down.”

0:45:30.1 PS: And so we basically one Friday got the team together. And we were like, let’s just crib, like, let’s crib. What are the biggest, like frustrations this week? And like the team talked about it, and we laughed about it, and it came out in the open. That was also a great way for us to figure out like recurring issues in the team, which like data enablement could pick up and solve. But it was also like, it was also a good way for the just the team to like, come out and talk about their problems and their issues and creating a safe space, frankly, where people know that, “Hey, you’re not alone, right?” Like typically, you know, sometimes if you’re working on a project, and you’re just like, the one analyst on the project, you know, it’s easy to like feel like feel alone. And it just created this network where we were like, you know, we’re all in this together.


0:46:16.1 MK: You seem so happy, though, and I can see this working. But I can also see if you have a couple like, super negative people on your team, or even just for some reason, morale is low…

0:46:28.4 TW: Or just me, just one.

0:46:29.1 MK: Or just him, yeah. You just have Tim on your team.

0:46:29.8 TW: It could be just one.

0:46:31.9 MK: It could vary, it could also really backfire. So I feel like it’s one of those things as a lead that you probably really need to gauge your team and see if that would be the right thing. And you would also really need to balance the expectations of the team of like, is this actually just about a place to vent or do they expect the action out of this, because if it’s intended as like a venting thing and it’s gonna be funny but no action is gonna come out of it. Do you know what I mean?

0:47:00.8 PS: Yeah.

0:47:01.3 MK: I feel like it sounds like an amazing idea with the right people.

0:47:03.5 PS: Absolutely.

0:47:03.5 MK: But you also have to likewise be careful.

0:47:05.9 PS: I fully agree and I mean even just like setting, just boundaries, right? Like, this is not about… It’s not about people, you’re not coming here to crib about an individual person. You cannot make this personal, you cannot… I think just setting the foundation of, just like the rules of what this space means. It was also interestingly a great way for us to actually help foster collaboration which was a surprising side effect. But the thing is that in a data team, right? Another reason that people are frustrated is the analyst is like, “Oh, my god, the pipeline broke again. Oh, my god, this data engineer really needs to fix it.” Right? And the data engineer is like, “Oh, my god, the analyst just couldn’t scope out the problem statement, again. I have to go redo this again?”


0:47:54.7 PS: And then the business users are like, “I just want annual recurring revenue and they’re not getting it right.”


0:48:02.8 PS: And so in this meeting or in this setting, when the data engineers document their frustrations and if they do it the right way, right? Which is that I had to go back and re-write my code five times this week because of this. You know what, it actually helps people understand the other person’s work.

0:48:21.9 MK: Yes.

0:48:22.4 PS: It makes it… And if you have a core environment of trust built in the team, right, it helps you understand the person is doing their best, and this is not… And they’re really trying hard, but this is why something that frustrates you on a daily basis is not getting done the way it should because these are the five challenges that this person is dealing with, right? And I think that humanizes it in a way that is very hard to do in any other setting but I absolutely… I think that’s true for every ritual, right? Like the one thing about culture is every company is different, every organization is different, everything is different. Just because it works for somebody else, it will not work for you and that’s why it’s so important to keep the North Star as the value. And experiment with the rituals ’cause the rituals will also change over time. What works for you when you’re a ten member data team is not gonna work for you when you’re a hundred member data team. And so I think that’s why you need to keep sort of revisiting these rituals and making sure that they’re working, that you get the values that you wanna see.

0:49:25.4 MH: That’s a great point. Have you dealt with this as the team grows and rituals change, dealing with the loss of the ritual from the team? Because I think that’s something I’ve heard from my teams before. It’s like, “We used to be like this.” And people think the ritual is the culture. How have you communicated that or has that been something you’ve run into?

0:49:48.1 PS: Yeah, I think it’ll always happen, right? Things change as the team changes. I think that’s why it’s important that the rituals don’t become the culture and the culture… Like, you have to keep aligning to the values. And I think, honestly, I think we learned over time to communicate that, like today when we communicate a ritual, we actually can communicate that like, “This works for us now, it might not work for us when we scale.” And just aligning the team to that as well so that they… You keep talking at the values level and not at the ritual level is important. I can be messed up with this, and lots of learnings and scars from that, right? And I think…

0:50:28.4 MH: Okay.


0:50:31.6 PS: But I think a part of it is making sure that your team also realizes that things will change. I think that’s true for any growing team. And the good outweighs the bad, right? You do want to grow and you do want to scale, and you do want to specialize over time, you do… These are things are just like eventualities and I think there’ll always be a certain set of people who might not get it. I think all you can do as a leader is continue to communicate that the values haven’t changed but the way we do certain things will change.

0:51:04.2 MH: This is… Man, I did not expect to end this up where we are ending up, Prukalpa. You are helping me so much today. I don’t… You don’t even know.


0:51:16.0 MK: I feel like she’s helping everyone. We’re all taking notes.

0:51:21.0 MH: No, I know, but I’m just like, “This podcast has not become about anything else. It’s like this is sort of me and my problems.” And learning from you and I’m so thankful, but we do have to start to wrap up. And I’m sad because I have so many more things I would love to talk to you about. So this is so good, so good. And I’m sorry to make it about me ’cause it’s not, it’s about all of us, but what you’re talking about is so relevant. Anyway, I love it. Alright, well, one thing we do on the show is we go around the horn and we share a last call. Something maybe our audience you might find interesting. Prukalpa, you’re our guest, do you have a last call you’d like to share?

0:52:00.1 PS: Oh, man. I feel like this whole call has been about my last call.


0:52:05.8 MH: And it could be anything.

0:52:07.6 PS: I was gonna talk about data enablement but I think we’ve already touched that. No, I mean, I think my last call is, I feel like the one thing I would really encourage people to think about is, we’re living in this world, it’s this… It’s the renaissance era for the modern data stack, right? There’s a lot of tooling, there’s a lot of innovation, there’s a lot of… And I think that’s great but the one thing I would really encourage people to think about is data culture and I hope, like, one of my dreams is that we actually have a… In 2023 and ahead, we actually start talking about the culture stack that our data teams need to work with and I hope the conversation starts shifting to that rather than tools and technology and push versus pull architecture. I feel like it’s easy as engineers and it’s easy as data… Well, obviously technical people, to like focus our energy on that. But I think we very quickly like culture value of data. Like these are things that will actually move the needle. Like that’s why we all do what we do. And I really hope the conversation starts shifting towards that.

0:53:20.6 MH: Yeah. And if people wanna just have a link to go to, I noticed you have your own newsletter, so metadataweekly.substack.com. So I’ll shout that out for you.

0:53:34.6 PS: Thank you. Thank you.


0:53:35.6 MH: Yeah. Yeah. So well worth it. Moe, what about you? What’s your last call?

0:53:39.7 MK: I have a weird one today. I feel like I often do weird last calls. So I suppose since I had a child, I’ve been thinking a lot more about what it’s like to be a working parent, because obviously I am one now. So I feel like I have all this new empathy for working parents. And I came across an article that someone sent the other day, which was there’s an organization in Australia called the Women’s Agenda. And it’s actually about the 10 things that will improve the life of women in Australia right now. But the one thing it really talks about is like paid parental leaves and access to early childhood education and what a barrier it is to some people working. But it kind of got me thinking a lot about the fact that like, those are things that I can’t really control, right?

0:54:31.6 MK: But it’s actually, yeah. I’ve been kind of reflecting because as a people manager, I don’t want things to be unequal, right? Where you get people that are non-parents to have to do more of the team heavy lifting, but I’ve noticed that I’m much more aware of the parents in the team and how their week is going. And if they’re having like a particularly hard week, I’ll kind of be like, “You know what? I’m not gonna ask you to organize like the team outing or take notes in this meeting because like, guess what? You pretty much have two jobs.” So this week, like you get a whole pass because you’re having a really shit week. And it’s just, I don’t know. I think it’s made me think a lot about what… Like who you are when you turn up to work and what you can offer the team and like how you balance that really well. And I don’t know. Anyway, that article kind of spurred me to do lots of thinking about this. I definitely don’t have it right. I would love people to reach out on Slack or Twitter and let me know if they found a way to like, I guess get that balance right of being empathetic and thoughtful about people’s circumstances, but also having like an even playing field for everyone because it’s a work in progress, anyway.

0:55:45.5 MH: Wow. That’s good. All right, Tim, what about you? What’s your last call?

0:55:51.5 TW: So my actual last call is gonna be super, super short, so I’m going to ramble anyway. So we didn’t quite hit on the fact that I tend to have a frustration with technology platforms that ignore the people and process and culture side. So I just kind of want to acknowledge that we didn’t really get to talk about it, but Prukalpa with Atlan, which is a technology platform. But this whole discussion is just like reinforcing, like it’s so coming at it from sort of the people and process side of things. So I will kind of throw in that, if you, dear listeners are intrigued by any of this, there Prukalpa did have at Coalesce 2021, there’s a video if you wanna see that and there’s kind of more backstory. So, some more Prukalpa on that, and she also wrote an article.

0:56:46.3 TW: That’s kind of what sparked us to ask her to come on, called, It’s Time For The Modern Data Culture Stack. So there is more content on this and I’m gonna probably go back and read and watch those again. So my official last call is just that the, R weekly newsletter is back, which I missed it for like a full quarter. And I’m thrilled that it’s back and that it is now back in my weekly digest to go through and find what cool and fun and little interesting things people are doing with R, so there’s my kind of mixed bag.

0:57:17.0 PS: Thank You. Can I do a last, last call, actually?

0:57:21.4 MK: Yeah.

0:57:21.6 TW: Sure. Yeah.

0:57:23.0 MH: Totally allowed.

0:57:23.9 PS: I’m also looking for a few people to come work with me on like actually building out like culture enablement for data teams in general in the community. I feel like there’s a lot of best case practices that get lost. And I think we’re trying to sort of, as a side project build out, like what does culture enablement look like and what should it look like? And just like learnings and practices and, and pieces like that. So if there are any… If there’s anyone who listens to this and is really excited about what culture should look like in data teams and data enablement as a function, I would love for you to reach out to me on Twitter, Slack thing, and wherever. And you know, maybe we’ll find ways to work together.

0:58:03.7 TW: Damn it. If I hadn’t just shown myself as being grossly unqualified for the task.


0:58:07.8 TW: Curses. I already flanked the interview.

0:58:10.1 MH: Oh man.

0:58:12.7 TW: Michael, what did you have as your last call?

0:58:16.6 MH: Well, somewhat off topic, but still kind of cool for the community. One of our listeners and a good friend of the show, Jim Gianoglio recently came out with a little website that I find myself popping over to check in on actually pretty regularly. It’s called mmmhub.org, which stands for marketing mix modeling. And he’s just sort of gathering resources and things. I think initially probably just for his own use, but he’s made it a community resource and you can add resources to it. And as you know, more and more companies are experimenting with how to do that. And more technology and startups are actually focusing on this problem and innovating in this space. I think it’s a great resource. I found it to be a really nice resource for learning more myself. So that was really good.

0:59:02.6 MH: And then on another topic, I’m a big fan, as I’ve said before in the show of Aaron Dignan’s book, Brave New Work, which is a lot about culture and we’ve recently started working with a product that he’s building called Murmur and it’s in beta right now in our company. And so, I’m like, this all, this is sort of like very current, because we’re trying to figure out, well, how does this technology we’re using, how will this work and those things. But Murmur is kind of pretty cool as well. So if you want to take a look at that, it’s a kind of a very interesting…

0:59:32.0 TW: What’s the two sentence, like, what is it?

0:59:34.0 MH: So it’s basically a collaborative decision making tool. So basically, it’s a way to not leverage email or Slack or other things, but actually do a structured, how do we come to agreement on certain things, and there’s lots of layers to that. So our company specifically, we have a lot of agreements that we need to manage. And we’re trying to use the beta chip, and he was kind enough to let us into the beta, so we’re experimenting with that. But anyways, top of mind. Alright, as you’ve been listening, you’ve probably been becoming a massive fan of Prukalpa, like I have over the course of this episode. And you probably want to comment on that or learn more or anything like that. We would love to hear from you. You can reach us on the Measure Slack group, you can also reach us on our Twitter, or on our LinkedIn page. Prukalpa, are you active on Twitter and social media?

1:00:25.2 PS: I am active on Twitter and social media. So it’s just me Prukalpa@prukalpa. And so Twitter, LinkedIn, my email is p@atlan.com.


1:00:37.0 MH: Perfect.

1:00:38.7 PS: This is a topic I’m super passionate about, so would love to hear from folks on it.

1:00:43.9 MH: Yeah. So thank you so much for being open to that. And we’d love to hear from you, your ideas, your thoughts, and once again, Prukalpa, thank you so much for coming on the show. It’s been a pleasure. And no show would be complete if we didn’t talk about the person behind the culture here at the Analytics Power Hour, yeah, that’s our producer, Josh Crowhurst. We give him a lot of props, because he deserves all of them. Thank you, Josh, for everything you do behind the scenes.

1:01:12.9 TW: That’s a podcast enablement role that he’s in.

1:01:14.5 MH: Yeah. Ooh, I like that. And it’ll probably get you a raise somewhere.


1:01:18.0 MH: Alright. We too have to go. But again, Prukalpa, thank you again. Really, really appreciate having you.

1:01:25.8 PS: Thanks for having me.

1:01:26.6 MH: It’s been such a pleasure. Yeah, like it’s been great. Alright. So no matter what rituals you’re using to drive your culture forward, and how you’re designing the enablement of the data in your work, one thing I know that I can say with confidence from my two co-hosts, Tim and Moe, is that you should definitely keep analyzing.

1:01:50.7 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.

1:02:08.3 Charles Barkley: So, smart guys wanted to fit in, so they made up a term called analytics. Analytics don’t work.

1:02:16.3 Tom Hammerschmidt: Analytics. Oh my god. What the fuck does that even mean?


1:02:20.8 MH: Alright. Let’s get started.

1:02:24.6 TW: And I guess the other thing, we’ll go for about 45 minutes once we start and then Michael will kind of head us towards that like last call and then we’ll wrap from there.

1:02:39.4 MH: Or we could go longer than that. You never know.

1:02:42.3 TW: We know. Alright.

1:02:45.1 MH: It could be a six-hour marathon.

1:02:46.0 TW: Well, no. Hopefully no.

1:02:50.0 MK: I think I would be… Yeah, I was like, I think I’ll be asleep, but you guys crack on. That is the most beautifully organized bookcase I think I’ve ever seen.

1:03:01.4 MH: Yeah, this is pretty disgusting.


1:03:08.2 MK: Do you think that’s… It must be, it’s real, right?

1:03:10.8 MH: Oh, it’s real.

1:03:12.1 MK: You know sometimes how these beautiful backgrounds and you’re like, oh.

1:03:14.6 TW: We got the… There’s an excuse to admire your bookcase.

1:03:18.5 MH: Yeah, we were all like, wow what a bookcase.

1:03:19.3 MK: Although you’re still on mute though, but your bookcase is beautiful.


1:03:29.3 TW: Rock flag and data cribbing parties.

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