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[Announcer]: Welcome to the Analytics Power Hour.

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

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[Michael Helbling]: Hey everybody, welcome.

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[Michael Helbling]: It's the Analytics Power Hour and this is episode 276.

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[Michael Helbling]: Business Intelligence.

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[Michael Helbling]: It's sort of like an oxymoron, but BI tools, I mean, they come and they go and somehow we're still rebuilding the dashboards for the third time in three years.

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[Michael Helbling]: Well, there you go, Moe, fixed it for you.

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Thanks.

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[Michael Helbling]: You know, besides providing analytics engineers somewhat Sisyphean job security, I don't know why anyone, I don't know if I know anyone who's actually truly happy with their BI tool.

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[Michael Helbling]: I mean, are we expecting too much?

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[Michael Helbling]: Are we trying too hard to make them fit every possible use case and leads to a graveyard of unused and obsolete reports from the last time someone got hired in as the head of data and tried to build what the org was asking for?

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[Michael Helbling]: I don't know, maybe newer concepts like semantic layers or AI are going to usher us into a golden age of self-serve data brilliance.

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[Michael Helbling]: Speaking of data brilliance, let me introduce you to my co-hosts, Moe-Kiss.

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[Michael Helbling]: How are you going?

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[Moe Kiss]: I'm going great and super pumped to talk about this topic.

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[Michael Helbling]: I know, and I'm excited to ask you how you're going finally because I keep wanting to with the other co-hosts and it doesn't really fit.

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[Moe Kiss]: It doesn't work, no.

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[Michael Helbling]: And Tim Wilson, glad to have you.

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[Tim Wilson]: I've shown remarkable restraint already up to this point.

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[Michael Helbling]: Yeah, I am glad you're here, Tim.

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[Michael Helbling]: I know you probably don't have any strong opinions about this topic, but glad you're here.

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[Michael Helbling]: And I'm Michael Helbig.

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[Michael Helbling]: We also wanted to get on a guest, someone with deep experience navigating these challenges.

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[Michael Helbling]: Colin Zima is the CEO of Omni, a modern business intelligence platform.

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[Michael Helbling]: Prior to that, he was the chief analytics officer at Looker and helped lead that product through its acquisition by Google.

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[Michael Helbling]: And today he is our guest.

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[Michael Helbling]: Welcome to the show, Colin.

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[Michael Helbling]: Thank you for having me.

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[Michael Helbling]: Colin, why do we struggle so much?

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[Michael Helbling]: with BI tools, maybe close it down a little bit more.

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[Colin Zima]: Yeah.

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[Colin Zima]: I mean, I think it actually comes down to a very simple idea, which is everyone at some level can do things with data.

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[Colin Zima]: Some people are just calculating the change on paying for money at the store, and some people are doing hardcore data science and machine learning.

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[Colin Zima]: But at some level, data is something that everyone is doing.

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[Colin Zima]: Everyone takes math.

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[Colin Zima]: And the challenge with building a business intelligence tool itself is that you actually need to build a product that is used by that entire spectrum of users.

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[Colin Zima]: So you have a CEO that is expecting perfect visual reporting and clear data and just stuff that looks and feels amazing.

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[Colin Zima]: You have a data science team that

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[Colin Zima]: probably hates the tool that you bought and wants to run something in a notebook on their desktop.

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[Colin Zima]: You've got a marketing team that just doesn't even want to use your tool, but is just trying to get their thing done.

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[Colin Zima]: And then you've got your finance team that's just like, why isn't this Excel?

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[Moe Kiss]: Why?

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[Moe Kiss]: Where is the download to CSV button?

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[Colin Zima]: Exactly.

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[Colin Zima]: That doesn't want your tool either in a different way.

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[Colin Zima]: And I was mentioning this to Moee a couple of weeks ago, but one of these things that you see is with most business software, so if you take Slack or email, for example,

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[Colin Zima]: Everyone uses it the exact same way.

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[Colin Zima]: Like you open your emails, you send emails.

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[Colin Zima]: It doesn't really do anything else.

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[Colin Zima]: Like there's no automation on top of it.

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[Colin Zima]: With a BI tool, you have a variety of users that has completely different desires in the platform.

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[Colin Zima]: So some people are frustrated if they can't get SQL.

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[Colin Zima]: Some people are frustrated if they do see SQL.

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[Colin Zima]: And I can't even enumerate the number of times where I've gotten requests on the same day for two opposite opinions that are both antithetical to each other.

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[Colin Zima]: It's like, why is this the default?

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[Colin Zima]: No, like why isn't the inverse the default?

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[Colin Zima]: And I think this is actually the core challenge at Building BI is like, how do you make a tool that is perfect for everyone at all of these different levels?

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[Colin Zima]: And we're working on it, but like, I think that's actually the core problem.

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[Tim Wilson]: But the premise of BI is often that you, of a BI tool is that you just have to, I like the framing of the different users and it's like the data team or the BI team says, we just need to get everybody to their starting point based on who they are.

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[Tim Wilson]: The marketers, we just need to give them just the right dashboard and then they'll have interactivity and flexibility.

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[Tim Wilson]: And we need to give, and it just, it doesn't,

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[Tim Wilson]: It literally never works, and it just instead winds up being, well, it's the next day, and there's one thing this doesn't do for me, and now what do I do?

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[Tim Wilson]: And then it seems like it forks into, it's the person who's a hacker who goes in and figures it out, but then it's the other overwhelming majority who say, well, now I gotta go back to the team that was trying to offload the work from their plate and ask them to do more for me.

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[Moe Kiss]: Is it about the tool or is it our expectations often of like, I don't know, like the dashboard?

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[Tim Wilson]: It's definitely not about the tool, but I'm just going to say that I'll put that on.

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[Colin Zima]: I can give you an example where I do think the tool doesn't help it.

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[Colin Zima]: And this is one of the most visceral ones for me, which is if you build a BI tool, it usually is writing SQL.

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[Colin Zima]: So SQL is a core language of the tool.

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[Colin Zima]: The whole back end of the tool is writing SQL at some level.

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[Colin Zima]: And then you need to build these front-end layers on top of it, like something that's called table calculations or some sort of post-processing language.

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[Colin Zima]: And inevitably, the users that you're exposing that language to are not SQL people.

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[Colin Zima]: They're Excel people.

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[Colin Zima]: But if you look at every single tool's implementation of that final language, it's always this weird hybrid between Excel and SQL, because you're trying to bring a back end that does one thing and deliver a front end that does another thing.

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[Colin Zima]: And it's one of those examples where if you try to thread the needle and create this half language, it's like you're back to the XKCD 15th standard of a new language.

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[Colin Zima]: And I do think this is one of those tools where everyone thinks they can do it better and they try to reinvent.

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[Colin Zima]: And again, I'm mildly talking my book because we made our front end Excel and our back end SQL.

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[Colin Zima]: But I do think that sometimes there's a learning curve associated because the builder is trying to solve a problem.

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[Colin Zima]: And in solving the problem, they create new things.

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[Colin Zima]: And then there's a learning curve for the user that they need to come along with that makes it hard.

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[Colin Zima]: And that's why Excel as this lowest common denominator is always sort of the release valve for every single tool in existence.

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[Colin Zima]: It's like, I don't know how to do this, but I can make it make tables.

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[Colin Zima]: And then I can go put it in this other app that I actually know how to use.

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[Colin Zima]: And I do think that at some level that is a failing of the tool stack because the user is saying like, hey, I can't quite understand this, but I know I can understand it over here.

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[Colin Zima]: I do think that underlying all of this is like, you know, business is hard and they change and it's hard to make like perfect metrics.

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[Colin Zima]: Like the reason that marketers dashboard doesn't work is because it probably worked and then they changed the definition of a thing.

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[Colin Zima]: Oh, yeah.

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[Moe Kiss]: And now like.

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[Colin Zima]: Now, we've got two time series that need to attach to each other.

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[Colin Zima]: And that's just hard.

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[Tim Wilson]: But even going from Eminem Excel, one, I think, fair dividing line is Excel, people who are comfortable and regularly used to the tables and people who don't.

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[Tim Wilson]: And the concept of metrics and dimensions and aggregation and group by, I've getting somebody from

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[Tim Wilson]: If they get pivot tables, which has a deeper level of I get the manipulation of data and I'm like, oh, well, you actually kind of get some intuition around a group buying sequel.

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[Tim Wilson]: You get the idea of a dimension with dropping a metric on it.

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[Tim Wilson]: I mean, there's a ton of people who don't get that.

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[Tim Wilson]: And yet they're jumping into a BI platform.

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[Tim Wilson]: And I don't know that it's a tooling.

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[Tim Wilson]: I think you kind of nailed it.

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[Tim Wilson]: Like the promise of the BI tools is we're going to be everything to everybody.

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[Tim Wilson]: And that just winds up being a feature bloat and you're imperfect for everyone.

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[Tim Wilson]: And no one's really even defined what

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[Tim Wilson]: The focus becomes on if I get the right thing in the tool, and I'm just going to obsess about that, and it misses the step.

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[Tim Wilson]: I guess, Moe, this is to my answer to the emphatic, no, it's not about the tool.

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[Tim Wilson]: They're not actually going in with clarity on what they're trying

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[Tim Wilson]: to do.

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[Moe Kiss]: Sorry, the person using the tool you mean doesn't have clarity on what they're trying to do.

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[Moe Kiss]: Is that what you're saying?

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[Tim Wilson]: They're saying, I'm supposed to be like, I kind of want to know how that campaign did.

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[Tim Wilson]: What does that really mean?

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[Tim Wilson]: Well, I mean, I guess like, how many registrations did we get?

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[Tim Wilson]: Where can I go get the number of registrations?

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[Tim Wilson]: There's already that's kind of broken because they're kind of setting off for sort of

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[Tim Wilson]: aimless wandering in the data with the hope that some useful insight will emerge.

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[Tim Wilson]: And where they hit the most tangible blocker is they have some sort of frustration or limitation with the tool.

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[Tim Wilson]: And those could be a million different frustrations.

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[Tim Wilson]: And then they start to say, well, my problem is that the tool is not giving me this.

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[Tim Wilson]: And I think, in my experience, the problem is often, no, you're just kind of trying to wander through the data

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[Tim Wilson]: and find something.

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[Moe Kiss]: They're trying to do exploratory analysis, right?

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[Moe Kiss]: No, not while I take issue with your... Fine, whatever you want to call it.

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[Moe Kiss]: They're trying to wander through the data, but potentially don't have the skill set to do that in a structured way that they don't end up meandering.

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[Moe Kiss]: And so then the question is, instead of trying to teach people to use a BI tool, do we actually need to teach people how to do analysis, how to answer a business question?

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[Tim Wilson]: Well, how to validate that it's a good business question in the first place, right?

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[Tim Wilson]: I mean, because even that, not to a pedantic, like, well, if somebody in the business asks the question, isn't that a business question?

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[Tim Wilson]: Definitionally, yes.

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[Tim Wilson]: Is it useful?

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[Tim Wilson]: Many, many times, no.

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[Tim Wilson]: They're, yeah.

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[Moe Kiss]: I underestimated your level of soapboxness on this topic.

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[Moe Kiss]: I do think there's another layer of this that I've experienced in the past.

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[Colin Zima]: I've managed data teams and been sort of like

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[Colin Zima]: like disconnected enough that I've asked for things from the data team.

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[Colin Zima]: And I actually think one of the other challenges, and this isn't a product problem either, it's a people problem, is the language of data people and how data people think is actually quite different from how business people think.

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[Colin Zima]: And the translation can actually be very challenging.

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[Colin Zima]: I'll give you an example.

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[Colin Zima]: We did this at Looker where I had left the data team at this point and was sort of doing product stuff and we were doing a giant repricing.

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[Colin Zima]: And I went to the data team and I said, hey, would love to do some analysis.

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[Colin Zima]: We're going to do some repricing.

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[Colin Zima]: And we cut the data a bunch of different ways.

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[Colin Zima]: But they essentially came back with a dashboard that had 30 tiles on it.

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[Colin Zima]: And my first question was, great, this all looks good.

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[Colin Zima]: What should we do with pricing?

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[Colin Zima]: You did all this analysis, what should we do?

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[Colin Zima]: And their answer was, I don't know.

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[Colin Zima]: We cut the data a bunch of different ways.

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[Colin Zima]: What do you think?

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[Colin Zima]: And I was like, guys,

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[Colin Zima]: The point of this was not to make the charts.

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[Colin Zima]: The point of this was to get the conclusion.

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[Colin Zima]: And the charts were for you, and I guess for me, to help get to the conclusion.

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[Colin Zima]: But really, if you had just shown up and been like, these are the pricing tiers, that would have also been equally good.

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[Colin Zima]: And I think this, like because the BI tool is the vehicle for communication between these two teams, teams being everyone and the data team, you get these sort of lost in translation conversations where, like,

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[Colin Zima]: the data team might be concerned with sort of like pedantically correct, well-structured semantic layers and models.

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[Colin Zima]: And the business is just like, hey, I'm just trying to go spend some money right now on marketing.

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[Colin Zima]: Like where should I go put it?

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[Colin Zima]: And I think that is actually one of the big problems as well.

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[Tim Wilson]: But I think it's a great point that everyone's well-intentioned.

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[Tim Wilson]: Everyone has good intentions and is very capable.

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[Tim Wilson]: But if you have the business saying, I'm trying to speak the data team's language, they wind up

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[Tim Wilson]: speaking in that language and saying, like, I think we need to kind of slice it a bunch of different ways to see if we can figure out what this is.

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[Tim Wilson]: And what the data team hears is, oh, the ask is to slice it a bunch of different ways.

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[Tim Wilson]: And then the business will be able to look at it and the answer will emerge and materialize.

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[Moe Kiss]: Yeah.

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[Tim Wilson]: And you wind up with both being saying, well, that's what you asked for.

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[Tim Wilson]: I mean, the classic, I mean, the line I like to use is like you have all these dashboards and

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[Tim Wilson]: the business at the end of the just destroys the data team when they say, I know you're doing everything I've asked and it's a lot of stuff.

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[Tim Wilson]: And I basically understand what this is, but what am I supposed to actually do with it?

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[Tim Wilson]: And it's like, it's a dagger to the heart of the data team that then gets frustrated saying, what the hell?

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[Tim Wilson]: You just said I did everything.

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[Moe Kiss]: Tim, that happens at mature companies.

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[Moe Kiss]: I think what happens is they like you do all the things and then they go, oh, can you just have this filter or I just need to have this view or like, oh, what if we just tweak it this way?

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[Moe Kiss]: And what they're actually saying is this dashboard is not answering my question.

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[Moe Kiss]: Like I can't and they just keep adding to it.

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[Moe Kiss]: And then you end up with this hot, hot mess.

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[Michael Helbling]: Hey, you listen to the analytics power hour so you know that data is everywhere and it is growing fast.

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[Michael Helbling]: In fact, we just generated some data together since you're listening right now.

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[Michael Helbling]: You know, you put all the world's data together and we generated 149 zettabytes last year.

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[Michael Helbling]: And next year it will be 181 zettabytes.

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[Michael Helbling]: I mean, that is a lot of data.

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[Michael Helbling]: Thankfully, you don't have to manage it all, but you do have to manage yours, and that's why there's 5TRAN.

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[Michael Helbling]: It's your one-stop platform for all your data sources with over 700 managed data connectors, 5TRAN seamlessly centralizes your ever-growing data streams, and now it's even easier with the new connector SDK.

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[Michael Helbling]: Data engineers can use simple Python scripts to build custom connectors into any data source.

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[Michael Helbling]: That's worth saying again, any data source.

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[Michael Helbling]: That's F-I-V-E-T-R-A-N dot com slash aph.

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[Michael Helbling]: All right, let's get back to the show.

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[Moe Kiss]: Colin, I need to just revisit Tim.

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[Moe Kiss]: Go ahead.

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[Moe Kiss]: The topic you were just kind of on about that specific example with the pricing.

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[Moe Kiss]: I'm really curious because full disclosure, we are doing a lot of thinking internally about BI tooling and semantic layers and all that sort of stuff.

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[Moe Kiss]: It seems to be the topic of the moment because we've had a BI tool for three to five years.

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[Moe Kiss]: Obviously, that's the thing that we're looking at at the moment.

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[Moe Kiss]: That's what you're blaming at the moment.

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[Tim Wilson]: That's what's being blamed at the moment.

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[Moe Kiss]: That was sarcasm, Tim.

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[Moe Kiss]: One of the observations I've had.

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[Moe Kiss]: There's no secret in this, but we have been a big user of mode for a very, very long time.

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[Moe Kiss]: I pointed out to someone the other day, one of the challenges that I feel that we have is it is used as an exploratory tool by data scientists a lot.

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[Moe Kiss]: It is very good.

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[Moe Kiss]: You can make hacky stuff, you can quickly get to an answer, do what you need to do, write SQL, awesome.

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[Moe Kiss]: But then there are other people in the business that have really been trying to use it as a dashboarding tool.

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[Moe Kiss]: that is stakeholder facing and does all the things in my view, not super well, because it's really ugly and I like beautiful things.

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[Moe Kiss]: Then ultimately, you end up with this tool that is like data scientists trying to build hacky exploratory shit, and then you have trying to have business stakeholders use it to go to look at dashboards for specific things.

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[Moe Kiss]: You end up with this massive amount of bloat.

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[Moe Kiss]: because no one can find anything.

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[Moe Kiss]: It just becomes unruly.

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[Moe Kiss]: Are we getting to a point where we need to think about this from a more mature point of like, lots of businesses now have multiple BI tools for different purposes?

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[Moe Kiss]: Is that just the evolution that if you get to a big enough stage, that's kind of what you have to do?

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[Moe Kiss]: Or is it, again, back to that, we're trying to use it for too many things?

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[Colin Zima]: I mean, I'd like to think that someone could solve this.

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[Colin Zima]: I think the reality is like when you start building different tools, I actually, not to answer your question directly, but I'll give you like a direct analogy, which is Looker had semantic layers, you know, like, I feel the burn of it.

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[Colin Zima]: You're aware of them.

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[Colin Zima]: DBT didn't exist when Looker started.

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[Colin Zima]: At some level, you're always trying to turn this knob between self-service and control.

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[Colin Zima]: What you're talking about is this knob between self-service and control.

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[Colin Zima]: It's like, either people can do stuff and they make a mess and they're wrong or they can't and they get frustrated.

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[Colin Zima]: I'd like to think that at Looker, we democratized a lot of sort of getting it data because we had the semantic layer and people could do things on top of it.

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[Colin Zima]: But in some ways, a lot of people thought that Looker's semantic layer was even too open and too many people were touching it.

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[Colin Zima]: And for a lot of early customers,

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[Colin Zima]: DBT was an interesting option just because they might have only had four people that had access to the DBT layer.

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[Colin Zima]: And thus, there was a new gate and a different level of control and a different version of self-service where Looker was even abstracted away from the transformation in the warehouse.

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[Colin Zima]: And I think the mode looker balance that you have is very similar to like the Excel Tableau balance or like pick any pair of tools.

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[Colin Zima]: Like there are progressive layers of freedom that you get the more disconnected you are from the core business system.

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[Colin Zima]: And in some ways, it's nice to have a disconnected tool because you can just point at it and say over there, we don't trust anything, but you can do whatever you want.

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[Colin Zima]: But over here, we have control.

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[Colin Zima]: The downside of that philosophy is

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[Colin Zima]: like I'm coming up with terrible analogies as I talk, but it's like if you had like a crime-free zone and a no crime zone, like I can't remember the purge, like if you go crazy on the purge day, then like, yeah, you've made a mess in one day, but like the other days are clean.

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[Colin Zima]: And all you're kind of trying to do is like side pocket and figure out how much of a side pocket you want.

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[Colin Zima]: So like maybe the purge is a good structure for your data org, like chaos and then order.

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[Colin Zima]: Or maybe you should try to create order every single day and create more balance.

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[Colin Zima]: That was a bad analogy, I was testing that one.

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[Moe Kiss]: I was going to say, we often talk about the path of there's a structured path to go down that has been built.

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[Moe Kiss]: If you want to go off road, it's fine.

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[Moe Kiss]: But then you're responsible for your four wheel drive and your own safety.

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[Colin Zima]: Exactly.

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[Moe Kiss]: One of the things, I'm really curious to hear your view on, is we also are users of Looker.

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[Moe Kiss]: We have built LookML.

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[Moe Kiss]: This is my personal observation.

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[Moe Kiss]: I'm sure there are people that potentially don't agree.

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[Moe Kiss]: I feel like what we've done with our LookML, and this gets us very much into that hot topic at the moment of semantic layers because it seems to be all anyone's discussing, I feel that our LookML essentially replicated what we had in our data warehouse of basically like report tables, right?

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[Moe Kiss]: Like super lean, but very structured.

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[Moe Kiss]: And what it meant is like we have 70,000 tables.

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[Moe Kiss]: In snowflake in our data warehouse and we duplicated that in looker where like you don't have a view for.

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[Moe Kiss]: Users you have a view for users by country you have a view of users by platform you have a view of users by marketing channel by users by and you end up with this.

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[Moe Kiss]: What seems to a data person as a very beautiful, like look ML layer, but to a user, they're like, sorry, there's 50 tables.

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[Moe Kiss]: It's like 50 looks at say user, which table do I use?

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[Moe Kiss]: I sometimes feel like it's not even like it's the data team wanting to structure it.

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[Moe Kiss]: to be perfect from their view of like this is the perfect architecture, but then it creates this usability problem.

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[Moe Kiss]: I'm so curious to hear your views on that.

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[Colin Zima]: I think this is why we bounce between these centralized and decentralized platforms.

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[Colin Zima]: So like a lot of my thesis is essentially like people appreciate business objects and like very centralized data teams.

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[Colin Zima]: And so they were like,

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[Colin Zima]: perfect semantic layer, data team controls everything, great.

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[Colin Zima]: Quality very high, agility very low.

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[Colin Zima]: And then someone gets their hands on Tableau desktop, starts building things and everyone's like, oh wow, that side of the world looks really good.

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[Colin Zima]: Ditch the business objects, everyone runs over there.

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[Colin Zima]: That builds for five years, maybe three years.

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[Colin Zima]: And you've got all this reporting and then you're like, wait a second,

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[Colin Zima]: Like we just did a meeting where eight people brought eight different metrics.

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[Colin Zima]: Like this is like, oh wait, look at Looker over there.

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[Colin Zima]: Like that looks pretty good.

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[Colin Zima]: Like let's go run over to that side of the house.

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[Colin Zima]: And I think you end up with this oscillation back and forth.

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[Colin Zima]: And like maybe you settle with a little bit on both sides.

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[Colin Zima]: I'd like to think that the ideal version of this is you're doing it in a bunch of different layers, and you're sort of differentiating between what happens and what layers.

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[Colin Zima]: So the example I always give, and this is a pretty trivial example, is like five trans sucks data out of Salesforce.

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[Colin Zima]: For whatever reason, Salesforce delivers deleted records.

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[Colin Zima]: No one ever needs that.

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[Colin Zima]: Like clean that up in the warehouse, make sure that no one can ever touch a deleted record.

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[Colin Zima]: Like I've built up on to reporting on deleted records.

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[Colin Zima]: It's very frustrating.

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[Colin Zima]: In the inverse, if you're building like a daily, weekly, monthly table and telling your user to go navigate the BI tool to go figure out like daily active users and they've got to go like exploring for 14 different cuts of users, I think then you've probably abstracted too much into the warehouse and you've not left enough in the BI tool.

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[Colin Zima]: It's really hard to actually strike a proper balance, which is why everyone now is like, hey, maybe AI fixes this and like,

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[Colin Zima]: I can just throw a text box on top of everything and like magic beans, like everyone can get the reporting they need.

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[Michael Helbling]: Because somehow the LLM will know which of those user tables is the right one.

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[Moe Kiss]: Exactly.

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[Tim Wilson]: It does feel like, I mean, that's everyone says the requirements are very simple for my BI tool.

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[Tim Wilson]: I just want a tool that's intuitive, easy to use, gives me access to all the data and I won't get in trouble with it.

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[Tim Wilson]: And that is the behavior

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[Tim Wilson]: that people act under.

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[Tim Wilson]: I will call out that we had Ben Stansel on episode 190 to talk about metrics layers.

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[Tim Wilson]: Remember when those were kind of a hot, and I think he's gone beyond it.

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[Tim Wilson]: I still am firmly in the camp of when people show up with those different reports, different numbers, different revenue numbers.

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[Tim Wilson]: Sometimes it's not particularly material.

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[Tim Wilson]: Sometimes it's not even a metric that fucking matters.

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[Tim Wilson]: But in, we have conditioned people to say, ah, we have found the discrepancy and everybody feels actually good about going to track down why these metrics are different.

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[Tim Wilson]: And another way to like solve for that is to look at a lot fewer metrics when you're sitting

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[Tim Wilson]: in a meeting.

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[Tim Wilson]: I get that there is a tooling desire, and I love the way you're articulating the range of, it's a balance, and there's never going to be a perfect balance.

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[Tim Wilson]: How do you handle it?

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[Tim Wilson]: To me, it feels like it's

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[Tim Wilson]: It's kind of going to be an impossible fix as long as businesses jump to the data.

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[Tim Wilson]: Everybody wants to come.

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[Tim Wilson]: We're supposed to be data driven.

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[Tim Wilson]: So let me bring my 75 charts.

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[Tim Wilson]: If everybody brings their 75 charts, we're going to find charts to disagree and we're going to have

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[Tim Wilson]: an argument about which chart.

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[Tim Wilson]: And then somebody is going to say, we got to figure this out.

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[Tim Wilson]: So then it goes back to the data team who they're they're both right.

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[Tim Wilson]: But now the data team is putting together an explanation of reconciling these two different revenue counts.

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[Tim Wilson]: And that's the next meeting.

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[Tim Wilson]: And nobody's saying, what the hell are we doing?

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[Tim Wilson]: Like, this doesn't matter.

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[Tim Wilson]: But everybody's felt good.

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[Tim Wilson]: They've been they've had activity and they've discovered things.

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[Tim Wilson]: But OK, I'm sorry that.

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[Tim Wilson]: Sorry, sorry.

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[Tim Wilson]: It's going to happen every seven minutes mode.

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[Tim Wilson]: They're just going to be like a little pressure valve and then I'll go quiet again.

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[Tim Wilson]: Will that work?

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[Tim Wilson]: No, I'm here for it.

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[Tim Wilson]: OK.

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[Colin Zima]: The what I was going to say is like I can't remember.

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[Colin Zima]: I think I took I think this might have been like a pop science book that I read that effectively the thesis of the book was like happiness in life is about exceeding expectations.

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[Colin Zima]: for everything.

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[Colin Zima]: I might have gotten the wrong message out of it, but that is what I personally took away, is like, always have lower expectations than exceed them.

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[Colin Zima]: And I actually think this is the problem with data tools, is if your expectation is like, I'm gonna do some product analytics and they'll mostly be right and we'll make a better decision, you'll exceed expectations.

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[Colin Zima]: If your expectation is like, we've got a little bit of a mess over here and we've got some slower metrics over here, but like you'll mostly get what you want,

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[Colin Zima]: If the expectation inversely is, I can answer every single question I have in eight words with no context whatsoever, then you have a very mismatch expectation for how messy data is in reality.

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[Colin Zima]: And so I think the funny thing when you think about people that really are happy with their data tools, I think sometimes it's the most technical people.

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[Colin Zima]: that are businessy.

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[Colin Zima]: And the reason is because they can find the balance between these things.

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[Colin Zima]: I don't know Ben that well, but I bet if we had a conversation about managing product with data, both of us would be like, a little data input is good, but mostly just make good decisions and pick stuff.

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[Tim Wilson]: Yeah, but he'd have like a long footnote and links to some obscure movie in a clip.

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[Tim Wilson]: He would say it much more eloquently.

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[Colin Zima]: But like, I do think a lot of it is just being able to communicate what you are doing and have people understand it well.

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[Colin Zima]: If people, I remember this at the tail end of Looker, for example, like you start getting bigger and everyone's like, let's make metrics based product decisions.

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[Colin Zima]: So the canonical example is like, if we move these pixels around, do people click more stuff on my e-commerce site?

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[Colin Zima]: It's great if you're Amazon because like moving things by a couple of pixels like increases, you know, click through by 50%.

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[Colin Zima]: It's like for Looker, it's not gonna tell us whether we should go build like trellis charting or go work on like the SQL interface.

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[Colin Zima]: It's just like,

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[Colin Zima]: be an adult and make a decision and choose between the two of them.

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[Colin Zima]: And then like monitor and make sure that it's doing what you want it to do or that, you know, like people aren't getting stuck in some weird way, but like data is not going to do your job for you.

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[Colin Zima]: And I feel like a lot of people are expecting a data person to come in and be like, go make me some money for the business.

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[Colin Zima]: Like I gave you the database, like where's the money now?

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[Colin Zima]: And it's, it's not usually that simple.

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[Moe Kiss]: Okay, so just hypothetically, Colin, I feel like I could talk to you for like 5,000 years about all of the problems that I'm trying to deal with.

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[Moe Kiss]: So I love this point of expectations and you want everything to exceed if

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[Moe Kiss]: if things exceed your expectations, then that's great.

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[Moe Kiss]: With BI tools, fundamentally, I do think the issue is that what is sold to the business is also, we're going to migrate from tool X to tool Y, we're going to do POCs, people spend way too long gathering requirements, doing all the things, and then it's sold as like, this is the perfect solution, which disappoints everybody because it doesn't do any of the things completely perfectly.

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[Moe Kiss]: It sounds like

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[Moe Kiss]: the win is to message it differently of like, we have this tool, it's going to be able to do 90% of what we need.

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[Moe Kiss]: And then when for some people it does 95%, everyone's going to be like, yeah, this is awesome.

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[Moe Kiss]: Like, is it actually we just need to get better at how we message this?

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[Colin Zima]: I'd like to think the answer is sort of yes.

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[Colin Zima]: I think if you, I'll give you an example, which is like, if we go turn an AI on our company and we're like, this thing's gonna make us 100 times as much money next year.

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[Colin Zima]: Your CEO's gonna be pretty disappointed.

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[Colin Zima]: If you make one and you're like, I think this will search docs a little bit better.

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[Colin Zima]: And then you come back and you're like, wow, this thing can kind of answer all of our support questions.

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[Colin Zima]: That's pretty good.

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[Colin Zima]: I think then you've won a little bit.

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[Colin Zima]: you're probably not going to be able to go to your boss or your boss's boss's boss or whatever and be like, I'm going to rip out my BI tool and monopolize the data team for six months.

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[Colin Zima]: And we're going to get an OK product.

357
00:29:34,579 --> 00:29:36,782
[Colin Zima]: Like it needs to go solve some problem.

358
00:29:37,463 --> 00:29:45,373
[Colin Zima]: But I think you need to the thing that we would do in Looker POCs, for example, is we would always try to solve a really tangible problem.

359
00:29:45,733 --> 00:29:48,497
[Colin Zima]: Like if marketing was the problem,

360
00:29:48,477 --> 00:29:51,984
[Colin Zima]: let's go make marketing a little bit more efficient in the POC.

361
00:29:52,504 --> 00:29:56,933
[Colin Zima]: And like, of course, you can get sidetracked and get into like the 100 thing checklist.

362
00:29:56,953 --> 00:30:00,179
[Colin Zima]: But ultimately, you're just actually solving a marketing problem.

363
00:30:00,319 --> 00:30:07,853
[Colin Zima]: And frankly, you might not have even needed to use Looker like you might have just gotten a sales engineer and whatever tool that they wanted and been able to do it.

364
00:30:07,833 --> 00:30:09,835
[Tim Wilson]: The incentive structure, I mean, I think this is the challenge.

365
00:30:09,855 --> 00:30:11,436
[Tim Wilson]: I think I agree.

366
00:30:11,877 --> 00:30:13,638
[Tim Wilson]: Changing the messaging would be the solution.

367
00:30:13,658 --> 00:30:31,354
[Tim Wilson]: The challenge is that the BI platforms and their sales teams, like they, in marketing for the BI tools, RIT large and analytics platforms, they kind of have to have a, a pithy declarative statement that is the kind of extreme.

368
00:30:31,454 --> 00:30:37,840
[Tim Wilson]: And then I would claim that even when you go to the POC, when you do the demo, the demos are always simplistic.

369
00:30:37,820 --> 00:30:43,669
[Tim Wilson]: They look at the example of how this works on this one page that's the lowest hanging fruit.

370
00:30:45,511 --> 00:30:50,399
[Tim Wilson]: What happens, I will claim, is people see that and say, that's awesome.

371
00:30:50,419 --> 00:30:57,349
[Tim Wilson]: That's not my exact use case, but mine's adjacent to it, and I'm sure it will do the same thing for me.

372
00:30:57,489 --> 00:31:00,874
[Tim Wilson]: The same thing happens when picking a POC.

373
00:31:00,854 --> 00:31:05,964
[Tim Wilson]: The most solvable thing happens, and I'm not opposed to POCs.

374
00:31:05,984 --> 00:31:18,610
[Tim Wilson]: There's a lot of value coming out of them, but the next level of managed expectations is saying, we picked a tight and defined POC that had a high likelihood of success and would let us kick the tires a little bit.

375
00:31:18,590 --> 00:31:35,248
[Tim Wilson]: Now everyone's going to assume that that's going to happen in an instant for all of us and neither the BI platforms nor the consultants who are being brought on to help implement have any incentive whatsoever to disabuse them of that.

376
00:31:35,228 --> 00:31:40,795
[Tim Wilson]: notion because they're getting paid when the licensing gets signed or when it gets implemented.

377
00:31:40,835 --> 00:31:47,384
[Tim Wilson]: They're not getting paid a year from now when value has really been delivered.

378
00:31:47,444 --> 00:31:54,213
[Tim Wilson]: I think it is a massive, it's just a structural challenge.

379
00:31:54,814 --> 00:32:02,063
[Tim Wilson]: I think the more people who are trying to manage those expectations, it does fall, I think, to the internal person that says,

380
00:32:02,245 --> 00:32:04,448
[Tim Wilson]: Take everything that's being said with a grain of salt.

381
00:32:04,508 --> 00:32:11,138
[Tim Wilson]: It's shocking how little skepticism, seasoned, experienced people have.

382
00:32:11,258 --> 00:32:15,143
[Tim Wilson]: When the vendor tells them X, they just accept it.

383
00:32:15,524 --> 00:32:19,409
[Tim Wilson]: When the vendor doesn't know the internal complexities, absolutely.

384
00:32:19,750 --> 00:32:22,093
[Moe Kiss]: Moere than you think.

385
00:32:22,073 --> 00:32:24,416
[Moe Kiss]: I feel like I'm a bit of an asshole.

386
00:32:24,676 --> 00:32:27,821
[Moe Kiss]: I'm like, this could go wrong.

387
00:32:28,281 --> 00:32:33,408
[Tim Wilson]: But the thing is, if you look at their incentives for many, many people, people have been beating up on the tool.

388
00:32:33,448 --> 00:32:36,312
[Tim Wilson]: So it's either our tool or it's my team and the process we run.

389
00:32:36,832 --> 00:32:38,755
[Tim Wilson]: What's the easier psychological thing?

390
00:32:39,095 --> 00:32:40,577
[Tim Wilson]: Yeah, I will pile on the tool now.

391
00:32:40,677 --> 00:32:41,238
[Tim Wilson]: And guess what?

392
00:32:41,579 --> 00:32:46,545
[Tim Wilson]: If I buy a new tool, and Colin, you said it, that buys us six months or 12 months,

393
00:32:46,525 --> 00:32:52,552
[Tim Wilson]: of time where we get to tell people, I know business objects sucked, but we're rolling out Tableau.

394
00:32:52,572 --> 00:32:55,956
[Tim Wilson]: I think the opposite.

395
00:32:56,216 --> 00:32:57,377
[Moe Kiss]: Well, you're wrong.

396
00:32:57,397 --> 00:32:59,420
[Tim Wilson]: That is a thousand percent what happens.

397
00:32:59,740 --> 00:33:09,972
[Moe Kiss]: I know that that is what happens, but that whole... I fundamentally find it so weird how people are like, we're going to do this migration at six to 12 months.

398
00:33:10,092 --> 00:33:10,913
[Moe Kiss]: I am like,

399
00:33:11,551 --> 00:33:18,084
[Moe Kiss]: I get mortified when I need to tell the business that of like, we're going to spend 12 months migrating something.

400
00:33:18,164 --> 00:33:20,047
[Moe Kiss]: Like that's unacceptable.

401
00:33:20,248 --> 00:33:23,634
[Moe Kiss]: Like I find it weird that people would want to use that to pad.

402
00:33:23,674 --> 00:33:26,219
[Moe Kiss]: Like, I'm like, that means we're not moving fast enough.

403
00:33:26,259 --> 00:33:27,582
[Moe Kiss]: We're not delivering value.

404
00:33:27,882 --> 00:33:32,191
[Tim Wilson]: But once they get over that hurdle, they're in this like glorious window.

405
00:33:32,631 --> 00:33:34,555
[Michael Helbling]: The overarching point is,

406
00:33:35,530 --> 00:33:45,949
[Michael Helbling]: We take a process problem and we try to slap a tool on top of it as opposed to reevaluating the process and its lack of functionality within our organization.

407
00:33:46,791 --> 00:33:53,544
[Michael Helbling]: And that's kind of, I think, Tim, what point you're making, whether it's three months to replace it, whether it's 12 months, it doesn't kind of doesn't matter.

408
00:33:54,205 --> 00:33:57,791
[Michael Helbling]: All right, I'm going to mute Tim in motion, just a second comment.

409
00:33:57,811 --> 00:33:58,112
[Colin Zima]: You and me.

410
00:33:59,122 --> 00:34:00,363
[Michael Helbling]: No, I'm just kidding.

411
00:34:00,904 --> 00:34:13,955
[Michael Helbling]: But I do wanna just switch gears just a little bit because we've talked a lot about a lot of the challenges in our industry around BI, but there's lots of things happening right now in our space, specifically AI.

412
00:34:14,635 --> 00:34:23,143
[Michael Helbling]: And one of the things we all wanna do is get analytics people to sort of think where we're headed with this stuff.

413
00:34:23,183 --> 00:34:29,128
[Michael Helbling]: And so maybe you can start to share some of your perspective on, okay, there's a lot of

414
00:34:29,108 --> 00:34:37,160
[Michael Helbling]: talk and hype around AI, but when the rubber hits the road, what's real and what's the hype cycle?

415
00:34:37,721 --> 00:34:41,326
[Colin Zima]: I think it's actually this conversation even magnified.

416
00:34:41,346 --> 00:34:47,635
[Colin Zima]: I remember when I joined Hotel Tonight, there was almost this expectation that was like, hey, you're a data scientist.

417
00:34:47,655 --> 00:34:48,596
[Colin Zima]: We don't have one of those.

418
00:34:49,418 --> 00:34:50,339
[Colin Zima]: Go make us some money.

419
00:34:52,041 --> 00:34:53,884
[Colin Zima]: Find us the insights.

420
00:34:53,864 --> 00:34:54,485
[Colin Zima]: Yeah, exactly.

421
00:34:54,526 --> 00:34:56,550
[Colin Zima]: It's just like, isn't that what data science is?

422
00:34:56,631 --> 00:35:01,342
[Colin Zima]: It's like, you take data and you make, and I feel like AI is actually that more magnified.

423
00:35:01,382 --> 00:35:06,474
[Colin Zima]: It's like, people are sort of like, you know, why can't I just hook this up to my database and have it optimize my business?

424
00:35:07,236 --> 00:35:09,662
[Colin Zima]: I'm pretty far along the side of like,

425
00:35:09,642 --> 00:35:12,565
[Colin Zima]: I think that that's what humans are for, is like interpretation.

426
00:35:13,166 --> 00:35:21,735
[Colin Zima]: And if you have a true optimization problem, it's because you've cleaned the data so well that you've created essentially an optimization problem, not actually a business problem.

427
00:35:22,696 --> 00:35:31,084
[Colin Zima]: I think at the same time, it is remarkable the things that can happen with so little human input that AI can do.

428
00:35:31,925 --> 00:35:34,468
[Colin Zima]: The examples I always cite are

429
00:35:34,448 --> 00:35:44,239
[Colin Zima]: Like you can go hand at your database and it'll go write you like a query with eight CTs and go spit out some sort of cohort analysis like something that would have taken a user hours to do.

430
00:35:44,819 --> 00:35:46,021
[Colin Zima]: And it can be right.

431
00:35:47,082 --> 00:35:53,509
[Colin Zima]: I think the flip side is that the control of what AI is doing I think is actually really important.

432
00:35:53,649 --> 00:35:57,333
[Colin Zima]: And the way the human fits in the loop I think is really, really important.

433
00:35:57,934 --> 00:36:03,780
[Colin Zima]: The examples I like to give for stuff like this are I think that if you look at the AI use cases that are

434
00:36:03,760 --> 00:36:07,284
[Colin Zima]: most well adopted now, it's writing and it's coding.

435
00:36:07,844 --> 00:36:11,188
[Colin Zima]: And the reason is because they are so heavily human in the loop.

436
00:36:11,669 --> 00:36:20,098
[Colin Zima]: Like if it writes a block of text, you don't just press enter and like get a picture of a block of text, you can touch it, you can feel it, like you can manipulate words.

437
00:36:20,759 --> 00:36:26,645
[Colin Zima]: Similarly with code, it's not like go build this app for me, though there's obviously stuff that is doing, trying to do things like that.

438
00:36:27,206 --> 00:36:33,092
[Colin Zima]: It's much more like I took a cut at this, you can pull in the things that are valuable and throw out the things that aren't.

439
00:36:33,629 --> 00:36:40,185
[Colin Zima]: When I see it applied to data, I think that there's a pretty hard fork of AI that will write SQL.

440
00:36:41,027 --> 00:36:50,710
[Colin Zima]: So truly like black box, the stuff that Anthropic and OpenAI are spending a lot of time working on, like I've heard they have a couple hundred engineers that are working on text to SQL.

441
00:36:50,690 --> 00:36:52,513
[Colin Zima]: or sending it through a semantic layer.

442
00:36:53,014 --> 00:36:57,400
[Colin Zima]: And again, this comes back to governed analytics, the classic BI concepts.

443
00:36:58,202 --> 00:37:00,385
[Colin Zima]: I think that people will have to make a decision.

444
00:37:00,445 --> 00:37:10,200
[Colin Zima]: Implicit is obviously, I think semantic layers are really, really important because I think if you cannot maintain control and tie it back to UI, it's going to be impossible for users.

445
00:37:11,061 --> 00:37:13,044
[Colin Zima]: I think there's still going to be a place for text to SQL.

446
00:37:13,585 --> 00:37:18,132
[Colin Zima]: If I do need to go write 200 lines of SQL, it can be really valuable.

447
00:37:18,112 --> 00:37:27,185
[Colin Zima]: But I think that the control level in manipulating SQL is just not high enough to address all of the users that we're talking about.

448
00:37:27,665 --> 00:37:32,091
[Colin Zima]: And so I think for it to apply well in data, it's going to need to attach to UI.

449
00:37:32,652 --> 00:37:47,072
[Colin Zima]: So what I mean by that is you're going to need to send it through some sort of semantics or some sort of intermediate layer that can let a user touch and feel the results, touch the filters, understand the subqueries, understand what the aggregations mean.

450
00:37:47,052 --> 00:37:55,110
[Colin Zima]: And what I would say is, I think when I do see it applied in those types of contexts, it's actually unbelievable how good it is.

451
00:37:55,150 --> 00:38:05,432
[Colin Zima]: We turn this on our own Salesforce data, and I routinely now, instead of building a dashboard that is like an opportunity lookup, I say,

452
00:38:05,412 --> 00:38:08,076
[Colin Zima]: Give me some information about opportunity XYZ.

453
00:38:09,058 --> 00:38:18,334
[Colin Zima]: It just picks out eight random fields or whatever, but it finds me who the sales rep is, who the sales engineer is, whether there's three opportunities, and it does it in four words.

454
00:38:19,436 --> 00:38:26,307
[Colin Zima]: That kind of stuff, while it feels like a very low bar, no one would call an opportunity record lookup data analytics.

455
00:38:26,287 --> 00:38:31,336
[Colin Zima]: I actually think those are the areas where end users are struggling the most very frequently.

456
00:38:32,157 --> 00:38:34,922
[Colin Zima]: Show me the Zendesk tickets for customer XYZ.

457
00:38:35,383 --> 00:38:36,465
[Colin Zima]: That's not data analytics.

458
00:38:36,685 --> 00:38:38,088
[Colin Zima]: It's data retrieval, basically.

459
00:38:38,108 --> 00:38:38,528
[Colin Zima]: It is.

460
00:38:38,829 --> 00:38:44,339
[Colin Zima]: But that is actually, I would argue, the most impactful version of data analysis.

461
00:38:44,359 --> 00:38:47,404
[Colin Zima]: What I know is the least sexy thing that anyone would ever say.

462
00:38:47,424 --> 00:38:50,910
[Colin Zima]: But looking up stuff is kind of hard.

463
00:38:50,890 --> 00:38:56,079
[Colin Zima]: And it's all sitting in the warehouse and it's all attached together and you need to be able to link it all up.

464
00:38:56,620 --> 00:39:03,052
[Colin Zima]: And I think AI is actually unbelievable for those types of problems because it can get things mostly right.

465
00:39:03,813 --> 00:39:09,844
[Colin Zima]: And then the human can say like, oh, actually I want these two fields or I want to tweak the filter or I want to change the sort.

466
00:39:09,824 --> 00:39:12,928
[Colin Zima]: And so I'm pretty excited about those things.

467
00:39:13,688 --> 00:39:18,214
[Colin Zima]: I think the sort of next layer is, can it ask the next question or sort of point you at follow-ups?

468
00:39:19,055 --> 00:39:20,436
[Colin Zima]: And I think it sort of can.

469
00:39:20,496 --> 00:39:22,779
[Colin Zima]: There's just always the danger that it does the trivial.

470
00:39:23,420 --> 00:39:24,641
[Colin Zima]: And like, I see this a lot.

471
00:39:24,661 --> 00:39:26,103
[Colin Zima]: It's like, oh, I can cut this by time.

472
00:39:26,123 --> 00:39:27,344
[Colin Zima]: I can cut this by region.

473
00:39:27,384 --> 00:39:36,034
[Tim Wilson]: It can cut it so many ways that it can, it's going to find anomalies, just like mathematically, it's going to, I can slice, if you had to slice it,

474
00:39:36,368 --> 00:39:39,573
[Tim Wilson]: every time and look for something that would slow you down.

475
00:39:39,853 --> 00:39:44,059
[Tim Wilson]: And if you found something you would have had, it would have had your logic behind it.

476
00:39:44,379 --> 00:39:49,206
[Tim Wilson]: If you just let the machine slice it a thousand ways, it's going to pop up like 20 things.

477
00:39:49,667 --> 00:39:51,670
[Tim Wilson]: And that's just statistics.

478
00:39:51,790 --> 00:39:53,012
[Tim Wilson]: Like that's, yeah.

479
00:39:53,032 --> 00:39:54,574
[Colin Zima]: We have 300 customers now.

480
00:39:54,854 --> 00:39:57,999
[Colin Zima]: Like I don't need it doing statistical analysis on our opportunities.

481
00:39:58,219 --> 00:40:03,186
[Colin Zima]: Like they're coming from just like the growth of our business and like the signal means nothing.

482
00:40:03,166 --> 00:40:07,012
[Moe Kiss]: You have such a wonderful perspective of this.

483
00:40:07,072 --> 00:40:22,635
[Moe Kiss]: I'm curious to understand how you're balancing it, though, because I've been looking at lots of BI tools and it feels like everyone is trying from a product perspective to sell the dream of just natural language questions with an interface.

484
00:40:22,655 --> 00:40:26,281
[Moe Kiss]: Anyone can ask anything and like, how are you balancing that?

485
00:40:26,421 --> 00:40:31,148
[Moe Kiss]: Because that's what is being sold to execs that we're going to be able to do this in months.

486
00:40:31,128 --> 00:40:35,374
[Colin Zima]: I mean, we do a little bit of it too, don't worry.

487
00:40:35,394 --> 00:40:41,823
[Colin Zima]: I think that I'm trying to encourage people to climb the slope more gently, which is not always the most appealing statement.

488
00:40:42,204 --> 00:40:53,099
[Colin Zima]: But again, going back to the Salesforce or Lookup example, I truly think that Lookups are the most killer use case for natural language right now, immediately.

489
00:40:53,119 --> 00:40:58,126
[Tim Wilson]: But is that genuinely what people are struggling to do without a simple, where is this data?

490
00:40:58,146 --> 00:41:01,050
[Tim Wilson]: It feels like the most solvable with traditional BI.

491
00:41:01,199 --> 00:41:02,941
[Michael Helbling]: I think more than you think actually.

492
00:41:02,961 --> 00:41:10,328
[Michael Helbling]: I see it a lot because a business user doesn't know how to address the underlying data in a way that'll get that for them.

493
00:41:10,989 --> 00:41:16,014
[Michael Helbling]: So like being able to just ask it and the AI kind of know like, okay, this table, this table, this table.

494
00:41:16,615 --> 00:41:19,818
[Michael Helbling]: It's sort of like a little bit of a analytics engineer in a box.

495
00:41:20,058 --> 00:41:27,966
[Michael Helbling]: So instead of prioritizing in a queue where I now need to wait a month until they can get to my report, if they ever get to it, I can just have it right now.

496
00:41:27,946 --> 00:41:28,307
[Colin Zima]: Yeah.

497
00:41:28,607 --> 00:41:40,624
[Colin Zima]: And the other example that I think that people, and again, this is going to sound trivial, but the idea of reverse value lookup, I can't tell you how many times I've walked into a tool where someone's like, I need to filter for customers in the US.

498
00:41:41,825 --> 00:41:49,236
[Colin Zima]: That's not a complex query, but to find US, you need to know whether that's region or country or geo.

499
00:41:50,137 --> 00:41:53,802
[Colin Zima]: And I know that sounds incredibly trivial and every tool should be able to do that.

500
00:41:53,782 --> 00:41:54,844
[Moe Kiss]: No, I hear you.

501
00:41:55,024 --> 00:41:56,246
[Moe Kiss]: I deeply hear you.

502
00:41:56,687 --> 00:41:59,232
[Moe Kiss]: And did they write US or United States?

503
00:41:59,372 --> 00:42:01,295
[Colin Zima]: Or USA, or yeah.

504
00:42:02,417 --> 00:42:09,349
[Colin Zima]: And I think those are, again, in terms of expectations, I know that's solvable.

505
00:42:09,971 --> 00:42:16,482
[Colin Zima]: And I think I see our sales reps literally using the product more because they can do stuff like this.

506
00:42:16,462 --> 00:42:24,278
[Colin Zima]: I think the follow on is like, if you get really good at this thing, like you unlock the next thing and the next thing and the next thing, I think it's less of like a

507
00:42:24,460 --> 00:42:28,005
[Colin Zima]: a regime change stepwise, like everyone's great at data now.

508
00:42:28,626 --> 00:42:40,123
[Colin Zima]: I think it's that people get a little bit more comfortable, like in the same way that instead of doing a lookup on Yahoo message boards, now you went to Google and you typed in search and like, you know, ThoughtSpots had this message for 15 years.

509
00:42:40,764 --> 00:42:50,518
[Colin Zima]: But I think in many ways, like that has become trivial for every BI tool to implement and will now become much more native in all products that we are using.

510
00:42:50,785 --> 00:42:55,453
[Tim Wilson]: This is kind of going back a little bit, but I would have loved to have you just riff a little bit.

511
00:42:55,493 --> 00:42:58,098
[Tim Wilson]: It's Microsoft, so we can say good or bad things are huge.

512
00:42:58,118 --> 00:42:58,679
[Tim Wilson]: They'll be fine.

513
00:42:58,699 --> 00:42:59,821
[Tim Wilson]: We're going to have no influence.

514
00:42:59,921 --> 00:43:09,418
[Tim Wilson]: But as you were like the Excel to power pivot, to power query, to power BI, DAX or no DAX in

515
00:43:09,398 --> 00:43:14,044
[Tim Wilson]: I know this is still bugging me from when you were saying it.

516
00:43:14,165 --> 00:43:27,323
[Tim Wilson]: Is it fair to say that was Microsoft's attempt to address that balancing act that you're basically there's like a Peter principle for the business user that they're going to progress

517
00:43:27,303 --> 00:43:40,160
[Tim Wilson]: Now, if they got to go to actually Excel to a pivot table to then power pivot to power query, that's a complicated and he hit the limitations in Power BI of which desktop versus window who has what access, what data can I pull into.

518
00:43:40,180 --> 00:43:47,910
[Tim Wilson]: So you wind up with all the other challenges we've been talking about, but was that kind of a viable and intentional approach by them?

519
00:43:49,207 --> 00:43:55,517
[Colin Zima]: I think they are the only company I cite that is trying to do what at least I say that we're trying to do.

520
00:43:55,537 --> 00:43:58,621
[Colin Zima]: I think they have tried to layer everything into one place.

521
00:43:59,803 --> 00:44:03,649
[Colin Zima]: You can layer in PowerPoint and presentations on top of that as well.

522
00:44:04,070 --> 00:44:13,324
[Colin Zima]: I think that because they have such a wide customer base and they're very good at producing product, they've realized that the semantic layer is different than the spreadsheet.

523
00:44:13,304 --> 00:44:15,429
[Colin Zima]: is different than the halfway point in between them.

524
00:44:16,210 --> 00:44:19,037
[Colin Zima]: And the products evolved probably a little bit more naturally.

525
00:44:19,558 --> 00:44:24,368
[Colin Zima]: So the way that they link together is maybe not as elegant as it would be if it was like centrally planned.

526
00:44:24,990 --> 00:44:28,698
[Colin Zima]: But I think the reason that all of those exist is because of these gradients.

527
00:44:29,159 --> 00:44:32,967
[Colin Zima]: Like DAX is really just a natural evolution of Excel.

528
00:44:32,947 --> 00:44:37,500
[Colin Zima]: attached to sequel, it's their intermediate language halfway between sequel and excel.

529
00:44:38,523 --> 00:44:41,551
[Colin Zima]: But like it does a thing that neither of them do.

530
00:44:41,591 --> 00:44:48,711
[Colin Zima]: And so like, yes, I think it's actually the best done version out there by a single player.

531
00:44:49,163 --> 00:44:56,177
[Tim Wilson]: If they had just stopped at Excel 2003 and then started building instead of getting all the bloat and Excel trying to make Excel do, I mean, actually that's interesting.

532
00:44:56,237 --> 00:44:59,523
[Tim Wilson]: Excel tried to do too much.

533
00:45:00,245 --> 00:45:03,892
[Tim Wilson]: Now all that legacy overhead is still there before they built up the other.

534
00:45:04,052 --> 00:45:05,014
[Tim Wilson]: Okay.

535
00:45:05,034 --> 00:45:05,896
[Tim Wilson]: Oh, okay.

536
00:45:05,916 --> 00:45:06,497
[Tim Wilson]: That's helpful.

537
00:45:07,422 --> 00:45:07,883
[Moe Kiss]: Yeah.

538
00:45:07,903 --> 00:45:18,816
[Moe Kiss]: I mean, that was actually going to be my next question was just on that topic is like, there's seen, there does seem to be more like strong newer players kind of in this space at the moment.

539
00:45:19,557 --> 00:45:25,404
[Tim Wilson]: And part of me is wondering like, they're also garbage charlatans who are AI hype monkeys.

540
00:45:25,424 --> 00:45:25,925
[Moe Kiss]: Totally.

541
00:45:25,945 --> 00:45:26,486
[Moe Kiss]: Totally.

542
00:45:26,526 --> 00:45:29,790
[Moe Kiss]: But that's always the case, right?

543
00:45:29,810 --> 00:45:32,333
[Moe Kiss]: For some, um, it,

544
00:45:32,735 --> 00:45:39,024
[Moe Kiss]: It does seem like there is like this not race, but like maybe a bit of a pushback from us.

545
00:45:39,044 --> 00:45:40,486
[Moe Kiss]: And I mean, I work in tech.

546
00:45:40,566 --> 00:45:50,540
[Moe Kiss]: So, you know, we have a very different perspective on tooling and what we're willing to try or like appetite to try new things, maybe then some more traditional businesses.

547
00:45:51,321 --> 00:45:56,248
[Moe Kiss]: But there does seem to be this thing of like some of the more legacy BI tooling, like a bit of pushback.

548
00:45:56,448 --> 00:46:01,155
[Moe Kiss]: And I just wonder, like, I feel like so many companies have tried some of those tools and it hasn't worked.

549
00:46:01,135 --> 00:46:07,926
[Moe Kiss]: We need to go to something that's a bit more new and innovative and thinking about this problem with a different perspective.

550
00:46:08,106 --> 00:46:10,630
[Moe Kiss]: I'm curious to see what you're seeing.

551
00:46:10,670 --> 00:46:17,862
[Moe Kiss]: Is that a fair observation or is it like, no, this is just the standard run of the mill and people are still choosing the legacy tools?

552
00:46:18,263 --> 00:46:19,505
[Colin Zima]: Yeah, I think there's two things at play.

553
00:46:19,765 --> 00:46:26,856
[Colin Zima]: So one is, I can't tell you how many people I talked to that are still using micro strategy, now strategy and business objects.

554
00:46:26,916 --> 00:46:28,177
[Tim Wilson]: They're just in it for the crypto.

555
00:46:28,197 --> 00:46:28,678
[Colin Zima]: Yeah.

556
00:46:28,698 --> 00:46:31,663
[Colin Zima]: Well, maybe they like Michael Saylor and his business strategies.

557
00:46:32,564 --> 00:46:38,452
[Colin Zima]: But like, I would argue that a lot of those are there purely because they literally just do what they need to do.

558
00:46:39,013 --> 00:46:40,896
[Colin Zima]: Like, to your point,

559
00:46:40,876 --> 00:46:42,459
[Colin Zima]: they could go transition off of them.

560
00:46:42,740 --> 00:46:56,186
[Colin Zima]: I think that over the infinity of time, they will all be deprecated, but maybe they do need to produce 12 spreadsheets a day and send them to S3 and let the team pick them up.

561
00:46:56,587 --> 00:46:58,611
[Colin Zima]: They are happy with that workflow and it exists.

562
00:46:59,172 --> 00:47:02,378
[Colin Zima]: I think the challenge is a lot of modern tools

563
00:47:02,358 --> 00:47:06,187
[Colin Zima]: didn't pick up the functionalities that business objects and micro strategy have.

564
00:47:06,769 --> 00:47:18,898
[Colin Zima]: So like a lot of the hardcore legacy modeled BI tools, I think actually still don't have like 2025 comps that can replace them, ourselves included, like we have work to do to do some of those things.

565
00:47:18,878 --> 00:47:20,401
[Colin Zima]: So I think that's one piece of it.

566
00:47:21,043 --> 00:47:34,393
[Colin Zima]: I would say the mediating factor on the other side is I'm also talking to a lot of like CIOs of big companies now that are looking to buy any AI tool under the sun to POC it because they've got to go buy some AI.

567
00:47:34,793 --> 00:47:35,976
[Colin Zima]: Because they have a mandate.

568
00:47:35,956 --> 00:47:41,602
[Colin Zima]: It's a weird dichotomy of, like, I've got MicroStrategy producing PDFs over here.

569
00:47:41,722 --> 00:47:49,431
[Colin Zima]: We talked to one customer that was like, I need to spit out 200 pages of PDFs of all the products that we sold yesterday, every day.

570
00:47:49,952 --> 00:47:51,133
[Colin Zima]: Like, I need your tool to do that.

571
00:47:51,253 --> 00:47:54,076
[Colin Zima]: And we were like, well, we don't do that yet, but we will do it.

572
00:47:54,577 --> 00:47:59,162
[Colin Zima]: But it's like they have business objects doing that, and they decided they need it.

573
00:47:59,142 --> 00:48:02,905
[Colin Zima]: But at the same time, they're like, also, we want to replace our whole front end with natural language.

574
00:48:03,726 --> 00:48:10,332
[Colin Zima]: And so you have this weird tension between the dream and the reality that people are trying to navigate.

575
00:48:10,793 --> 00:48:23,404
[Colin Zima]: And I think that's what's making it a kind of strange time, because you do have the YC two-person companies doing text to sequel that are able to go talk to Fortune 500 companies.

576
00:48:24,305 --> 00:48:27,968
[Colin Zima]: And then you've also got a 22-year-old business objects deployment.

577
00:48:28,201 --> 00:48:29,944
[Tim Wilson]: Moe made the comment a while back.

578
00:48:29,964 --> 00:48:35,011
[Tim Wilson]: I'm going to do another callback to mode and maybe the not the most attractive visuals.

579
00:48:35,031 --> 00:48:37,875
[Tim Wilson]: So broadening it, I'm not into beautiful.

580
00:48:38,056 --> 00:48:39,237
[Tim Wilson]: I'm into effective.

581
00:48:39,638 --> 00:48:42,442
[Tim Wilson]: But it's not about making the data pretty.

582
00:48:42,683 --> 00:48:44,826
[Moe Kiss]: Beautiful can be effective.

583
00:48:45,287 --> 00:48:46,969
[Moe Kiss]: Pretty means being understood.

584
00:48:47,084 --> 00:48:49,168
[Tim Wilson]: I have modules and classes I've taught on that front.

585
00:48:49,348 --> 00:49:04,435
[Tim Wilson]: That's another reason I think people have stayed with Excel, is Excel still seems to have more flexibility on the charts and every BI tool I've worked with, I can't get

586
00:49:04,415 --> 00:49:12,010
[Tim Wilson]: I can't shift the get another couple of pixels between the grid and the label on it.

587
00:49:12,050 --> 00:49:23,993
[Tim Wilson]: I'm deep down the RGG plot world, love that, which every tool was working in that, but that would be stupid because nobody would understand it because it's a nightmare to learn.

588
00:49:24,293 --> 00:49:36,850
[Tim Wilson]: So I put myself in the camp of the like super precious about the specifics of the visualization, the palette, the color, the font size, all the stuff to follow Steven Fuse and Edward Tufty's best practices.

589
00:49:38,212 --> 00:49:45,823
[Tim Wilson]: BI tools, I understand it because they're trying to serve all these masters and they have to plug in the limitations.

590
00:49:45,963 --> 00:49:51,651
[Tim Wilson]: Like how much is the front end visualization a,

591
00:49:51,631 --> 00:49:54,855
[Tim Wilson]: impossible, it has to be good enough.

592
00:49:55,956 --> 00:50:00,661
[Tim Wilson]: We're going to differentiate ourselves for a long time, Tableau, that was the way they were differentiating themselves.

593
00:50:01,302 --> 00:50:01,542
[Colin Zima]: Still.

594
00:50:02,363 --> 00:50:02,943
[Colin Zima]: Literally still.

595
00:50:02,963 --> 00:50:11,092
[Tim Wilson]: So where do you fall on that on the importance of saying, when I do a chart, I need to be able to pick it and control it.

596
00:50:11,272 --> 00:50:17,419
[Tim Wilson]: And if I want a bar with one series and a line with the other series and this on the right side, where does that fall?

597
00:50:17,990 --> 00:50:20,473
[Colin Zima]: I'd say I'm closer to the most side of the house on this one.

598
00:50:20,693 --> 00:50:23,436
[Colin Zima]: When it comes down to it, again, you have many masters here.

599
00:50:23,716 --> 00:50:31,164
[Colin Zima]: The example I always give is if you look at the chart of sort of dashboard consumption in an organization, it is just absurdly skewed.

600
00:50:31,485 --> 00:50:36,069
[Colin Zima]: Like the top three dashboards are 80% of the usage in the entire environment.

601
00:50:36,510 --> 00:50:43,137
[Colin Zima]: If that's the case, you should spend time on those three dashboards, making sure that they look and feel amazing.

602
00:50:43,117 --> 00:50:55,898
[Colin Zima]: Inversely, for the other 2000 dashboards that exist, have an average of two views apiece, you need to optimize for how fast it is to build them, how flexible they are, like how well they attach to different shapes of data.

603
00:50:56,599 --> 00:50:59,944
[Colin Zima]: And this again gets back to this really difficult challenge of like,

604
00:50:59,924 --> 00:51:06,113
[Colin Zima]: It needs to be both the fastest to build in the whole world, but it also needs to have the most extensibility.

605
00:51:06,553 --> 00:51:13,143
[Colin Zima]: So a thing that we did is you can build it in the UI, but you can also unlock the Vega spec and literally write Vega code.

606
00:51:13,743 --> 00:51:17,689
[Colin Zima]: And if I said that to a business user, they just glaze over.

607
00:51:17,709 --> 00:51:18,951
[Colin Zima]: They're like, I don't know what you're talking about.

608
00:51:19,451 --> 00:51:21,374
[Colin Zima]: But the point is it's not for that person.

609
00:51:21,775 --> 00:51:27,062
[Colin Zima]: It's for the one dashboard where you do need to move the title 15 pixels to the left.

610
00:51:27,042 --> 00:51:28,224
[Colin Zima]: being able to go do that.

611
00:51:28,785 --> 00:51:31,089
[Colin Zima]: And again, like it's so hard to do both.

612
00:51:31,710 --> 00:51:35,617
[Colin Zima]: And the reason Excel can do so much is like, frankly, they've just built these features over 40 years.

613
00:51:36,418 --> 00:51:37,620
[Colin Zima]: And it's really hard.

614
00:51:37,800 --> 00:51:42,368
[Colin Zima]: So like, I like to say that we want to be better than every tool at everything, except Tableau.

615
00:51:42,428 --> 00:51:50,502
[Colin Zima]: We want to be almost as good at them as visualization, which is like, it's kind of sad, but it's just like, it's hard.

616
00:51:50,633 --> 00:51:58,745
[Tim Wilson]: And just to clarify, and I think that can probably go back over a decade where I think most sister and I had a shared presentation.

617
00:51:58,765 --> 00:52:08,940
[Tim Wilson]: Just to clarify, I think the visualizations that I produce are beautiful, but the big point making when teaching analysts who say, what do you need to make it pretty?

618
00:52:08,960 --> 00:52:11,123
[Tim Wilson]: There is a tendency to say,

619
00:52:11,103 --> 00:52:16,989
[Tim Wilson]: I'll do this crap like dropping shadow or adding more color or doing all the stuff that's additive and terrible.

620
00:52:17,329 --> 00:52:20,252
[Tim Wilson]: So I say it's not about being pretty.

621
00:52:20,352 --> 00:52:21,534
[Tim Wilson]: It's about being understood.

622
00:52:21,594 --> 00:52:28,681
[Tim Wilson]: Now, the fact is if you nail, they being understood, it is a more, it is a, it has a, it's a lower cognitive load.

623
00:52:29,021 --> 00:52:32,585
[Tim Wilson]: People think that it looks good, but they don't start off by trying to make it.

624
00:52:32,605 --> 00:52:35,728
[Tim Wilson]: So I just want to clarify when you said you came down on the side of Moee.

625
00:52:36,328 --> 00:52:39,912
[Tim Wilson]: I, there are many analysts who listened to this, who I have worked with who were like,

626
00:52:39,892 --> 00:52:44,598
[Tim Wilson]: Tim is a fucking stickler about making the stuff look effective.

627
00:52:44,778 --> 00:52:52,327
[Colin Zima]: So I mean, I tried to I tried to like full band pie charts and word clouds at Looker for a while, like fully resist them.

628
00:52:53,168 --> 00:52:56,632
[Colin Zima]: And like I still really don't think that you should use them.

629
00:52:56,973 --> 00:53:03,621
[Colin Zima]: But like we built both immediately at Omni because like what I realized is just like

630
00:53:03,601 --> 00:53:06,067
[Colin Zima]: You need to pick your battles with your users.

631
00:53:06,568 --> 00:53:13,927
[Colin Zima]: And if the person wants to build a word cloud, like I do, I can't convince them every time that it's not a good visualization.

632
00:53:13,947 --> 00:53:17,897
[Colin Zima]: I just need to be like, here's your word cloud, like good luck.

633
00:53:17,995 --> 00:53:27,306
[Tim Wilson]: You should have the requirement that says if you put more than like four categories in your pie chart, it pops up an alert like buried in the ggplot documentation.

634
00:53:27,326 --> 00:53:27,887
[Moe Kiss]: Don't do this.

635
00:53:27,907 --> 00:53:39,701
[Tim Wilson]: To do a pie chart in ggplot, you have to use this like change the coordinate system to pull in the help documentation that it basically says, this is generally a bad idea.

636
00:53:39,801 --> 00:53:43,625
[Tim Wilson]: Like we get that you're trying to make, you were probably trying to do something that's a bad idea.

637
00:53:43,846 --> 00:53:46,669
[Tim Wilson]: So that would be a killer feature for a BI platform.

638
00:53:46,649 --> 00:53:48,571
[Michael Helbling]: You just pop up a nuke, form a clippy.

639
00:53:48,631 --> 00:53:50,673
[Michael Helbling]: Looks like you're trying to commit a truck crime.

640
00:53:52,394 --> 00:53:53,315
[Michael Helbling]: You want some help with that?

641
00:53:54,176 --> 00:54:00,302
[Michael Helbling]: Okay, so I think we've nailed the solution here, which is... We haven't even talked about APIs.

642
00:54:00,462 --> 00:54:01,643
[Moe Kiss]: I mean, there is so much more.

643
00:54:01,763 --> 00:54:02,084
[Moe Kiss]: Anyway.

644
00:54:02,764 --> 00:54:06,107
[Michael Helbling]: Well, we're over time, though.

645
00:54:06,828 --> 00:54:14,175
[Michael Helbling]: Persona-based quizzes, when people onboard to the new tool to determine what features they get,

646
00:54:14,155 --> 00:54:16,838
[Michael Helbling]: It's like, oh, you can't answer these questions.

647
00:54:17,118 --> 00:54:18,600
[Michael Helbling]: OK, you get the static charts.

648
00:54:19,201 --> 00:54:20,322
[Michael Helbling]: Oh, you know a little bit about this?

649
00:54:20,362 --> 00:54:21,424
[Michael Helbling]: You get some notebooks.

650
00:54:21,824 --> 00:54:23,106
[Michael Helbling]: I don't hate it, actually.

651
00:54:23,146 --> 00:54:25,288
[Michael Helbling]: Like, the philosophy of that is correct, I think.

652
00:54:25,789 --> 00:54:32,236
[Michael Helbling]: Yeah, because then you can say, like, hey, answer these questions, and then we'll get you into the right part of the tool for you.

653
00:54:32,256 --> 00:54:40,486
[Tim Wilson]: What's the persistent avenue when they butt up against the limit and they have a need and they've gotten to that point, there's an avenue for them to say, can you move me to the next level?

654
00:54:40,546 --> 00:54:41,948
[Tim Wilson]: I think that, I like it.

655
00:54:41,968 --> 00:54:42,148
[Tim Wilson]: Yeah.

656
00:54:42,268 --> 00:54:44,010
[Tim Wilson]: It's a killer feature right there.

657
00:54:43,990 --> 00:54:46,333
[Michael Helbling]: And you get a gold star for the day.

658
00:54:46,893 --> 00:54:49,176
[Michael Helbling]: Okay, but we do, we have to start to wrap up.

659
00:54:50,738 --> 00:54:58,347
[Michael Helbling]: Obviously, Colin, thank you for managing to get a word and edgewise around all of us.

660
00:54:58,948 --> 00:55:02,552
[Michael Helbling]: Cause like Moe has strong opinions, loosely held.

661
00:55:02,812 --> 00:55:04,935
[Michael Helbling]: Tim has strong opinions, strongly held.

662
00:55:04,915 --> 00:55:11,185
[Michael Helbling]: So great conversation and really illuminating, really appreciate your perspective.

663
00:55:11,225 --> 00:55:15,331
[Michael Helbling]: One thing we love to do, go around the horn and share a last call, something you might find of interest.

664
00:55:15,772 --> 00:55:17,294
[Michael Helbling]: So you're our guest, Colin.

665
00:55:17,394 --> 00:55:18,215
[Michael Helbling]: Do you have a last call?

666
00:55:18,496 --> 00:55:19,016
[Michael Helbling]: You'd like to share?

667
00:55:19,036 --> 00:55:20,058
[Colin Zima]: I have two quickies.

668
00:55:20,559 --> 00:55:22,321
[Colin Zima]: One, I'm sort of embarrassed to say this.

669
00:55:22,682 --> 00:55:24,224
[Colin Zima]: The games on LinkedIn are pretty fun.

670
00:55:24,264 --> 00:55:27,349
[Colin Zima]: They're like 30 seconds and you should play them every day.

671
00:55:27,369 --> 00:55:28,611
[Colin Zima]: What?

672
00:55:29,182 --> 00:55:30,766
[Tim Wilson]: I went for three or four days.

673
00:55:30,846 --> 00:55:33,212
[Tim Wilson]: I went down that I said, I need to step away.

674
00:55:33,252 --> 00:55:34,134
[Tim Wilson]: I've already got.

675
00:55:34,154 --> 00:55:36,299
[Colin Zima]: You can see your leaderboard against like other people.

676
00:55:36,460 --> 00:55:37,302
[Colin Zima]: It's, it's a lot of fun.

677
00:55:37,442 --> 00:55:39,928
[Moe Kiss]: I go on LinkedIn like once every six months.

678
00:55:39,948 --> 00:55:42,234
[Moe Kiss]: Like I don't want to, I don't want to open the trap.

679
00:55:42,254 --> 00:55:43,357
[Colin Zima]: This was their viral hook.

680
00:55:43,698 --> 00:55:45,081
[Colin Zima]: Anyway, their games were fun.

681
00:55:45,061 --> 00:55:51,752
[Colin Zima]: The second one, and this is like my little trick, turn off images by default in your email.

682
00:55:52,493 --> 00:55:55,178
[Colin Zima]: And the reason you do it is because it blocks all the pixel tracking.

683
00:55:55,698 --> 00:56:00,386
[Colin Zima]: And you can decide whether you want to turn images on, which is effectively alerting people that you've opened their email.

684
00:56:00,727 --> 00:56:04,012
[Colin Zima]: But otherwise, it by default blocks all the pixel tracking.

685
00:56:04,352 --> 00:56:07,137
[Colin Zima]: And so no one can actually track whether you've opened their emails or not.

686
00:56:07,167 --> 00:56:27,631
[Tim Wilson]: So when you have over those 300 clients who has their email dashboard, and some jackass is looking at click to open rate, and the poor analyst has been saying, I've been trying, I mean, there are a million reasons that, I mean, that pixel is the most imperfect thing anyway, and it is treated as sacred.

687
00:56:27,651 --> 00:56:32,297
[Tim Wilson]: So that is, you just tried to make

688
00:56:33,020 --> 00:56:36,325
[Tim Wilson]: I mean, it's a horrible metric to be looking at anyway.

689
00:56:36,406 --> 00:56:39,491
[Tim Wilson]: So I like the, you know, mess around with mess with them a bit more.

690
00:56:39,591 --> 00:56:41,454
[Tim Wilson]: So I endorse that.

691
00:56:42,215 --> 00:56:42,676
[Michael Helbling]: All right.

692
00:56:43,237 --> 00:56:44,018
[Michael Helbling]: Moe, what about you?

693
00:56:44,058 --> 00:56:45,080
[Michael Helbling]: What's your last call?

694
00:56:45,260 --> 00:56:49,647
[Moe Kiss]: Okay, guys, you know how I go through this phase and I like get really into something.

695
00:56:49,767 --> 00:56:52,632
[Moe Kiss]: And then I like read everything on that topic and like,

696
00:56:53,489 --> 00:56:55,192
[Moe Kiss]: I've reached a new one.

697
00:56:55,393 --> 00:57:00,042
[Moe Kiss]: This is similar to the Why We Sleep book, where I'm going to be talking about this for the next 18 months.

698
00:57:00,062 --> 00:57:01,365
[Moe Kiss]: So prepare yourself, friends.

699
00:57:02,247 --> 00:57:09,081
[Moe Kiss]: I finished Careless People, a story of where I used to work, and the author, Sarah Wynne Williams, about her time.

700
00:57:09,101 --> 00:57:12,207
[Moe Kiss]: It was called Facebook when she joined.

701
00:57:12,406 --> 00:57:14,049
[Moe Kiss]: Holy shit, man.

702
00:57:14,209 --> 00:57:19,878
[Moe Kiss]: I actually wasn't going to read it because I was like, I don't want to read a worky thing.

703
00:57:19,918 --> 00:57:20,639
[Moe Kiss]: I need a bit of space.

704
00:57:20,659 --> 00:57:22,482
[Moe Kiss]: And someone's like, oh, I actually think you should read it.

705
00:57:22,502 --> 00:57:23,384
[Moe Kiss]: It might be good for you.

706
00:57:24,245 --> 00:57:29,153
[Moe Kiss]: And I've discussed the book genuinely with so many people.

707
00:57:30,035 --> 00:57:36,405
[Moe Kiss]: But it's kind of just reaffirmed for me how much my own values

708
00:57:36,385 --> 00:57:39,809
[Moe Kiss]: are important to me and my job and like, I don't know.

709
00:57:39,869 --> 00:57:48,539
[Moe Kiss]: It's just, it's got me thinking a lot about like the kind of places I want to work and the kind of people I want to work with and also like, I mean, she just dropped some great tea.

710
00:57:48,599 --> 00:57:49,881
[Moe Kiss]: Like it is a good time.

711
00:57:51,082 --> 00:57:52,003
[Moe Kiss]: Careless people.

712
00:57:52,183 --> 00:57:54,126
[Moe Kiss]: Ah, okay.

713
00:57:54,146 --> 00:57:55,207
[Moe Kiss]: Yeah.

714
00:57:55,367 --> 00:58:00,553
[Moe Kiss]: So she worked at Facebook very, very early on with Zuck and Sheryl Sandberg and like,

715
00:58:01,242 --> 00:58:02,925
[Moe Kiss]: There are some great anecdotes in there.

716
00:58:03,586 --> 00:58:05,690
[Moe Kiss]: There's apparently a word for it.

717
00:58:05,730 --> 00:58:10,159
[Moe Kiss]: The morning you get when you finish a really good book, I had that for days.

718
00:58:10,279 --> 00:58:11,020
[Moe Kiss]: I was so upset.

719
00:58:11,601 --> 00:58:12,864
[Moe Kiss]: But I was straight on to the next one.

720
00:58:12,964 --> 00:58:19,737
[Moe Kiss]: And so the summary is I am deep in my reading books that there's lots of tea about tech companies.

721
00:58:20,238 --> 00:58:23,183
[Moe Kiss]: So I'll show the next one on the next episode.

722
00:58:23,635 --> 00:58:24,136
[Michael Helbling]: Nice.

723
00:58:24,997 --> 00:58:26,180
[Michael Helbling]: All right, Tim, what about you?

724
00:58:26,220 --> 00:58:28,704
[Tim Wilson]: Well, I'm going to go with the book as well.

725
00:58:29,185 --> 00:58:30,127
[Tim Wilson]: And this is not a book.

726
00:58:30,167 --> 00:58:34,054
[Tim Wilson]: I have not finished reading it, but I've enjoyed it so far.

727
00:58:34,094 --> 00:58:34,755
[Tim Wilson]: It's pretty random.

728
00:58:34,775 --> 00:58:39,544
[Tim Wilson]: It's called Once Upon a Prime, The Wondrous Connections Between Mathematics and Literature.

729
00:58:39,584 --> 00:58:42,088
[Tim Wilson]: So it's a mathematician.

730
00:58:42,128 --> 00:58:43,811
[Tim Wilson]: And you're like a whole book.

731
00:58:44,172 --> 00:58:46,977
[Tim Wilson]: She's literally, it goes down

732
00:58:46,957 --> 00:58:50,284
[Tim Wilson]: Tristan Shandy, a gentleman in Moescow, metamorphosis.

733
00:58:51,666 --> 00:58:54,211
[Tim Wilson]: Some of them are like experimental literature.

734
00:58:54,271 --> 00:59:08,880
[Tim Wilson]: She talks about easy things like haikus and there are other versions of haikus, but it is this deep exploration by somebody who loves reading and is a professional mathematician.

735
00:59:08,860 --> 00:59:19,773
[Tim Wilson]: It's not particularly useful for anything other than she feels like the math and the arts have gotten too far apart, and she's trying to bring them together.

736
00:59:19,913 --> 00:59:27,443
[Tim Wilson]: But it's just got the whole concept of experimental literature that is math-based, and she's got multiple examples of that.

737
00:59:27,483 --> 00:59:31,948
[Tim Wilson]: So it's just kind of an odd, but interesting read.

738
00:59:33,599 --> 00:59:34,861
[Tim Wilson]: What about you, Michael?

739
00:59:35,302 --> 00:59:36,263
[Tim Wilson]: What's your last call?

740
00:59:36,383 --> 00:59:37,725
[Michael Helbling]: Well, I'm glad you asked.

741
00:59:38,286 --> 00:59:48,501
[Michael Helbling]: So MIT recently did a study of the cognitive debt when using AI assistance for essay writing.

742
00:59:48,802 --> 00:59:49,182
[Tim Wilson]: Oh, dear.

743
00:59:49,242 --> 00:59:51,726
[Tim Wilson]: Have you read Cassie's teardown of this thing?

744
00:59:52,111 --> 00:59:52,712
[Michael Helbling]: No, I haven't.

745
00:59:52,833 --> 00:59:54,075
[Michael Helbling]: So I'll read that next.

746
00:59:54,216 --> 01:00:02,534
[Michael Helbling]: But I did read the study, and it's not everything that the media folks are making it out to be like, if you use AI, you're going to get dumber.

747
01:00:03,156 --> 01:00:06,103
[Michael Helbling]: But there are some interesting conclusions that they come to.

748
01:00:06,143 --> 01:00:11,655
[Michael Helbling]: And it's, I think, worth a look at what they're saying, because I think

749
01:00:11,635 --> 01:00:37,413
[Michael Helbling]: It goes even to something Colin, you said earlier, which is sort of like, if you just accept the response that the data gives you that it's not ready the same with an AI, if you just accept the response without critically analyzing what's going on or that person in the middle, you run the risk of maybe giving up just a little bit of your intellectual critical abilities and Lord knows we need those.

750
01:00:37,673 --> 01:00:40,157
[Michael Helbling]: So just be careful out there.

751
01:00:40,357 --> 01:00:43,452
[Tim Wilson]: I'm just saying, Michael, I've already, it is 90% drafted.

752
01:00:43,533 --> 01:00:49,000
[Tim Wilson]: I referenced that study in a post that will be, will have long been out by the time we,

753
01:00:49,621 --> 01:00:51,384
[Michael Helbling]: We'll add that to the show notes then.

754
01:00:51,744 --> 01:00:52,125
[Michael Helbling]: Perfect.

755
01:00:52,826 --> 01:00:53,226
[Michael Helbling]: All right.

756
01:00:53,387 --> 01:01:00,197
[Michael Helbling]: Well, I'm sure as you've been listening, you've been having your own thoughts about this topic and we would love to hear that.

757
01:01:00,657 --> 01:01:02,059
[Michael Helbling]: Feel free to reach out to us.

758
01:01:02,080 --> 01:01:03,401
[Michael Helbling]: There's some great ways to do that.

759
01:01:03,662 --> 01:01:13,857
[Michael Helbling]: Obviously, you can get to us on LinkedIn or on the Measure Slack chat group or via email at contact at analyticshour.io.

760
01:01:13,837 --> 01:01:15,319
[Michael Helbling]: So please reach out.

761
01:01:15,520 --> 01:01:18,845
[Michael Helbling]: Colin, once again, thank you so much for coming on the show.

762
01:01:18,905 --> 01:01:21,790
[Michael Helbling]: Appreciate you taking the time to do that and share some of your insight.

763
01:01:22,270 --> 01:01:33,969
[Michael Helbling]: And as you go through this and you're listening and if you like what you're hearing on the show, please feel free to drop a rating, a review on whatever platform you listen to podcasts on.

764
01:01:34,610 --> 01:01:35,792
[Michael Helbling]: That helps us out quite a bit.

765
01:01:36,293 --> 01:01:37,615
[Michael Helbling]: See, I'm still not ready for this.

766
01:01:37,715 --> 01:01:40,840
[Michael Helbling]: Like I still want to thank Josh, but you know,

767
01:01:41,107 --> 01:01:42,449
[Tim Wilson]: He did a lot.

768
01:01:42,669 --> 01:01:43,511
[Tim Wilson]: You can thank him again.

769
01:01:43,871 --> 01:01:48,238
[Michael Helbling]: Hey, thanks, Josh, for everything you have done in the past.

770
01:01:49,260 --> 01:01:58,795
[Michael Helbling]: So anyways, but I know that no matter what BI tool you use, I think I can speak for both of my co-hosts, Moe and Tim, when I say keep analyzing.

771
01:02:01,593 --> 01:02:14,772
[Announcer]: Let's keep the conversation going with your comments, suggestions, and questions on Twitter at @analyticshour on the web at analyticshour.io, our LinkedIn group, and the Measure Chat Slack group.

772
01:02:14,792 --> 01:02:17,176
[Announcer]: Music for the podcast by Josh Grohurst.

773
01:02:18,217 --> 01:02:22,664
[Announcer]: So smart guys wanted to fit in, so they made up a term called analytics.

774
01:02:22,684 --> 01:02:24,006
[Charles Barkley]: Analytics don't work.

775
01:02:24,897 --> 01:02:27,640
[Charles Barkley]: Do the analytics say go for it, no matter who's going for it?

776
01:02:28,001 --> 01:02:31,145
[Charles Barkley]: So if you and I were on the field, the analytics say go for it.

777
01:02:31,165 --> 01:02:37,132
[Charles Barkley]: It's the stupidest, laziest, lamest thing I've ever heard for reasoning in competition.

778
01:02:38,553 --> 01:02:45,922
[Michael Helbling]: I think they might be going direct to video at this point or something, but yeah, I thought that was a very bold analogy and I was, I was there for it, you know.

779
01:02:47,544 --> 01:02:51,529
[Michael Helbling]: One night a year you get to kill any dashboard you want.

780
01:02:54,817 --> 01:02:55,979
[Colin Zima]: Hey, you're a data scientist.

781
01:02:55,999 --> 01:02:57,221
[Colin Zima]: We don't have one of those.

782
01:02:57,742 --> 01:02:58,663
[Colin Zima]: Go make us some money.

783
01:03:00,566 --> 01:03:02,730
[Colin Zima]: And I feel like... Find us the insights.

784
01:03:03,070 --> 01:03:03,631
[Colin Zima]: Yeah, exactly.

785
01:03:03,651 --> 01:03:08,439
[Moe Kiss]: That's what is being sold to execs that we're going to be able to do this in months.

786
01:03:08,659 --> 01:03:12,606
[Colin Zima]: I mean, we do a little bit of it too, don't worry.

787
01:03:12,626 --> 01:03:16,131
[Tim Wilson]: There are also garbage charlatans who are AI hype monkeys.

788
01:03:16,372 --> 01:03:17,433
[Moe Kiss]: Totally, totally.

789
01:03:17,473 --> 01:03:18,796
[Moe Kiss]: But that's always the case.

790
01:03:20,739 --> 01:03:21,600
[Moe Kiss]: For some.

791
01:03:22,846 --> 01:03:26,596
[Michael Helbling]: All right, I'm gonna mute Tim in motion, just the second call, just you and me.

792
01:03:27,859 --> 01:03:29,102
[Michael Helbling]: No, I'm just kidding.

793
01:03:29,925 --> 01:03:32,090
[Moe Kiss]: I feel like I'm a bit of an asshole.

794
01:03:32,331 --> 01:03:34,837
[Moe Kiss]: I'm like, this could go wrong.

795
01:03:34,898 --> 01:03:35,860
[Moe Kiss]: You're a snowflake.

796
01:03:36,481 --> 01:03:53,157
[Michael Helbling]: Obviously, Colin, thank you for managing to get a word in edgewise around all of us, because Moe has strong opinions loosely held, Tim has strong opinions strongly held.

797
01:03:53,626 --> 01:04:14,082
[Tim Wilson]: So when you have over those 300 clients who has their email dashboard, and some jackass is looking at click to open rate, and the poor analyst has been saying, I've been trying, I mean, there are a million reasons that, I mean, that pixel is the most imperfect thing anyway, and it is treated as sacred.

798
01:04:14,102 --> 01:04:18,790
[Tim Wilson]: So that is, you just tried to make

799
01:04:19,462 --> 01:04:22,827
[Tim Wilson]: I mean, it's a horrible metric to be looking at anyway.

800
01:04:22,847 --> 01:04:25,932
[Tim Wilson]: So I like the, you know, mess around with mess with them a bit more.

801
01:04:26,013 --> 01:04:38,112
[Tim Wilson]: So I endorse that.