Episode Transcript
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(00:05):
Welcome to the Analytics Power Hour. Analytics topics covered conversationally
and sometimes with explicit language. Hey everybody, welcome. It's the Analytics
Power Hour, and this is episode 262. Hey, Happy New Year.
2025, that'll probably be the year of, well, what exactly?
(00:26):
There is a pretty steady flow of prognostications every year about the things
that will define the coming year, and we're not
completely immune to desire to define the future.
I didn't say that very clearly, but we do wanna define the future.
So what will 2025 bring? It's probably the year of Tim Wilson still
(00:47):
being frustrated with people calling stuff the year of. That's... Yeah,
yeah, yeah. That's fair. Probably accurate. Yeah. You could try in with
Tim being frustrated with people. You don't really need to say...
Oh, uh, there you go. Further qualifiers. Wow. Not necessary. We still like
you. And 2025 probably be the year of Moe still liking Adam Grant
(01:08):
and Brene Brown. Hey, Moe. Yeah, probably, actually. That's a very good
prediction. Oh, there's gonna be a huge scandal with one of them between
like recording and that coming out. Oh, geez. It's gonna be... Yeah. That's...
Alright. Well, and I'm Michael Helbling. Well, some attempts at categorizing
the future that is coming at us awfully fast is definitely warranted.
(01:30):
So what better time than the first episode of 2025?
Insert Zager and Evan's pun here. And to do this right,
we wanted to have a guest who has a great track record of
observing our industry and seeing where the puck is going. Barr Moses is
the co founder and CEO of Monte Carlo, the data reliability company.
As part of her role as CEO, she works closely with data leaders
(01:52):
at some of the foremost AI driven organizations like Pepsi, Roche, Fox,
American Airlines, hundreds more. She's a member of the Forbes Technology
Council and is a returning guest to the show. Welcome back,
Barr. Thank you so much. I am honored and pleased to be a
returning member. No, we're serious. We love the way that you take such
(02:13):
an interest in really having, from your level, a real good clear view
of where our industry is and the data industry is going. Before we
get started, let's just get a recap of what's going on with
you and Monte Carlo. Yeah. It's been a whirlwind couple of years for...
(02:33):
Not only for Monte Carlo, but I'd say for the entire data industry.
I'm just reflecting last time I was here, this was 2021.
Is just kinda coming out of COVID, I think, we were all like getting
comfortable behind the camera and feeling comfortable at home. And the world
is obviously very different today, but maybe just to kind of give a
(02:55):
quick recap. Monte Carlo was founded to solve the problem of what we
call data downtime. Periods of time when data is wrong or inaccurate.
And five, 10 years ago, that actually didn't seem important at all.
I think people spend some time thinking about quality of data and you
guys know this better than I do, but it probably didn't get the
(03:16):
diligence that it deserved back then. You could kind of like skirt around
the issue, could probably... It was very common at the time to just
have like extra eyes on the data to make sure that a report
is accurate. And if it was wrong, you'd kind of be like,
ah, shucks, so sorry. And kind of like move on.
I also, but sorry to interrupt, but I also think it maybe wasn't
as complex and so like as complexity has grown, that the ability to
(03:43):
troubleshoot and dig into the why it's not reliable is even harder.
But sorry to break your stride there. Not at all. No,
I think that's spot on. And maybe just to unpack that a little
bit, I think it was less complex because one, the use cases were
limited. So today, we call it data products and very fancy names for...
(04:03):
But the use case was maybe just revenue reporting to the street.
And the... So the use cases were fewer,
the timelines were fewer. So you maybe used data like once a quarter
to report the numbers. And also there were fewer people working with data.
So maybe it's like a couple of analysts under the finance team.
And so you really had a lot more time, less use cases,
(04:25):
less complexity in which... And the stakes were lower.
And so in all of those instances, like it kind of didn't really
matter if the data was accurate or not. And then there was this
big wave of actually people starting to use data. Remember when people would
say, oh, we're data driven, and you kind of like didn't really believe
them. That whole thing... There was a period back in time. It's still happening.
(04:48):
Yeah. Still happening. Totally agree with you. So I think there was this
big push, and that's sort of when Monte Carlo created the category of
data observability, which is basically allowing people creating data products,
whether those are data engineers, data analysts, data scientists, anyone
working with data to make sure that they are actually using trusted,
(05:10):
reliable data for that. And sort of kinda like helping when someone's looking
at the data and like, what WTF the data here looks wrong.
Helping those people come answer the question of what's wrong and why.
That was sort of kinda like the reason how Monte Carlo was born.
Now fast forward today, I can't believe it's almost 2025. It's like four
years since. I like to say that I think the data industry a
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little bit like Taylor Swift, we kind of like reinvent ourselves every year.
We need to do like an Eras tour and kind of like go
through all of the periods of time of the data industry.
And I think the most recent era being swept by generative AI, the
implication of that means that bad data is
even worse for organizations. And we kind of unpack what that means. But
(05:57):
at a very high level, what Monte Carlo does is help organizations,
enterprises, make sure that the data that they're using to power their pipelines,
power their dashboards, power their generative AI applications is actually
trusted and reliable. And we do that by
first and foremost, knowing when there's something wrong.
(06:18):
Knowing if the data is late or inaccurate, but then also being able
to answer the question of why is it wrong
and how do I actually resolve an issue? I'll sort of pause there
instead of a long answer and a lot more that we can go
into. But, whew, it's been a fun couple of years. Nice. Well, but also,
I mean, one, I guess just to clarify, we're not saying that in
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2021 people weren't using data. I mean, that's been a... That's been ramping
up for a while. I think also the modern data stack,
I'm not sure where that phrase was in the
inflated expectations versus... It definitely, I feel like since the last
time you were on the modern data stack as a phrase has slid
(07:01):
into the trough of disillusionment at least a little bit, which is
kind of interesting. I don't know exactly how that applies to kind of where
we're going from here, but I feel like there was a point where
it was like, if we just have all these modules plugged in together
with the right layers on top of them, then like all will be
(07:23):
good. And it feels like we're a little
past that, that that nirvana, even if we got there, wouldn't actually necessarily
yield the results that were being promised. But...
Yeah. I mean, I think, look, putting myself in sort of the shoes
of data leaders today, you're facing a really tough reality because like
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every 12 to 18 months you're being thrown at with sort of a
new concept. Call it modern data platform, call it generative AI, call it
whatever you want you're sort of expected to be on top of your
game and sort of understand the words or trend du jour. But I
think if you sort of unpeel that for a second and go back
to fundamentals, there are a couple of things that I think remain true
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regardless and have remained true for the last 10, 15 years,
which is first and foremost, like organizations want to use data,
and data is a competitive advantage. How you use it and in what
ways, like, I think that is undisputable. Like strong companies have strong
data practices and use that to their advantage. You can talk about how,
for example, you can use it for better decision making internally.
(08:28):
That was sort of one of the dominant use cases in the beginning.
You can use it to build better data products. Like for example,
you can have a better pricing algorithm. And I think today,
you can talk more about this, but I think data is the moat for generative
AI product innovative solutions. And so regardless of where the hype cycle
is, I think one core truth is that data matters to organizations.
(08:51):
What we do matters. And so data continues to be a core part
for organizations. I think the second sort of fundamental truth that we
believe in is like reliable data matters. Like the data's worthless if you're
working with... Completely. Yeah. This even goes without saying, but like
having something that you can trust in is sort of fundamental to your
(09:11):
ability to deliver it. And then I think the third thing that's sort
of always remained true is like innovation matters. Like you have to be
at the forefront. And so organizations that are doing nothing about generative
AI or doing nothing to kind of learn what's next will be in
a difficult position. I'm curious for your takes about that the modern data
platform in particular. I think one of the
(09:33):
benefits of that was that data leaders were met with many solutions for
many problems, but actually were inundated with perhaps too many solutions.
And so ended up in a position where they had to
make bets on a variety of solutions and ended up with maybe sort
of a proliferation of tools. And now, there's a big movement to actually
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consolidate that or cut back to what's necessary. And so
if you're not solving a core fundamental truth, then you probably don't
deserve to live in the modern data stack, if that makes sense.
You don't deserve to live in the modern data stack. I'm sorry. That was.
I so deeply love when the podcast intersects with things that are like completely
(10:20):
churning through my brain at the moment. And it is like this beautiful,
like chef kiss because these are all kind of concepts that I've been
giving a lot of thought to over the break.
I wanna dig into what you... You mentioned data can be a moat. Can
you say more about that? Especially you said, I think relative to GenAI.
(10:42):
Yeah, for sure. I'm happy to. So I think what's happened to,
let's sort of think about like the last, I wanna call a year
or two in generative AI. I'll actually start by sharing
a survey that we did that I thought was really, really funny.
We basically interviewed a couple hundred data leaders and asked them
(11:06):
what percentage of data leaders are building with generative AI. Can you
guess what percentage of data leaders? Oh. Probably
all of them are saying that they are at least. Yeah. Yeah. Really?
It's... Yeah. So like, I think like 97% said it. Like not a
single person... Stop it. Yeah. That's... You're just spot on, Michael.
(11:30):
Oh no. We're all doing it for sure. We're all doing it.
We're all doing it. Everyone... 2025 is the year of maybe building with
AI, maybe. Yeah. Maybe. We're all doing it. Which I think... How often
do you do a survey and get almost a hundred percent response rate.
Like for a question? It's pretty outlier. Second question that we asked
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was, what percentage of you're like, do you feel confident in the data
that you have? Like do you trust the data that you have that's running
it? What do you think is... What percentage of people trust the data
that they're using for generative AI? That's 70%. That's not bad.
It was... Was it 70? Okay. 'Cause usually, the Duke Business School used
(12:13):
to do a CMO survey every year and they would ask
data questions like that, and there was usually about a 60%
gap between how important it is versus how much they trusted it.
It was always a very big delta. So yeah. It's exactly right.
So 60% said they don't trust it. So I think there is, that's
exactly the doubt there. So only one out of three trust and two out of three
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don't trust the data. So it's interesting that
everyone is building generative AI, but no one has the core component to
actually deliver the said generative AI. I think that speaks more to kind
of human nature. And will we wanna be where we are?
Can I ask, this concept has been rolling around and I've been like digging
up old blogs on it, but it just seems to have dropped off.
(12:58):
There was a lot of hype. I feel like it was probably two
years ago, but I mean, the last four years have blurred together,
so it could be anywhere between two to six years, about a metrics
layer. And it's... I feel like I've done all this, like had to
do all this like mental processing around like how does a metrics layer
or a semantics layer differ from like a store schema, data warehouse to
(13:21):
like have a reliable data set. But it doesn't seem like anyone is
talking about that right now. And I'm curious to hear your perspective.
Wow. This is a... That's a really good question, Moe. I think there's...
Yeah, I'm curious for your opinions, but I think sort of going back
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to like sort of the Taylor Swift kind of analogy from before,
there is this like, I think there's this desire to like chase the
shiny object right now. And going back to the survey, like if you're
not talking about generative AI, you're gonna be left behind.
And I think there's a lot that goes into delivering generative AI right
now. You can talk about what those things are, and I'll go back
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to your Moe question for a second as well. But I think if
you're not on track or have a really strong solid answer to how
you're on track, you're kind of on the hot seat right now as a
data leader. And I think that has just sucked the air
out of the room in every single room where there is a data
leader or an executive leader. And I'll explain what I meant by sort of
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data is the moat. I think the... If you think about like what
a data leader needs to do now, basically, like the first thing that's
being asked is like, what models are you using? What foundational models
are you using? Like what LLMs are you using, et cetera. Like between like
OpenAI and Anthropic, like et cetera. There's lots of options. The thing
is, every single data leader today has access to the latest and greatest
(14:51):
model. Everyone has access to that. And so
I have access to that, Moe, you have, Michael, you have,
everyone here has access to models that's like supported by
10,000 PhDs and a billion GPUs. And that is true for me and
every other company around me. So in that world,
how do I create something that's valuable for my customers?
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How do I create something that's unique? Like what is, what is the
advantage? Like I can create a product just like you can create a
product. And so what's a distinguishment here? Like why how... If like for
example, if I'm a bank, how can I offer a differentiated service if
I have access to the exact same model as you do and the
exact same ingredients of the generative AI product, if that makes sense.
(15:36):
And so I think what we're learning is that in putting together these
generative AI applications, which are today really limited to chatbots,
if you will, or sort of a gen tech solution, et cetera, and
all of those instances, the way in which companies make those products personalized
or differentiated is by marrying... By introducing their enterprise data.
(16:01):
Basically corporate data. And so let's take a practical example. Like let's
say I'm a bank and I wanna build a financial advisor solution.
I want to be able to help Tim fill out his taxes.
And so I'm gonna be able to do that better if I have
data about Tim's background, his car, his house, whatever it is.
And so I can offer you a much better differentiated product if I
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have reliable data about Tim that I can use. And so that's the
only difference between bank one and bank two, it's what kind of data
do we have to power that product? Yeah. So just to summarize,
like we all have access to latest, greatest models, but the only thing
that differentiates... Differentiating generative AI products is the data
that's powering them. And so that's why data is actually your moat in
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the world of generative AI. I guess counterpoint, like I feel like that
is coming from a... That's coming from a super data centric perspective.
I mean, and I guess this is what terrifies me is that year
2025 could be supercharging this obsession with more and more and more and
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more, more data. As you throw more data in, then it's harder to
keep it clean. You've got more things that can conflict. And so
absolutely. And we fought this battle in the past where there's...
You chase all this data because anytime something isn't seen as valuable,
the easy thing to default to is to just to point to some
data that's not clean enough or not clean. It may be clean enough,
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but it's never gonna be perfectly clean or data that's missing.
And so that can feed this like horrendously vicious cycle where we completely
lose sight of like, what are we trying to do? And oh, what
we're trying to do is get as much data as possible.
Like the counterpoint is those banks could differentiate by
thinking about with way less data what their customers really
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value, what they most need. And it's not an either or,
but if there's a deep understanding of their customer and they value something,
it may need very little data. It may be using data in a
different way from they already have it. So I think there has to
be that balance. I would hope that we get to that point of
like, we can't just be in this arms race for more and more
(18:16):
models, more data, more whatever. So okay, Val. Unleash... Okay. So my visceral
reaction, my visceral reaction is like, I can absolutely see
that some people would use, like, what you're saying the GenAI hype train
to be like, we need more data. I don't think that's what Barr is
(18:38):
saying, but I will obviously give you the opportunity to speak for yourself
because my reaction is, but it's not about the quantity, it is about
the quality. It is not about let's collect more data, it's that we
have... The last few years has been all about, like, let's have fucking
data lakes, let's just dump data from backend services into anywhere and
(19:00):
it's created, I mean, I think we've said a swamp before,
but it's like, you can't ask important questions, like, what do my customers
value if the data that's there is a complete trash fire and I don't think
it's about quantity. You're drawing, there's also this distinction of, like,
it is so easy to say, I found an error in the data,
(19:21):
this field is missing or this field is incorrect.
Fix it as opposed to, you just... You just said if your data
is a dumpster, a trash fire, there is a gradation of which... So put
aside the more, more, more data and bring in the pristine data.
That point, it is so easy to find a problem in the data
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and chase that and extrapolate from that. So, absolutely, we need proper
governance, but you can replace either more, more, more data, which they're
absolutely... You can Google for it and find all sorts of articles and say
who's gonna win are the ones who collect all the data,
you will find I completely grant you a... The data has to be
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garbage in garbage out. I mean, that is like a path that may
become my next favorite thing to hate on after in god we trust,
all others must bring data. It's so easy to say garbage in garbage
out. It's like, well, people are not pouring
garbage in. Yes, there are errors. Yes, there is process breakdown.
Yes, there needs to be governance and observability, but it is so easy
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to say that if we're not getting value out, oh, it's a data
quality issue and now you can get equally obsessed around over chasing that.
So Moe, I feel like you were putting... You were again,
putting words in my mouth and like, well, you, it's not bad at
all, but... No, no, no, I just, I think sometimes that like when
we're discussing this concept, there are like extremes and it's... Says
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the one who said dumpster fire. Or like it sometimes is interpreted as a
binary thing and it's not like, I do think there is a spectrum.
It just often happens that you're at one end of the spectrum and
I'm at the other end. But let me just elaborate what I mean
by quality, because I, again, can see a situation where a business goes,
we must have perfect data. And that's not what I'm saying.
(21:13):
I'm saying the data has to be meaningful so that you can create
connections between different data sources and that the way they relate
to each other is consistent so that different areas of the business are
not like tripping over themselves, making mistakes, because it's like fundamentally
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so unstructured and so... To me, it's about how all those things connect
together. It's not just about like, is this number accurate to the 99th
percent or whatever. It's... I don't know. I'm going to just shut up
and let Barr talk because I feel like she probably... No, I love
this. I've been, I love hearing y'all's thoughts. I'm, yeah, I love it.
(21:58):
Well, okay. So a couple of thoughts. One, obviously I'm biased.
I have a very data centric view. I will not, for a minute,
pretend that I have nothing but bias. And I think my bias comes
from a place of like, yeah, I think data is like the most
interesting place to be in in the past five, 10 years and in
the next five, 10. I think it's like the coolest party that everyone
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wants to be a part of and like they should and,
I'll continue thinking that I have strong, I wake up every day and
choose to be part of the data party. And I think it's where
we're having fun. So yes, I'm a 100% biased and I agree with
you. I think data hoarding has been a huge issue, a huge problem.
And I think it's been sort of a strategy that has largely failed. Oh,
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let's just collect all the data and like hope that it solves or
think that more data is more helpful. It's actually interesting. I was just
sitting down with the founder of a data catalog company a couple of
days ago and we were talking about how
95% of the problems that people... 95% of the questions that people have
of data have already been answered. And so their challenge is just finding
(23:01):
the answer and surfacing it, there's very, very
net new insights being created, if that makes sense. And so really,
their challenge is about how do we help
company or help people, users, discover the answer versus create a new answer,
which is actually mind blowing if you think about
what a small percentage of new insights are generated. It sort of made
(23:24):
me a little bit sad for the human race, but also happy that
maybe we can solve this. But I think that I digress here.
But my point is, I think the point that you're making,
Tim and Moe, is an important point. I
don't think that more data is necessarily better. In fact, I think there
are a lot of areas where less is better and more precise answers
(23:46):
are better. For a minute, I'm not advocating for that, not at all.
I think what I am saying is, most of the...
If you look at like chat GPT or kind of things that like anyone
has access to that is trained on data that everyone has access to
like we can all sort of... It's funny, people used to say let
me Google that for you and I was trying to think what's the
(24:07):
new like let me Perplexity that for you I don't know it doesn't
doesn't like roll it off the tongue just as much. Well,
let me ask Claude would work you know so. Exactly let me ask
what Claude says, but I think the point is like from that perspective
everyone has access to that and also everyone can use those models
to train their data And so everyone sort of has access to that.
(24:30):
But if you have some data about your users, let's take like I
don't know like a hotel chain that's trying to create a personalized experience
for their users. No one knows as much as they do about,
I don't know, they're like... How you like to travel, the kind of
food you like to eat, the kind of
ads that would speak better to you. Not that I'm advocating for like an
(24:53):
ad centric world but my point is like the power today and where
I think the leverage lies in is in having things that not everyone
has access to. And the reality is everyone has access to the latest
and greatest LLM. So that cannot be your moat or your advantage.
And by no means means that we have to have too much data
(25:13):
or a lot of data. I'm not advocating for that, and I think
it's a very important clarification. I actually will say that oftentimes
in the companies at least that I work with, one of the biggest
challenges is that they have so much data they don't even know where
to get started. And so a lot of the work is actually saying,
let's try to... You can think of like layers of important data tier one,
(25:34):
tier two, tier three. Then think about like what's the core data sets
that we care about making sure that those are really pristine and reliable.
So oftentimes like actually starting small is the winning strategy. I find
when companies... When we work at the company, company is like,
I wanna observe everything wall to wall. I'd be like, whoa, whoa, whoa.
Hold on. You're gonna... That's gonna be really hard. Tell me why.
(25:58):
Are you actually using all of that data? And that strategy often fails.
And so I'd much rather start with, what's a small use case that you
actually really are using the data for, and that's really important for
users. Let's start with making sure that that's really highly trusted and
reliable. So I agree with you is my point here. And I think
it's an important clarification. Moe, are you gonna? No, I am like waiting
(26:21):
for the next like rant. We can rant, by the way,
I'm happy to rant about garbage in garbage out. I think that is
a great rant. I'm happy to like carry the torch on ranting against
that, Tim, if you'd like. I don't know if you wanna share why
you wanna rant, I'm happy to share my rant about it.
(26:43):
Go for it. So I'm curious, Tim, like when I said that stuff
about like connectivity. What, like, what's your views on that? Because
I feel like you can only answer important questions
if the data is kind of, I don't wanna say structured,
(27:05):
but I'm thinking about, like, Barr's comment of
the competitive advantage that you have is your data set, like, it's not
the models. So how that all works together then to me becomes the
most important bit. And I really like Barr's concept. Actually, someone
(27:26):
in my team did this recently, where they went through of,
like, what's tier one, tier two, tier three. And I think it's such
a great framework to help the business understand the different levels of
importance. But, Tim, what's your thoughts on that connectivity piece?
So, one, I mean, there is nuance. I try to not say things
like, it all has to be connected, or it's a dumpster fire,
(27:47):
or it's perfectly pristine. And maybe I fell into it a little bit,
and then we chase the more and the more and the more.
But, I mean, I would love for there to be a little bit
more discipline and nuance. Like as Barr when you said, starting small,
that is... There is no pressure, no force in business right now that
(28:09):
says, when doing anything with your data, you should go lock yourself in
a room with some smart people on a whiteboard, and then come out
with a mandate, that it's an absolute minimalist approach. And then you
build from there. Because when you say some... And I feel like I
see this, and I see it, I mean, I'm spending too much time
(28:32):
on LinkedIn and reading articles, that if someone says,
this is data that we uniquely have as a bank or a hotel
chain, therefore, they make the leap to we have it. Therefore,
we need to feed it in and connect it
because that is something unique to us and therefore, it provides competitive
(28:53):
advantage. And there's kind of a... That's the default position is it's
our unique data, we must use it. And
where I see that going wrong is there's a missed step to say,
really, just because we have it uniquely doesn't mean it's necessarily valuable.
(29:15):
If somebody says, here's why we think it can be valuable,
what's our minimum viable product? What's our minimum way to test that it
would be valuable? But instead it kind of is like, there's this tendency
to say, it's ours, put it in the system.
Make sure it goes through that it's pristine.
Which when you flip it around to LLMs they're doing stuff probabilistically,
(29:41):
like hallucinations are coming out, all of that's getting better, but it's
like, even with pristine data going in, it's going to give kind of inconsistent
results. And we're kind of like, oh, that's cool. Well, it's like,
well, then I can't remember who wrote... Might have been Ethan Malik or
somebody who pointed out like, yeah, data that's got noise in it,
(30:03):
putting into some... It's not that if you put pristine data in, you're
gonna get a definitive deterministic answer out. If you put pristine data
in you're gonna get a probabilistic answer out, if you put noisy data
in, you're gonna get probabilistic with a bigger range of uncertainty.
And I just I think there's just thought
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and nuance to say if you had a bias towards
less, and it's not saying don't do it, it's just saying,
move with deliberation so that like you figure out something is a tier
one. And then you say that's tier one, it's a differentiator,
lock that in and make sure that it is clean. And when you're
connecting it to something else... So that's what I was... I guess that
(30:49):
was... I was like, I'm not gonna rant about this, I'm gonna have
a very nuanced thing to say. And then whoop, here it comes. That
was very eloquent. No, that was eloquent. But okay, can I add some
color to the situation. I feel like there are some companies that still
have a highly centralized model for how they store their data or how
(31:10):
it's built, that sort of stuff, my world is very different to that.
Everything's done completely decentralized. So like in marketing, we have
marketing analytics engineers and data scientists creating data sets. And
then over in the growth team, there are people creating data sets,
and over in teams in education. And even if you start with that,
(31:31):
like, let's do something small, it's often created in isolation. And
the problem is, is like, it's really hard to answer
a cross cutting business question, like, what's important to our customers
or what do our customers value when everything is
built in this completely decentralized model because like, if I take my
(31:53):
tier one tables and data sets, that will be completely different to another
department's tier one data sets. And you might not be able to answer
that question. I agree, just to be clear, I totally agree.
I love this idea of like starting with less, but
you can only start with less if it is, I don't know if
(32:15):
the right word is like company wide or like it's centralized.
I feel like there's this tension in how technology is built in some
companies. Can I quickly, unfairly, I'm gonna admit this is unfairly picking
on an example that you just threw that if it's like,
what do our customers value? And it's like, well, I have to have
all the data and hook it all together or
I could field a study and ask them.
(32:38):
There is this story out there of, I'm gonna plug in,
I'm gonna launch my internet and I'm gonna say, what do our customers
value the most and then through all of this magic, it's gonna
generate it and you say, well, why can't it? It has to connect
all of this stuff? If that's a fundamental question,
then there are alternative techniques that have been around for 50 years,
(33:02):
which is usability testing or focus groups or panels
for some of that. That's unfair 'cause you just yanked that out as
one example. So I'm gonna acknowledge fair point. It was just a random example,
but yes, I agree that there are other research methods that would be
more appropriate there. Again, I'm gonna shut up and let Barr speak.
(33:23):
No, not at all. I love this. I feel like I'm asking questions
that I haven't thought of in a while, so that's good.
No, I mean, listen to this. My reaction is a couple of things.
One is going back to sort of data as being faced with sort of a
really tricky part of their journey, I think,
(33:45):
and you talked a little bit about sort of what does a great
model look like for a team? Is it sort of centralized or decentralized?
And I think organizations go back and forth on that, and it also
is a little bit of like a function of the environment in which
they operate. So we work with highly regulated... Companies who operate
in a highly regulated environment. So think like financial services or healthcare
(34:07):
or anything like that, and in those instances, they're actually privy to
significant regulations and audits, and in those instances, you really need
to have really strong data management and data quality controls in place,
and oftentimes that needs to be across your entire data estate,
and that is sort of... It's sort of like a table stake, so you
(34:28):
can't really operate without that. I think that's very different from like
a retailer organisation or retail company or an e commerce company.
So first and foremost, I think this is really dependent on
what the environment you're operating, and also what problem are you trying
to solve when, we say data products or generative AI applications It's very
(34:49):
broad. And I think if you really think about what actually is being
used, there's a couple of things. One is creating personalized experience
for your customers, but it can also be inwardly looking for a company
sort of automating internal operations. So an example, Fortune 500 company
that we work with, they have a goal to have their IT organisation, 50%
(35:13):
of their IT work needs to be either completely AI automated or AI
assisted. That's sort of their goal. And that's in terms of internally automating
sort of human manual tasks. And so I think it sort of depends
on what you're trying to solve. And I think that that's sort of
what data leaders need to ask themselves today. Maybe sort of one thing
that's coming out of that is I think there's this sort of blurring
(35:36):
line between different people working with data. So in the past,
there's sort of you can really draw the lines, I think more clearly
between engineers, data engineers, analyst, data scientist, all of that
is becoming a lot harder to distinguish and I think
my view is sort of in, the teams that will be building generative
(35:57):
AI applications will be a mix of that. So it will include both
engineering and data people. I don't think... I think
how does this work? Someone wakes up one day in a company and is
like, hey, CTO, go build a generative application. And so like a bunch
of engineers like run off and build something. And then someone's like,
hey, CDO, chief data officer, go build a generative application. And then
(36:19):
like the data team runs off and like build stuff. And so you
end up having data teams trying to build stuff that software engineers should
be doing and software engineers trying to build data teams. But at the
end of the day, a strong generative AI application or any data product
needs a good UI, which should be built by software engineers.
You're not gonna like, that's not the data team's job. And it also
(36:40):
needs good data pipelines and reliable pipelines. And that doesn't make
sense. You don't need a front end engineer to build like a data
pipeline. And so I think at the end, there will be some convergence
of like what the roles are. But right now
there's a lot of people sort of crossing lines and lots of blurry lines
in between. And what's your perspective on data products
(37:05):
being more as like a platform product versus...
I don't know. I feel like there's been...
There are many kind of ways you could cut it.
Sometimes data products seem to sit more in like a marketing technology
space or whatever. But it seems at the moment there is kind of
a lot of perspective about it really sitting in that product platform sphere.
(37:27):
And product PMs are quite different as well to like a customer facing
product manager. Yeah, I mean, I think if you look at like the
product... Oh, go for it, Tim. Well, I just want to clarify. So when you
say a platform product, are you saying the data product is a platform
that then gets kind of in... Winds up serving a bunch of different
(37:49):
use cases? Or are you saying just where... Are you saying organizationally
or are you saying what the data product is
a platform with a bunch of features? What do you mean by...
Yeah, when I say platform product, I'm more meaning like the products that
you build, I suppose, in house that serve as
the platform for internal stakeholders and the tools that you're building
(38:11):
to service your organisation. And I suppose, as I'm saying this out loud,
I'm like, I suppose you could have data products that would be doing
that. And you could also have customer facing data products. And those things
would probably be different. Oh, wow. I really answered my own question
there, haven't I? No, it's okay. I can elaborate. But I think you
did answer part of it. So maybe also just like take a step
back for a second. If you think about data products and where they
(38:32):
are in the hype cycle, I think there's sort of... It's like there's
this hype and then they plateau. And then you're like, oh,
now I can actually make use of this. Yes. And I think that's
where data product is like, oh, now I can actually really use this
thing, which is good, I think. I think data products can really mean
whatever you want. It can both be... It can be
let's walk through a simple example, like an internal dashboard that
(38:54):
the chief marketing officer is using every day. And so
it's basically like a set of dashboards or a set of reports. And
then there's a lot of tables with this... Followed by a particular lineage
that feed into that report. And so it can be a combination of
user attributes and some different information about those users and also
(39:15):
some user behaviour. And it can be a bunch of sort of different
third party data sources. And so all of that can be part of
a data product. Sort of from... And you can describe that as basically
like all the assets that are contributing to said report or dashboard that
the CMO is looking at. My point is,
you can basically use data products as a way to organise your data
(39:37):
assets and to also organise your users and data teams. And so
to me, it's less of a question of
is this part of a platform or not? Because that varies,
as I mentioned, by the organisation, the size, the maturity of the organisation.
For me, it's more a way for companies to organise what they care
about. And so oftentimes if we will work with a data platform team,
(39:58):
we'll say, Hey, what's the data that you care about? And then they
might tell us, Oh, we have a marketing team and
that really focuses on our ads business. And the CMO there looks at
this dashboard every morning and they are so sensitive to any changes that
they have there. And so we wanna make sure that all the data
pipelines from ingestion, third party data sources, through transformation,
(40:23):
all the layers through to that report, we want that to be very
high quality and accurate. So we wanna make sure that that entire data
product is trusted. That's one way to think about it. Now,
the ownership of those assets can be by the data platform itself or
it can be by the data analysts that are actually running the reports.
Oftentimes, it's a combination of both. So you might have data analysts
(40:44):
looking at the reports, the data platform running the pipelines, the totally
separate engineering team that's owning the data upstream and sort of the
different sources. And so oftentimes, it's actually all of them are contributing
to sort of the said data product, if you will. But to me,
where data products are most useful is in a way to organise data
(41:05):
assets and organise a view of the world
for a particular domain, for a particular use case, for a particular business
outcome, if that makes sense. Do data product... And this is,
I guess, for both of you, data product, product managers,
what's the breadth. Do they go... Do they engage all the way up
(41:26):
to the upstream engineering, owning the data creation all the way through
to the use case and the need? Or does it... Is there a
natural cutoff where they say, this is engineering's problem, they're just,
they need to be managing the data coming in? Or
(41:46):
how broad does that role go? Assuming it, I guess, maybe there's a
precursor question, does that role get defined and exist as you are a
data product, product manager for this data product or set of data products?
And if so, what's the scope of that role? Yeah, doesn't it depend
on the organisation? I mean, we're having lots of conversations at the moment,
(42:09):
'cause like I said, we have a decentralised model,
which is quite unique. Because well, it's not unique, but it creates
different layers of accountability. 'Cause if you have engineers that have
a back end service, and they're pushing that data to you,
and then you're building a data product off it,
the question that comes to mind for me is like, who's accountable?
(42:31):
Well, it's not an easy answer, in that model, I think it's the
responsibility of the team that are in the backend service to make sure
that the data is getting pushed correctly out. But then likewise,
for the people who are receiving it, they have layers of accountability
as well as the people that are using that data. But in a
completely different model, where you don't have that... Like you have a
(42:55):
more centralised model, those lines of ownership could be different.
And so I think it's so dependent on the company,
and how they're structured to understand where something starts and ends.
I think it's probably impossible to think that a data product PM would
own everything completely end to end. I can't envisage a world where that
(43:21):
would happen, just because there are so many different parts of the bit
I don't know, anyway, I'm not making a lot of sense now. Yeah,
yeah. I mean, this is a maybe not what you'd wanna hear.
But I think it's a it depends answer.
It depends on the maturity of... I mean, I don't wanna repeat what
(43:43):
Moe said, but I strongly agree with that.
It's hard to draw the lines, I think some of
the teams that do this better are those that are able to have
like a strong data governance team that can actually sort of
clearly sort of lay out what that looks like. The most common model
is something like a federated model where you have a centralised data platform,
(44:05):
like what you said, Moe, the centralised data platform sort of defines what
excellence looks like, what great looks like. And so they might define like,
these are the standards for security, quality, reliability, and scalability.
And so whenever you're building a new data pipeline or adding a new
data source, you need to make sure that
it passes these requirements on each of those elements. And so in that
(44:27):
way, the centralised data platform defines what great looks like. And then
no matter what team you're on, this could be the data team serving
the marketing team, or finance team, or sort of whatever use case it
is, will adhere to the same requirements that the centralised team has defined.
So we see a lot of that. I think that's, again,
with generative AI, we will see more of that. Because maybe going back
(44:50):
to sort of what we said at the very, very beginning of the
call, how we use data 10 years ago was a lot simpler.
There were very few use cases and very few people using data.
But today, because there's so many more use cases, so many more people
using it, and more in real time, the need for a centralised sort of governance
(45:11):
definition is more important. I mean, this is also you kind of see
this... I think the sort of LLM or generative AI stack is still
being defined. But one of the questions you raised this, Tim,
was hallucinations are very real. And when you release a product,
and the data is wrong, it could have
(45:33):
colossal impact, both on your revenue and your brand. Maybe the example
that I like to give them the most is, I don't know if
you all saw this sort of went viral
on Twitter, or X, I'm not gonna get used to that thing.
But when it went viral on X, someone did this thing on Google,
basically, the prompt was something like, what should I do if my cheese
(45:57):
is slipping off my pizza? And the answer was like, oh,
you should just use organic super glue. And the... Oh, wow. It's obviously
a bad answer. And honestly, I think Google can get away with it
because of such strong brand that Google has these days. And so,
yeah, I'll probably continue to use Google, even though they gave me a
(46:17):
shit answer about organic super glue for my pizza. But most brands,
if I'm an esteemed bank, or an airline, or a media company,
I can't afford to have those kind of answers in front of my
users. And so like, actually getting that in order is... Again,
(46:37):
Google, can get away with it. But like 99.9% of us cannot.
Nice. I wanna switch gears just a little bit and talk about something
else that kind of obviously ties in, but also kind of reintroduces a
lot of challenges, which is unstructured data. And going into next year,
one of the articles I was reading that you'd written, Barr, was kind
(46:59):
of like saying, well, it's gonna be one of the things,
could you kind of give a perspective about, okay, so we're gonna be
using a lot more unstructured data, but then doesn't that... How do how
do we then take all the things we've just been discussing about how
challenging data is? And now, we're just gonna slam on
now a new set of challenges on top of that, they're gonna kind of redo
(47:19):
the whole thing. What do people do about this?
Yeah, great question. We should do at some point, like a 2025 will
be the year of and see what we come up with.
I don't know if it'll I guess be... Round robin. Yeah, exactly.
Yes, Claude. I'll ask perplexity. Yes, ChatGPT. Please. Yeah, exactly. Exactly.
(47:43):
I mean, honestly, if like, if we could foresee that we probably wouldn't
be in this business. We'd be doing something else if we could be
forecasting that. But I think as will 2025 be the year of unstructured data?
I don't know. But I can tell you this for the last 10,
15 years, most of the data work has been done with structured data.
And structured data is very easy. It's like data that's like in rows, columns,
tables that you can analyse in a pretty straightforward way with a schema
(48:05):
and and most of like the modern data stack and whatever solutions that
we all use and love and day to day has been have been
focused on structured data. That being said, if you look at where the
growth is, I think there's like some crazy estimates from Gartner like 90%
of the growth in data will come from unstructured data or something like
that. Or and just to define when we talk about unstructured data,
(48:28):
things like text, images, et cetera... Well, 80% of unstructured data will
be generated by an LLM. So no, I'm... It's turtles all the way.
Yes turtles. If you know what I mean.
I think the former founder of OpenAI said something like, we're at the
peak data of AI now. We're at the time we're like,
(48:51):
this is the most data that we have to train.
And from now on, we're gonna have to like rely on synthetic data
in order to do that. And that goes back to your question of
like hoarding data. But going back to the unstructured point, I think
unstructured data is becoming more and more important.
And we're seeing organisations not only start to collect more of that,
(49:11):
but also understand how to use it and how to what to do
with it. I think this is very early days for this space.
And I think we're still sort of watching and kind of understanding what's
happening. But I think one of the things just to make this really
concrete with an example, I think is a cool example.
We work with a company that's a Fortune 500 insurance company.
And one of the most important types of data for them,
(49:34):
unstructured data is actually customer service conversations. So let's say
I have a policy or something that I'm upset with, and I wanna
chat with someone and then have this conversation. And you can analyse that
conversation to understand my sentiment. How pissed off am I? Am I like
yelling representative? I don't know, I'm like, get me my manager or whatever
(49:56):
it is, or and I'm like, super happy. Thank you so much.
That's what I mean by sentiment. So you can sort of analyse like, what
is a conversation like? And basically you can also
ask the user for feedback. Sort of scoring that. One of the things
that this customer does actually uses LLM to create structure for this unstructured
(50:17):
data. What do I mean by that? They basically take a conversation and
then score that conversation. So like, zero to 10, this conversation was
a seven or an eight or something like that. Now, what's the problem?
The problem is that sometimes LLM hallucinate, and they might give a score
that's, let's say, larger than 10. What does that mean if a score... If
a conversation scored a 12, for example. And
(50:38):
so actually, the way in which we were working with this company is
allowing them to observe the output of the LLM to make sure that
the structured data is within the bounds of what a human would expect
to score an unstructured data, which is the customer conversation.
And so in that instance, we're sort of using automation in a way
(50:58):
that we maybe hadn't expected before, in order to add value and to
sort of... In this instance, is actually like reduce the cost and improve
the experience for the users in this case. But it's one of those,
that brings up the case of, say that it just... That scoring,
that model, it just, it shits the bed 10% of the time,
(51:19):
but it does way better 60% of the time. And it does about
the same as a human, and it's overall,
a little bit cheaper. I think that there are the trade offs.
And I mean, maybe this goes back to
earlier, or the discussion that if it's like, well, we're gonna pull out
the one that it said at 12, and say,
(51:41):
you got to fix that from happening. That's one approach, make this never
happen. The other option is, it's gonna happen. So the process needs to
be human in the loop or human on the loop, like don't completely
hand this over, so that you can catch the ones because a human
would catch it. And there the trade offs are...
(52:01):
And you know what, maybe they're even it's okay, you're gonna have a
small percentage who are totally pissed off, even if you're just running
humans, 'cause their wait time was too long or something else.
Is your goal to have every customer have a delightful experience?
Or is it to actually have fewer customers have
(52:22):
a horrible experience? It may be a different set of customers that are
having a horrible experience. And then probably mode of your connected,
you wanna make sure the ones with the highest predicted lifetime value,
you're not saying, great, we have way fewer customers are pissed off.
Unfortunately, it tends to skew towards the ones that are the
highest lifetime value so. I think that's... Yeah, I mean, I think that's
(52:45):
spot on. And I think it's... I mean, one of the questions that
I remember sort of thinking through is like what's worse, like no answer
or a bad answer? I'm not sure I can tell you,
we're not creating sort of agents, if you will, in order to say,
(53:06):
oh, I don't know. That's not how you create them. But oftentimes,
like, that actually might be the better answer.
I think Tomasz Tunguz, who we sort of collaborated with on predictions for
next year, sort of mentioned to us that like what you'd expect is
like 75% to 90% accuracy is considered like state of the art for
AI. However, what's often not considered, I mean, on the face of it,
(53:30):
75% to 90% seems really legit and reasonable. But what's not considered
is like, if you have three steps, and each is 75% to 90%
of accuracy, the combination of that is actually ultimate accuracy of only
50%, which is, by the way worse than the high school student would
(53:52):
score in that sense. And so is 50% acceptable? Probably not.
And so what ends up happening is, is actually what I think we
were seeing in markets is like, the market actually took this big step
back. I think a year ago, there was this huge rush to adopt
generative AI and to try to build solutions. But as we were seeing
that the accuracy is sort of at those ranges, companies did take a
(54:16):
step back and actually are re evaluating or rethinking where to place their
bets or place their chips, if you will. I still find that most
companies evaluate a solution with a human thumbs up or thumbs down. Was
this answer good or not and allowing users to just mark like,
yep, this was great, or no, this kind of sucked.
Companies still have that. And I don't think we're moving away from that
(54:37):
unless there's sort of big, big change in the near future.
I have a totally unrelated random question, Barr. With the companies you're
working with, is the focus of reliability and the work you do quite
different depending on whether data's structured or unstructured?
(54:58):
In the use case you just gave, like, it sounded like it was
quite different. But what are you seeing across the industry?
Yeah, 100%. I think the use cases that we cover vary tremendously based
on industry and company. And I think that's a reflection of the variability
in what you can do with the data across the industry.
(55:19):
So it can range. The sort of types of products that we work
with can be data products that are more like a regulatory environment,
where one mistake in the data could actually put you at risk of
regulatory fines if you are using data in some incorrect way,
(55:40):
or not following what is defined as sort of best practises for data
quality, sort of like this blanket statement that's very high level,
but actually, is very important in these environments. That's like one.
The second could be where you have a lot of internal data products,
so like a lot of reporting or product organizations that are doing analysis
(56:01):
based on cohorts or segmentation of your user base.
A third could be data products that are sort of customer facing.
So for example, if we have the easiest thing that is like a
Netflix recommends your next best view, for example. And then a third...
I guess a fifth use case could be
(56:21):
a generative AI application. So for example, an agent chat bot that helps
you ask questions and answer about your internal process or your internal
data. So you can ask really basic questions like, how many customers do
we have? And how many customers have renewals in the last few years?
Or if I'm in support, I can ask, how many support tickets has
(56:42):
this customer submitted in the last year? And in what topics?
And what was their CSAT, sort of questions like that. And so
each of these can include structured or unstructured data, and each of these
can cover very, very different use cases and very different applications
of the data. So if anything, I see that there are less homogenous
(57:06):
sort of applications of the data, if that makes sense. And I actually
anticipate that this will carry through to the generative AI stack.
So there's people create software in a multitude of different ways,
in a multitude of different stacks, the same can be said for data.
There's not one single stack that rules at all, there's not one single
(57:27):
type of data that rules at all in order to create data.
I think the same will be true for generative AI. There's not one
single stack or one single preferred language of choice, and there's not
one single preferred method, whether it's structured data or unstructured
data. I think that this does very much sort of vary. I will
say from my bias point of view is
(57:49):
the thing that is common, sort of going back to like the foundation
of truth and sort of what is very important is like every organisation needs
to have, or needs to rely on their enterprise data to make sure
that it's high quality trusted data so that they can actually leverage and
capitalize on that and I think it's a messy, messy route to get
there. Maybe 2025 will be the year of messiness. Sometimes you just gotta
(58:12):
like lean into the messiness on our like path, like this random random
path to kind of figure it out. But there's a lot more to
figure it out there. But I don't see us sort of converging on
like one single path or use case or even type of data.
All right, we've gotta start to wrap up. This is so good.
(58:33):
And yeah... Oh, we figured it all out. So we're good to wrap. Maybe we will
do this before... Exactly. 2025 will just be the year of leaning into
the mess. And maybe that's the best we can do right now. Anyway,
one thing we love to do is go around the horn,
share last call, something that might be interesting to our audience.
Barr, you're our guest. Do you have a last call you wanna share?
(58:56):
Sure. So this concept that someone has shared with me recently,
which I'll call sort of watching the avocado, if you will.
I don't know if you have experienced this, but you buy an avocado
and it's like, it's not ready, not ready, not ready. Boom,
you're too late. It's already like you can't eat it anymore. That happens
(59:16):
to you. And so I think the idea is like a lot of
sort of new technologies and trends are like that. And in this case,
sort of this is like generative AI. We're too early, we're too early,
we're too early. Boom. You missed the boat. And so I think one
of the things that I take away from that is like as data
leaders, as sort of data practitioners, how do we keep watching the avocado?
(59:38):
We gotta hit the avocado before it's too ripe. But the timing matters
here, especially for a lot of these sort of trends and technologies.
Nobody likes bad guacamole. If any listener now uses that when they're talking
somewhere internally, if they use the analogy, please
let us know. I wanna... I like that. We gotta watch the avocado.
(01:00:01):
Yeah, that's awesome. All right, Moe, what about you? What's your last call?
Okay. I've been doing lots of thinking about
how I make 2025 really great. And I think
one of the tensions I've found is that I'm naturally inclined to like, wanna
go fast and get to the place that I wanna get to.
(01:00:22):
And so this is not anything other than just
kind of a personal learning or a personal goal that I've set for
myself. It is the start of 2025 after all, that I wanna be
more intentional about enjoying the journey. And the
analogy I have is I love going to the beach, going to the
(01:00:45):
beach with two small humans is really fucking hard. There's all this shit
to pack. You've got a card at all down there. Everyone needs sunscreen
on like... And so sometimes the bit of getting to the beach is
so unpleasant that by the time you get there, you're all like flustered
and hot and you don't wanna be there and you're like,
Oh, fuck it. Let's all just go home. So I'm trying to enjoy
the journey to get there more. So I went to the beach the
(01:01:09):
other day, it took us an hour to get there. My kids wanted
to stop at this playground. They wanted to look at the bird.
They wanted to have a snack and I'm like, you know what?
That's okay. I am just going to lean into letting... Enjoying the bit
to get there and not focusing so much on kind of the end
state. And it's not just about kids. It's also about work.
(01:01:31):
'Cause like, if you're constantly trying to like come up with this huge,
amazing strategy and deliver this project, but you're miserable in the months
delivering it, that kind of defeats the purpose. So anyway, that's just
my intention for the year that I chair. What about you,
Tim? Well, my publisher is gonna hurt me if I don't
plug Analytics the Right Way. So if you're, depending on when you're listening
(01:01:53):
to this, it is less, 15 or fewer days from actually being available,
but Analytics the Right Way is available for
pre order until January 22nd, in which case it will be available as
a print book or an ebook, and the audio book's coming out four
or five weeks after that. So that does have a section talking about
(01:02:13):
human in the loop versus on the loop versus out of the loop
and some of the AI trade offs, but it is not an AI
heavy book at all. So that's my obligatory self... My log rolling last
call. But for fun, I've definitely last called stuff from The Pudding before,
but one that they recently had, it's at pudding.cool, but it was Alvin
(01:02:37):
Chang got a data set that looked at a whole bunch of different
roles and it was how much they spent of their time sitting versus
standing. So it's kind of one of those like scrolling visualizations.
You enter kind of some stuff about your job first. So it can
then kind of locate you on it. But it's just a simple X
axis that goes from sitting all the time for work versus standing all
(01:02:58):
the time for work. And then it looks at a whole bunch of
different, it varies what the Y axis is as you scroll through it.
So it's kind of just a fun visualization. And it also starts to
call out like how tough on bodies a lot of professions are because
they're required to crouch or stand all the time. They can't take breaks
(01:03:20):
and that sort of thing. But it's just kind of a fun interactive
visualization. So worth checking out to roll X. What about you,
Michael? What's your last call? I mean, it was going to be the
book. Tim, I was actually ready to do one on the book for
you just in case you didn't cover it. So good job.
We'll report back to your publisher. You're doing it. You're doing what
(01:03:43):
you can do. So actually mine is recently
Recast, who I think is some of the best in the game when
it comes to media mix models, they've started publishing a series of YouTube
videos on how to think through the creation of those models.
And I think it's a great watch for anybody who's engaging with that
(01:04:05):
kind of data. So I'd highly recommend it. And they've put a couple
out already and then I think there's some more to come.
So that would be my last call. All right. So what is
2025 the year of? I would just have one word. Everybody has to
go around and do like a one word. It's more like a faster.
(01:04:28):
No, nothing. Moderation. I think 2020... Yeah, there you go. I think 2025
is gonna be the year of being thoughtful,
keeping with the work, increasing insights, maybe helping with process.
That's none of that's actually gonna happen, but I just sort of like wish
(01:04:50):
it were. So that's my take on it. So you use the one
word for all of us. You just, you kind of took,
we all deferred or... Well, nobody answered, Tim. So I just figured we
were not gonna... No, I yielded my one word to you. That's good.
I like it. So I couldn't think of a better person to help
us kick off 2025 with than you, Barr. Thank you so much for
(01:05:10):
coming on the show. It's been awesome. Absolutely. I hope
2025 will be even better and greater than 2024. And
I would probably be remiss if I wouldn't say that 2025 would be
the year of highly reliable data in AI. That's right. Hey,
(01:05:31):
what's the saying from your mouth to God's ears or whatever.
We absolutely would want that. Amen. Thank you so much. Awesome.
Thank you so much for coming on the show again. And of course,
no show would be complete without a huge thank you to Josh Crowhurst, our
producer, just getting everything done behind the scenes. As you've been
listening and thinking about 2025, we'd love to hear from you.
(01:05:55):
Feel free to reach out to us. You can do that via our
LinkedIn page or on the Measure Slack chat group, or via email at
contact at analyticshour.io. We'd love to hear your thoughts other things
that you think are big topics for 2025 in the world of data
and analytics. So once again, Barr, it's a pleasure. Thank you so much
(01:06:15):
for taking the time. We really appreciate having you on the show again.
And you're on track now there we keep talking about the five timers
jacket. That's gonna be a thing. So you're in the running.
There's only been a few people have done this a couple of times. Are you
prepared to have five kids, I guess is the question. We might need to break
(01:06:35):
right now. Yeah, anyway, so of course I think I speak for both
of my co hosts, Tim and Moe, when I say no matter where
your data is going, no matter the AI model you're using,
keep analyzing. Thanks for listening. Let's keep the conversation going
with your comments, suggestions, and questions on Twitter @analyticshour,
(01:06:58):
on the web at analyticshour.io, our LinkedIn group, and the Measure Chat
Slack group. Music for the podcast by Josh Crowhurst. So smart guys want
to fit in, so they made up a term called analytics.
Analytics don't work. Do the analytics say go for it no matter who's
going for it? So if you and I were on the field, the
(01:07:20):
analytics say go for it. It's the stupidest, laziest, lamest thing I've
ever heard for reasoning in competition. Yeah, my smart speaker decided
to weigh in on that. I love it. What did they have to say about that? It's
(01:07:40):
the perfect little end note to that particular thing. Yeah. Yeah. Tim, I'm
coming for you. thumbs up or thumbs down, man. I'm in the background
saying, nope, I don't think I can... Actually it basically said I don't
know now that I think about it. It was like whatever it decided
it had heard, which was nothing. Yeah, perfect.
(01:08:00):
Rock flag and lean into the mess!