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Hello everyone, and welcome to another
episode of the day of Standard audio experience.
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I'm your host, Catherine Tau,
and the day we have another amazing
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guests. We have Dave Milillo,
the Director of data from Cloud Bey's,
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and today we're going to be speaking
about leveraging data to improve your businesses.
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So welcome to the show, Dave. I'm very excited to speak with you.
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Thanks, Catherine. I'm really excited
to be here. Yeah, so
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tell me more about your background.
What kind of got you interested in data?
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Yeah, I guess the best way
to answer that as I've always been
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kind of a puzzle person throughout my
life. It's manifested in different ways,
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like it's very big into video games
for a little while. I've been to
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music. That was something we were
talking about offline, but it's all around
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like solving things right and as I
started mature, to mature in my professional
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life and my as a student as
well, I started to realize that data
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was really on the cutting edge of
solving problems with businesses and solving the puzzle
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of how is a business successful or
what are good traits, what are bad
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traits? I don't really think that
was as much of a thing when I
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was younger, but it's really coming
to the forefront now. But I've always
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been connected to data that way,
even through sports. been into like the
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statistics of sports. I thought I
was going to graduate and be the statistician
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for the New York Yankees. That
didn't happen, but but again, it's
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always revolved around the puzzle of data
right and every day I almost feel like
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I'm presented with a rube's cube and
for someone like me it's really fun to
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go through that root rubeks cube and
try to figure out how to make everything
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work. So that's awesome. And
so I know that a lot of companies
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are really striving to be very data
driven and a lot of them want to
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say, Oh, I we want
to be more AI, AI driven.
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But it's important to also know that
there's a statistic everyone saying that about eighty
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seven percent of companies are not able
to deploy ai into their company. And
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so my next question for you is
just how is your company more data driven?
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Yeah, I think that I'm very
lucky to be a company where there's
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just a culture of being data driven. So have a lot of engineers,
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a lot of stem people via the
what the company sells and what we do.
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So in that way everyone leads either
with the data point that they've see
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or they're trying to prove a hypothesis
wrong er right, and so that's really
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great. So part of it is
cultural, but part of the part of
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the data driven aspect that I can
drive is that we've built a framework for
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that right. So is one thing
to say that you want to be data
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driven, to hack together some analysis
every now and then. It's a completely
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different thing to dedicate yourself to data
as a company, and that requires do
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it a lot of things that might
not be very exciting, setting up data
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pipelines, setting up a data warehouse, getting all that infrastructure in place so
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that you can anticipate questions. You
could do things that scale. Those are
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really the reasons that I feel most
AI and ML projects fail is because you
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usually do a POC's off a small
data set with the limited audience, and
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that could go well, but then
when you start to try to roll that
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out and you know really at scale, make your predictions or your analysis means
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something to the company, that's where
things fall apart, because you're never going
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to convince an analyst to, you
know, upload a CSV file every day
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in order to fuel your predictions,
or you know you're never going to get
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that salesperson who needs your prediction to
log into the dashboard that you need or
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whatever end point you have your prediction
at. So if you don't have that
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whole data platform thought out, you
can even be producing some really great stuff
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and your projects can be successful,
but they don't really take at the level
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that they need to because there's no
infrastructure there. Maybe people don't trust the
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data, maybe people can access it, but there's so many things that go
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into the success of data and again, most of it is not very exciting
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and most people don't do it.
They skip right to the fun stuff.
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But, as you can imagine,
that's not necessarily recipe for success. Yeah,
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I completely agree, and I've talked
to a lot of different people on
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the podcast as well, saying that
it's important to how quality data as well,
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and that's that's a huge problem of
why a lot of projects aren't being
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deployed. It out with, for
with AI, at a lot of companies.
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It's a lot of people say it's
scarbage in, garbage out, and
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I think that's a great way to
kind of think about it. Too,
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and so what are some ways that
you think would be best to kind of
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mitigate these challenges that companies are having
in terms of being able to deploy ai
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within their company and be more data
driven? Yeah, it's great question.
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One of like if I could put
words on my wall, I don't have
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any, like live, laugh,
love stuff, but if I could,
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it would say automate everything until the
fun stuff is left, right and for
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me and I think most data people, the ML AI visualization actually providing insight
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is the fun stuff. Right,
you're actually driving the business like it's fun,
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it's engaging. You could see how
your work translates to dollars or success
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on the other side. But again, you have to automate a lot to
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get to that fun stuff, and
that's what I always preach to people.
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Right, if you don't have a
data pipelining solution or some type of at
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least concept of that in your company, you don't have data engineers or someone
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that's minded that way, it's going
to be very hard to get to the
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fun stuff. Same thing with architecture. If you don't have the concept of
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a data warehouse, you don't have
someone who's managing that, it's you're not
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gonna be able to get to the
fun stuff. And even more recently that's
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coming to focus is the data model
of a company. Right. So,
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even if you have all the physical
pieces of that data platform, even if
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you have the skills producer prediction,
if you don't have the data model well
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defined, and it's going to be
really hard for anyone in the business to
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take your insights seriously, your predictions
seriously, because you can't give them any
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transparency into the definitions of those data
how that relates to everything, you can't
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even, you know, give confidence
that you're pulling the right information, that
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you have clean informations. So,
again, all that's not fun stuff.
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Their long roads setting up those programs, making sure that they're automated, that
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they work. Those are heavy lifts. But if you are successful in automating
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that and you put your time there, the ML in the AI will be
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there. You'll have a framework to
build anything that you want to in the
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future. Yeah, I completely agree. And I think another part about companies
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kind of failing to be able to
deploy AI. It might be also the
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company structure in terms of WHO's collaborating
with WHO, and so, as a
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director of data. I'm sure you
speak to a lot of different people on
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their teams, such as the business
people, the executives, the engineers and
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everyone in between, and so what
is the company team structure like and what
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are some bust practices to encourage collaboration
and just make sure that the end goal
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is met? Yeah, I love
this question because it feeds into something that
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we're, I wouldn't say experimenting with, but we've we've grown out here at
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our company and it's a it's a
service model that I really like. And
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I call this service model because I
draw a lot of comparisons between data and
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HR as functions out of company and
people kind of scratch their head to like,
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what do you mean? It's you
know, there's not a lot of
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hr people that necessarily sit in marketing
or product or sales. It's usually a
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centralized function in GNA and what they
do is that HR people are business partners
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with sales, marketing, whatever.
They have kind of a deep understanding of
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that business or SEMIDEP, but what
their specialization is is really an hr and
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you would never expect like a demand
generation person in marketing to also be an
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HR person. There's really not that
there's not that expectation there, and that's
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really the way it should be with
data. But now, if you think
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about how data works, right,
a lot of times you just have data
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people embedded all over the company.
Right, that person that does Legion in
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marketing probably also has a bunch of
spreads. Jeets, does some attribution modeling,
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you know, something on the side, right, and so they have
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a split job. What we've tried
to do here at cloudy's is create a
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center of excellence, right, and
all of our data people are data generalist.
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They know how to do anything across
the scope of data, whether it's
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pipelinding, architecting or producing results in
analysis. And what we do is,
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like hr, we partner with the
business. Right. There's a there's someone
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who has a better understanding of sales
and marketing data. Are they a marketer?
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Are they a salesperson? No Way, their data to person, but
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they are assigned to that marketing organization
and they can really understand their pains.
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And so you really have now a
full time data person WHO's working on your
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data problems rather than just trying to
allocate some CPU of someone someone else on
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the team. And again, that
model, I think, is really the
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future for data and at these companies
that are taking data seriously, because it
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also allows you to leverage a mind
share. Right, like maybe the person
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who's the partner with the product team
is doing something really cool with side kit
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learn and now they could turn around
and they could share with the person in
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marketing. You know, if those
people are both embedded in the businesses or
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those business units, it's really difficult
to do that. So again, the
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short answer is that business partner model, I think is has made as very
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successful. Yeah, and I think
that's great that you kind of talked about
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really getting to know everyone within your
team and your organization to be able to
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share your ideas with within the organization. One of my mentors recently she told
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me that just at going into a
new job or anything like that, it's
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important to just set aside some time
and get to know each person within your
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team just so you can build that
trust, because trust is also very important
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within organization and it's important to kind
of build that up from the beginning.
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So great insight there. You know, a great point right about trust data
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people are definitely they don't do enough
of that. Like there is a whole
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psychological piece to to data right and
and we're trying to actually conceptualize that and
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graph what our engagements look like because, again, in order to gain someone
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trust that you have the right data, that you have those skills, you
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know, that's a journey, right. It's very consultative, but there is
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a way that you can build that. Rapport I'll be honest. It's tougher
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now in the days of covid because
it was much different when we were able
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to get it together with people.
We could show them things, we could
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demonstrate things a little bit more easily. There's more walls now, but you
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can still, to your point,
cultivate that trust by doing a lot of
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things that data people don't do,
talking right, scoping projects, so being
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more of that project manage in that
business partner, not just like technical engineering
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pece. So in that way,
data people are really the Jack of all
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trades, right, so it's a
wide breath of skills that you need,
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I think, to be really successful. Yeah, and that's one of the
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big reasons why I really like the
data field, because you kind of have
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to know a little bit of everything, a little bit of programming, a
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little bit of just networking and the
business side of things. It's just a
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little bit of everything. So I
think that's an awesome field to kind of
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be in. And so what how
do you kind of see the future of
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business is changing with AI and data
in general? Yeah, I think there's
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I think most companies are going to
have one of two paths that they take.
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They're either going to double down on
actually being data driven, and you
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see that from some of the best
companies. They've already been that way,
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like I think of someone like Instat
Cart Right. They built out their data
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science team from day one because they
knew it was going to be a strategic
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thing. Same thing with door.
Like these major companies, you can see
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that their success is tied to the
fact that they took data seriously. They
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have data science departments, they have
those data platforms right. So I think
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that more companies will be forced to
do that. Again, making the parallel
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between HR, probably back in the
S S, maybe even S, there
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might have not been like an HR
department. Maybe there was an HR person
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here there, but nowadays, now
that we understand the value of emotional intelligence,
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diversity and inclusion, it's kind of
silly if you go to a company
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and there's not an HR department,
you'd kind of scratch your head and say
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what's going on? It should be
the same thing with data, but we're
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not. Most companies aren't there yet. They still have, you know,
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that data wizard who serves the whole
company or a small Tigroup team of people
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that is serving people. But,
but again, I think one of the
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paths that people will go down as
they'll take data seriously, they'll create centers
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of excellence and real functions within the
business that are right on part with marketing,
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sales, finance, etc. So
the other option, I think,
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and and you see it emerging with
tools like snowflake and five tran becoming major
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players in the data space, is
that people are going to double down on
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technology that allows their small data team
to scale themselves and become super powered.
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Right. So again I mentioned five
trans something like that. I could see
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getting bigger and bigger and bigger,
because if you're not going to invest in
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building a real data pipeline platform with
like airflow or something like that, five
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trains great out of the box.
It works and, as one person,
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you could probably a minister, connections
to a lot of different applications. Right.
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Same thing with Snowflake, it makes
managing a day to warehouse pretty easy
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for one person or a couple people
to do so. Again, I think
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it'll either be people build those functions
out or they just double down this technology
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that makes their day to team,
the small data team, superpowered. Now
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awesome insight there. Everything that you
just brought up is super important within the
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industry and just helping the future of
businesses really grow. But yeah, and
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so I just wanted to kind of
transition into talking more about less technical topics
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now. And so just outside of
the technical data field, what are some
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things that you do outside of work
and do me hobbies or interest that you
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do on the side? Yeah,
I'm so again it's all around numbers.
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Unfortunately. I'm a big I'm a
big music person, right, so I'm
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always writing songs people. There's a
big math component to that. I can't
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always quantify it as well as I
can with my job, right, but
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there's something to that. So,
like music, I'm a big fitness person.
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There's a lot of numbers behind that, believe it or not, especially
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if you want to get to optimal
performance. And then I love sports.
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I'm a big sports fan and,
no, needless to say, a there's
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you could dive into so many numbers
when it comes to sports, and that's
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cool for me. Like I like
watching baseball games and keeping the score and
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diving into all the whacking metrics like
war and stuff and what they mean.
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So yeah, like most of my
time is split up between music, fitness,
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your data and I also have a
bunch of cats, so I'm very
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surprised that they actually didn't crash this
podcast. But you know, between all
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that, I keep pretty busy.
Yeah, that's awesome. And how you've
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ever worked on any like sports data
analytics at all? Yeah, I actually
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one of the things I'm really proud
of is that recently I got a block
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post published on towards data science,
which is a popular medium publication, and
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it was all about how you can
automate, or how I automated my football
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Sunday picks. Right, I had
like this process where I was copying pasting
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from all these different places in order
to make a prediction on what games,
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you know, what the outcome should
be, were fantasy players I should be
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putting my line up, and I
actually walk through that and automated it to
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a certain extent, and I published
that out on medium. So, yeah,
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if you're interested in any of that
stuff, I have some of that
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on the Internet. That sounds awesome. So thank you so much for sharing
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more about your outside technical stuff that
you do is that's awesome. And so,
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yeah, we have the data standard. We're trying to build a community
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of data enthusiast and data thought leaders
so that everybody has a place where they
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can just kind of notework and collaborate
with each other. So, so what
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is something the community can do for
you? Yeah, I think that sharing,
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you know, ideas is the most
important thing. Just what people how
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people are solving problems. You know, I think it's very hard in data
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to anticipate what the problems will be
because we're really on the bleeding edge of
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all technology, right. You know, back, five, ten years ago
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is all about big data. Right, how do we handle Big Day besets?
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Today? It's a very different problem
and I think when people just share
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the problems that they're trying to solve
for themselves that that starts everyone thinking.
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It's almost like a group think at
that point. That's why I love something
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like Cagle, right. That's like
I go to Cavil when I have a
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question and I try to see how
other people are solving for that question,
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and I think the same thing could
be done in this community right just people
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being willing to share what they're doing, even if it's half baked. You
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know, that could be someone else's
afha moment and then it could lead them
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to the answer that they've been looking
for for years. So, you know,
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just people being open and participating the
community like that's that's more than I
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could ever ask for, you know. Yeah, and I think it's great
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that you brought that up, because
a lot of companies are also struggling on
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similar problems too, and if everyone
just collaborated and talk to each other,
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then these problems could be solved,
probably much sooner than later, but definitely
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collaboration is super important within the community. Yeah, and we're can our community
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find you online? So, yeah, I am on medium. If you
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look for my name, I believe
it's Davelow. I'll get you the link.
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Obviously on Linkedin, you know,
same thing. That's probably professionally where
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the best places to look for me
are. So yeah, awesome. and
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to re viewers. For more information
on the data standard, you can find
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us at www dot data standard dot
io, as well as our linkedin and
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Youtube Channel, and this episode is
sponsored by Pandio. They're innovating the text
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base with their patchy pulsar messaging system, helping companies have more streamline of data
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and do more data driven so learn
more about their work up pandiocom and thank
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you so much day for joining us. So great to speak with you and
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just learn more about the industry and
just more about your experiences. So thank
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you so much. Happy to do
with Catherine. Thank you, wo
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Hello everyone, and welcome to another
episode of the day of Standard audio experience.
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I'm your host, Catherine Tau,
and the day we have another amazing
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guests. We have Dave Milillo,
the Director of data from Cloud Bey's,
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and today we're going to be speaking
about leveraging data to improve your businesses.
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So welcome to the show, Dave. I'm very excited to speak with you.
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Thanks, Catherine. I'm really excited
to be here. Yeah, so
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tell me more about your background.
What kind of got you interested in data?
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Yeah, I guess the best way
to answer that as I've always been
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kind of a puzzle person throughout my
life. It's manifested in different ways,
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like it's very big into video games
for a little while. I've been to
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music. That was something we were
talking about offline, but it's all around
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like solving things right and as I
started mature, to mature in my professional
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life and my as a student as
well, I started to realize that data
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was really on the cutting edge of
solving problems with businesses and solving the puzzle
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of how is a business successful or
what are good traits, what are bad
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traits? I don't really think that
was as much of a thing when I
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was younger, but it's really coming
to the forefront now. But I've always
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been connected to data that way,
even through sports. been into like the
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statistics of sports. I thought I
was going to graduate and be the statistician
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for the New York Yankees. That
didn't happen, but but again, it's
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always revolved around the puzzle of data
right and every day I almost feel like
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I'm presented with a rube's cube and
for someone like me it's really fun to
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go through that root rubeks cube and
try to figure out how to make everything
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work. So that's awesome. And
so I know that a lot of companies
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are really striving to be very data
driven and a lot of them want to
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say, Oh, I we want
to be more AI, AI driven.
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But it's important to also know that
there's a statistic everyone saying that about eighty
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seven percent of companies are not able
to deploy ai into their company. And
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so my next question for you is
just how is your company more data driven?
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Yeah, I think that I'm very
lucky to be a company where there's
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just a culture of being data driven. So have a lot of engineers,
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a lot of stem people via the
what the company sells and what we do.
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So in that way everyone leads either
with the data point that they've see
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or they're trying to prove a hypothesis
wrong er right, and so that's really
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great. So part of it is
cultural, but part of the part of
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the data driven aspect that I can
drive is that we've built a framework for
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that right. So is one thing
to say that you want to be data
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driven, to hack together some analysis
every now and then. It's a completely
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different thing to dedicate yourself to data
as a company, and that requires do
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it a lot of things that might
not be very exciting, setting up data
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pipelines, setting up a data warehouse, getting all that infrastructure in place so
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that you can anticipate questions. You
could do things that scale. Those are
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really the reasons that I feel most
AI and ML projects fail is because you
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usually do a POC's off a small
data set with the limited audience, and
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that could go well, but then
when you start to try to roll that
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out and you know really at scale, make your predictions or your analysis means
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something to the company, that's where
things fall apart, because you're never going
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to convince an analyst to, you
know, upload a CSV file every day
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in order to fuel your predictions,
or you know you're never going to get
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that salesperson who needs your prediction to
log into the dashboard that you need or
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whatever end point you have your prediction
at. So if you don't have that
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whole data platform thought out, you
can even be producing some really great stuff
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and your projects can be successful,
but they don't really take at the level
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that they need to because there's no
infrastructure there. Maybe people don't trust the
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data, maybe people can access it, but there's so many things that go
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into the success of data and again, most of it is not very exciting
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and most people don't do it.
They skip right to the fun stuff.
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But, as you can imagine,
that's not necessarily recipe for success. Yeah,
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I completely agree, and I've talked
to a lot of different people on
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the podcast as well, saying that
it's important to how quality data as well,
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and that's that's a huge problem of
why a lot of projects aren't being
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deployed. It out with, for
with AI, at a lot of companies.
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It's a lot of people say it's
scarbage in, garbage out, and
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I think that's a great way to
kind of think about it. Too,
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and so what are some ways that
you think would be best to kind of
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mitigate these challenges that companies are having
in terms of being able to deploy ai
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within their company and be more data
driven? Yeah, it's great question.
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One of like if I could put
words on my wall, I don't have
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any, like live, laugh,
love stuff, but if I could,
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it would say automate everything until the
fun stuff is left, right and for
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me and I think most data people, the ML AI visualization actually providing insight
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is the fun stuff. Right,
you're actually driving the business like it's fun,
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it's engaging. You could see how
your work translates to dollars or success
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on the other side. But again, you have to automate a lot to
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get to that fun stuff, and
that's what I always preach to people.
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Right, if you don't have a
data pipelining solution or some type of at
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least concept of that in your company, you don't have data engineers or someone
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that's minded that way, it's going
to be very hard to get to the
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fun stuff. Same thing with architecture. If you don't have the concept of
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a data warehouse, you don't have
someone who's managing that, it's you're not
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gonna be able to get to the
fun stuff. And even more recently that's
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coming to focus is the data model
of a company. Right. So,
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even if you have all the physical
pieces of that data platform, even if
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you have the skills producer prediction,
if you don't have the data model well
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defined, and it's going to be
really hard for anyone in the business to
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take your insights seriously, your predictions
seriously, because you can't give them any
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transparency into the definitions of those data
how that relates to everything, you can't
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even, you know, give confidence
that you're pulling the right information, that
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you have clean informations. So,
again, all that's not fun stuff.
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Their long roads setting up those programs, making sure that they're automated, that
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they work. Those are heavy lifts. But if you are successful in automating
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that and you put your time there, the ML in the AI will be
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there. You'll have a framework to
build anything that you want to in the
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future. Yeah, I completely agree. And I think another part about companies
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kind of failing to be able to
deploy AI. It might be also the
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company structure in terms of WHO's collaborating
with WHO, and so, as a
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director of data. I'm sure you
speak to a lot of different people on
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their teams, such as the business
people, the executives, the engineers and
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everyone in between, and so what
is the company team structure like and what
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are some bust practices to encourage collaboration
and just make sure that the end goal
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is met? Yeah, I love
this question because it feeds into something that
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we're, I wouldn't say experimenting with, but we've we've grown out here at
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our company and it's a it's a
service model that I really like. And
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I call this service model because I
draw a lot of comparisons between data and
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HR as functions out of company and
people kind of scratch their head to like,
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what do you mean? It's you
know, there's not a lot of
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hr people that necessarily sit in marketing
or product or sales. It's usually a
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centralized function in GNA and what they
do is that HR people are business partners
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with sales, marketing, whatever.
They have kind of a deep understanding of
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that business or SEMIDEP, but what
their specialization is is really an hr and
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you would never expect like a demand
generation person in marketing to also be an
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HR person. There's really not that
there's not that expectation there, and that's
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really the way it should be with
data. But now, if you think
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about how data works, right,
a lot of times you just have data
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people embedded all over the company.
Right, that person that does Legion in
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marketing probably also has a bunch of
spreads. Jeets, does some attribution modeling,
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you know, something on the side, right, and so they have
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a split job. What we've tried
to do here at cloudy's is create a
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center of excellence, right, and
all of our data people are data generalist.
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They know how to do anything across
the scope of data, whether it's
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pipelinding, architecting or producing results in
analysis. And what we do is,
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like hr, we partner with the
business. Right. There's a there's someone
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who has a better understanding of sales
and marketing data. Are they a marketer?
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Are they a salesperson? No Way, their data to person, but
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they are assigned to that marketing organization
and they can really understand their pains.
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And so you really have now a
full time data person WHO's working on your
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data problems rather than just trying to
allocate some CPU of someone someone else on
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the team. And again, that
model, I think, is really the
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future for data and at these companies
that are taking data seriously, because it
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also allows you to leverage a mind
share. Right, like maybe the person
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who's the partner with the product team
is doing something really cool with side kit
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learn and now they could turn around
and they could share with the person in
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marketing. You know, if those
people are both embedded in the businesses or
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those business units, it's really difficult
to do that. So again, the
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short answer is that business partner model, I think is has made as very
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successful. Yeah, and I think
that's great that you kind of talked about
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really getting to know everyone within your
team and your organization to be able to
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share your ideas with within the organization. One of my mentors recently she told
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me that just at going into a
new job or anything like that, it's
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important to just set aside some time
and get to know each person within your
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team just so you can build that
trust, because trust is also very important
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within organization and it's important to kind
of build that up from the beginning.
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So great insight there. You know, a great point right about trust data
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people are definitely they don't do enough
of that. Like there is a whole
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psychological piece to to data right and
and we're trying to actually conceptualize that and
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graph what our engagements look like because, again, in order to gain someone
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trust that you have the right data, that you have those skills, you
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know, that's a journey, right. It's very consultative, but there is
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a way that you can build that. Rapport I'll be honest. It's tougher
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now in the days of covid because
it was much different when we were able
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to get it together with people.
We could show them things, we could
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demonstrate things a little bit more easily. There's more walls now, but you
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can still, to your point,
cultivate that trust by doing a lot of
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things that data people don't do,
talking right, scoping projects, so being
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more of that project manage in that
business partner, not just like technical engineering
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pece. So in that way,
data people are really the Jack of all
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trades, right, so it's a
wide breath of skills that you need,
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I think, to be really successful. Yeah, and that's one of the
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big reasons why I really like the
data field, because you kind of have
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to know a little bit of everything, a little bit of programming, a
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little bit of just networking and the
business side of things. It's just a
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little bit of everything. So I
think that's an awesome field to kind of
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be in. And so what how
do you kind of see the future of
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business is changing with AI and data
in general? Yeah, I think there's
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I think most companies are going to
have one of two paths that they take.
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They're either going to double down on
actually being data driven, and you
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see that from some of the best
companies. They've already been that way,
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like I think of someone like Instat
Cart Right. They built out their data
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science team from day one because they
knew it was going to be a strategic
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thing. Same thing with door.
Like these major companies, you can see
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that their success is tied to the
fact that they took data seriously. They
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have data science departments, they have
those data platforms right. So I think
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that more companies will be forced to
do that. Again, making the parallel
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between HR, probably back in the
S S, maybe even S, there
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might have not been like an HR
department. Maybe there was an HR person
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here there, but nowadays, now
that we understand the value of emotional intelligence,
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diversity and inclusion, it's kind of
silly if you go to a company
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and there's not an HR department,
you'd kind of scratch your head and say
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what's going on? It should be
the same thing with data, but we're
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not. Most companies aren't there yet. They still have, you know,
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that data wizard who serves the whole
company or a small Tigroup team of people
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that is serving people. But,
but again, I think one of the
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paths that people will go down as
they'll take data seriously, they'll create centers
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of excellence and real functions within the
business that are right on part with marketing,
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sales, finance, etc. So
the other option, I think,
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and and you see it emerging with
tools like snowflake and five tran becoming major
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players in the data space, is
that people are going to double down on
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technology that allows their small data team
to scale themselves and become super powered.
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Right. So again I mentioned five
trans something like that. I could see
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getting bigger and bigger and bigger,
because if you're not going to invest in
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building a real data pipeline platform with
like airflow or something like that, five
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trains great out of the box.
It works and, as one person,
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you could probably a minister, connections
to a lot of different applications. Right.
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Same thing with Snowflake, it makes
managing a day to warehouse pretty easy
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for one person or a couple people
to do so. Again, I think
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it'll either be people build those functions
out or they just double down this technology
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that makes their day to team,
the small data team, superpowered. Now
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awesome insight there. Everything that you
just brought up is super important within the
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industry and just helping the future of
businesses really grow. But yeah, and
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so I just wanted to kind of
transition into talking more about less technical topics
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now. And so just outside of
the technical data field, what are some
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things that you do outside of work
and do me hobbies or interest that you
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do on the side? Yeah,
I'm so again it's all around numbers.
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Unfortunately. I'm a big I'm a
big music person, right, so I'm
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always writing songs people. There's a
big math component to that. I can't
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always quantify it as well as I
can with my job, right, but
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there's something to that. So,
like music, I'm a big fitness person.
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There's a lot of numbers behind that, believe it or not, especially
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if you want to get to optimal
performance. And then I love sports.
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I'm a big sports fan and,
no, needless to say, a there's
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you could dive into so many numbers
when it comes to sports, and that's
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cool for me. Like I like
watching baseball games and keeping the score and
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diving into all the whacking metrics like
war and stuff and what they mean.
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So yeah, like most of my
time is split up between music, fitness,
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your data and I also have a
bunch of cats, so I'm very
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surprised that they actually didn't crash this
podcast. But you know, between all
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that, I keep pretty busy.
Yeah, that's awesome. And how you've
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ever worked on any like sports data
analytics at all? Yeah, I actually
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one of the things I'm really proud
of is that recently I got a block
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post published on towards data science,
which is a popular medium publication, and
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it was all about how you can
automate, or how I automated my football
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Sunday picks. Right, I had
like this process where I was copying pasting
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from all these different places in order
to make a prediction on what games,
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you know, what the outcome should
be, were fantasy players I should be
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putting my line up, and I
actually walk through that and automated it to
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a certain extent, and I published
that out on medium. So, yeah,
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if you're interested in any of that
stuff, I have some of that
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on the Internet. That sounds awesome. So thank you so much for sharing
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more about your outside technical stuff that
you do is that's awesome. And so,
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yeah, we have the data standard. We're trying to build a community
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of data enthusiast and data thought leaders
so that everybody has a place where they
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can just kind of notework and collaborate
with each other. So, so what
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is something the community can do for
you? Yeah, I think that sharing,
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you know, ideas is the most
important thing. Just what people how
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people are solving problems. You know, I think it's very hard in data
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to anticipate what the problems will be
because we're really on the bleeding edge of
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all technology, right. You know, back, five, ten years ago
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is all about big data. Right, how do we handle Big Day besets?
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Today? It's a very different problem
and I think when people just share
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the problems that they're trying to solve
for themselves that that starts everyone thinking.
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It's almost like a group think at
that point. That's why I love something
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like Cagle, right. That's like
I go to Cavil when I have a
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question and I try to see how
other people are solving for that question,
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and I think the same thing could
be done in this community right just people
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being willing to share what they're doing, even if it's half baked. You
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know, that could be someone else's
afha moment and then it could lead them
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to the answer that they've been looking
for for years. So, you know,
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just people being open and participating the
community like that's that's more than I
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could ever ask for, you know. Yeah, and I think it's great
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that you brought that up, because
a lot of companies are also struggling on
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similar problems too, and if everyone
just collaborated and talk to each other,
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then these problems could be solved,
probably much sooner than later, but definitely
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collaboration is super important within the community. Yeah, and we're can our community
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find you online? So, yeah, I am on medium. If you
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look for my name, I believe
it's Davelow. I'll get you the link.
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Obviously on Linkedin, you know,
same thing. That's probably professionally where
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the best places to look for me
are. So yeah, awesome. and
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to re viewers. For more information
on the data standard, you can find
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us at www dot data standard dot
io, as well as our linkedin and
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Youtube Channel, and this episode is
sponsored by Pandio. They're innovating the text
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base with their patchy pulsar messaging system, helping companies have more streamline of data
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and do more data driven so learn
more about their work up pandiocom and thank
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you so much day for joining us. So great to speak with you and
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just learn more about the industry and
just more about your experiences. So thank
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you so much. Happy to do
with Catherine. Thank you, wo
