September 6, 2022 • 19 mins
In this episode, Brandon Cosley (Director Artificial Intelligence) and Andreas Welsch discuss how business stakeholders can become more familiar with data science & Artificial Intelligence (AI). Brandon shares expertise on increasing AI literacy in business teams and provides valuable insights for listeners looking to get value from AI projects faster.
Key topics:
- Upskill your team on data science
- Ask the right data science questions
- Make work easier for the data science manager
Listen to the full episode to hear how you can:
- Understand the AI product lifecycle of data science
- Maintain constant communication with domain experts
- Find solutions through experimentation and alignment
Watch this episode on YouTube: https://youtu.be/WIDlcDWKi6s
Questions or suggestions? Send me a Text Message.
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Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Andreas Welsch (00:00):
Today we'll talk about data science for non-data
scientists.
And who better to talk to aboutit than someone who's passionate
about doing just that?
Brandon Cosley.
Hey Brandon.
How's it going?
Brandon Cosley (00:13):
Hey, it's going well.
Thanks for having me on theshow.
I really appreciate it, Andreas.
Andreas Welsch (00:19):
Great.
Hey, why don't you tell us alittle bit about yourself, who
you are, what do you do?
Brandon Cosley (00:24):
Sure, absolutely.
I direct a data scienceorganization.
We are responsible for all ofthe customization that happens
for the business artificialintelligence, machine learning,
data science our buzzwords.
Everybody's selling them.
But what I think the realchallenge is trying to figure
(00:44):
out how to use thosecapabilities in the fabric of
your own business.
And so I'm responsible for anorganization that tries to do
just that, bring thosecapabilities into the things
that are very unique and veryspecial to the industry, and to
the sort of the way in which wework in that industry.
Because not every company isgoing to sell you something
(01:06):
that's going to fit perfectlywith what you do, your
infrastructure, all the oldthings that you have going on
your legacy systems.
So I help bring thosecapabilities to those systems.
Andreas Welsch (01:18):
Awesome.
And I know following yourcontent for a while, you're also
quite passionate about teachingdata science to non-data
scientists outside of work.
So I'm really excited thatyou're able to join me.
And so for those of you justjoining the stream, please drop
a comment in the chat.
What business stakeholdersshould learn about more around
(01:39):
AI?
What do you think?
But hey to kick things off,Brandon, should we play a little
game?
Brandon Cosley (01:47):
Okay, let's do it.
Andreas Welsch (01:48):
Okay, perfect.
So this game is called In YourOwn Words.
When I hit the buzzer, thewheels will start spinning and
when they stop, you'll see asentence.
And I'd like you to answer withthe first thing that comes to
mind and why, in your own words.
(02:09):
So Brendan, are you ready for,What's the BUZZ?
Brandon Cosley (02:14):
Let's do this.
Andreas Welsch (02:16):
Good.
Alright, so then let's getstarted.
If AI were a rock band, whowould it be?
Brandon Cosley (02:24):
That is a really fun question for me to think
about.
And I think if AI were rockband, the most obvious choice
probably would be Daft Punk.
I'm a little bit of a technoguy, but if AI were a rock band,
it's definitely Daft Punk.
Andreas Welsch (02:41):
Alright.
And why do you think it's them?
Brandon Cosley (02:43):
One, they dress like robots.
It's their shtick.
Two, they write great music.
It's all electronic.
So, when I think of AI, that'swhat conjures up for me They're
dance they're oriented towardsdigital and and they even wear
suits that make them look likeartificially intelligent robots.
(03:06):
But they're not, they're people.
Andreas Welsch (03:08):
Awesome.
So one more time, huh?
Brandon Cosley (03:11):
Absolutely.
Cool.
That's right.
Yeah.
Makes sense.
Andreas Welsch (03:14):
Perfect.
Thanks for answering that on thespot in your own words.
Hey I remember you had mentioneda while ago that you have a dual
role and you alluded to thatearlier in the intro as well.
On one hand, leading a team ofdata scientists and on the
other, enabling your businessstakeholders on AI, what it is
and what you can use it for.
(03:35):
Now I'm really curious, how doyou do that kind of upskilling
and what kind of prerequisitesdo you see that these
individuals on business teamsshould have to make this a
successful endeavor?
Brandon Cosley (03:49):
Yeah, so I think that there's a couple of
different angles.
I think that from the oneaspect, you have data scientists
who are coming into newenvironments.
So data scientists coming out ofboot camps and degree
organizations coming intobusinesses trying to figure out.
What does it mean to take whatI've learned about training
(04:10):
models and turning that intoactual data science products?
I think that there is a lot tobe learned, not just from those
who understand how to trainmodels, but I take that same
framework and try and teach thatto business stakeholders.
Let business stakeholdersunderstand that there is a
framework just as there is forbuilding.
(04:31):
That there's a framework forbuilding data science products.
And if we can think about thatframework together, we can
identify those use cases thathelp us advance the business
forward by building upon thatframework, and then allow those
subject matter experts whounderstand the modeling part to
come in and add those pieces.
But to me, the most importantthing is building understanding
(04:56):
around the proper framework forbuilding data science products.
Fabric of the businessunderstands those use cases.
Make sense when they make sense,and then how to actually go
about implement implementingthem because they know who to
talk to.
You don't need to know how to doeach part of the framework,
right?
What you do need to know how todo is who to talk to, the right
(05:19):
people when each different pieceof that framework needs to come
into play.
As you're building those.
Hey, enablement.
It's all about teaching peoplewhat framework is for data
science as a solution.
Andreas Welsch (05:35):
That's awesome.
I would like to pick up on onething that I noticed because I
feel in a lot of cases we thinkabout AI as projects, but I hear
you talk about it as a product.
How do you see that beingdifferent?
And why do you feel it'simportant to call it a product
as opposed to a project?
Brandon Cosley (05:54):
So for me, user experiences are fundamental.
And if you can't have the properuser experience, then at the end
of the day a project will fail.
And so for me it makes a lotmore sense to call artificial
intelligence agents a product,right?
Because what it's doing is it'senabling a user.
And so users work with products,users work in the context of
(06:18):
projects, but at the end of theday, what they want is something
that they can take away fromthat project so that they get
more value from it day in andday out.
And to me, that's why a productis a much more fitting term than
the word project.
Sure, it takes a project tobuild a product, but at the end
of the day, what you want is aproduct that lasts for a very
(06:41):
long time that continues todrive value for that team, for
that business, for that process.
So I really prefer the termproduct.
I think that what we're buildingas data scientists are truly
products.
And to me, that orients ustowards that user experience,
which is fundamental for the wayin which this, those products
can be successful.
Andreas Welsch (07:02):
I think that's a perfect way to summarize and to
frame it and to call it thatdistinction.
I really like how you thinkabout it and how you seem to
approach it.
Really putting the user at thecenter.
Yeah.
So thanks for sharing that.
Now, like you said there shouldbe a framework.
There are data scientists, thereare business stakeholders, and
(07:24):
they will eventually worktogether on building an AI
product.
How should business stakeholderswork with data scientists?
What should they be prepared toanswer?
And on the other hand, whatshould business analyst, data
scientists be prepared toanswer, to help understand the
problem much better and muchmore quickly?
Brandon Cosley (07:45):
Great question.
I think probably the mostfundamental challenge as a data
scientist is really trying tounderstand how to turn a
business problem, or what Ioften call real problems into
data science problems.
Fundamentally, there aredifferent degrees of
sophistication that we can turnbusiness problems into data
(08:05):
science problems.
Those data science problems arethe ways in which we reframe
business problems in the contextof the capabilities that we have
available to us, whether they betools, technologies, models, you
name it, right?
All the things that fit underthat umbrella of data science.
But the better we a, the betterable we.
(08:27):
To take all of those realproblems and reframe them as.
Data science problems, thebetter able we are to identify
whether or not there are datascience solutions for those
problems.
Now, will the problemfundamentally change or the
solution fundamentally change?
Absolutely.
And that's where communicationbecomes fundamental to that
(08:50):
entire process.
So there needs to be acommunication between the
business stakeholder and thedata science team that work.
To say, Hey, here's how wetranslate the pain point that
you have, using the capabilitiesthat we know to how to
potentially solve that painpoint.
And so trying to tie those twothings together is where the
(09:13):
magic happens, right?
That's the magic in the middle.
And that I think is the mostchallenging part to what I do on
a day-to-day basis, is trying tobring my business stakeholders.
Where they're dealing with realeveryday problems into my world
to say, Hey, there's a datascience solution for that, but
we need to reframe it indifferent ways.
(09:34):
And that means we need toreframe the way that you think
about your real problem.
And so we look at thecapabilities of data science.
To try and do, it doesn't alwaysfit.
And when it doesn't fit, then wehave to understand that and be
ready to move on.
And more importantly, we have tobe ready to experiment because
oftentimes we don't know if itfits.
So when we look for that magicin the middle, we have to have
(09:56):
that clear communication.
We take those real problems, weturn them into data science
problems, and then we find allthe different possible.
That may fit in the middle, andthat's where the value is.
Whenever we fail fast, weidentify the solution more
quickly and hopefully, Deploythat solution project so that it
(10:18):
becomes, here it is again,product
Andreas Welsch (10:23):
Perfect.
I've seen in my own experiencethat a lot of times you start
with something like, Hey, let'slet's either see where or how we
can apply AI to this problem.
Or here's an idea that will helpsomebody.
Reduce the number of clicks orget better insights, and then
(10:45):
you need to start drilling down,right?
Why are you doing this?
What does it mean?
What's the next step?
What does the business impactlook like?
What are you seeing there?
Do you see that be part of thatconversation as, as well in your
work.
Brandon Cosley (11:01):
Yeah, absolutely.
So I think to revisit what Ijust said in, in the previous
answer, real problems have KPIstied to them, right?
They have key performanceindicators, businesses about
metrics.
Obviously it drive our bottomline, but where data scientists
(11:23):
really have chows trying tounderstand how.
Work drives and affects thoseKPIs.
And so what's really importantis, again, to try and understand
how we turn all these that we'vebeen trained to do and bring
those in to impact those KPIs.
That's where the business canunderstand you.
(11:43):
That's where you can have animpact, but that's also
fundamentally the hardestchallenge lies because it's not
always the case that when wetranslate that real problem into
a business pro or into.
Data science problem, excuse me,that we have an impact on that
kpi.
So we need to make sure thatfundamentally the things that
we're doing with our datascience are having an impact on
(12:05):
that KPI, so that way thebusiness understands the value
of what we do build.
Andreas Welsch (12:13):
Great summary.
And again, tying it back tobusiness KPIs and something that
your business stakeholders careabout and are it's eventually
measured on.
So keeping an eye on the chat.
I see Lisa saying, Hey, keepdiscussion simple and using
analogies.
It's helpful when you work withbusiness stakeholders.
(12:33):
And quick shout out to Oliver inGermany who said, Hey I've built
a course in, in English andGerman called Data Science for
Business Leaders for smallmedium enterprises.
So check that out.
And Sujata is saying, Hey wehave to be willing to experiment
(12:53):
like you.
She emphasizes with that so truethat it's not always a fit.
And that also needs to be theexpectation that it is
experimentation in some casesmore research than, a
straightforward process in thatsense.
Now to close it out with ourlast question.
(13:15):
I know you've seen your fairshare of AI projects, obviously,
and I think one perspective thatis usually not covered that much
is what does it actually looklike from a data science
manager's perspective?
How can your businessstakeholders and your business
analysts make your life as adata science manager easier?
(13:36):
What do you see in your work?
Brandon Cosley (13:38):
Fundamentally, for me, it comes down to
communication.
I'm maybe sometimes described asan over communicator.
I always check in.
I really like to have theconversations.
And I think to Sujata's point,it's really important to
understand where the languagecrosses different boundaries and
(13:58):
different domains.
You have to take a lot of time,a lot of attention, and you have
to spend a lot of real care totry and understand where the
language is using differentterms, but it means the same
thing and that's fundamentallywhere I think we struggle as
data science managers to helpcommunicate the value of the
(14:21):
work that our teams do with ourbusiness stakeholders.
And so the more that we can getin front of them, the more that
we can have those conversations,the better able they are to
articul.
The relationship between theirreal problems and the ways in
which my teams and my developerscan solve for them.
So I think at the end of theday, for me, if I were to wrap
(14:43):
it up in a single sentence, itwould be communicate, spend lots
of time talking.
And I know it seems sofundamentally simple.
But it is so important that wespend time communicating with
those individuals.
It is always people in the loop.
If we don't keep them in theloop.
Then whatever we're doing isdestined to fail.
(15:04):
So it's always keeping them inthe loop.
It's always making sure that wecommunicate, and then being
willing and also beingempathetic.
Understand that we are talkingabout the same things, just
using different words.
And so trying to find thoserelationships is where we really
drive value.
Andreas Welsch (15:23):
I think that's golden that, that advice really
keeping people in the loop andworking as one team to have a
shared understanding.
There's not much more I can add.
Maybe one last question from thechat before we wrap it up
altogether.
Arek is asking, Hey Brandon whendo you conduct projects, are you
mostly looking for specificdomain expertise or more AI
(15:45):
strategy?
Brandon Cosley (15:46):
Fundamental question, both actually.
It's important I always trainboth data scientists and
non-data scientists tounderstand that the more
knowledge that you have of howdata science algorithms work to
(16:07):
train their models, right?
The.
The more options will beavailable to you for potential
solutions, right?
For turning real problems intopotential data science
solutions.
So it's important that you haveAI strategists that understand
the framework through which youbuild products.
(16:28):
But I am most interested indomain expertise because at the
end of the day, they're the oneswho are struggling with the real
problems that we're trying to.
And so what we need to be ableto do is we need to be able to
connect the two together.
So my answer to art's questionis that you need both.
(16:48):
Fundamentally, thesophistication that you have on
the AI side is important becauseit opens you up to new
possibilities.
But at the end of the day, it'sreally fundamental that your
domain expert helps toarticulate what their real
problem.
And how those potentialsolutions may actually impact
(17:10):
the metrics that they're tryingto change.
Andreas Welsch (17:15):
That's a great summary.
So having both domain expertiseand technical expertise to, to
make the biggest impactpossible.
Brandon Cosley (17:22):
You need apples and dough to have apple pie.
Andreas Welsch (17:25):
Perfect.
Now you're making me hungry.
Hey let's wrap it up.
And maybe if I can toss it overto you to quickly summarize the
key points for each of thetopics that we talked about.
Brandon Cosley (17:38):
So for me, for data scientists and non data
scientists understanding theframework through which we build
solutions which I have calledproducts understanding the full
product lifecycle of a datascientist is fundamental and
communication.
Constant communication withthose domain experts who are
(17:59):
dealing with the real problemsthat data scientists need to be
able to turn into data scienceproblems so that way we can look
for experimentations to findproper solutions that actually
fit.
And have impacts on the KPIsthat those business domain
stakeholders are reallystruggling with.
So communication, understandingthe framework, and then enabling
(18:22):
that communication so that youfind that magic in the middle
and understand that it will takeexperimentation.
And then oftentimes thatexperimentation does take.
But the more we communicate, thefaster we're able to fail.
The quicker we able, the quickerwe are able to get to a solution
that develops a product that hasa lasting impact for a given
(18:43):
organization.
Andreas Welsch (18:45):
Awesome.
Thank you so much for wrappingit up.
And folks in the audience, we'recoming up to the end of our show
today.
Thank you so much for joining usBrandon, and for sharing your
expertise with us.
It's been great having you on.
I really appreciate it.
Brandon Cosley (18:58):
Thanks, Andreas.
Andreas Welsch (18:59):
Alright, thanks Brandon.
Today we'll talk about data science for non-data
scientists.
And who better to talk to aboutit than someone who's passionate
about doing just that?
Brandon Cosley.
Hey Brandon.
How's it going?
Brandon Cosley (00:13):
Hey, it's going well.
Thanks for having me on theshow.
I really appreciate it, Andreas.
Andreas Welsch (00:19):
Great.
Hey, why don't you tell us alittle bit about yourself, who
you are, what do you do?
Brandon Cosley (00:24):
Sure, absolutely.
I direct a data scienceorganization.
We are responsible for all ofthe customization that happens
for the business artificialintelligence, machine learning,
data science our buzzwords.
Everybody's selling them.
But what I think the realchallenge is trying to figure
(00:44):
out how to use thosecapabilities in the fabric of
your own business.
And so I'm responsible for anorganization that tries to do
just that, bring thosecapabilities into the things
that are very unique and veryspecial to the industry, and to
the sort of the way in which wework in that industry.
Because not every company isgoing to sell you something
(01:06):
that's going to fit perfectlywith what you do, your
infrastructure, all the oldthings that you have going on
your legacy systems.
So I help bring thosecapabilities to those systems.
Andreas Welsch (01:18):
Awesome.
And I know following yourcontent for a while, you're also
quite passionate about teachingdata science to non-data
scientists outside of work.
So I'm really excited thatyou're able to join me.
And so for those of you justjoining the stream, please drop
a comment in the chat.
What business stakeholdersshould learn about more around
(01:39):
AI?
What do you think?
But hey to kick things off,Brandon, should we play a little
game?
Brandon Cosley (01:47):
Okay, let's do it.
Andreas Welsch (01:48):
Okay, perfect.
So this game is called In YourOwn Words.
When I hit the buzzer, thewheels will start spinning and
when they stop, you'll see asentence.
And I'd like you to answer withthe first thing that comes to
mind and why, in your own words.
(02:09):
So Brendan, are you ready for,What's the BUZZ?
Brandon Cosley (02:14):
Let's do this.
Andreas Welsch (02:16):
Good.
Alright, so then let's getstarted.
If AI were a rock band, whowould it be?
Brandon Cosley (02:24):
That is a really fun question for me to think
about.
And I think if AI were rockband, the most obvious choice
probably would be Daft Punk.
I'm a little bit of a technoguy, but if AI were a rock band,
it's definitely Daft Punk.
Andreas Welsch (02:41):
Alright.
And why do you think it's them?
Brandon Cosley (02:43):
One, they dress like robots.
It's their shtick.
Two, they write great music.
It's all electronic.
So, when I think of AI, that'swhat conjures up for me They're
dance they're oriented towardsdigital and and they even wear
suits that make them look likeartificially intelligent robots.
(03:06):
But they're not, they're people.
Andreas Welsch (03:08):
Awesome.
So one more time, huh?
Brandon Cosley (03:11):
Absolutely.
Cool.
That's right.
Yeah.
Makes sense.
Andreas Welsch (03:14):
Perfect.
Thanks for answering that on thespot in your own words.
Hey I remember you had mentioneda while ago that you have a dual
role and you alluded to thatearlier in the intro as well.
On one hand, leading a team ofdata scientists and on the
other, enabling your businessstakeholders on AI, what it is
and what you can use it for.
(03:35):
Now I'm really curious, how doyou do that kind of upskilling
and what kind of prerequisitesdo you see that these
individuals on business teamsshould have to make this a
successful endeavor?
Brandon Cosley (03:49):
Yeah, so I think that there's a couple of
different angles.
I think that from the oneaspect, you have data scientists
who are coming into newenvironments.
So data scientists coming out ofboot camps and degree
organizations coming intobusinesses trying to figure out.
What does it mean to take whatI've learned about training
(04:10):
models and turning that intoactual data science products?
I think that there is a lot tobe learned, not just from those
who understand how to trainmodels, but I take that same
framework and try and teach thatto business stakeholders.
Let business stakeholdersunderstand that there is a
framework just as there is forbuilding.
(04:31):
That there's a framework forbuilding data science products.
And if we can think about thatframework together, we can
identify those use cases thathelp us advance the business
forward by building upon thatframework, and then allow those
subject matter experts whounderstand the modeling part to
come in and add those pieces.
But to me, the most importantthing is building understanding
(04:56):
around the proper framework forbuilding data science products.
Fabric of the businessunderstands those use cases.
Make sense when they make sense,and then how to actually go
about implement implementingthem because they know who to
talk to.
You don't need to know how to doeach part of the framework,
right?
What you do need to know how todo is who to talk to, the right
(05:19):
people when each different pieceof that framework needs to come
into play.
As you're building those.
Hey, enablement.
It's all about teaching peoplewhat framework is for data
science as a solution.
Andreas Welsch (05:35):
That's awesome.
I would like to pick up on onething that I noticed because I
feel in a lot of cases we thinkabout AI as projects, but I hear
you talk about it as a product.
How do you see that beingdifferent?
And why do you feel it'simportant to call it a product
as opposed to a project?
Brandon Cosley (05:54):
So for me, user experiences are fundamental.
And if you can't have the properuser experience, then at the end
of the day a project will fail.
And so for me it makes a lotmore sense to call artificial
intelligence agents a product,right?
Because what it's doing is it'senabling a user.
And so users work with products,users work in the context of
(06:18):
projects, but at the end of theday, what they want is something
that they can take away fromthat project so that they get
more value from it day in andday out.
And to me, that's why a productis a much more fitting term than
the word project.
Sure, it takes a project tobuild a product, but at the end
of the day, what you want is aproduct that lasts for a very
(06:41):
long time that continues todrive value for that team, for
that business, for that process.
So I really prefer the termproduct.
I think that what we're buildingas data scientists are truly
products.
And to me, that orients ustowards that user experience,
which is fundamental for the wayin which this, those products
can be successful.
Andreas Welsch (07:02):
I think that's a perfect way to summarize and to
frame it and to call it thatdistinction.
I really like how you thinkabout it and how you seem to
approach it.
Really putting the user at thecenter.
Yeah.
So thanks for sharing that.
Now, like you said there shouldbe a framework.
There are data scientists, thereare business stakeholders, and
(07:24):
they will eventually worktogether on building an AI
product.
How should business stakeholderswork with data scientists?
What should they be prepared toanswer?
And on the other hand, whatshould business analyst, data
scientists be prepared toanswer, to help understand the
problem much better and muchmore quickly?
Brandon Cosley (07:45):
Great question.
I think probably the mostfundamental challenge as a data
scientist is really trying tounderstand how to turn a
business problem, or what Ioften call real problems into
data science problems.
Fundamentally, there aredifferent degrees of
sophistication that we can turnbusiness problems into data
(08:05):
science problems.
Those data science problems arethe ways in which we reframe
business problems in the contextof the capabilities that we have
available to us, whether they betools, technologies, models, you
name it, right?
All the things that fit underthat umbrella of data science.
But the better we a, the betterable we.
(08:27):
To take all of those realproblems and reframe them as.
Data science problems, thebetter able we are to identify
whether or not there are datascience solutions for those
problems.
Now, will the problemfundamentally change or the
solution fundamentally change?
Absolutely.
And that's where communicationbecomes fundamental to that
(08:50):
entire process.
So there needs to be acommunication between the
business stakeholder and thedata science team that work.
To say, Hey, here's how wetranslate the pain point that
you have, using the capabilitiesthat we know to how to
potentially solve that painpoint.
And so trying to tie those twothings together is where the
(09:13):
magic happens, right?
That's the magic in the middle.
And that I think is the mostchallenging part to what I do on
a day-to-day basis, is trying tobring my business stakeholders.
Where they're dealing with realeveryday problems into my world
to say, Hey, there's a datascience solution for that, but
we need to reframe it indifferent ways.
(09:34):
And that means we need toreframe the way that you think
about your real problem.
And so we look at thecapabilities of data science.
To try and do, it doesn't alwaysfit.
And when it doesn't fit, then wehave to understand that and be
ready to move on.
And more importantly, we have tobe ready to experiment because
oftentimes we don't know if itfits.
So when we look for that magicin the middle, we have to have
(09:56):
that clear communication.
We take those real problems, weturn them into data science
problems, and then we find allthe different possible.
That may fit in the middle, andthat's where the value is.
Whenever we fail fast, weidentify the solution more
quickly and hopefully, Deploythat solution project so that it
(10:18):
becomes, here it is again,product
Andreas Welsch (10:23):
Perfect.
I've seen in my own experiencethat a lot of times you start
with something like, Hey, let'slet's either see where or how we
can apply AI to this problem.
Or here's an idea that will helpsomebody.
Reduce the number of clicks orget better insights, and then
(10:45):
you need to start drilling down,right?
Why are you doing this?
What does it mean?
What's the next step?
What does the business impactlook like?
What are you seeing there?
Do you see that be part of thatconversation as, as well in your
work.
Brandon Cosley (11:01):
Yeah, absolutely.
So I think to revisit what Ijust said in, in the previous
answer, real problems have KPIstied to them, right?
They have key performanceindicators, businesses about
metrics.
Obviously it drive our bottomline, but where data scientists
(11:23):
really have chows trying tounderstand how.
Work drives and affects thoseKPIs.
And so what's really importantis, again, to try and understand
how we turn all these that we'vebeen trained to do and bring
those in to impact those KPIs.
That's where the business canunderstand you.
(11:43):
That's where you can have animpact, but that's also
fundamentally the hardestchallenge lies because it's not
always the case that when wetranslate that real problem into
a business pro or into.
Data science problem, excuse me,that we have an impact on that
kpi.
So we need to make sure thatfundamentally the things that
we're doing with our datascience are having an impact on
(12:05):
that KPI, so that way thebusiness understands the value
of what we do build.
Andreas Welsch (12:13):
Great summary.
And again, tying it back tobusiness KPIs and something that
your business stakeholders careabout and are it's eventually
measured on.
So keeping an eye on the chat.
I see Lisa saying, Hey, keepdiscussion simple and using
analogies.
It's helpful when you work withbusiness stakeholders.
(12:33):
And quick shout out to Oliver inGermany who said, Hey I've built
a course in, in English andGerman called Data Science for
Business Leaders for smallmedium enterprises.
So check that out.
And Sujata is saying, Hey wehave to be willing to experiment
(12:53):
like you.
She emphasizes with that so truethat it's not always a fit.
And that also needs to be theexpectation that it is
experimentation in some casesmore research than, a
straightforward process in thatsense.
Now to close it out with ourlast question.
(13:15):
I know you've seen your fairshare of AI projects, obviously,
and I think one perspective thatis usually not covered that much
is what does it actually looklike from a data science
manager's perspective?
How can your businessstakeholders and your business
analysts make your life as adata science manager easier?
(13:36):
What do you see in your work?
Brandon Cosley (13:38):
Fundamentally, for me, it comes down to
communication.
I'm maybe sometimes described asan over communicator.
I always check in.
I really like to have theconversations.
And I think to Sujata's point,it's really important to
understand where the languagecrosses different boundaries and
(13:58):
different domains.
You have to take a lot of time,a lot of attention, and you have
to spend a lot of real care totry and understand where the
language is using differentterms, but it means the same
thing and that's fundamentallywhere I think we struggle as
data science managers to helpcommunicate the value of the
(14:21):
work that our teams do with ourbusiness stakeholders.
And so the more that we can getin front of them, the more that
we can have those conversations,the better able they are to
articul.
The relationship between theirreal problems and the ways in
which my teams and my developerscan solve for them.
So I think at the end of theday, for me, if I were to wrap
(14:43):
it up in a single sentence, itwould be communicate, spend lots
of time talking.
And I know it seems sofundamentally simple.
But it is so important that wespend time communicating with
those individuals.
It is always people in the loop.
If we don't keep them in theloop.
Then whatever we're doing isdestined to fail.
(15:04):
So it's always keeping them inthe loop.
It's always making sure that wecommunicate, and then being
willing and also beingempathetic.
Understand that we are talkingabout the same things, just
using different words.
And so trying to find thoserelationships is where we really
drive value.
Andreas Welsch (15:23):
I think that's golden that, that advice really
keeping people in the loop andworking as one team to have a
shared understanding.
There's not much more I can add.
Maybe one last question from thechat before we wrap it up
altogether.
Arek is asking, Hey Brandon whendo you conduct projects, are you
mostly looking for specificdomain expertise or more AI
(15:45):
strategy?
Brandon Cosley (15:46):
Fundamental question, both actually.
It's important I always trainboth data scientists and
non-data scientists tounderstand that the more
knowledge that you have of howdata science algorithms work to
(16:07):
train their models, right?
The.
The more options will beavailable to you for potential
solutions, right?
For turning real problems intopotential data science
solutions.
So it's important that you haveAI strategists that understand
the framework through which youbuild products.
(16:28):
But I am most interested indomain expertise because at the
end of the day, they're the oneswho are struggling with the real
problems that we're trying to.
And so what we need to be ableto do is we need to be able to
connect the two together.
So my answer to art's questionis that you need both.
(16:48):
Fundamentally, thesophistication that you have on
the AI side is important becauseit opens you up to new
possibilities.
But at the end of the day, it'sreally fundamental that your
domain expert helps toarticulate what their real
problem.
And how those potentialsolutions may actually impact
(17:10):
the metrics that they're tryingto change.
Andreas Welsch (17:15):
That's a great summary.
So having both domain expertiseand technical expertise to, to
make the biggest impactpossible.
Brandon Cosley (17:22):
You need apples and dough to have apple pie.
Andreas Welsch (17:25):
Perfect.
Now you're making me hungry.
Hey let's wrap it up.
And maybe if I can toss it overto you to quickly summarize the
key points for each of thetopics that we talked about.
Brandon Cosley (17:38):
So for me, for data scientists and non data
scientists understanding theframework through which we build
solutions which I have calledproducts understanding the full
product lifecycle of a datascientist is fundamental and
communication.
Constant communication withthose domain experts who are
(17:59):
dealing with the real problemsthat data scientists need to be
able to turn into data scienceproblems so that way we can look
for experimentations to findproper solutions that actually
fit.
And have impacts on the KPIsthat those business domain
stakeholders are reallystruggling with.
So communication, understandingthe framework, and then enabling
(18:22):
that communication so that youfind that magic in the middle
and understand that it will takeexperimentation.
And then oftentimes thatexperimentation does take.
But the more we communicate, thefaster we're able to fail.
The quicker we able, the quickerwe are able to get to a solution
that develops a product that hasa lasting impact for a given
(18:43):
organization.
Andreas Welsch (18:45):
Awesome.
Thank you so much for wrappingit up.
And folks in the audience, we'recoming up to the end of our show
today.
Thank you so much for joining usBrandon, and for sharing your
expertise with us.
It's been great having you on.
I really appreciate it.
Brandon Cosley (18:58):
Thanks, Andreas.
Andreas Welsch (18:59):
Alright, thanks Brandon.
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