Today we're talking about how to manage a
team of data scientists.
It's a crucial topic.
We're speaking with Bülent Kiziltan, who
is one of the most articulate and outspoken
data scientists that I know.
Thank you.
Great to be on the show again, Michael.
Give us a flavor of your background to set
some context for us.
I'm trained as a physicist and an astronomer.
I have spent my career searching for neutron
stars and black holes.
During that pursuit, I used different aspects
of apps like math and machine learning for
more than 20 years.
Now I'm applying those skills in the industry.
All right, so I'm hoping that the topic we
talk about today is going to be a little bit
less complex than looking for neutrons and
black holes but, hey, we're talking about
people, so maybe it's more complex.
That's exactly what I was going to say.
Okay.
Bülent, what is unique about data science,
unique about AI when it comes to building
teams and managing teams?
Right.
First and foremost, it's a new domain for
the business and the industry.
There is a lot of experimentation going on.
The strategy, when it comes to managing, building
data science teams and creating value with
those teams is still in its infancy.
There is a lot of experimentation.
Companies change strategies all the time.
There is no single answer.
There is no single right answer.
Bigger companies versus smaller companies,
industry domains matter a lot, and the culture
is very, very important.
I think we're going to talk about that as
well.
Is there a distinction between managing data
science and managing AI?
I think there's a lot of confusion between
those two things.
Right.
Just a few days ago, I wanted to go online
and look what people say on blogs about AI,
data science, and analytics, what are the
differences.
Once you go through the blogs and the information
that are in it, it's really confusing.
All of those posts have a certain level of
truth and correctness in them but, mainly,
it's an area that's very new to the industry.
AI is a more generic term that is considered
more general an umbrella term that sits above
data science, and data science typically would
sit above analytics in terms of its comprehensiveness,
if you will.
But there is a lot of confusion.
When it comes to etymology and how the context
of the world changes, I think a culture and
industry will play its part.
So, it's currently reshaping itself.
There are overlapping things about all those
three areas.
There are things that are very different about
all those three.
From a management standpoint, do we need to
think about them in very unique ways?
I think, not based on whether it's called
AI, data science, or analytics, but I think
what is more important in managing data science
teams, if you will, is in what type of company
and what type of domain you are operating
in.
Depending on the business objectives, I think
one can come up with a more appropriate designation
for the team.
For instance, data science versus analytics.
All right.
I think that that sets the stage.
Fundamentally, the issue is one of this being
a new domain, and so we're still trying to
figure it out.
Is that the correct sort of ground level beginning
that we need to start from?
That's one important aspect.
Another aspect of this is data science is
science.
It is still a hybrid of an academic culture
and a business culture.
Companies have a hard time hitting the right
balance that aligns well with their business
objectives.
This is one area that we all are struggling
and we're experimenting with, hitting the
right cultural balance within an operation.
I would say, one can come up with the most
interesting strategy, but cultural eats strategy
for breakfast.
If your culture is not set right, you cannot
execute on the strategy that you are thinking
of.
Let's talk about the ROI and the organizational
expectations for a team that's being managed.
Organizationally, where should a data science
team or a data science department fit inside
a company?
If I were to think only like an academic,
I would say they should be independent.
Obviously, people who have been in the industry
for a very long time, they have a different
mindset.
Depending on the company size and their business
objectives, it could be considered as a supporting
department and then many companies have data
science operating on their engineering, but
their business objectives are--I would imagine--more
short-term.
AI has delivered on its promises.
It's creating a lot of ROI.
Executives and the companies are well aware
of what the ROI can be if analytics, operations,
or data science operations can set their strategy
independently of the engineering strategies
because engineers have different priorities
as opposed to analytics folks or data science
folks.
In my opinion, now we are at the stage where
AI, data science, or analytics operations
should be independently reporting to the board
or be represented at the C-level at least
regardless of the size of the company.
I want to remind everybody that we're speaking
with Bülent Kiziltan, and we're talking about
how to manage AI and data science organizations;
what are the unique aspects of this?
Bülent, the kind of expectations that companies
have of their AI departments or their data
science departments, you made a distinction
between the business expectations and the
technology or the engineering expectations.
Can you elaborate on that?
Everybody knows there is a certain level of
hype that comes with AI or data science.
Early, when this hype started, we had seen
some justified skepticism from high-level
business executives because the teams that
were formed were not, essentially, executing
or delivering the ROI.
I would say there were some unrealistic expectations
at the beginning but, right now, we are at
the level where we can utilize more mature
tools.
The talent pool has diversified itself.
Still, there is not enough talent out there,
but we hire data scientists from very different
profiles.
Data science, I would say that it is more
of a creative process than an engineering
process.
Engineering processes require creativity as
well, but I think that that creative aspect
is very dominant in data science processes.
An engineering mindset typically has an output
and input and they are, generically speaking,
trying to optimize that whole process whereas,
in data science, if managed properly and has
an aligned vision with the company, is about
discovery, extracting new information.
That is very different from a sole engineering
operations perspective.
This is why I think companies who are trying
to make an impact, who are trying to come
up with disruptive innovation on the large
scale or on the small scale, I think, are
moving in the direction where data science
operations now are led by domain experts that
really have done data science, have written
code, and they're leading the data science
operations by example.
This is why we have all sorts of titles floating
around.
That leader type is also very scarce right
now, but people and leaders who are domain
experts in data science or AI but also have
a business acumen and experience are the leaders
that are highly sought after today.
You mentioned that data science needs to be
aligned with the goals of the organization.
Right.
Please, talk about that.
That seems like a very crucial dimension here.
There are, I think, two extremes when it comes
to building a data science strategy.
One extreme is the academic mindset where
you do research for long-term impact.
The other extreme is the short-term pragmatism
that comes with short-term deliverables in
the business setting.
I think, in data science operations, the balance
has to be set right to align with the business
objectives of any company.
There are larger companies such as Google
and Facebook which have the resources to make
mid-term and long-term investments only.
They have research teams that really operate
like academic institutions.
Most of the companies don't have those resources.
They don't have those objectives.
Any leader who is coming in, I think, has
to first and foremost identify the business
objectives and what sort of short-term, midterm,
and long-term deliverables he can deliver
to the board in order to justify the operation's
existence.
This is very important.
On the other side, and we see that more often
than not, AI or analytics operations are managed
by non-domain experts.
I would call them managers.
There are some pragmatic reasons why you would
want a person that doesn't have a data science
background but has really the business experience,
especially in larger companies, mainly because
the value that one can create is most often
hindered by the internal dynamics and the
stakeholders.
A leader that goes into a bigger company has
to really consider the balance of different
stakeholders and convince them.
That comes with business experience.
I think the right balance is somewhere in
between where you really deliver on the short-term.
There are lots of low hanging fruits in all
sorts of business settings, especially in
larger companies.
A smart leader would focus on delivering the
short-term and invest in the long-term.
Given the importance of aligning the data
science efforts--and you could say the same
thing for AI--can you share with us any examples
where the business goals, where the data science
efforts were not aligned with the business
goals?
What's the kind of outcome that then happens?
I will talk about what we generalists see
in larger companies today is data science
and analytics efforts are led by non-domain
experts.
They don't know much about data science itself,
but they know really how to manage groups,
how to maybe build groups, and how to talk
to different stakeholders internally.
Through that relationship, they can justify
their existence and large budgets by delivering
on the short-term.
When you have an AI manager, if you will,
I wouldn't call them leaders.
If you have a manager that has a sole business
objective and business background, they will
have the shortsightedness that comes with
the business pragmatism.
They will go after the low hanging fruit only
because this is how they have been operating
and delivering it to the business executives.
Data science operations, especially when it
comes to sustaining the value that AI promises,
requires long-term investment.
One of that investment is to attract talent
and to retain that talent.
If you have a data science operation that
focuses on short-term goals only without giving
the data scientists creative space, those
folks will be very difficult to be retained
in any type of operations.
In that relationship, an employer has to ask
what type of value do I bring to the table
and do I invest in the continual mentorship
and training of the data scientists.
In different domains, the domain knowledge
that the company has adds to the employee.
But, within data science, one of the nice
things about data science is it's somewhat
domain agnostic.
So, when a data scientist comes onboard, they
will build the required domain knowledge,
whether you're in healthcare or in finance,
about what they do, and the value that they
create is not directly related to that domain
expertise.
So, they know their worth and they can switch
from one domain to another.
As an employer, the companies have to really
consider how to retain that talent.
There are methods one can do that.
There is a clear ROI case to build a culture
in which data scientists collaborate rather
than compete, it's more human-centric, and
there is an academic aspect to that operation.
It's because if you are looking just at the
skillset of data scientists and work them
150%, that skillset will become obsolete in
six months.
Continual training and an environment in which
intellectually they get enriched is essential
in any type of data science operations, in
my opinion.
Let's shift gears here and talk about data
science talent.
First off, why is this such an important issue?
It's a new domain and there are a lot of people
who are trying to come into that domain from
very diverse perspectives, very diverse trainings.
I think all of them bring an important aspect
from their own domain into the creative process
of doing data science.
Having folks with a psychology background,
having a data scientist coming from a math
background or an astrophysics background,
they all bring in interesting ideas.
As I said, data science is more about the
creative process and problem solving than
just using certain tools.
To know how to use certain tools is important,
but what is more important is problem-solving
skills.
Each diverse background brings an interesting
aspect and perspective to the problem-solving
process.
So, that is very important.
One of the negative sides of being in a new
domain is every data scientist is above average
in one of those three aspects when it comes
to statistics, code writing, or the domain
expertise or when it comes to contributing
to a problem solving a certain domain.
There has to be a continual training for data
scientists, and I value very much diverse
backgrounds.
That is one of my approaches to building data
science teams.
Diversity is very important.
You've mentioned culture several times now
as being really important.
We have a question from Twitter.
Arsalan Khan asks, "How do you create an AI-focused
culture when employees are fearful that their
jobs will be taken away by that AI?"
By that AI?
Well, the fear that AI will come and take
jobs is largely unjustified, I think.
Some folks who are popularizing AI, they come
up with figures and claim that AI will take
away jobs.
I ask them, "What's the data?
How do you justify that claim?"
I don't know whether AI will take away jobs,
but what I know for sure is the demographics
in how the labor market is going to look in
five years will be very different from today.
Just looking back in innovations that are
similar to AI, like the invention of electricity
or engines in the past, it changed the labor
market dramatically but didn't diminish the
labor market in numbers.
I don't think so.
I don't think that AI, in general, will have
a negative impact on the job security for
the future.
I think that fear is not justified.
this has to be addressed.
Setting the culture, I think that fear is
not the most important issue.
The most important issue is to create an environment
which is collaborative and human-centric.
It's intellectually very rich mainly because
the problems that we're facing today are very
complex and we need creative ideas to solve
those problems.
Data science, AI, and machine learning is
providing very powerful tools to solve those
problems.
People coming with all sorts of different
backgrounds are bringing in something that's
valuable.
As a leader, we have to make sure that each
individual's opinion is valued.
The merit of the opinion wins over the title.
This is my approach to things.
Ideas are very important and data science
is a new domain.
None of us are formally trained in data science.
Everybody brings in something different which
has value.
What about the relationship between academics
and data science inside, say, especially large
companies, but I think it's probably equally
true for smaller venture-funded startups.
Yes.
The bleeding edge know-how is still created
and produced within academic settings or in
larger companies that operate like academic
institutions.
We have to consider this if you want to continue
and sustain the value that data science creates
for companies.
I'm an advocate to continue the relationship
with my colleagues from the academic institutions
and building really productive relationships
both ways.
There is a lot that we can learn from folks
that are operating in academia.
I still read The Archive every day and see
what's coming out in my own domain, in both
domains, in astrophysics and in data science
or AI-related fields.
But also, create a relationship with them
that gives them incentives to contribute to
solving problems in the industry.
Some bigger companies find different solutions
for that where they fund academics full time
or they give them the resources so that they
don't have to think about bringing in grants
and focus on problem-solving, which is their
main job.
But in any setting, in smaller companies or
bigger companies, I think there has to be
some sort of an ongoing productive relationship
with the academic world in order to sustain
the value that AI is bringing today.
What about smaller companies?
How do smaller companies manage?
It's clear with a large company.
They have the resources to do that, but what
about a smaller company?
Smaller companies actually hire folks directly
with very strong academic backgrounds.
Larger companies, on the other hand, because
they have internal dynamics and silos, they
typically hire people who are not domain experts
in data science but are business managers.
I don't think startups have a problem in building
that relationship very informally and casually
with academic institutions because this is
their main source where they hire that talent.
Whereas, at bigger companies, they have their
own problems bringing academics into their
operations.
It sounds like you're a big fan of the kind
of work that's done, innovation--put it that
way--inside smaller companies.
That's right.
I mean I've operated and worked in both domains,
if you will in the startup space and in bigger
companies.
Both have pros and cons, positive and negative
aspects to their operations.
But, when it comes to innovation, I think
moving quickly is very important and, startups,
they don't have the stigma of having different
silos.
They are very fluid in terms of hierarchy,
so they can provide and make that impact really
quickly.
Time is an important asset today, especially
when it comes to AI.
Things are changing on a weekly basis, so
we have to be quick.
Having larger companies having a lot of people
to manage, internal dynamics to overcome is
a lot of wasted time and resources for bigger
companies.
This is why I've seen a trend where bigger
companies are acquiring smaller companies
for their AI operations or they are having
a more organic merger with different companies.
Smaller companies definitely have their advantages
when it comes to innovation.
The challenge that small companies face is
they have speed, they can move quickly, but
they often don't have the resources that large
companies do.
And so, what are you seeing inside smaller
companies when it comes to AI and data science
to let them overcome the lack of resources?
That's correct.
The lack of resources is one of the disadvantages
smaller companies have.
One important resource that bigger companies
have is access to data.
Data is currency when it comes to doing data
science.
Bigger companies cannot move fast, whereas
smaller companies can move really quickly.
Depending on the problem that they're working
on, sometimes working with smaller teams is
more advantageous than working with bigger
teams, to be honest.
As long as smaller companies have access to
data, I think the day-to-day operations, the
hardware requirements are not that enormous
and you don't need an army of data scientists
to address a certain problem or come up with
an interesting solution.
I think the asset that bigger companies have
is the data that they're owning and smaller
companies have the speed, so I encourage companies
to build synergistic relationships in that
regard.
That's a really interesting point; smaller
companies have speed and larger companies
have data.
That's a uniquely AI focused proposition today.
I think, yes, it's very different from what
we've seen until the emergence of data science,
how that relationship worked.
I think startups today have a lot of leverage
doing data science and AI as long as they
have access to that data.
Sometimes the data is being produced in academic
settings, and this is why we see a lot of
academics and professors who are building
companies, startups, and moving quickly and
producing value that way.
Let's talk about finding data scientists.
When I speak with executives, the lack of
resources seems to be a constant complaint.
There's definitely a lack of talent.
That's for sure.
I think an important part of the searching
process is how the recruitment process works.
What I've seen in companies, especially bigger
companies, their recruitment teams still use
the old fashioned way of going after data
scientists and, most of the time, they're
missing the real talent.
They're searching for certain keywords.
They're looking for a certain type of experience
that is not out there and is not essentially
required in the data science operations.
I think reforming, restructuring, and retraining
the HR and the recruiters is an important
part in proactively going after the data science
talent.
They are looking in the wrong places most
of the time.
What are the wrong places and what are the
right places?
How they scan CVs, for instance, they're looking
for certain keywords like Python and the type
of learning anybody can put on their CV.
But it's very difficult to gauge the creative
aspect of a certain individual, so there are
metrics that one can use to gauge the creativity
that an individual has.
But I'm a face-to-face person, so a five-minute
face-to-face meeting is worth any type of
strategy in recruitment.
Meeting a person face-to-face and talking
to them gives a lot of insight into what type
of person they are, how they go after a certain
type of problem.
Many companies are filtering based on certain
skill sets, and I think that's the wrong way
to go about searching talented data scientists.
The problem is many recruiters are searching
for specific languages, as an example, none
of which can point to creativity, and creativity
is so essential.
That's right.
In data science, in my opinion, what language
you use, in most of the use cases, the language
is not relevant.
I advise them to use the language that they
are most comfortable with.
When it comes to certain tools, they are looking
for tools.
Tools are being produced on a weekly basis.
They are dynamically changing.
What you put on your CV today is becoming
irrelevant in a couple of months and there
is a new tool.
So, what I look for is creativity and the
willingness to learn rather than a certain
keyword on your CV.
I think recruiters, most of them are going
about this the wrong way.
Is that any different from hiring software
developers?
Yes, it is somewhat different.
In software development, things are more mature,
experience is very important, and the process
is really well defined, whereas data science
operations, as I said, is still in its experimentation
and there is no single answer.
You can take two very similar companies, based
on their leadership and management style,
you have to hire different people that align
better with the culture of the particular
company.
So, it's a very dynamic domain and one cannot
go about this in a deterministic manner.
you really have to meet the candidate and
talk to them.
Obviously, screening is very important, and
we really appreciate the efforts of recruiters
and how they help us in the hiring process.
But we have to go about data science hiring
in a different way.
What makes a company attractive to a data
scientist, if you want to set up the right
kind of environment to attract folks?
Yeah.
Everybody has a different background.
They have different experiences that they
bring to the table.
For me, for instance, right now, I think what
I focus on is the culture.
The culture has to be right for the data scientists.
When I mean right, I mean a place where they
can continually learn, where they can bring
interesting ideas to the table, and the ideas
are being valued.
The merit of the idea is more important than
the title of a person and an intellectually
rich environment.
Then, obviously, the problems that they're
working on, how interesting they are is important
for people to choose one company over another.
If a company doesn't set the culture right,
as I said, the turnover will be very high,
which is a very high cost for companies.
That high turnover will make a data scientist
question whether it's a right choice or not.
If a company has a high number of turnover,
they have to really rethink their strategy
and approach to data science, in general,
in my opinion.
Arsalan Khan, on Twitter, makes a really interesting
point.
He says, "Very often HR is not equipped to
hire data scientists because they don't know
enough to evaluate who is good and who is
not."
I think this gets back to the point that you
raised earlier that searching, scanning for
keywords on a CV or a resume does not help
you evaluate the creativity or the potential
of that person at all.
Right and wrong.
Certainly, he is right when it comes to domain
expertise.
They don't.
But recruiters and HR is not required to be
a domain expert when they are screening the
candidate.
How they screened in the past based on keywords
regarding domains that are mature like software
development or certain types of engineering
should be very different than looking for
a particular type and profile of a data scientist
is what I'm saying.
They don't require domain expertise to do
that.
What you're essentially saying is you need
to be looking for innovation potential.
Right.
For instance, has a person operated in different
domains and still remain productive?
I think that's an important metric to gauge
the learning willingness of a candidate and
the creativity of a candidate.
If a person comes from an academic background,
have they produced academic work in different
topics, for instance, is an important metric.
If they have been in the industry, have they
been in different domains?
If they have remained in the same domain for
15 years, that makes them a really deep domain
expert, but will this make a person the right
candidate for data science?
I don't think so.
Gus Bekdash, on Twitter, makes a really interesting
point.
He says, "One principle that's helped him
in the past is looking at the kinds of problems
that a person has solved.
What do you think about that?
Yes, but not everybody has the luxury to choose
the problems that they want to work on, both
in the academic setting and in the industry
setting as well.
I kind of evaluate this more dynamically.
I look into the problem.
It has a certain weight.
Certainly, they have some leverage in choosing
it.
But once they're in an operation, typically
they are assigned certain problems, and I
look at what they have brought to the solution.
That's the key.
It's not just the problem they're working
on but the nature of the solution that they're
applying.
Absolutely.
This also will give insight into how you can
build your CV and make it more transparent
to the hiring manager.
You should probably include some of the relevant
solutions that you brought to a certain type
of problem.
I think that is very relevant.
Yeah, that's a very, very interesting point.
What you're really trying to do is help the
hiring manager gain insight into the way that
you think.
Yes, that's a very tough one.
It's a challenge for everyone, including recruiters,
including hiring managers.
It is an area that we also keep experimenting.
Some companies, they have online challenges,
coding challenges, which work or it doesn't
work.
I don't like online challenges, but I like
to sit down with candidates and stay in front
of a board and have a conversation, focus
on some problem, and see how they think.
This is a very subjective process.
I agree with that.
This is why data science candidates should
not be discouraged if you're turned down by
certain companies because everybody has a
different approach.
It doesn't reflect their worth, so it's a
very subjective process.
One of the issues, I think, that comes up,
especially in larger companies, and you alluded
to this earlier, is that the aspect of data
science can get lost relative to the importance
of process, corporate flow, desire to look
good, and so forth.
That's right.
This is why choosing the right leader is very
important.
My approach to leadership is to lead by example.
I would imagine a leader who really has written
code and models and knows about a process
itself is an important asset in this regard
to hit the right balance when it comes to
focusing on short-term business objectives
and deliverables and then also investing in
the long-term future.
If the manager of a certain type of data science
operations is focused only on one aspect,
whether it be R&D for the long-term or just
short-term deliverables, I think will face
some hardship in the midterm.
So, in big companies, as I said, because the
domain experts have operated mainly in academic
settings, have somewhat limited business experience
compared to managers, consultants, or executives
who have been in the industry for decades.
You cannot compare their business experience,
for sure.
I think what is more important is to bring
a person that has a reputable domain expertise
and has business acumen leadership skills
as well and invest into that person if you
want to break ground using AI.
The ultimate issue is, as you say, how are
we breaking ground?
Right?
That's very different from, say, developing
traditional enterprise software business applications.
Yes, breaking ground.
Everybody has a different approach to breaking
ground or doing disruptive innovation.
But what I've seen in many companies is, when
it comes to AI, when you look at the nuts
and bolts of what they actually do, sometimes
what they promise is actually not there.
This has to do with not having the right talent
or not having the right level of investment
into AI, but they want to move in the right
direction, so that's an understandable position
to take where they advertise a vision and
they are trying to build a team that can align
with that vision, which is very important.
It's, I think, critically important to have
the right leader that is setting the tone
for the culture.
A leader versus a manager is very important.
Go after the leaders that have some domain
expertise.
What about at the senior level inside a company,
at the board level, the senior executive level,
folks who are setting the goals for the business?
How does that translate down into AI efforts
and data science efforts?
Again, I think the leader who is managing
data science operations is critically important
here.
The person has to be an educator, coming from
a background in which they have operated at
a level where they can break down technical
stuff to simple terms.
This is how a leader that is operating within
data science has to talk with the board.
You cannot just go about a deep learning network
and talk about backpropagation in the board
meeting.
With startup companies, this may not be a
big problem.
But with bigger companies, I think the communication
skill plays a critical role when the data
science leader comes to the meeting and talks
about the objectives that they have and how
it aligns with the business objectives.
On the other side of the table, it requires
an executive board and a CEO that is willing
to learn.
We had this conversation before.
We live at a time in which every business
leader has to be articulate and learn some
aspects of machine learning and AI in order
to make the right decision.
If it's a person that is not open to learning,
that doesn't value the best ideas but is looking
for a certain type of presentation or looks
at the fonts and the colors of a PowerPoint
rather than look at the content of the PowerPoint,
I think that's the place where that relationship
can break ground or fail.
Isn't this really just like It folks?
How is this different from the historical
problem that technical folks have had communicating
with businesspeople?
It seems like that's the same issue.
When it comes to communicating, I think, yeah,
the problems are similar, but the business
objectives, the investment types, and the
strategies are very different.
There will be a certain structure of communication
and a certain expectation from the board that
they are used to.
The leader, the data science leader either
conforms into this expectation, I think which
is the wrong way to do.
The data science leader has to be bold but
also has to align the business objectives
of the data science operations with the board.
What the data science leader brings to the
table will be very different from an IT leader.
As we finish up, can you describe those differences
between managing an IT function like the CIO,
for example, and managing an AI or data science
operation?
I don't want to talk too much about the IT
folks because it's not my area of expertise,
I would say, per se.
But I would imagine that the IT infrastructure
is much more mature.
The day-to-day expectation, how they set up
certain goals is more a set as opposed to
a domain, such as data science, in which the
leader has to reinvent himself or herself
on a weekly basis.
You basically have to encourage your team
to fail but fail quickly rather than in IT.
That margin for failure might be very, very
small as opposed to in data science.
We are experimenting quite a bit, and so there
is a certain overhead that comes with that
experimentation.
We face different types of challenges, but
by no means do I want to say that IT leadership
is less challenging.
We all face different challenges.
That is certainly the truth.
As we finish up, Bülent, any final thoughts
on this topic?
Data science, AI, machine learning, and analytics
is a great place to be.
I see a lot of younger people who are moving
into the field.
I want to encourage them to move in.
Most of the people are coming in, they are
suffering from a syndrome where they think
they are not properly trained as a data scientist,
so they are less worth as opposed to more
formally trained computer scientists.
I don't think this is the case.
A diverse background has a lot of business
value.
From a leaders perspective, creating that
creative space for data scientists, regardless
of their level, produces a lot of ROI.
It has business value that comes with it.
I encourage the leaders to create that space
for the incoming data scientists.
Invest into their continual training if you
want to sustain the value that you have produced
in the short-term.
Okay.
Bülent Kiziltan, thank you so much for joining
us today.
It's been a very fascinating discussion.
It's been a pleasure, Michael.
You have been watching this interesting discussion
on how we manage data science and artificial
intelligence operations.
Thanks for watching.
Please subscribe on YouTube to our YouTube
challenge and go to CXOTalk.com to see more
videos.
Be sure to subscribe to our newsletter.
Thanks, everybody.
Have a great day.
Check out our videos and we'll see you next
time.
Bye-bye.
