In this episode Kevyn Rustici Area Vice President - Strategic Human Capital Consultant for Gallagher - talks about human analytics. How companies can use data to improve the lives of their employees and the company. Listen to the episode to learn Kevyn’s tips for getting a job or internship and how to stay up to date as a data scientist.
(electronic music) - [Announcer] This is
Buffalo State Data Talk, the podcast where we introduce
you to how data is used, and explore careers that involve data. - [Brian] Hello, and welcome back
to another episode of
Buffalo State Data Talk. I'm your host, Brian Barrey, and thank you for joining
us for episode 17. Today, we'll be talking to Kevyn Rustici, the Area Vice President
and Strategic Human Capital
Consultant for Gallagher. Thank you for joining us today, Kevyn. - [Kevyn] Hey Brian, thank
you so much for having me. - [Brian] Could you start us off by giving a general overview of the work that you do
as a VP at Gallagher? - [Kevyn] Yes, so I am new to the team as in four months new. We get to have strategic
and visionary conversations with some of the top
employers throughout our area.
So you get to really be
on the front lines of understanding the trials and tribulations that they're experiencing right now with this war for talent. And you get to see all these
cool articles that come out
and really get to see
it in real life, right? And how it's starting to have
an impact on the business. So I love what I get to do, and really bring forth the best solutions to our partners, because
at the end of the day,
it's how we can provide
enough value to them, but more specifically, the employees. And then if we improve
the lives of employees at the end of the day,
they're going to drive better results for the business.
So that's my fun part of my
job, is connecting those dots for a lot of business owners and leaders, and they have a lot more
questions than answers right now. So it's fun. The data side of it as well,
from a people analytics perspective. - [Brian] Well, thanks for the overview. Now that we have an idea of what you do, can you tell me about what a typical day or week looks like for you?
- [Kevyn] God, that's all over the map. Life is crazy, right? The world of work has totally
shifted and totally changed. I'm a new father, so
you add that to the mix. But finding the time, that's the one thing
I learned most in college,
was time management. There's a lot of different
things that happen throughout the day, right? You never know when you're
going to get an email, frantic email, from a customer that
they're having a major issue, and you have to step in and help them. Or potentially, it's a
fascinating conversation that you're having with
an up and coming business that people have not heard of yet,
or they're really interested
in getting their name out and they want to have a
conversation with you. So a normal, typical day
is a lot of fascinating conversations with business leaders. I do a lot of prospecting,
and unfortunately
as much work that I would love to do, I still have to go out
and get my work today. So a lot of that is building
and maintaining relationships. It's doing speaking engagements like this. It's having opportunities
to really connect
with those in the HR community. But I love what I do, right? I think at the end of the day, if I really was to tell
you what I get to do, is improve the lives of employees
while still driving
results for the business, and really get to see that impact that you're having on people's lives. There's nothing better than that. - [Brian] Yeah, it
sounds like a lot of fun.
So what kind of data does
your team collect and use? - [Kevyn] The tons of tons
and tons of different data. So the side that I'm still
learning daily is the benefits, the insurance side of things. Benefits are confusing.
I never got the education
that I probably needed around benefits. I'm learning the data
side from the healthcare, the pharmaceutical
understanding, the claims, understanding all of that stuff.
Obviously that is extremely,
extremely data driven. What I focus on on the HR
side, from a data perspective, is really 30,000 foot view. And I say 30,000 foot view,
is I look at turnover data. I look at voluntary and
involuntary turnover.
I look at absenteeism, I look at time off. And the reason, and what
it all makes sense is it matters what question that we're asking of the data, right? The thing that I've
learned most about data,
and my buddy is a genius,
because he basically is a pharmaceutical statistical analysis. He's got his doctorate. He's a genius, right? The guy is teaching me how to do R,
and all this other stuff. But what I learned from that was these data scientists are amazing. But if they don't know what
question to ask the data, it's just numbers on the page.
And that's really where
it's like a translator, if you want to call it a translator. But you need to understand
what business question or human related question
to ask of that data, and how you translate that
back to these key stakeholders
where it's almost like foreign language. You're challenging their
opinions, their thoughts, and their emotions. We get into a lot of the workforce data. So you talk about hours worked over time.
Like I said, turnover,
turnover by department. You find these little
crumbs, and these crumbs that your employees are leaving you are telling you almost
how you tell a doctor I have a cough, I have back
aches, I have knee pain.
So we get a prescription
as to what is going on. It's the same thing as
how we use people data in organization, because
a lot of organizations are sick right now. They're not really sure really
where the challenges are.
Is it a five alarm fire, or is it only a smoking, smoldering waste basket, right? Size fire. And the data that we collect particularly is what question we're looking to solve
from a business challenge. - [Brian] Cool. So I don't know how detailed
you can be about this question, but what is the volume of
data that you work with, and how do you store that data?
- [Kevyn] Great question. This brings up an important point. So I don't want to comment
on that particularly, but what I will say is there
is a lot of responsibility when it comes to data, right?
And that part needs to be at the forefront of all data science, and all data programs and conversations, is because it is scary, especially when you're
talking about employee data. People want to know what
you're doing with that.
The privacy laws in New York state, and obviously United
States are not as strict as some of the European countries. But still, you have to be careful, make sure that we remove all names, right?
We make sure that we remove
all identifying characteristics of some of these individuals
for these projects. And really understanding the intent behind some of these projects first before we get involved in
those is really important,
because sometimes you can find
some scary things. (laughs) You can find some really scary things. And depending on what type of leadership are in leadership, they could take that in a
good way or a really bad way.
So you really have to be cautious with how you're using
data, and the expectations that you're creating around it. Because I always say it's
data, insights, action. You still have to apply that
human intellectual capital
and social capital that we talk about in roles and positions. That's where you have insights. And then you really work
together collectively to drive results, and
have actionable results.
That's what I would say
about the data privacy side. It needs to be talked
about more, probably, especially as we explore. There's some scary
things that employers do. Read every email that you're sending,
every text, everything. That's to the degree of people analytics that I don't want to see. I don't think you need to be that invasive to really get a temperature of the room.
Is this person happy? Are they fully engaged? Are we getting the most out of them? It seems a little bit too intrusive for me, personally. (laughs)
- [Brian] Yeah, me too. - [Kevyn] Yeah, but there's a lot. You think that's not out? That's out there, a lot of it. And probably more so than I even know.
- [Brian] The SHEILD Act
and the GDRP come to mind when you're talking about that. And again, I'm not sure how
far you can get into this, but once you've collected
and cleaned the data, what happens next?
How is the data being used? - [Kevyn] So that's
probably the hardest part. So let's be clear, the clean data, right? We can almost obsess over the cleanliness of the data, right?
Almost to a fault. It's almost like overanalysis
paralysis, right? So the clean data part, I, again, I focus first on the people, the process, and the technology.
If we have the right
people in the right seats and the process is clean, then we know that we're
getting clean data. And I like to leverage
technology to automate as much administrative task as possible.
But then once we're dealing
with a clean data set, that's when the fun starts, right? That's when we start to work backwards. A lot of the people in
biology classes today, you know the scientific method.
That's the same way that I
apply to people data, right? What question? What do we want? And then we work backwards, that's it. And we start asking questions.
- [Brian] Nice. So what do the data scientists produce from the results that you find? Like a dashboard, written reports? - [Kevyn] Typically I like dashboards.
Except when dashboards are
used inappropriately, right? You see a lot of dashboards thrown up on, if I was to talk about the
working world, managers, right? Managers have these dashboards. Some of them are beautiful, right?
But if it's just numbers, again, and they don't really
sure how they impact them or what's particular to them,
or why they should care, it's just a beautiful dashboard on a page that somebody spent a
lot of time on, right?
And a lot of money, probably. So really it depends. I like to put it in concise reports. I think that that's probably the best way, because in data, it's
only as good as the story
you're able to tell with it. If you're putting up,
oh, we did this analysis, and we did this and this and this, and you're sitting in a
room with key stakeholders and business owners, they're
going to not follow you
and not care what you're saying. So you've really got to
talk in their language is what I found. So storytelling is probably
the most valuable part of that. And being able to relate it back to them.
That's really, if I was to tell you, too, where the money is, that's where the money and magic happen. All data is you have to
be able to be, again, that master translator to understand
what's on the minds of
these business leaders and how can you take that
data and tell them a story in the words that they're
going to know and understand? - [Brian] Storytelling
is such a big part of it. - [Kevyn] Massive, massive.
And charts, because the
charts are also the, that's the other, the
visualization piece is another. That's an art in data
science, is visualizations and knowing which visualization that you need to use, and when.
It's a very, very powerful
tool when used effectively, but if you use the wrong graph
or pie chart or something, your massive finding could
totally miss the mark. - [Brian] I was just
going to speak to that. So are there any specific
software technologies,
or programming languages
like Tableau or Excel? - [Kevyn] I'm good at Excel. My buddy that I referenced earlier, R. I have two books of R from O'Reilly that I am cranking through,
as well as a couple other
tools that he gave me. Because what I've learned in R so far is the ability to rip
through massive sets of data. I am rarely dealing with
the sets of data sets that I would need R to do right now.
Because again, you don't want
to get too, too in the weeds in some of these projects, because they don't have enough data to start doing predictive
analytics in most cases. R is what I really like.
Python is another one that
obviously a lot of people use, but R I have found easier, I guess, to make the transition from Excel to, because you can still use
some of the common language, or if you understand how
to write that language.
So that's what I've been using. R, and then I like Tableau. Tableau is probably the
easiest charting technology out there for visualizations. I would say that's the cat's
meow from my perspective,
as far as ease of use. - [Brian] I think many
would agree with you, too. And I know this is really maybe difficult to try and predict, but what technologies do you think
would be the most important
to know for the future? - [Kevyn] God, this is a passion of mine. I love talking about the future of work. And especially if students
are listening, right? I would say, don't get in
love with any technology,
because it's changing at
such an exponential rate that what you are training on today, again, might not even be
relevant in six months to a year. So that's why I think you're
seeing more organizations, too, outside of college, switch
to these skills based
talent management strategies. Where it's, hey, we're hiring for traits, character traits, that
people can learn skills. We need characters, we need the ability, we need the individuals that
are willing to learn, listen,
and learn new tricks kind of thing. So you're seeing these people
create those types of models. But technology, I don't
want to give you any names because I think they're going
to change in such a pace that it doesn't matter. (laughs)
- [Brian] So could you
talk about a challenge that you and your team had to overcome, and what you did to solve it? - [Kevyn] One project in
particular comes to mind, because it also tells
you, I guess, the infancy
of upstate New York and its
comfort level with data. I won't name any names, but I
will say a healthcare system, or a provider, put
together an analytics team. They put the CEO in the room, they put the CFO in the room, VP of HR.
A lot of highly, highly,
highly compensated people in a room, right? So when the project was
brought to me, I said, "Well, what's the CEO's goal?" Right?
Does he want to be the best
of the healthcare system in this particular area, or why? You need a why behind what
he wants out of this, right? And I think there's that perception that a lot of people still don't
know the difference between
data analytics and metrics. I think there's a lot of, people still don't understand
the difference between quantitative and qualitative data. I think people see data, right?
They see it everywhere. They see it in more
and more news articles. So therefore they need to have it, except they don't know
what they need it for. So I challenged them.
And then finally they came back, and came back with a little
bit different of an image. Well, we want to know
this about the workforce, or we want to know this. And again, I pushed back a little bit more
as like, that's fine,
but you need big picture. You're spending a lot of money. You're putting a lot
of people in one room, and they're all looking at each other as what problems are we supposed to solve?
So the deeper that we dove
and the more questions that we asked, they started
answering their own questions, like I told you earlier. Oh, we should be looking at this. Yes you should.
You should be looking
at this, yes we should. And by the end of it, it's like, okay, now I know what I can get out of data. And that's really what
you want people to learn. - [Brian] What first made you interested
in working in your field? - [Kevyn] I stumbled into HR, and everybody says the
same thing. (laughs) I've worked for
organizations that treated HR like the redheaded ugly stepchild.
That's how I viewed HR. ADP, when I started with them, and they had a lot of data sets, because they paid one in five Americans, and they had this data cloud.
It was fascinating to me, and
that's where I really clicked. And I said, "That's the ticket for me." Data's not going anywhere. Data jobs are really picking up, and aren't going to go
anywhere, and any time soon.
And I just said, "This
is interesting to me." To be able to finally
get businesses to see how people impact the
business was fascinating. And I'll tell you the two
things that I needed here. 22% of businesses use data and science
to drive business decisions. I found that erroneous,
I could not believe that. And then just how many organizations continue to not understand the value of their employees, (laughs) I guess.
- [Brian] That's amazing. - [Kevyn] And that's really
where I was like, okay, so you're telling me, why do
you promote who we promote was the first question
that I wanted to answer, because I've been passed
over for promotions.
Why aren't we challenging
these ways of thought, and just asking the question why? And I got obsessed with the question why, which brings you to data. (laughs) Because it's the only way to get down
to the real answers of it. - [Brian] So I know that time
is limited as a new father, but are you still able to set time aside for professional development? And if so, what kind of
activities do you do?
- [Kevyn] Every single day. Every single day, it's
really, really important to find your time. I am constantly, I call it
reinvesting in yourself. You really are.
For that same point that I made earlier, that we're going to be
in this cycle of things changing so fast that you better have that intellectual skillset,
or that social skillset or capital, like I call
it, because that really is
the only thing that makes you stand out and differentiate yourself. It really is. What you have learned on the job, what you've taken away
from those experiences,
that's all stuff that you own. And then what do you do
with that information is the next question. And then keep asking that same question, keep peeling back that onion,
because that's really where
I got to where I am today. So I do a lot of reading,
to answer your question. A lot of scientific journals I read. I like scientific studies when
it comes to human capital, because they have a lot
more time and energy.
A lot of it's academic. They have a lot more resources
than I could ever feasibly do to put together some of these
case studies that they do, and it's just fascinating to
see what they come out with. - [Brian] Yeah, yeah, it was perfect.
Many of our listeners are younger, like high school undergrads. So as someone in a leadership role who has mentored students,
what advice would you give to someone who's interested
in working as a data analyst?
- [Kevyn] Get good at listening. Probably is my one. That's probably my biggest tip. I think, again, is you
have to be able to listen. Oh, I guess ask the right questions too.
Those are probably the two things. Because if you ask the right questions and you actually listen,
you can help people solve a lot of problems. (laughs) You just have to know
what questions to ask,
and then can you support that, or can you not support that with data? And so I think it's a
great field to be in, because it's so diverse. There's so much variance,
and that's really what I like
about the data field, is that
there is so much variance. So you'll find your, I
guess, more of your purpose with the alignment of
what you're studying. What data are you studying? Like my one buddy, he
loves cancer research.
That's what he loves, is understanding the trial information. That shit would be boring
as hell for me, Brian. I like the people perspective,
and my buddy can't stand it. But it's preference, right?
And I found my purpose and my meaning more towards the human
piece, because I'm a feeler. I feel for people, and I just want to have their voices heard and valued. And really that's what
I get to do every day,
is explain to the senior
level what the employees want, and what they're delivering
today is missing the mark, and where improvements
need to be made, and how. - [Brian] Cool. Since you've worked in HR
and understand data science,
could you give our listeners some tips for applying for jobs and internships, such as how to get through ATS, or applicant tracking system software? - [Kevyn] Networking is a
way around those ATS systems.
If you have somebody that is going to tell that HR representative, that recruiter, "Hey, I have a candidate coming over. His name is Kevyn Rustici, keep
an eye out for their resume. I'd really like you to pull that one,
and I'd really like you
to have a conversation." Now that gets you right
around the ATS system, and probably puts you front and center in the top 10 candidates for
that particular position. So I hated the system, I hated the game.
But instead of sitting there
and complaining about it, you had to figure a way around it. So that's where I'm telling you, learn networking and get
your butt on LinkedIn, because it's a great place
to start to build your brand,
define yourself on who you are,
and building the connections that you want to build at
some of these companies. And do yourself another favor. Do an internship before
you sign yourself up for a long career in a particular field.
Get exposed to a lot of
different things, because this thing called The Great Resignation, that's a lot of people doing
a gut check and saying, "I can't do this the rest of my life." - [Brian] And finally,
before we let you go,
is there anything else that
you'd like our listeners to know that we didn't cover today? - [Kevyn] If I can help you reach out. If you have questions, reach out. If there's anything I can do
to help you in any single way
and I can make an introduction, I would be more than happy to do that. If I was to leave,
things to think about is invest in yourself today,
because not a lot of other people are going to tomorrow.
So you're in charge of your own destiny. And I think building
those right relationships, developing trusting
relationships with your teachers, leveraging these professors. It's amazing what these people do,
and the commitment that they give you of their time and energy, and
I just wish I did more of that when I was in your shoes, because
it did myself a disservice and probably behind the eight ball. But the world is big, there's a lot
of opportunities out there, and
it's really an exciting time because when Brian
probably first got his job and I was first getting my job, there wasn't a lot of jobs like this, and the employee was never
in the driver's seat.
So seeing where we are today, and seeing everything that's happening, it's pretty incredible. - [Brian] Well Kevyn, thank you so much for joining us today.
It was truly a pleasure. - [Kevyn] Thanks, Brian. I really appreciate this
opportunity to speak to you and the students, and I really enjoyed the conversation with you.
- [Brian] And to all our listeners, if you haven't already checked
out our previous podcasts, they are available wherever
you listen to podcasts. For more information
about starting your career as a data scientist, go to
www.dataanalytics.buffalostate.edu. Don't forget to subscribe so
that you get notifications each time we release a new episode of Buffalo State Data Talk. (electronic music)
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