November 13, 2024 • 37 mins
In this episode of Making Data Matter, we are joined by Olivia Kew-Fickus - Chief Data Officer at Vanderbilt University.
This conversation offers you insights about:
- What does a Chief Data Officer do?
- How do you form a data strategy for your organization?
- Managing the complexities of systems in higher education
- Collecting data to measure student outcomes
and more.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Welcome to Making Data Matter.
(00:01):
We have conversations about data and leadership
at mission-driven organizations with practical insights
into that intersection of nonprofit mission strategy
and data.
I'm your host Sawyer Nyquist.
And I'm your co-host Troy Dewek.
And today we're joined by guest Olivia Koufikis.
Welcome to the show, Olivia.
(00:22):
Thank you so much both for having me today.
It's a real pleasure to be here.
Absolutely.
Olivia, for people just meeting you for the first time,
give us a little background of who you are
and what do you do.
I am the Chief Data Officer at Vanderbilt University.
Vanderbilt is a private research university
based in Nashville, Tennessee.
We have about 13,000 students.
(00:43):
About half of them are undergraduates,
almost all residential.
And half are graduate and professional students.
And I oversee the data team, as the name suggests.
And we work with both the academic side of the shop
and also with all of our administrative partners
across the university.
Now, correct me if I'm wrong,
(01:04):
but when people think of higher education
and data in higher education,
they might just think that you're kind of keeping track
of what students are enrolled and what classes.
And is that about the extent of it?
Or correct my perception of how complex data is in higher ed.
Sure, no problem.
So what I think people don't understand always
about a university, particularly a research university
(01:25):
like Vanderbilt is we're basically a small town.
So yes, we have students.
They're really important.
They're the reason we're there.
We also have huge research labs.
We have a huge dining facility.
We have a huge residential facility.
We maintain a campus.
We're an arboretum.
(01:46):
We have a huge kind of energy plant that supports us.
A lot of shared services around that.
We obviously have a lot of kind of classroom
and conferencing facilities, lots of buildings.
And we do a lot of work out in the community.
So we have all our engagements with our alumni.
(02:09):
We're embedded.
We're in the middle of Nashville.
So we're embedded in the city of Nashville.
We engage with the community and we're a huge employer.
So we have thousands of staff.
We have a big HR facility.
So we do kind of all sorts of stuff.
And that's what I love about working in the universities
because the data is actually really, really diverse.
(02:31):
And there's always something new to get involved with.
My team's just getting involved with working
with our athletics department.
And so that's a whole kind of different ball game,
if you'll forgive the pun.
And it's just a lot of fun, but it is, as you say,
it's not just counting the students
(02:51):
and making sure they get to class on time.
Well, when you said you had an arboretum,
I was half expecting you to say, and the data swamp
is right next door or something like that.
So that's amazing.
So the chief data officer role, would you
see that as more of a technical role, more of a strategic role?
A lot of people listening to this
(03:13):
may not have heard of a CDO role before.
And the only thing maybe you think about is
a manager of analytics or manager of data.
How does a CDO function, technical, strategic,
somewhere in between?
So CDO role, as it is evolving, and this is not just
at Vanderbilt, this is across the world, really,
(03:34):
because CDOs are some of the fastest growing new roles
right now.
It really is an intermediary role.
When done right, I think a CDO role spans everything
from deeply strategic and very, very embedded
in what does the business want to do.
(03:54):
And I'm going to use business, even though it's a university,
it's just shorthand, all the way through to needing
to understand the data stack, needing
to understand how those pieces fit together,
and what are the dependencies between those two things.
And so again, I think it's a role
you can come at from either side.
There's lots of CDOs who have a technical background.
(04:17):
I am not one of those.
I've come at it from the strategy side.
My previous role was as director of strategic planning
at a different university.
And so I have had to learn a lot of the technical stuff.
And I'm not a technologist.
I'm not going to go out and code for you.
(04:38):
That's not my wheelhouse.
But I do have a pretty good understanding
of how all the pieces fit together.
And I find that just fascinating.
It's a constant challenge.
Give us a day in the life of Olivia
as she bridges those two worlds of the strategy
and the technical.
So think of maybe a project that you worked on,
(04:59):
just something to give us more insight into what that
looks like in an earthy, nitty gritty kind of a way.
So we're working on a project right now
about research administration.
And research administration is, at Vanderbilt,
we collaborate with Vanderbilt University Medical Center.
(05:20):
They're a separate entity to us.
But together, we have over a billion dollars worth
of research activity going on every year
between Vanderbilt University and the University Medical
Center.
And so managing all of that money flowing
through the organization is not a small task.
Much of it is federal money.
And so there's a lot of requirements around that.
And just helping the individual researchers, what's
(05:46):
called the principal investigators, the PIs,
to understand how much money they have in their grants,
how much has been spent, how much will be spent,
how much is still free and unallocated,
and they can spend.
Are they aligning with the requirements of what they
were given by the federal government
in terms of what they're putting in there?
(06:07):
That's a huge task.
And we've been working to pull together all of that information.
And so those conversations for that project
will go anywhere from meeting with the provost
so that she explains to us why this is so important, what
is the demand coming into her from her deans
(06:27):
and from the PIs in the different schools
as to what they're saying they're not getting,
through to understanding what the research administrators,
the people who are sitting there day to day doing that work,
supporting the PIs need, through to getting into what
are the source data systems, where the information is held.
(06:48):
We actually have multiple systems that data is held in.
How can we bring that together?
And then how can we create them?
We're using Snowflake as our foundation there.
And then how can we create a front end interface
that the PIs will be able to use and their administrators
will be able to use easily that pulls on all that information
(07:11):
and can collect new information from them?
So they can model, OK, if I hire this person,
will I have enough money then to also buy
this particular piece of equipment I need to buy?
Or will that throw all of my other metrics out?
And so we're thinking about data sources.
We're thinking about platforms.
We're thinking about warehousing.
We're thinking about data modeling.
(07:33):
And we're thinking about what do the end users need?
How are we going to get that in front of them?
How are they going to be trained?
And of course, they want it all six months ago.
And at a scale of a billion dollars of research,
which is not the scale that a lot of organizations operate at.
And so those are problems that people solve at small levels,
(07:54):
but you're solving them at a scale
that's different than most places.
Now, I think we need to pause, though,
and just clarify something.
So Olivia, do you have the IT personnel
right directly on your staff?
Or is there a separate IT organization
that you're partnering with?
And what does that relationship look like?
So my office is located in the office of the chancellor.
(08:16):
That's the president's office.
And that is to recognize that data is fundamentally
about supporting the needs of the business
rather than a technical item.
And so we have in my office a lot of the people
who work closely with leaders.
They work closely with data users.
(08:37):
They work with our data stewards.
We run our data governance.
But the IT stack, the data engineering stack,
sits within our IT department.
And we have a Vanderbilt culture of what
we call radical collaboration.
And so we work really closely with them in partnership,
in daily, weekly partnership, to make sure they understand
(08:58):
what we need and that we understand
what are the issues they're facing
and how we prioritize things, how we bring things together,
how we make sure we've got the right technology.
And that's just constant communication.
I know you think a lot about strategy.
And you mentioned earlier on, you came from the strategy side
and you've mentioned how strategy is kind of what
(09:18):
holds these pieces together.
Tell us a little bit about what does data strategy
look like at Vanderbilt. And I think
there's a few more questions we'll go to after that.
But I want to start there, I guess.
Tell us about data strategy at Vanderbilt.
So when I came to Vanderbilt five years ago,
I was actually hired into what's called institutional research,
which is the, it's seen as largely being around the, what
(09:41):
we started with, counting the students, sometimes counting
the faculty, doing all of that thing,
doing some basic reporting to the federal government,
sometimes to state governments.
It's, institutional research has been around a long time.
And I was hired, I had already been in a broader role
than that.
And I said to them when they hired me,
(10:01):
what do you want from this?
And they said, Olivia, make it strategic.
And I remember meeting during my recruitment process
with a couple of the vice chancellors, the vice presidents
who were from non-academic areas.
And they said, we really just, we
want this team to really engage across the university.
So I came in with a brief to say, OK,
(10:22):
what does it mean to make data strategic at Vanderbilt?
And my first year, I spent doing a lot of listening
in order to understand that.
And what I heard was the same as you hear
a lot of organizations.
We don't know where the data is.
We can't get to it.
We're not sure who to ask for for it.
When we do get it, we don't really know what it means.
(10:45):
It was nothing unusual, but it was clear
that there was a real desire to create something
that was bigger than that.
So the first part of what was data strategy at Vanderbilt
was, in fact, moving my office to be not
institutional research, but to be data,
and to be all sorts of data.
And so we were moved into the president's office
(11:07):
at that point.
The next challenge you face at a university
is that you have a lot of systems.
There's the ones you would think you have, the big ones,
the student system, for instance, the HR system,
the finance system.
Those are your fundamentals.
But there are so many little systems.
There's a different system that does admissions than the one
(11:30):
that holds student information.
We have a system that just handles
what we call immersion, which is the students,
they kind of extracurricular, or they're doing a piece,
they're doing a special project, or they're
doing service learning, or something like that.
They study abroad.
We have a system that handles the immersion work.
(11:52):
We have a system that handles internal scholarships.
So when somebody's come, if they want
a grant to do something else, we have a system
that kind of does that.
Mental health has its own system.
Housing has its own system.
I mean, there's just dozens of them.
And so one of the real challenges in a university
is how do you pull that together?
(12:13):
And so we started exploring.
We already had a good on-prem data warehouse
for our student information system.
But we said we need to be able to warehouse and pull together
and read across a lot more systems than just these ones
that we already have.
And so it was obvious we needed to go cloud.
(12:33):
We ended up going with Snowflake for that.
And we are in that process now of slowly moving things
into Snowflake, starting to be able to integrate them much
more easily than we could before.
And then, so really, the first kind
of stage of our data strategy was
about building those foundations,
the technical foundations, and then also,
(12:54):
like in many organizations, the data governance foundations.
So we had a strand of activity around data governance.
And then, and I shouldn't really have left this till last,
you've got the people foundations.
So we had our team.
And I was really lucky to inherit
a really, really good team.
We had our IT team, also really strong.
(13:18):
But what's happened is that we found different units
around the university have wanted
to hire in a data person or have wanted
to tap more professional data expertise.
And so we slowly built up a team,
some people who are located in my office,
some people who are located in other units
around the university, who are increasingly having
(13:40):
data as their primary role.
And we're building a community of practice around that,
trying to build up their skills.
And so that's been super exciting.
And what I will say about our data strategy now
is that I feel like we've built the foundations.
And so it's not that we haven't done anything,
it's not that we haven't delivered anything,
but we're now at a place where we're ready to do takeoff.
(14:01):
And so we're really trying to understand,
OK, what's the next stage?
What can we do that will really be transformational?
Now, my conversations are shifting quite rapidly
to leaning into talking to leaders and saying, OK,
what do you want data to do for you?
And they often start by being like, well,
(14:23):
just get it all available to me.
And it's like, no, what do you actually want to do with it?
It sounds like that difference between the descriptive
and diagnostic to, can you be a little more forward thinking
and, OK, how do you want this to forecast for you?
Is that what you mean by these conversations?
It is.
(14:43):
And so I'll give you an example.
We have done an awful lot of work with our alumni team.
And they moved to Salesforce system
two and a half, three years ago.
And at that point, that was a real opening for us
to come in and work with them.
And they were some of our first tenants,
(15:04):
if you will, in our Snowflake environment.
We were able to pull the data out of Salesforce.
We did it really easily, put it into Snowflake,
started to see how they needed to be thinking
about some of their business processes differently.
They brought in some really data forward leaders
around the same time.
And so we've been able to build out some great dashboards
(15:28):
that are embedded in their business processes.
And so when we go in, we can see that they're
using these on a regular basis to make decisions.
First of all, first they started being just monitoring.
OK, can we see what's happening?
But now we've built for them dashboards that, for instance,
we have some great mapping dashboards.
So if somebody is saying, OK, I've
(15:49):
got a development officer going to St. Louis
to visit this person, you can actually then
put in the parameters.
And you can see within five miles of where you're going,
there might be three other people
it would be worth going to see.
And so now you spent your money to go.
You were going to go anyway.
But now in that one day or day and a half,
you've identified people you can reach out to,
hopefully set something up.
(16:11):
And so they're doing that daily.
As they plan their travel, they're going in
and they're using that data.
And they continue to want more and more.
So it's kind of like, OK, how can we think about how do we
model some of our alumni so that we understand which alumni
(16:32):
might be really interested in Vanderbilt,
but they've given, but they haven't given a lot.
But we've not really developed a proactive relationship
with them.
How can we reach out to them, proactively start
to build those relationships?
How can we understand people who are younger, who won't be
necessarily giving, but who are really engaged?
How can we find them, figure out what turns them on,
(16:54):
gets them excited, so that we can keep them engaged?
Because our goal with our alumni is
to maintain a lifelong relationship.
We're not trying to just sell them something tomorrow.
We want them to be engaged with Vanderbilt 20, 30 years from now.
So those are some of the things that we found
(17:15):
that it is an evolution.
That you start by, yes, just exposing some of the data,
giving people insight into things they didn't have before.
And then they start to see how they can use the data.
And they also start to see how they
need to improve their business processes so that their data
quality will be better.
Because the more they want to rely on it,
the more they realize, oh, hang on a second.
(17:35):
It wasn't that great.
And we need to change something so that our data gets better.
And that was what happened with our maps.
We had to completely update our addresses
so that we could map them.
That's what I was going to ask is,
how did you get them to actually realize the business process was
broken based on the data that was coming in?
(17:56):
That seems like an age-old battle where the data person
says, well, you're giving me garbage data
through your business process.
And they're like, no, we're not.
You just have to do this and this,
and it'll make total sense to you.
So how did you convince them that they
could identify their own brokenness
in their business processes?
Well, it's not necessarily that the business processes
(18:17):
were broken.
Sometimes it is about, we just need
to raise the profile of something.
But sometimes it is that they had historic data that
had been put in a long time ago.
And fixing that is actually going
to be a significant amount of work.
It's just walking through it with them.
(18:40):
In that case, we had all the data in Salesforce.
It was all data that had been ported over
from a previous system or almost all of it.
We were able to bring the US Postal Service address
database together with that.
And that was not a small lift.
And my team walked alongside them going through identifying
(19:04):
addresses that weren't making sense, things like that.
And we've more or less got there.
Now we're turning to international addresses.
How can we fix our international addresses, many of which
have been put in more or less as a string?
And so how can we start to do that?
And how can we work with our Salesforce team
(19:25):
so that we can force some of those things
like drop down menus or auto filling
rather than just people continuing
to put in garbage data that we continue to have to clean up?
A labor of love, that's what I heard.
It is.
It is.
It's step by step.
And it takes a lot of resource from them.
So they have to figure out what they're
(19:45):
going to prioritize as well.
And that's just one sliver we're talking about of alumni
and really donor relations.
And I imagine you could go down through all
the different pillars of Vanderbilt
and talk about different use cases and different ways
data can be activated.
Is there a way the data strategy helps
you to think about prioritizing which
of those pillars and silos are you going to dive into next?
(20:08):
And how do you handle the competing needs of a small city
that you have at your hands?
It's a great question because it's
something we grapple with on a regular basis.
We started by working where we had enthusiasm.
And as it happened, we did all this work with alumni.
(20:31):
And without really realizing it at first,
but it became apparent pretty quickly
that the thing about the alumni relations
is they have dollar goals that they're
trying to meet.
And so we can actually measure our effectiveness really
easily in that area.
And this is something that a lot of not-for-profits
(20:54):
will recognize.
As you move out from that, a lot of your other areas
don't have easy dollar goals.
And so with our student data, and especially
at a place like Vanderbilt where we're not looking to grow,
particularly our undergraduate student population,
it's all about how do we serve them better?
(21:18):
Doing that better is not going to result in more dollars,
for the most part.
We have very low dropout rates.
We have very high graduation rates.
There are other institutions where they can do that better
and they can see a dramatic increase in their retention
rates or their graduation rates or whatever.
That's not the case at Vanderbilt.
And so we have to really think hard
(21:41):
about how are we going to show that return on investment?
What is that going to look like?
What is success going to look like?
And we've had some where it looks
like we weren't getting very far.
And then a year or two years later,
we suddenly see a decision being made.
And we're like, oh, they're doing that because the data was
better.
(22:01):
OK, that's good.
What are some of those things then
that you do start to look at and measure?
If it's not student retention, which is really strong,
graduation rates, which is really strong, what
are other things that are important to the student
experience that Vanderbilt wants to enhance
and that maybe you can use data to get insights into?
One of the major challenges that we've had is graduate outcomes.
(22:23):
So where do our students go after they graduate?
A lot of people will think about that and say, well,
of course you know that, don't you?
But if you then think about how is the data
flow going to work for that?
Who's going to tell us where our students have
gone after they've graduated?
The students are.
Yeah, self-reported.
And so you can often get a good slice
(22:46):
of what we call first destinations
before they graduate.
And we do hit them up with all sorts of surveys about that
before they graduate.
But there will be some who don't know at the point
that they graduate where they're going to go.
And there will be some for whom that first year or so out
is not where they're headed.
(23:07):
We have some who take a break, they travel for a year,
or they do something else.
Or they might be, we have a music school,
so we have many who are kind of jumping
into these very unstable, they're
not sure what that's going to look like gig type professions.
And so those people look really weird in the data.
And so we've spent a lot of time on data collection
(23:33):
for first destinations, understanding where they
go that first year out.
And then we are now trying to grapple with where
have they gone after that?
And how do we understand what those trajectories look like?
And do we think that what we at Vanderbilt
gave them during their four years
(23:55):
in undergraduate education with us,
did that change their trajectory?
Did that improve their trajectory?
And so we've done a lot of data scraping, as you can imagine.
We've worked with companies that scrape LinkedIn,
and then tried to match that up to our students.
That's a lot of fun, because that's name based matching.
(24:17):
Wildly consistent, I'm sure.
How many John Smiths are there in the world?
Yeah, yeah.
And the thing is, we tend to have some of their address
information.
Many people will put what their degree was,
so they often get flagged as being Vanderbilt people
from LinkedIn as well.
But we also have a lot of people who
(24:38):
go into medical professions, as you can imagine,
at a place like Vanderbilt. Those people
have a very long trajectory between when they leave us
and when they start working.
And a lot of doctors actually don't use LinkedIn heavily.
And so we've got these kind of data gaps.
So we've been working with, we've
done a lot of work with third parties,
both as I say, these data scraping firms,
(24:59):
but also with some more sophisticated labor
economist type people, trying to say, OK, what
does a Vanderbilt education actually do?
Because those are the kind of questions,
even before we get to how much do they make,
people seem to think we know how much our graduates make.
Where would we get that information from?
(25:21):
How many have founded a company?
How many are chief executive officers?
These are questions we get asked on a regular basis.
And we kind of grope towards answers.
But there's not good data sources for those.
And so we're always playing a little bit of a scavenger
hunt on that.
But it is really important information.
(25:42):
Because ultimately, if we want to convince people
that it's worth coming to private university
and spending the money that you might have to,
although we have very good financial aid,
spending the money you might have
to spend to come to a private university,
well, then you would hope that there's something better
out the other end.
That it's worth that investment.
(26:02):
From the technology side, you've talked
about a few of the challenges of just all the different source
systems that come together.
What are the other technologies that you
found to be really useful?
So I think you've mentioned Snowflake so far
as one of the main pieces.
What other pieces make up the technology stack
that help you start to enable these use cases you're
trying to get to?
We have three technologies that we've invested in,
(26:24):
significant technologies we've invested in
since I've been at Vanderbilt. So Snowflake is one of them.
We talk a little bit more about that.
Tableau is another one.
We already had Power BI.
We had Oracle Analytics Cloud.
But we wanted a tool that was really heavily visual.
And so we went with Tableau.
(26:46):
And it intersects as well, obviously,
with our Salesforce environment really well.
So that's a massive win for that environment.
And then the last is our data catalog.
And we have ended up going with a relatively small data
catalog.
It's a company called Data Edo.
It is right sized for us.
(27:06):
So a lot of the big data catalogs
are extremely expensive and do a lot of things
that when you're just starting out in data governance,
you're probably not ready to do yet.
And so we found Data Edo.
We could kind of scale it desk by desk, basically.
But it does a lot of data lineage work for us.
It provides us with those foundations.
(27:29):
It enables us to not have to do everything by hand,
which was important.
And so that data catalog is really important to us.
So I'm going to bring this back to data strategy.
Because in data strategy, you talked about people
and kind of like the processes of how data is collected
and managed and then the technologies.
If somebody is just approaching data strategy
(27:51):
for the first time, where do they kind of
start on that paradigm?
How do they approach the challenges that
come from technology, processes, and people?
I mean, it's the classic triumph, isn't it?
People, process, technology.
And you do need all three.
You don't want to be driven by the technologies.
Because if you don't have the people in the processes,
(28:12):
the technologies are just money you're
flushing down the toilet.
And so I think you really want to start with the people first.
You want to have that core of people who understand
what you're trying to do.
And as I say, when I came to Vanderbilt,
I was lucky that that already existed.
That not only did I have a good team in the team
(28:34):
that I inherited, but also there was demand for data.
There was interest in data.
I had senior leaders who were saying, we want more data.
I have not had anybody ever say to me, is this worth it?
Which has been tremendously helpful.
It doesn't mean that it's always easy, then,
(28:56):
to get the resourcing you need.
You're still in the resource.
You're still in a resource-constrained environment.
Even a lot of institutions will say, well, yeah,
Vanderbilt, you've just got a big endowment.
We're still resource-constrained.
Everybody is.
And so we're still having to make those decisions.
But I would say you've got to start with the people.
(29:17):
You have to have both some good people to work with to do
the work.
And you have to have enough leaders who understand
what you're trying to do that they're going to at least
support you in it.
Olivia, what is it about higher education?
You've spent a lot of your career in the higher education
space.
Why did you land your career there?
And why have you stayed in higher ed and at Vanderbilt?
(29:40):
What about the space is exciting or interesting to you?
I started my career working internationally.
When I finished college, I was really interested in going
into international space of some kind.
And I actually spent several years working in Ukraine.
I worked on the USAID project.
(30:00):
I learned Ukrainian.
I've got this whole strand over there.
And I realized that what I learned
was that there were a lot of universities that were doing
a lot of international stuff.
And so when my USAID project time ended,
I was looking for what the next step was.
And I found myself.
(30:21):
I had moved at that time as well, got married and moved.
And I found myself working in one of the Cal State
institutions, Cal Poly Pomona, doing international work.
And I discovered that universities
are just these fabulous, as I say, they're towns.
So they are their own communities.
Most of them have heft.
(30:41):
They're big enough that you've got real good interaction
of people.
And they're smart people.
People in universities are smart.
And they're interested in doing good for the world.
And there's very few people that go into universities
to get rich.
(31:03):
And so people have chosen that role
because they do want to make the world a better place.
I just it's endlessly fascinating.
And although I spend a lot of time talking to people
about data and just the hows of running a big organization,
(31:23):
I always try and chat particularly
with my academic colleagues about what do you really do?
What's your research about?
What's exciting to you?
And I've talked to people about drilling ice cores in Antarctica
and measuring the different debris in them,
how you can improve your biofauna,
(31:49):
your internal microfauna, whatever it is,
the bacteria in your gut and stuff like that,
right the way through to how children get language
and how they think about it.
And so these people are just fascinating.
And they know all sorts of stuff.
And they do like crazy stuff.
I was talking to one researcher one day.
(32:10):
He was giving me a ride somewhere.
And he's like, yeah, and I use these mice to do this and that.
And I'm like, where do you get your mice from?
And he's like, I make them.
You do what?
These are bioengineered mice.
So I just think you never can be bored in a university
because you're always learning something new.
(32:31):
You're always finding something different.
So what is the future, maybe specifically
for you and your team when there's so much excitement
happening, like bioengineered mice and microfauna in your gut?
I mean, sounds like there's so much to explore.
What's the future for a CDO in that environment?
(32:52):
Where do you strategically want to see your team go,
say, in the next 5, 10 years?
So we're currently really getting
excited about some of the new technologies out there.
Right now, I mean, I can't tell you 5, 10 years.
That's a really long time, Troy, in data land.
But right now, I am excited because I
(33:17):
see a lot of these technologies moving from proof of concept
to some real maturity.
And I see part of my role as scanning the horizon
and finding things that are exciting that I
can bring back to Vanderbilt. So I
need to understand what matters to senior leaders.
What are some of the challenges they're facing?
(33:39):
And then I need to be able to say, OK, that's interesting.
Maybe this is something that we might be able to pick up
and use at Vanderbilt. Now, I'll give you an example.
Unstructured data.
You've heard the statistics.
What is it?
80% of all information is unstructured.
(34:00):
And that's true at Vanderbilt as well.
But how do you actually get value out of that?
And Gen.ai is obviously the answer somehow.
But that's not as simple as just throw a chat GPT at it
and get on with it.
And so I'm currently looking at how do we start to have,
(34:21):
I'll come back to our alumni example again.
Our development officers, when they go out to meet with folks,
they will take notes and they will log all their emails
and stuff like that in their system.
I want to figure out how we mine those notes to figure out
what are those alumni actually interested in?
What were they talking about?
What are the patterns that we can fill from that?
(34:42):
And I'm trying to figure out how we do something like that.
How can we really bring those technologies to bear?
How can we think about surveys differently?
Finding out what people are thinking.
We always want to know what people are thinking.
How can we make sure that surveys are broken?
The traditional way of doing surveys is broken.
(35:02):
People are not responding anymore.
How can we use these new technologies
to help us do some of these things differently?
And so to me, that's the future of where my role is going.
We've got the foundation.
Now let's find those exciting things
that we couldn't do before and bring them back and plug them
into real problems that we have.
(35:23):
How do you do that in a resource-constrained environment?
Next major question, right?
Yeah.
I mean, so I am increasingly, I started out
doing a lot of time in higher education conferences.
And I now spend quite a bit of time at conferences.
But quite a few of them are not higher education.
Because I'm trying to learn what other people are doing.
(35:48):
Then I try and kind of grab those ideas.
Often, a lot of those companies are not
working with higher education.
So sometimes I can kind of get some preferential relationships
with them because they want to figure out
how to work in this market.
But sometimes it's just about, OK,
(36:08):
I've got to work with a particular area
of the university.
And if this is going to happen, they're
going to have to make some investment.
It can't come from my budget because I don't have it.
There you go.
Just get the business to pay for it, right?
I mean, that is, if they're going to get the value,
they're going to have to have some skin in the game as well.
(36:28):
We started this conversation talking about a CDO being
strategic and technical.
And I think as this conversation has wandered,
we've pretty much been bouncing between those two worlds
constantly.
And so I really do see, it makes a lot of sense.
You're sitting right in between those spaces.
Olivia, this has been really fun.
If people want to reach out and have questions about what
it's like to be a CDO or your role at Vanderbilt,
(36:50):
where could people find you online?
Or how could people connect with you further?
The best way to find me is via LinkedIn.
And I am the world's easiest person
to find on LinkedIn because I'm the only Olivia Q
ficus there.
Easy name match right there.
Your alumni organization loves you.
They absolutely do.
They don't have a problem with me.
(37:12):
All right.
Well, thank you so much for joining us today, Olivia.
And for listeners out there, thanks
so much for tuning into this episode of Making Data Matter.
Thank you, Troy.
Thank you, Sawyer.
Welcome to Making Data Matter.
(00:01):
We have conversations about data and leadership
at mission-driven organizations with practical insights
into that intersection of nonprofit mission strategy
and data.
I'm your host Sawyer Nyquist.
And I'm your co-host Troy Dewek.
And today we're joined by guest Olivia Koufikis.
Welcome to the show, Olivia.
(00:22):
Thank you so much both for having me today.
It's a real pleasure to be here.
Absolutely.
Olivia, for people just meeting you for the first time,
give us a little background of who you are
and what do you do.
I am the Chief Data Officer at Vanderbilt University.
Vanderbilt is a private research university
based in Nashville, Tennessee.
We have about 13,000 students.
(00:43):
About half of them are undergraduates,
almost all residential.
And half are graduate and professional students.
And I oversee the data team, as the name suggests.
And we work with both the academic side of the shop
and also with all of our administrative partners
across the university.
Now, correct me if I'm wrong,
(01:04):
but when people think of higher education
and data in higher education,
they might just think that you're kind of keeping track
of what students are enrolled and what classes.
And is that about the extent of it?
Or correct my perception of how complex data is in higher ed.
Sure, no problem.
So what I think people don't understand always
about a university, particularly a research university
(01:25):
like Vanderbilt is we're basically a small town.
So yes, we have students.
They're really important.
They're the reason we're there.
We also have huge research labs.
We have a huge dining facility.
We have a huge residential facility.
We maintain a campus.
We're an arboretum.
(01:46):
We have a huge kind of energy plant that supports us.
A lot of shared services around that.
We obviously have a lot of kind of classroom
and conferencing facilities, lots of buildings.
And we do a lot of work out in the community.
So we have all our engagements with our alumni.
(02:09):
We're embedded.
We're in the middle of Nashville.
So we're embedded in the city of Nashville.
We engage with the community and we're a huge employer.
So we have thousands of staff.
We have a big HR facility.
So we do kind of all sorts of stuff.
And that's what I love about working in the universities
because the data is actually really, really diverse.
(02:31):
And there's always something new to get involved with.
My team's just getting involved with working
with our athletics department.
And so that's a whole kind of different ball game,
if you'll forgive the pun.
And it's just a lot of fun, but it is, as you say,
it's not just counting the students
(02:51):
and making sure they get to class on time.
Well, when you said you had an arboretum,
I was half expecting you to say, and the data swamp
is right next door or something like that.
So that's amazing.
So the chief data officer role, would you
see that as more of a technical role, more of a strategic role?
A lot of people listening to this
(03:13):
may not have heard of a CDO role before.
And the only thing maybe you think about is
a manager of analytics or manager of data.
How does a CDO function, technical, strategic,
somewhere in between?
So CDO role, as it is evolving, and this is not just
at Vanderbilt, this is across the world, really,
(03:34):
because CDOs are some of the fastest growing new roles
right now.
It really is an intermediary role.
When done right, I think a CDO role spans everything
from deeply strategic and very, very embedded
in what does the business want to do.
(03:54):
And I'm going to use business, even though it's a university,
it's just shorthand, all the way through to needing
to understand the data stack, needing
to understand how those pieces fit together,
and what are the dependencies between those two things.
And so again, I think it's a role
you can come at from either side.
There's lots of CDOs who have a technical background.
(04:17):
I am not one of those.
I've come at it from the strategy side.
My previous role was as director of strategic planning
at a different university.
And so I have had to learn a lot of the technical stuff.
And I'm not a technologist.
I'm not going to go out and code for you.
(04:38):
That's not my wheelhouse.
But I do have a pretty good understanding
of how all the pieces fit together.
And I find that just fascinating.
It's a constant challenge.
Give us a day in the life of Olivia
as she bridges those two worlds of the strategy
and the technical.
So think of maybe a project that you worked on,
(04:59):
just something to give us more insight into what that
looks like in an earthy, nitty gritty kind of a way.
So we're working on a project right now
about research administration.
And research administration is, at Vanderbilt,
we collaborate with Vanderbilt University Medical Center.
(05:20):
They're a separate entity to us.
But together, we have over a billion dollars worth
of research activity going on every year
between Vanderbilt University and the University Medical
Center.
And so managing all of that money flowing
through the organization is not a small task.
Much of it is federal money.
And so there's a lot of requirements around that.
And just helping the individual researchers, what's
(05:46):
called the principal investigators, the PIs,
to understand how much money they have in their grants,
how much has been spent, how much will be spent,
how much is still free and unallocated,
and they can spend.
Are they aligning with the requirements of what they
were given by the federal government
in terms of what they're putting in there?
(06:07):
That's a huge task.
And we've been working to pull together all of that information.
And so those conversations for that project
will go anywhere from meeting with the provost
so that she explains to us why this is so important, what
is the demand coming into her from her deans
(06:27):
and from the PIs in the different schools
as to what they're saying they're not getting,
through to understanding what the research administrators,
the people who are sitting there day to day doing that work,
supporting the PIs need, through to getting into what
are the source data systems, where the information is held.
(06:48):
We actually have multiple systems that data is held in.
How can we bring that together?
And then how can we create them?
We're using Snowflake as our foundation there.
And then how can we create a front end interface
that the PIs will be able to use and their administrators
will be able to use easily that pulls on all that information
(07:11):
and can collect new information from them?
So they can model, OK, if I hire this person,
will I have enough money then to also buy
this particular piece of equipment I need to buy?
Or will that throw all of my other metrics out?
And so we're thinking about data sources.
We're thinking about platforms.
We're thinking about warehousing.
We're thinking about data modeling.
(07:33):
And we're thinking about what do the end users need?
How are we going to get that in front of them?
How are they going to be trained?
And of course, they want it all six months ago.
And at a scale of a billion dollars of research,
which is not the scale that a lot of organizations operate at.
And so those are problems that people solve at small levels,
(07:54):
but you're solving them at a scale
that's different than most places.
Now, I think we need to pause, though,
and just clarify something.
So Olivia, do you have the IT personnel
right directly on your staff?
Or is there a separate IT organization
that you're partnering with?
And what does that relationship look like?
So my office is located in the office of the chancellor.
(08:16):
That's the president's office.
And that is to recognize that data is fundamentally
about supporting the needs of the business
rather than a technical item.
And so we have in my office a lot of the people
who work closely with leaders.
They work closely with data users.
(08:37):
They work with our data stewards.
We run our data governance.
But the IT stack, the data engineering stack,
sits within our IT department.
And we have a Vanderbilt culture of what
we call radical collaboration.
And so we work really closely with them in partnership,
in daily, weekly partnership, to make sure they understand
(08:58):
what we need and that we understand
what are the issues they're facing
and how we prioritize things, how we bring things together,
how we make sure we've got the right technology.
And that's just constant communication.
I know you think a lot about strategy.
And you mentioned earlier on, you came from the strategy side
and you've mentioned how strategy is kind of what
(09:18):
holds these pieces together.
Tell us a little bit about what does data strategy
look like at Vanderbilt. And I think
there's a few more questions we'll go to after that.
But I want to start there, I guess.
Tell us about data strategy at Vanderbilt.
So when I came to Vanderbilt five years ago,
I was actually hired into what's called institutional research,
which is the, it's seen as largely being around the, what
(09:41):
we started with, counting the students, sometimes counting
the faculty, doing all of that thing,
doing some basic reporting to the federal government,
sometimes to state governments.
It's, institutional research has been around a long time.
And I was hired, I had already been in a broader role
than that.
And I said to them when they hired me,
(10:01):
what do you want from this?
And they said, Olivia, make it strategic.
And I remember meeting during my recruitment process
with a couple of the vice chancellors, the vice presidents
who were from non-academic areas.
And they said, we really just, we
want this team to really engage across the university.
So I came in with a brief to say, OK,
(10:22):
what does it mean to make data strategic at Vanderbilt?
And my first year, I spent doing a lot of listening
in order to understand that.
And what I heard was the same as you hear
a lot of organizations.
We don't know where the data is.
We can't get to it.
We're not sure who to ask for for it.
When we do get it, we don't really know what it means.
(10:45):
It was nothing unusual, but it was clear
that there was a real desire to create something
that was bigger than that.
So the first part of what was data strategy at Vanderbilt
was, in fact, moving my office to be not
institutional research, but to be data,
and to be all sorts of data.
And so we were moved into the president's office
(11:07):
at that point.
The next challenge you face at a university
is that you have a lot of systems.
There's the ones you would think you have, the big ones,
the student system, for instance, the HR system,
the finance system.
Those are your fundamentals.
But there are so many little systems.
There's a different system that does admissions than the one
(11:30):
that holds student information.
We have a system that just handles
what we call immersion, which is the students,
they kind of extracurricular, or they're doing a piece,
they're doing a special project, or they're
doing service learning, or something like that.
They study abroad.
We have a system that handles the immersion work.
(11:52):
We have a system that handles internal scholarships.
So when somebody's come, if they want
a grant to do something else, we have a system
that kind of does that.
Mental health has its own system.
Housing has its own system.
I mean, there's just dozens of them.
And so one of the real challenges in a university
is how do you pull that together?
(12:13):
And so we started exploring.
We already had a good on-prem data warehouse
for our student information system.
But we said we need to be able to warehouse and pull together
and read across a lot more systems than just these ones
that we already have.
And so it was obvious we needed to go cloud.
(12:33):
We ended up going with Snowflake for that.
And we are in that process now of slowly moving things
into Snowflake, starting to be able to integrate them much
more easily than we could before.
And then, so really, the first kind
of stage of our data strategy was
about building those foundations,
the technical foundations, and then also,
(12:54):
like in many organizations, the data governance foundations.
So we had a strand of activity around data governance.
And then, and I shouldn't really have left this till last,
you've got the people foundations.
So we had our team.
And I was really lucky to inherit
a really, really good team.
We had our IT team, also really strong.
(13:18):
But what's happened is that we found different units
around the university have wanted
to hire in a data person or have wanted
to tap more professional data expertise.
And so we slowly built up a team,
some people who are located in my office,
some people who are located in other units
around the university, who are increasingly having
(13:40):
data as their primary role.
And we're building a community of practice around that,
trying to build up their skills.
And so that's been super exciting.
And what I will say about our data strategy now
is that I feel like we've built the foundations.
And so it's not that we haven't done anything,
it's not that we haven't delivered anything,
but we're now at a place where we're ready to do takeoff.
(14:01):
And so we're really trying to understand,
OK, what's the next stage?
What can we do that will really be transformational?
Now, my conversations are shifting quite rapidly
to leaning into talking to leaders and saying, OK,
what do you want data to do for you?
And they often start by being like, well,
(14:23):
just get it all available to me.
And it's like, no, what do you actually want to do with it?
It sounds like that difference between the descriptive
and diagnostic to, can you be a little more forward thinking
and, OK, how do you want this to forecast for you?
Is that what you mean by these conversations?
It is.
(14:43):
And so I'll give you an example.
We have done an awful lot of work with our alumni team.
And they moved to Salesforce system
two and a half, three years ago.
And at that point, that was a real opening for us
to come in and work with them.
And they were some of our first tenants,
(15:04):
if you will, in our Snowflake environment.
We were able to pull the data out of Salesforce.
We did it really easily, put it into Snowflake,
started to see how they needed to be thinking
about some of their business processes differently.
They brought in some really data forward leaders
around the same time.
And so we've been able to build out some great dashboards
(15:28):
that are embedded in their business processes.
And so when we go in, we can see that they're
using these on a regular basis to make decisions.
First of all, first they started being just monitoring.
OK, can we see what's happening?
But now we've built for them dashboards that, for instance,
we have some great mapping dashboards.
So if somebody is saying, OK, I've
(15:49):
got a development officer going to St. Louis
to visit this person, you can actually then
put in the parameters.
And you can see within five miles of where you're going,
there might be three other people
it would be worth going to see.
And so now you spent your money to go.
You were going to go anyway.
But now in that one day or day and a half,
you've identified people you can reach out to,
hopefully set something up.
(16:11):
And so they're doing that daily.
As they plan their travel, they're going in
and they're using that data.
And they continue to want more and more.
So it's kind of like, OK, how can we think about how do we
model some of our alumni so that we understand which alumni
(16:32):
might be really interested in Vanderbilt,
but they've given, but they haven't given a lot.
But we've not really developed a proactive relationship
with them.
How can we reach out to them, proactively start
to build those relationships?
How can we understand people who are younger, who won't be
necessarily giving, but who are really engaged?
How can we find them, figure out what turns them on,
(16:54):
gets them excited, so that we can keep them engaged?
Because our goal with our alumni is
to maintain a lifelong relationship.
We're not trying to just sell them something tomorrow.
We want them to be engaged with Vanderbilt 20, 30 years from now.
So those are some of the things that we found
(17:15):
that it is an evolution.
That you start by, yes, just exposing some of the data,
giving people insight into things they didn't have before.
And then they start to see how they can use the data.
And they also start to see how they
need to improve their business processes so that their data
quality will be better.
Because the more they want to rely on it,
the more they realize, oh, hang on a second.
(17:35):
It wasn't that great.
And we need to change something so that our data gets better.
And that was what happened with our maps.
We had to completely update our addresses
so that we could map them.
That's what I was going to ask is,
how did you get them to actually realize the business process was
broken based on the data that was coming in?
(17:56):
That seems like an age-old battle where the data person
says, well, you're giving me garbage data
through your business process.
And they're like, no, we're not.
You just have to do this and this,
and it'll make total sense to you.
So how did you convince them that they
could identify their own brokenness
in their business processes?
Well, it's not necessarily that the business processes
(18:17):
were broken.
Sometimes it is about, we just need
to raise the profile of something.
But sometimes it is that they had historic data that
had been put in a long time ago.
And fixing that is actually going
to be a significant amount of work.
It's just walking through it with them.
(18:40):
In that case, we had all the data in Salesforce.
It was all data that had been ported over
from a previous system or almost all of it.
We were able to bring the US Postal Service address
database together with that.
And that was not a small lift.
And my team walked alongside them going through identifying
(19:04):
addresses that weren't making sense, things like that.
And we've more or less got there.
Now we're turning to international addresses.
How can we fix our international addresses, many of which
have been put in more or less as a string?
And so how can we start to do that?
And how can we work with our Salesforce team
(19:25):
so that we can force some of those things
like drop down menus or auto filling
rather than just people continuing
to put in garbage data that we continue to have to clean up?
A labor of love, that's what I heard.
It is.
It is.
It's step by step.
And it takes a lot of resource from them.
So they have to figure out what they're
(19:45):
going to prioritize as well.
And that's just one sliver we're talking about of alumni
and really donor relations.
And I imagine you could go down through all
the different pillars of Vanderbilt
and talk about different use cases and different ways
data can be activated.
Is there a way the data strategy helps
you to think about prioritizing which
of those pillars and silos are you going to dive into next?
(20:08):
And how do you handle the competing needs of a small city
that you have at your hands?
It's a great question because it's
something we grapple with on a regular basis.
We started by working where we had enthusiasm.
And as it happened, we did all this work with alumni.
(20:31):
And without really realizing it at first,
but it became apparent pretty quickly
that the thing about the alumni relations
is they have dollar goals that they're
trying to meet.
And so we can actually measure our effectiveness really
easily in that area.
And this is something that a lot of not-for-profits
(20:54):
will recognize.
As you move out from that, a lot of your other areas
don't have easy dollar goals.
And so with our student data, and especially
at a place like Vanderbilt where we're not looking to grow,
particularly our undergraduate student population,
it's all about how do we serve them better?
(21:18):
Doing that better is not going to result in more dollars,
for the most part.
We have very low dropout rates.
We have very high graduation rates.
There are other institutions where they can do that better
and they can see a dramatic increase in their retention
rates or their graduation rates or whatever.
That's not the case at Vanderbilt.
And so we have to really think hard
(21:41):
about how are we going to show that return on investment?
What is that going to look like?
What is success going to look like?
And we've had some where it looks
like we weren't getting very far.
And then a year or two years later,
we suddenly see a decision being made.
And we're like, oh, they're doing that because the data was
better.
(22:01):
OK, that's good.
What are some of those things then
that you do start to look at and measure?
If it's not student retention, which is really strong,
graduation rates, which is really strong, what
are other things that are important to the student
experience that Vanderbilt wants to enhance
and that maybe you can use data to get insights into?
One of the major challenges that we've had is graduate outcomes.
(22:23):
So where do our students go after they graduate?
A lot of people will think about that and say, well,
of course you know that, don't you?
But if you then think about how is the data
flow going to work for that?
Who's going to tell us where our students have
gone after they've graduated?
The students are.
Yeah, self-reported.
And so you can often get a good slice
(22:46):
of what we call first destinations
before they graduate.
And we do hit them up with all sorts of surveys about that
before they graduate.
But there will be some who don't know at the point
that they graduate where they're going to go.
And there will be some for whom that first year or so out
is not where they're headed.
(23:07):
We have some who take a break, they travel for a year,
or they do something else.
Or they might be, we have a music school,
so we have many who are kind of jumping
into these very unstable, they're
not sure what that's going to look like gig type professions.
And so those people look really weird in the data.
And so we've spent a lot of time on data collection
(23:33):
for first destinations, understanding where they
go that first year out.
And then we are now trying to grapple with where
have they gone after that?
And how do we understand what those trajectories look like?
And do we think that what we at Vanderbilt
gave them during their four years
(23:55):
in undergraduate education with us,
did that change their trajectory?
Did that improve their trajectory?
And so we've done a lot of data scraping, as you can imagine.
We've worked with companies that scrape LinkedIn,
and then tried to match that up to our students.
That's a lot of fun, because that's name based matching.
(24:17):
Wildly consistent, I'm sure.
How many John Smiths are there in the world?
Yeah, yeah.
And the thing is, we tend to have some of their address
information.
Many people will put what their degree was,
so they often get flagged as being Vanderbilt people
from LinkedIn as well.
But we also have a lot of people who
(24:38):
go into medical professions, as you can imagine,
at a place like Vanderbilt. Those people
have a very long trajectory between when they leave us
and when they start working.
And a lot of doctors actually don't use LinkedIn heavily.
And so we've got these kind of data gaps.
So we've been working with, we've
done a lot of work with third parties,
both as I say, these data scraping firms,
(24:59):
but also with some more sophisticated labor
economist type people, trying to say, OK, what
does a Vanderbilt education actually do?
Because those are the kind of questions,
even before we get to how much do they make,
people seem to think we know how much our graduates make.
Where would we get that information from?
(25:21):
How many have founded a company?
How many are chief executive officers?
These are questions we get asked on a regular basis.
And we kind of grope towards answers.
But there's not good data sources for those.
And so we're always playing a little bit of a scavenger
hunt on that.
But it is really important information.
(25:42):
Because ultimately, if we want to convince people
that it's worth coming to private university
and spending the money that you might have to,
although we have very good financial aid,
spending the money you might have
to spend to come to a private university,
well, then you would hope that there's something better
out the other end.
That it's worth that investment.
(26:02):
From the technology side, you've talked
about a few of the challenges of just all the different source
systems that come together.
What are the other technologies that you
found to be really useful?
So I think you've mentioned Snowflake so far
as one of the main pieces.
What other pieces make up the technology stack
that help you start to enable these use cases you're
trying to get to?
We have three technologies that we've invested in,
(26:24):
significant technologies we've invested in
since I've been at Vanderbilt. So Snowflake is one of them.
We talk a little bit more about that.
Tableau is another one.
We already had Power BI.
We had Oracle Analytics Cloud.
But we wanted a tool that was really heavily visual.
And so we went with Tableau.
(26:46):
And it intersects as well, obviously,
with our Salesforce environment really well.
So that's a massive win for that environment.
And then the last is our data catalog.
And we have ended up going with a relatively small data
catalog.
It's a company called Data Edo.
It is right sized for us.
(27:06):
So a lot of the big data catalogs
are extremely expensive and do a lot of things
that when you're just starting out in data governance,
you're probably not ready to do yet.
And so we found Data Edo.
We could kind of scale it desk by desk, basically.
But it does a lot of data lineage work for us.
It provides us with those foundations.
(27:29):
It enables us to not have to do everything by hand,
which was important.
And so that data catalog is really important to us.
So I'm going to bring this back to data strategy.
Because in data strategy, you talked about people
and kind of like the processes of how data is collected
and managed and then the technologies.
If somebody is just approaching data strategy
(27:51):
for the first time, where do they kind of
start on that paradigm?
How do they approach the challenges that
come from technology, processes, and people?
I mean, it's the classic triumph, isn't it?
People, process, technology.
And you do need all three.
You don't want to be driven by the technologies.
Because if you don't have the people in the processes,
(28:12):
the technologies are just money you're
flushing down the toilet.
And so I think you really want to start with the people first.
You want to have that core of people who understand
what you're trying to do.
And as I say, when I came to Vanderbilt,
I was lucky that that already existed.
That not only did I have a good team in the team
(28:34):
that I inherited, but also there was demand for data.
There was interest in data.
I had senior leaders who were saying, we want more data.
I have not had anybody ever say to me, is this worth it?
Which has been tremendously helpful.
It doesn't mean that it's always easy, then,
(28:56):
to get the resourcing you need.
You're still in the resource.
You're still in a resource-constrained environment.
Even a lot of institutions will say, well, yeah,
Vanderbilt, you've just got a big endowment.
We're still resource-constrained.
Everybody is.
And so we're still having to make those decisions.
But I would say you've got to start with the people.
(29:17):
You have to have both some good people to work with to do
the work.
And you have to have enough leaders who understand
what you're trying to do that they're going to at least
support you in it.
Olivia, what is it about higher education?
You've spent a lot of your career in the higher education
space.
Why did you land your career there?
And why have you stayed in higher ed and at Vanderbilt?
(29:40):
What about the space is exciting or interesting to you?
I started my career working internationally.
When I finished college, I was really interested in going
into international space of some kind.
And I actually spent several years working in Ukraine.
I worked on the USAID project.
(30:00):
I learned Ukrainian.
I've got this whole strand over there.
And I realized that what I learned
was that there were a lot of universities that were doing
a lot of international stuff.
And so when my USAID project time ended,
I was looking for what the next step was.
And I found myself.
(30:21):
I had moved at that time as well, got married and moved.
And I found myself working in one of the Cal State
institutions, Cal Poly Pomona, doing international work.
And I discovered that universities
are just these fabulous, as I say, they're towns.
So they are their own communities.
Most of them have heft.
(30:41):
They're big enough that you've got real good interaction
of people.
And they're smart people.
People in universities are smart.
And they're interested in doing good for the world.
And there's very few people that go into universities
to get rich.
(31:03):
And so people have chosen that role
because they do want to make the world a better place.
I just it's endlessly fascinating.
And although I spend a lot of time talking to people
about data and just the hows of running a big organization,
(31:23):
I always try and chat particularly
with my academic colleagues about what do you really do?
What's your research about?
What's exciting to you?
And I've talked to people about drilling ice cores in Antarctica
and measuring the different debris in them,
how you can improve your biofauna,
(31:49):
your internal microfauna, whatever it is,
the bacteria in your gut and stuff like that,
right the way through to how children get language
and how they think about it.
And so these people are just fascinating.
And they know all sorts of stuff.
And they do like crazy stuff.
I was talking to one researcher one day.
(32:10):
He was giving me a ride somewhere.
And he's like, yeah, and I use these mice to do this and that.
And I'm like, where do you get your mice from?
And he's like, I make them.
You do what?
These are bioengineered mice.
So I just think you never can be bored in a university
because you're always learning something new.
(32:31):
You're always finding something different.
So what is the future, maybe specifically
for you and your team when there's so much excitement
happening, like bioengineered mice and microfauna in your gut?
I mean, sounds like there's so much to explore.
What's the future for a CDO in that environment?
(32:52):
Where do you strategically want to see your team go,
say, in the next 5, 10 years?
So we're currently really getting
excited about some of the new technologies out there.
Right now, I mean, I can't tell you 5, 10 years.
That's a really long time, Troy, in data land.
But right now, I am excited because I
(33:17):
see a lot of these technologies moving from proof of concept
to some real maturity.
And I see part of my role as scanning the horizon
and finding things that are exciting that I
can bring back to Vanderbilt. So I
need to understand what matters to senior leaders.
What are some of the challenges they're facing?
(33:39):
And then I need to be able to say, OK, that's interesting.
Maybe this is something that we might be able to pick up
and use at Vanderbilt. Now, I'll give you an example.
Unstructured data.
You've heard the statistics.
What is it?
80% of all information is unstructured.
(34:00):
And that's true at Vanderbilt as well.
But how do you actually get value out of that?
And Gen.ai is obviously the answer somehow.
But that's not as simple as just throw a chat GPT at it
and get on with it.
And so I'm currently looking at how do we start to have,
(34:21):
I'll come back to our alumni example again.
Our development officers, when they go out to meet with folks,
they will take notes and they will log all their emails
and stuff like that in their system.
I want to figure out how we mine those notes to figure out
what are those alumni actually interested in?
What were they talking about?
What are the patterns that we can fill from that?
(34:42):
And I'm trying to figure out how we do something like that.
How can we really bring those technologies to bear?
How can we think about surveys differently?
Finding out what people are thinking.
We always want to know what people are thinking.
How can we make sure that surveys are broken?
The traditional way of doing surveys is broken.
(35:02):
People are not responding anymore.
How can we use these new technologies
to help us do some of these things differently?
And so to me, that's the future of where my role is going.
We've got the foundation.
Now let's find those exciting things
that we couldn't do before and bring them back and plug them
into real problems that we have.
(35:23):
How do you do that in a resource-constrained environment?
Next major question, right?
Yeah.
I mean, so I am increasingly, I started out
doing a lot of time in higher education conferences.
And I now spend quite a bit of time at conferences.
But quite a few of them are not higher education.
Because I'm trying to learn what other people are doing.
(35:48):
Then I try and kind of grab those ideas.
Often, a lot of those companies are not
working with higher education.
So sometimes I can kind of get some preferential relationships
with them because they want to figure out
how to work in this market.
But sometimes it's just about, OK,
(36:08):
I've got to work with a particular area
of the university.
And if this is going to happen, they're
going to have to make some investment.
It can't come from my budget because I don't have it.
There you go.
Just get the business to pay for it, right?
I mean, that is, if they're going to get the value,
they're going to have to have some skin in the game as well.
(36:28):
We started this conversation talking about a CDO being
strategic and technical.
And I think as this conversation has wandered,
we've pretty much been bouncing between those two worlds
constantly.
And so I really do see, it makes a lot of sense.
You're sitting right in between those spaces.
Olivia, this has been really fun.
If people want to reach out and have questions about what
it's like to be a CDO or your role at Vanderbilt,
(36:50):
where could people find you online?
Or how could people connect with you further?
The best way to find me is via LinkedIn.
And I am the world's easiest person
to find on LinkedIn because I'm the only Olivia Q
ficus there.
Easy name match right there.
Your alumni organization loves you.
They absolutely do.
They don't have a problem with me.
(37:12):
All right.
Well, thank you so much for joining us today, Olivia.
And for listeners out there, thanks
so much for tuning into this episode of Making Data Matter.
Thank you, Troy.
Thank you, Sawyer.
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