Success, to me, looks like
continuous improvement. And I know
sometimes those
those words "continuous improvement" get
tired or overused,
but that really is what a successful
analytics program would look like at a
college or university.
Welcome to Focus! A podcast dedicated to
the business of higher education.
I'm your host, Heather Richmond, and we
will be exploring the challenges
and opportunities facing today's higher
learning institutions. In today's episode,
I'm talking with Lindsay Wayt, NACUBO's
director of analytics.
We'll be discussing the ways higher
education can use data
to help them pave the way to a
successful future.
Well thank you for being here today,
Lindsay. 
Yeah, thank you for having me. I'm
really excited to be able to talk about
analytics with you.
Great, so can you share a little bit
about your role at NACUBO?
Yeah, I would love to. So I'm NACUBO's
director of analytics.
And so, in short, that means I
am focused on NACUBO's strategic
priority number five.
Which is to help institutions lead
higher education's integration of
analytics to achieve their institutional
strategic goals.
So I used to be a researcher at NACUBO,
and so data has always been a part of my
job.
And now the exciting part is I get to
talk about data with our members,
and plan professional development
opportunities, and create resources to
help them be able to harness
the power of data and analytics at their
institutions. So that's the the short
version of what I do now.
Well it sounds like the perfect role for
you then, being a data girl and all.
Yes, yes. Well, and it's definitely important in higher
ed as well
that we use data. I mean, I think like
any professional - whether you've grown up
as a data professional
or not in higher ed - I think the way
the world seems to be going,
everybody's going to have to be able
to use data and analytics to some degree.
And so when we think about, "Wel, why does
NACUBO have a strategic priority around
data? Isn't that just something for
IR or IT?" But it's not. You know, our members
really are
facing increasingly complex
challenges. I mean, if we just look
at, you know, some of the context even
before the pandemic that we're facing
now,
we already saw that state funding hasn't
really recovered since the great
recession. we know student aid
continues to be a concern. You know, we
see the Pell grant hasn't kept pace with
the price of higher education.
We see lots of individuals in the public -
whether it's students, their family
members, policy makers,
questioning the value of higher ed. And
so I think our our industry, our sector,
is really facing quite a few
challenges and they're going to have to
use data to navigate all those. And so
it's not just an IR or an IT
thing. It really is an entire campus
program, or at least it should be at
institutions. And business officers
really need to be involved with
analytics.
Yeah, you're absolutely right. And
so I'm assuming that that's why last
year
2019 NACUBO launched the study of
analytics and this was a new survey,
right?
Yes, yes, it's a new survey. So we launched
the survey itself last year in July.
So it's been a year since we collected
the data, and then
the report was published right before
our integrating analytics forum in 2019.
So that was in November.
So the data are still new, but as you
could imagine,
i'm sure there are questions now that we
all find ourselves in the midst of a
pandemic.
But I would say findings, though,
are still valid. Like, it's still
important to know where were we
with the use of data and analytics
before the pandemic,
and now that all of those concerns I
just mentioned about affordability, the
value of higher ed,
concerns about funding cuts, it's even
more important that we use data now.
You're absolutely right. And so curious:
speaking of that, so even though the
study kicked off in 2019,
when was the research actually completed?
So did you get a little bit of pre- and
post- COVID results in there?
No, so it was a one-time survey. So we
collected the data
in July of 2019 and the report came out
in November of 2019.
And we are not currently in a phase
of collecting
data again on that same survey.
Okay, that
makes a lot of sense.
The answers would be completely different
now anyway, wouldn't it?
Yeah, well yeah to some degree, I think,
honestly, our
business officers - I mean, as we kind of
dive into some of these findings, I'm
sure your listeners will agree -
I think a lot of what you would expect
is maybe just a little more
heightened sense of need, or a heightened
sense of purpose, for using data.
Although, I mean it already was evident
in the results
as they were in November, so before
the pandemic.
Yeah, that makes a lot of sense. So when
it comes to analytics for higher
education,
what type of data are business officers
and university leaders looking for?
That's a really great question.
So
I will say before we launched the
the survey itself, NACUBO's staff
conducted a series of focus groups
with our members at our different
regional meetings, as well as at a NACUBO
annual meeting,
really trying to get a sense of, you know,
if we have this,
you know, idea about data and analytics.
But what do you need? What do
business officers really want to do? How
do they want to be able to use data?
And the qualitative responses from those
focus groups - this is all before the
survey itself -
they pointed to three big buckets of
interest
for business officers for how they
wanted to use analytics.
One is not going to be a surprise:
that's finance.
So when we think about institutional
finances, business officers want to be
able to deploy those limited resources
the most efficiently and
as effectively as they can. So it's not
really a surprise that finance is one,
you know, bucket area, or one thematic
area where business officers want to use
analytics.
The next bucket - and I'm not listing
these in order of size, I'm saving the
biggest
one for last - but the next one is
facilities.
And that makes perfect sense, too.
Especially pre-pandemic.
You know, campuses have a really large
footprint and it makes sense that
business officers would want to make
sure that they're optimizing the use of
their space.
And then the third bucket - and this one
maybe would be a surprise to those,
especially if you're outside of the
business officer world, but I don't think
other business officers find this as a
surprise -
but the third big area where they really
want to see analytics used
is student success. And I will say one of,
there's a NACUBO member - he happens
to serve on the analytics advisory group -
and he always uses this phrase. He said,
"It's not just about return on investment,
it's about return on mission investment."
And I think that really shows the tie
about why business officers are so
interested
in analytics as it relates to student
success, because they want to know that
those resources that they're allocating -
which we know are limited -
are having an impact. And an impact for
students, and students are able to
achieve the outcomes that they want to.
Yeah, that makes a lot of sense.
And it's interesting when you say
student succes,s
because that really kind of is two-fold.
And we talk a lot about having success
outside the classroom in addition
to inside the classroom, and those really
being separate. And so obviously from a
business officer perspective,
we hear a lot of our schools, too, saying
that they're really looking at that
success outside the classroom.
Yes, well, and that's definitely important,
too. And I would, you know, point your
readers to NACUBO's the study of
analytics, where we show the results of
this survey,
and where we really break down some of
those items about how
analytics is currently being used
to support institutions and where
business officers are really seeing it
make an impact.
And we do break down that student
success piece into
maybe not necessarily the inside/
outside of the classroom, but we look at
the whole spectrum of,
you know, how are you using it for, you
know, for enrollment, or admissions?
And then, you know, you think about
student progress, you think about
retention.
And then, you know, as you think about
after completing a degree,
you know, how are students - how are we
using analytics to show that the success
for the post-graduation outcome.
So maybe we look at it slightly
differently, but it's the same thing
we, you know, obviously we have believed
that the experiences in the classroom,
there's definitely opportunities for
learning analytics
from the faculty side, from the provost
side, and there's also
lots of opportunities for understanding,
you know, how do different success
programs
influence student outcomes, you know? How
are we making that case for
investing into student support services
outside of the classroom?
And, you know, we've heard from talking to
members that there are some
interesting ways that that they're
working on, you know, addressing both the
inside and outside of the classroom
to make sure that students are being
successful. 
Yeah really all builds on
each other, right? so there's not really
this or that, it's a combination and
really one impacts the other to have
success
across the board. So yeah, that's great.
Well since this study is
relatively new to NACUBO,
can you talk more about your approach -
the NACUBO's higher ed analytics
framework?
Yeah, yeah definitely. I will say, so
the framework itself was built kind of
around,
or an extension of, the Gartner Framework
on Analytics. And I'm not sure if your
your listeners are familiar with that,
So I'll kind of just describe
what the the Gartner Framework looks
like. It's basically
a visual that shows progression so
that,
you know, if it's a little line graph
that's going up and to the right,
and it's showing the the different
levels
of analysis that you could conduct. So it
goes from hindsight,
to insight, to foresight. And what we're
showing using both the X and the Y
axis in that framework is you see
that as you move across -
so from hindsight, to insight, to
foresight - the level of difficulty of the
analysis increases,
but so does the value. And what we did -
and when I say we, I mean the analytics
advisory group at NACUBO -
we tried to use that as a framework for
understanding our own programming.
However when you think about higher ed
and you want to contextualize
all of that within a, you know, a
framework that business officers would
relate to,
we added a few elements to the Gartner
Framework. And the three elements
that we added
to it are: 1) to the far side of the
framework we added return on investment.
And so really encouraging our members to
think through, you know, as you
increase in difficulty and value of your
analyses that you're conducting,
you know, what is the return on
investment that you're getting for that?
And always look for how are you
actually improving things at your
institution. And then the other two
things we added
that I think are are of critical
importance to our members as they think
about analytics and how to have a
successful analytics program at their
campus
are culture and capacity. So when we
added capacity to the framework,
we're really thinking about asking
folks who are using the framework to
question, "Are we investing the right
amount of time,
resources, and staff?" You know, really the
institutional capabilities. And then the
other part of the framework is culture.
So you can hire all of the analytics
experts
that your heart desires. You can buy all
the latest and greatest tools,
but if you don't have a culture that, you
know, values being data informed,
then you're not gonna have the action.
And that's the whole point of doing
all the analysis and using analytics
is to change things for the better at
your campus.
Absolutely. I think that's great. And
like anything, we talk a lot about really
being tailored for higher education. So
it's great to be able to look at some of
these other frameworks and how is it
happening
in other business worlds. But at the
end of the day, higher ed is different
and unique and I think those are three
really good additions that you made to
the framework.
Yeah, thank you.
So can you go into
specifically the - I know that we talked
about having the six guiding
principles to use analytics - so can you
talk about what those six guiding
principles are?
Yeah so the six guiding principles
that are in a publication that NACUBO
wrote together with EDUCAUSE, which is
a professional association
for IT professionals in higher
education, and also with the Association
for Institutional Research.
So our three associations have
partnered, you know, on
professional development programs or, you
know, in other capacities
for the past six years if we're
thinking EDUCAUSE, and I think we're at at
least three years now working with
AIR. So our organizations had already
been working together in analytics.
And it was probably, it was November of
2018, there was a group of us meeting,
and we were thinking that it might be
good, you know - we keep collaborating, but
we really need to get the message that
it needs to be our members
and it also needs to extend beyond just
the members of our three organizations -
but we need to really have this call to
action.
So an idea was born. And the three
associations work together to write the
joint statement on analytics.
And the joint statement on analytics
really does two things:
first it's a call to action for everyone
in higher ed to really start
prioritizing analytics.
Because we know all the challenges that
institutions are facing, and they're
really going to have to start using
data as an asset. And then the other part
is these six principles that you're
asking about. And so there are six things
that
our three organizations thought
all institutions - so all colleges and
universities - should use these principles
to guide them. Whether they're just
starting an analytics program, or whether
they're already well on their way.
These are guiding principles that
everyone should consider
throughout their work. And so the six
principles - and I'll be brief about them.
but in no particular order - one of
them
simply says, "Go big." And we're really
asking
for institutions to make a commitment to
analytics.
And we think that that commitment needs
to come both from the top
top down, and also bottom up. But there
definitely has to be leadership buy-in.
If you don't have buy-in from your
president and your cabinet level-leaders,
then it's really difficult to maintain
any kind of data-informed culture.
The second: analytics is a team sport.
And maybe I should have said that one
first, but clearly collaboration is
important.
We know in higher ed, a lot of folks
operate in really siloed environments.
We also know that that means there's
probably an Excel spreadsheet
on one person's computer, in one
department, that nobody else knows about,
but it really could add value to
to the institution to have access to
that information.
So we think it's important to break down
silos and to collaborate.
And that's beyond, you know, IR/IT and
the business office, but also into
student affairs, the provost office, the
president, etc.
The third principle is to be prepared.
That one is more timely now than maybe
any of us
ever could have predicted it would be.
But in short,
we're calling on folks to prepare for
detours, or challenges, or,
you know, we're saying your first attempt
at using analytics, or developing a
campus-wide analytics program,
probably not going to be perfect - I mean
I can't think of an example
that's been perfect -
but it's prepared for detours
is the the principle because we know
it's hard for higher ed to change.
And, you know, whether it was a pandemic,
or or something else, there are going to
be other challenges
that your campus is going to experience.
But that doesn't mean you should let
your efforts to use analytics be
derailed.
So keep your focus, because ultimately
you're serving students,
which is one of the next principles.
So analytics has real impact on real
people.
We talked earlier about how higher ed as
a sector is very different
than other businesses. And that's
because
we're human-focused. So everything is
about students, or faculty, or staff
or, you know, hopefully a combination of
all of those. And so
behind every piece of data, behind those
numbers,
are real people. And so we need to make
sure that we take
data privacy, data security, very very
seriously at our institutions.
And the next principle is invest what
you can - you can't afford not to.
I love when I get to talk about
that one.
Especially being from, you know, NACUBO.
We always assume people are meaning, "Oh,
yep, you're the business officer, you're
talking dollars and cents."
And we are, and if you read the statement
it does say using analytics will require,
you know, investments in terms of like a
monetary investment. You might have to
buy new
software, or focus on your infrastructure.
But I would say
one of the most important aspects of
that investment
is investing in your staff. So it's
investing
by taking the time to offer
professional development,
to ensure that your campus has data
literacy skills
that help them be able to use the data
so that we're actually translating
everything to action.
So that's really what we mean by
investment. It's more than just the
dollars. It's all the time the staf,f the
professional development, the data
literacy, etc.
I'm so glad you said that, because I talk
often about as we
bring new tools or new systems to the
table,
you have to also - not just buy the tool,
but you have to invest your time,
and understanding it, and making sure
you're getting the value. And I think the
same thing is true of data,
right? And data, I'll say sometimes too,
data is only as powerful as it is
manageable.
And so if you don't know how to manage
it then it's just gonna be a bunch of
numbers.
Yes, yes, oh no, I agree. Yes numbers or,
you know, some folks they want that, you
know, we see those commercials on tv about
some kind of magic
button, or there's like a button you can
click on your computer, and it tells you
what to do.
That's not really how analytics works.
It's definitely a resource and you
should
use it especially when our institutions
are, you know, so financially constrained.
But but it's not magic, it definitely
requires effort. You have to be able to
understand the context around the data.
There's no magic answer
for higher education, unfortunately.
And then I would say the the last of
the six principles that we outlined in
the joint statement
is tick tock tick tock. What we
really mean by that
is the time to act is now. And we were
saying that,
you know, before the pandemic, so this
came out, you know, some time ago.
And it was already imperative
for institutions to really start to
leverage the power that data can bring
to support them.
And I would say you know if we could use
a bigger, bolder font
for that particular principle maybe we
should.
Because I do think the the pandemic
has highlighted, you know, all of the
things that we've just been talking
about.
It's made them seem,
you know, even more stark. And so I think,
you know, if we haven't already started
using data at institutions, we definitely
should be
thinking about it and thinking
quickly.
Because every day that passes is
another day lost.
You're absolutely right and I've talked
to some schools recently that they were
thankful that they
felt a little bit ahead of the curve, if
you will, by already starting to
track some data. And so then when they
were asked all of a sudden like, "Hey, do
you have this information? Do you have
this information?" they were able to say
yes
on there. So how have you then
talked with some other schools and, you
know, how do you see other business
officers using analytics? Is this,
you know, again we talked a little bit
about servicing their students, but are
you seeing them start to trend towards
some different ways with using analytics?
Yeah, so actually I'll share a couple
examples.
I have two I'm thinking of in my head
right now, and they're both examples
from members of my analytics advisory
group, so they're both business officers
who are clearly very involved with
analytics.
You know, being in part of a
volunteer group at NACUBO, focused on our
efforts on that.
So I'll share a couple examples. The
first one
is from Mike Gower and he's the
executive vice president for finance and
administration and is also the treasurer
at Rutgers University in New Jersey. And
I'd also say
Mike is a co-author of a chapter in a
book that's going to be coming out this
November. I think it comes out November
3rd of 2020. The book is called Big Data
on Campus:
Data Analytics and Decision Making in
Higher Education.
And there's a chapter that was
co-authored by a couple NACUBO staff -
myself included - and a couple NACUBO
members, and Mike is one of them.
And in that chapter, he writes about
a story about Rutgers using
analytics
to solve a problem that
they've been trying to tackle.
So in short, Rutgers University
surrounds the city of New Brunswick, and
so
for students to get from class to class
or as they travel -
you know, quote unquote - across campus,
they had to go through
a city to do it - to get to the, you
know, the other side of campus.
And that was creating a lot of
transportation issues.
When you think about the busing
routes or students trying to get from
class to class,
and what the university saw in terms of
the challenge
is that students were spending so much
time
on buses, that it made it difficult for
them to get to the courses that they
needed,
ultimately, to be able to graduate. So it
was delaying some students'
ability to complete a degree and they
even said the campus lore was
you'll spend more time on buses than in
the classroom.
So Rutgers had a problem and
the way they decided to, you know, to try
to better understand the problem and
ultimately develop a solution was to use
data and analytics.
And so they analyzed bus routes, they
analyzed
schedules of both the buses and
the students, they analyzed
travel patterns. And so they they pulled
together all of these data
to determine, you know, are there patterns,
are there better ways that we could
support
students? And it turns out there
was.
So they used all of that data to reduce
the amount of student travel.
So they travel from course to course while
having to get all the way through the
city of New Brunswick.
They also used that information to
create an app.
And the app was very much student-
focused and allowed students to optimize
their course schedules with the
consideration of travel built in.
And what they've started to see is that
this does help students reduce their
time to degree, because they're able to
get to the courses that they need.
And so I would say that's one example,
and I promised you a second. So the
second example
is from Sherri Newcomb, who's also
a member of NACUBO's analytics advisory
group,
and she's the the senior vice president
and chief operating officer at
Queensborough College, which is part
of the the City University of New York.
And, you know, I tried to pick a couple,
you know, diverse examples. So we just
heard about a big research university,
and now we'll hear about a community
college.
I'll also point out that Sherri's story
will eventually be released as one of
the resources in NACUBO's the solution
exchange,
where she's talking about planning and
budgeting, and how you can really use
analytics to support your planning and
budgeting efforts - especially
in a time of crises. So Sherri's story
actually starts
only a few months ago. So at the height
of the pandemic,
she was working with staff at
Queensborough College to
develop a schedule for this upcoming
fall, so fall 2020.
And I'm sure all of your listeners know
that has been
a huge challenge, for not just
our members, not just business officers,
but for really everybody in higher ed.
And some of that is there's a lot of
unknowns. So even as,
you know, we want to say we're going to
be data-informed, it's difficult to do
when your enrollment projections are,
you know, we might have 40 percent fewer
students, or we might have 25
percent fewer students, or 5 percent
fewer students. So all of that
modeling has been a challenge.
But I will say what Sherri did as she
was working,
is they actually pulled a lot of
historical data and looked at patterns
about course enrollment. So you know, from
everything from,
you know, when a course goes live and
students can register for it,
which ones fill faster. And you can
analyze that by time of day,
you know, by what courses they need to
complete their degree,
you know, what satisfies what
requirements, etc. And so they looked
through
all of their data to try to understand
what courses filled the fastest that
they
they knew would be most likely that
students would need to have for their
degree,
regardless of if enrollment was, you know,
expected to be 40 percent
lower, or 25 percent lower, than what
it had been the year prior.
And so they they dove into that, and they
also looked at
the data from the faculty perspective. So
they wanted to
look at these patterns ultimately to
figure out how can we make sure
that we offer the courses students need
to have
to satisfy their graduation requirements.
How can we make sure we're offering them,
you know, at the right time of day or,
you know, any of those things that you
would look at. And on the other side of
that,
because we do know institutions are
going to be financially constrained
because tuition revenue is an
important
revenue source for all institutions, not
just private ones, but how can we also
ensure
that we have full-time faculty who have
full schedules?
You know, and I know at
Queensborough College they have a tiered
system for how they will hire adjuncts,
you know, how do we have
a way to fill in adjuncts as we need
for the courses that our
students really need. So really coming up
with a way to
marry student needs and
faculty needs together.
And that's how they were able to develop
their schedule. So those are just a
couple examples of using analytics
in the business office world.
Those are
great examples! And I'm sure
a lot of insight of thinking, you know,
just thinking differently, right? And a
lot of times
being able to have access to the data to
analyze it, allows you to think
differently. And see some, like you said,
some patterns and some trends to help
you shift during that time.
Yeah, actually, you know, you saying
that made me think of something else
about Sherri's story,
or really the story at Queensborough
College. But one of the things that i
know
she did is, you're right, it is
important that as soon as you see data,
and you see the patterns, and you can
start to understand
what's behind them, and how we can use
this data to
to make informed decisions. Sherri didn't
make those decisions
in a vacuum and I would say one of the
most important things she did
is she collaborated with academic staff
at their institution, and you know, didn't
go in with like, "Here's what I think we
should do.
This is what the data say," but brought
the data and used it to have a real
conversation. A conversation where there
was,
you know, trust and transparency. And
I really think that
that's the real value when we think
about cultural changes in higher ed,
and making sure we're able to serve our
communities and our students
is the conversations that it
facilitates. 
So I think that's a
great point that you make.
Yeah, that's a real key too, because it
really shifts away from
just, you know, anecdotal beliefs,
to hard data saying
here are the numbers, or here's the
data, now let's figure out what this
means together.
And I like that it gives you buy-in on
both sides, and so it's not an opinion or
a belief,
but it really is taking that together
and working together to make a solution.
That's great.
Yes, yes, and I think -
and that really is, you know, I know
sometimes we're splitting hairs,
you know, when I look at
conference sessions, or you know, white
papers that I read.
But there seems to be almost a way
where
folks are, you know, interchangeably using
the words data-driven
and data-informed. And I do see
there's value in being data-driven, I
know when you can automate things and
have
the data say we're using too much
heat, and this room's empty, have the
power be turned off, or into something.
Like, there's ways to have data-driven
decisions,
but when we're thinking higher ed and
how it really is a
service industry, like you know, when you
think about the students, and the faculty,
and the staff, and how many people are
involved in all the real decisions that
are made,
being data-informed is much more
valuable.
Because we know our campuses hire
experts, you know, experts in student
affairs, you know, faculty who are experts
in their fields,
and we can empower them with data to
make sure we're
serving students the best we can and
making the most efficient use of our
resources.
And everyone really is involved in that
process. 
I think that's great, you know.
Data-informed just even as a word -
it's all about positioning sometimes,
I say, right? And so just
saying that we're being data-informed
just gives the ability to want to
bring that all together and
collaborate, versus we're going to be
data-driven.
So I think there's just a tone that
comes with. That's really good
insight.
But I'm betting that, you know, 
there's still, like you said, because of
some of the silos and
and just how some things have gotten
done on higher ed before,
there might be still some barriers to
to using data
in higher education. Do you want to talk
a little bit about what some of those
barriers might be?
Oh yes, I do. This has been the
focus
of my work, and will probably be the
focus of my work for a while to come.
So when we launched the survey
to business officers about the use of
analytics at their institutions, we asked
them about barriers.
And now our efforts are to help them
come up with ways to address those, or
come up with resources to help them
address those.
But in short the barriers really
fall into two different categories.
So one category of barriers are all
cultural.
And so just a couple of examples of what
I mean by cultural barriers are:
we asked institutions, we listed a few
different
kinds of cultural barriers, and we asked
them to indicate if they thought these
barriers
were never a barrier at their campus,
if they were were a barrier but are no
longer a barrier,
if they are a contributing barrier, or if
they're a pressing barrier. So a likert
scale question.
And I will share just a few of
those findings. And
I think some of them, your listeners
might find surprising, and others perhaps
not.
So one of the cultural barriers that I
know we hear quite a bit,
whether it be in white papers, or we
hear about it at conferences,
is that there is a fear around analytics.
That the data are going to be used to
punish.
You know, especially when we have
business officers starting to talk about
making data-informed decisions,
I think folks - and it's, you know, fair to
some degree - are worried that this
information will be used to cut programs,
or to cut courses,
or to cut other kinds of costs. And so
53.8 %
of our survey participants
indicated that that was either a
contributing or a pressing barrier. So
over half of our business officers think
that there's fear around the use of
analytics.
We also asked about the campus being
siloed, if that was a contributing
barrier. And 61.8 percent of business
officers said that the
lack of collaboration, or having a siloed
campus, is a cultural barrier to using
data to inform decisions.
And one more in the cultural space I
want to share
is 50.4% of our
our participants said that mistrust or
misunderstanding about how analytics
would be generated and/or used
- so kind of hitting at that ethics
piece that we were just talking about -
that they saw that as a cultural
barrier. And those are just a few of them
that stood out to me as I was reading
the results.
Yeah, that's really interesting,
especially the trust factor, and
it's interesting that
kind of relating that human
characteristic of trust to data.
Yeah, yes, definitely. Yeah
and then if you want me to share some of
the capacity barriers,
again I've just pulled a few that the
findings kind of stood out to me.
One I'll list because I think it's kind
of expected.
We asked, you know, if cost would be a
barrier, and we specifically asked about
the cost to invest
in the the skills, or the staff necessary
in order to allow your institution to
really leverage analytics.
And 66.2 percent of our survey
respondents said that the cost of
investing in skills and staff was a
barrier.
And in addition to that
78.9 percent said that they really just
didn't have the workforce capacity. There
simply just weren't the staff at their
campuses to be able to do this work.
And so that's why when I was
talking about investment earlier when we
talked about the the joint statement on
analytics from
AIR and EDUCAUSE and NACUBO, it really is
quite a bit about the people.
We asked about, you know, the the cost
of the technology
that was a barrier at institutions
as well,
but it's really the staff piece that I
found interesting.
And along with that, and I think
you'll find this really interesting, too.
And we
categorized it as a capacity barrier, but it
really could be part of the the cultural
barriers,
but we asked individuals to indicate if
they thought it was a barrier to have
end users - so those who you know aren't
in IR
or IT, or they're not the business
analyst, or the financial
analyst - but they're those who are seeing
dashboards or 
other data visualization pieces, or
they're getting the reports and they're
supposed to act on them, but
they're not
able to. For some reason, the end users
don't have the data literacy skills to
translate data to action.
And that almost 81%
of our participants said that was either
contributing or
pressing as a barrier at their
institutions. And so that's,
I think it kind of just, you know, puts a
finer point on what we were talking
about earlier.
That, you know, you can have all kinds of
fancy
tools, or buttons, or technology, but if
people can't
translate that information or that data
and
analytics into action to make a
difference at their campuses,
then it's almost, you know, moot to even
try.
Yeah that's really interesting. And I
think, you know, that really goes to
show and something I know that NACUBO
does a really good job of is always
educating and helping to make
sure people understand what are some
new, you know, new tools, or new
methodologies. And I think that's really
important,
a really big piece to keep going
these days, at least, is to keep your
staff educated on all the tools that
you've made investments on.
Yes, definitely. Yeah we have
ongoing professional development.
You know, the live programming -
although for this year it's online -
but the programming where
folks can come, and talk, and engage about
data and analytics. I will say
we do have a standalone workshop - the
NACUBO integrating analytics forum - that
folks can come to and learn about how
business officers are using data,
and the different kinds of analyses that
we're doing. You know, NACUBO was also
working to embed analytics content into
all of our other workshops and other
professional development offerings,
because it's not really a
standalone
you know skill or activity it's really a
part of everyone's job at institutions.
And I would say, you know, to go along
with that,
and not just because, you know, it's a
pandemic and it makes it difficult to
offer,
you know, face-to-face professional
development. But in addition to
the online programming, we're also
working on developing tools and
resources
to support our members as they move
forward. So it's
not out in the world yet, but we have a
series called accelerating analytics.
Which is,
you know, these brief explanatory
documents
that go over key key topics. For example,
data governance,
or academic cost modeling, or you know,
some of these topics that business
officers really need to understand.
And these resources will give them, you
know, here's the basics of what you need
to know,
and then here are some action items you
should be thinking about
at your institution.
Oh that's great. So
let's say when all this is in place, and
we're trained, and we're using it, we all
know we're doing, what does success look
like?
I love that question - very optimistic
and definitely what
keeps me going every day.
Although, my honest answer - it might
sound cliche at first -
but success to me looks like continuous
improvement.
And i know sometimes those those words
"continuous improvement"
get tired or overused, but that really is
what a successful analytics program
would look like at a college or
university.
You know, so at a successful campus
your administration, your faculty, your
staff, your students,
they all have data literacy skills. They
all can understand,
you know, the different kinds of
analyses that your institution is
putting out.
And not only do they understand it, but
your administration, your faculty, your
staff ,they know how to use that data for
their day-to-day roles and
make a difference for students. And so I
would say, you know, in my perfect world
of continuous improvement,
data really is used like an
institutional asset.
And analytics is leveraged to further an
institution's mission.
I think that's great. And to me that's
really at the core of higher education,
which is continuous learning.
Yes, yes, that is exactly right. 
That is wonderful.
Well thanks so much Lindsay, for all your
insights today. So
where's the best place to access all the
resources you talked about today?
Oh that's a great question. I would tell
your listeners to go to NACUBO's topic
page on analytics. And you can
get to that page by going to NACUBO.org/topics/analytics
and then from that page, there will be
links to most of the things that we
talked about today,
the exception is the book which is not
released until November.
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