I had a very narrow understanding of what graphic
design is. And now I'm like super
passionate about what designers do because I feel like we do so
much. There's like a lot of research
the process of getting to the end result is much more than
just diagrams and drawings you're listening to design
feature now a show about designing the future and the
future of design in this episode.
We hear from an educator and a student who are researching
teaching. King and practicing design in an
age of data and disruption Professor Deb Little John
demystifies the buzzword big data and explains why she has
her students tell lies with data MFA student Run 2 Hotty opens up
about her hopes and fears for the design profession and how
she's facing the future head-on all this and more on design
feature now from aiga the professional association for
design. I'm Li Shan Huang.
So, my name is Doug Littlejohn and I am an associate professor
here in the College of design at NC State and I teach graphic
design. My research interests are around
how we use digital tools for Learning and when I say learning
I'm talking about the novice or the non-expert, especially with
regards to data. No, I'm not looking at data
scientists. They already have their methods
in their tools and they know what they're doing.
But these days everybody's a data user.
Right and we have to understand how to use data and get some
sort of basic data literacy. And so that drives my research
topic who I am researching as well as the classes that I'm
teaching welcome Deb and thank you for coming on the show.
Could you give us an example of a project that you've worked on
related to helping people learn better or make more informed
decisions with data one of the data focused projects that I
have worked on involves collaboration with a plant
epidemiologist who studies plant diseases, especially in tomato
plants, right? So what causes something called
stem rot? And so what she does in her?
Work is she inserts sensors into plants and then purposefully
stresses them whether that is like a water too much light too
much something too much minerals fertilizer Etc.
And so what's happening with these sensors is that it's
collecting data on the plant at a constant rate 24/7 7 days a
week all year and my role in this project is how do we
visualize that data in a way that Someone who's not a data
expert who needs that to know that data and understand it.
For example, a farmer can take care of their crops.
So when should I when do I need to water because the plan is
getting stressed without so much water.
And so the tools the visualization tools that data
scientists might come up with are your typical bar charts pie
charts, maybe heat Maps things like that.
Whereas the tool I'm Come up with is something that tells
more of a story of the data and becomes something that's
glanceable. And so this idea of glance
ability and data visualization is important.
It gives you the big picture the big snapshot the bird's eye view
of the data and then for example, let's say the
visualization is a image of a plant that looks stressed in
some areas. It might have nodules and other
areas showing Health versus stress.
So the farmer could dive into certain points like a stress
point and understand what's happening with this crops to
kind of mend the situation. So again that the farmers using
the data in real time 24/7 as well at the data is being
collected 24/7 over the last few years.
We've seen the tools for Gathering and processing data
become cheaper and more accessible than ever.
We've also been hearing this term this buzzword big data.
On what does that term mean to you and your work?
So I have an analogy about big data and I think it is a
buzzword. Absolutely.
I think that companies are collecting data because they can
write the computer processing speed is able to keep up with
collecting data 24/7, but they are not collecting it for
specific purposes. They're just collecting it
because they can and my analogy here is that Nat Geo show called
hoarders buried alive. So initially the Some starts out
with this hobby or this thing that they do, right?
If you've seen the show, you understand that this hobby of
theirs is taken over their life and what made them happy has now
made the miserable and kind of destroy their lives and their
families lives. So not having a reason for
collecting data means that you just collect everything without
having a undergirding question for why you're doing it and I
think this is the difference between how data is collected
from a structured standpoint. So in Academia, we don't go out
and collect data just because we can we have a research question
that is guiding what data we collect and when and why and
from whom versus in the corporate world.
They're just collecting it because they have it and they
think they must That's a very broad generalization.
I'm sure that is not the case throughout but the problem is
not having specific research questions for why you're
collecting data. What kind of questions can I ask
of data? And what questions can I not ask
of data? And what do I do?
I need to collect to answer a certain question.
So those are kind of two camps that I'm coming from trying to
understand, you know a corporation.
Let's say our company. Or a farmer who is their own
self? They're self-employed.
They're collecting data, right because they can and it does
help them understand their crops.
But having that research question guides what you're
collecting so that it doesn't overwhelm you.
Listening to you talk about your work Deb has me thinking that
doing data data analysis is like kind of like doing design you're
designing the process and making choices along the way making
meaning with the data you collect what do you think of
that data analysis and research in general is a design you
design your research project you design the methods and you make
a plan for what you're going to do.
So it does have overlaps with Line in that aspect.
So we as humans, we as designers are designing data.
We are giving it structure we're making choices, but we're also
imperfect were also biased right and so how do we deal with this
bias? Any data that's collected is
going to have a bias just by the very nature that is a person
making the decision or a group of people making the decision to
collect it. So He's tell my students that
the most work you're doing and data analysis is the cleaning of
the data. The representation of it takes
no time. Once it's clean.
So 90% of your time is spent cleaning data 10% visualizing
and analyzing it so through that cleaning of the data through the
process of cleaning data is where the biases can come in,
but bias could be interpreted as a bad.
It's not it's just inherent to data collection that data is
biased and here I think as we're in the design world, when we you
know, release the visualization of the data, the thing that is
not told that the data analyst in the The Specialist the data
technicians do that. We don't we do not make
transparent the methods by which we collect data and the why of
why we're collecting the data, it's just a matter of kind of
out in your us because that is how other data readers can
understand where those biases are.
If you look at any kind of data set coming from the Census
Bureau or you know, what's published often in newspapers.
Let's say there's a little paragraph that talks about where
the data set came from and how they collected it.
So another bias that Creeps in as you know, unless you have a
situation which the Big Data proponents suggest that Equal
all so you are actually taking data from every single
representative of a sample, right?
That's kind of the Valhalla. I think of data collection.
If you're able to represent Reality by collecting data from
everybody in that group, that's a kind of an impossibility in
some depending upon the question.
So from that initial step of who you're collecting from and
determining a sample. Is it a representative sample if
It's not then the data is already not going to be
generalizable. Anyway, and I work a lot in the
world of qualitative data, which is very different.
It's unstructured and it's multi-channel multimodal data.
It's not all numbers in other words.
So how when you are collecting this kind of data, how do you
analyze different types of data becomes very important most data
from a traditional sense is numbers.
I swear that Excel spreadsheets come in and where we can so
easily hit that button to make me a pie chart and it just spits
one out, right? You can't do that.
Unless all of your data is in the same mode so numbers time X
and Y axes, you know time versus the timing years versus amounts
of money, you know, and you're comparing two variables.
The big data problem comes in when the data is unstructured.
Third right? So you have many different
channels of data are variables that you're collecting.
How how can you compare them? Deb what's inspiring you and
motivating you and your work right now this idea of data
literacy if data is, you know, becoming one of those Futures
topics and designers need to understand data and what it is
they need to learn how to work with it and they need to
understand how it is collected and why it's collected some very
interesting projects can be developed around that idea.
I have one project that I give the grad students called lies
damn lies and statistics. Sticks it's a project where they
are they are taking data and purposefully lying with it and
lying is a harsh word here. Right?
But it's some Mark Twain kind of quote, right?
He said there's three types of lies lies damned lies and
statistics, right? And there's also a wonderful
book called How to lie with Statistics you can manipulate
data to say what you want it to say and show what you want it to
show that is not common practice.
Is right for you and here's the perfect example right where you
get you collect some data and you have a couple of outliers
things that aren't matching with the pattern.
So you just delete them. You delete the outliers.
Well outliers can have some very interesting information for you.
So studying those outliers is important, but what we see in
the newspapers and on TV, and the election data visualizations
is the Rid of those outliers and why that happened.
So anyway back to the assignment.
It's I have the students take two data sets that are collected
for different reasons different purposes and it's different
types of data with one overlapping variable like time
and they are they break apart the data and try to understand
the two different sets and see a pattern of a correlation.
So we all heard correlation is not causation. right So let's
say you have two data sets that are both trending upwards.
That would be your correlation. But because the data are not
collecting are not collected for the same purpose.
You cannot claim there's a correlation but I have the
students make a plausible argument for that correlation.
For example data on the number of motorcycle registrations and
the births of babies, right? So both of these data's for a
certain year. We're going up the trend was
upward so you could make a Is correlation that the more babies
are born the more motorcycles the more motorcycle riders are
out there or vice versa, right? And so what does that look like
and it teaches the students how easy it is, I guess to
manipulate data and get it to behave how you want it to behave
and a little and you get to see a little bit of the behind the
scenes of how a data scientist works with data tab Little John.
Thank you for giving us a behind the scenes look at your work and
your research designing with data design future now we'll be
back with our next guest. My name is Rhonda Heidi.
I'm from Kuwait. I did my undergrad in
architecture at the University of Miami and I'm currently
getting my masters in graphic design at NC State and I'm
almost done welcome Rhonda and thanks for joining us.
On design feature. Now you're in your last year of
a three-year MFA program. Can you share some of your hopes
and fears for the design profession as you get closer and
closer to graduation? I do have a lot of hopes and
fears in terms of the design world right now.
We had talked about this in studio.
This was like one of our activities was to kind of write
down a list of hopes and fears that we had and I think my my
fear is that you know with design We have a you know, we
have a lot of like agency when it comes to creativity and I
fear that that might be something that's kind of slowly
going away with the rise of AI and that's also something
something else that I'm fearful of is just machine learning and
artificial intelligence kind of taking over this like design
world and questioning whether or not graphic design will exist in
the future. So kind of preparing ourselves
for that. This might be a little
pessimistic at me, but I feel like I More fears than hopes in
terms of you know, not necessarily just designed but
just you know, the world have a lot of fears towards people and
just emotions. I feel like with the rise of you
know, like social media a lot of like digital interfaces Ai and
machine learning people are kind of becoming less empathetic and
kind of that emotional wall is kind of like shut off right now.
And so how can we as designers create spaces for?
for people to be vulnerable and show empathy, you know, maybe
still using digital interfaces, but just I don't know how do we
create those spaces and make people comfortable with showing
those emotions because I feel like right now people are
constantly on their phones on their laptops, and we're not
having face-to-face conversations, but I'm also
hopeful that maybe with the rise of and machine learning that our
jobs will kind of In two more important spaces that we are
we're not necessarily sure of exist right now.
So we'll the rise of AI and machine learning create new jobs
and opportunities for designers. You've been helping us out and
sitting in on this small gathering of design thought
leaders convened by aiga over the last few days, are there any
particular themes or topics that are of particular interest to
you or resonate with you theme song?
The last few days or a year MFA program in general one of the
themes was designed Futures and I've heard a lot of people talk
about that. We had Stewart candy who's a
design futurist come in and give us a weekend workshop.
And I guess the way that I would Define design futures or just
for site is looking into the future and kind of speculating
what you know, what design would be like or what things would
emerge in the future and how We hope people see that or how can
we prepare people for certain things that we're speculating
might happen. So, you know, you could do that
through artifacts or just you know Holter and workshops that
you could you know, introduce people to these new ideas with
AI and machine learning. I feel like there's a lot of
fears around that and it's because people are not
necessarily understanding or like they don't really know
what's happening. and in terms of That kind of technology so
kind of bringing people into the table and making sure they
understand what that means. Are there any misconceptions
about design that you would like to dispel honestly when I first
came to this program specifically I had a very narrow
understanding of what graphic design is and now I'm like super
passionate about what designers do because I feel like we do so
much. There's like a lot of research
the process of getting to the And result is much more than
just diagrams and drawings. I guess my understanding of it
was more visual but there's a lot more that goes into and I
guess like the conversations that I've heard.
I when I first started I didn't realize that these were
conversations that designers were having like how do we think
of you know the future of design?
What can we do as designers to help other designers think about
the future and so like reading the a aiga Trends was really
interesting to me because as a studio we had we had to read all
of those and create workshops surrounding those different
Trends and that's not something that I thought we would do as a
studio because you think of studio is just creating things
that you know, I think people still think that graphic design
is just creating things that look pretty right and that's
conversations that I've had with my friends.
They're constantly asking me like, what are you doing?
I'm like, oh we're this what we're doing and they are like,
oh, I didn't realize that that's what you did as a graphic
designer and I'm like, yeah, so now it's important to me to kind
of make sure that other people realize that graphic design is
not just this small bubble. It's it's really this space that
a lot of people can do really whatever they want.
And there's a there's a lot more to it.
So making sure that I spread the word about what we're doing and
Ali are there things that are inspiring you giving you life
right now? I think that there's a lot to
look forward to right because it's the space is constantly
changing and it's I'm looking forward to what it can become
for both for me and for my peers and for people who you know are
going into this field. I'm interested in this just like
this transitional space and what what it could mean for us.
Thank you so much Rhonda for joining us on design feature now
and for opening up about your hopes and fears for the future.
We wish you the best of luck as you move forward in your career
and thank you for listening to design future.
Now remember to rate US leave a review that helps like-minded
listeners find our show and you can always email us what you
think at podcast at AIG a.org, or you can leave us a voicemail
on our anchor dot f m Pei Page you can find the design Futures
papers that Randall referred to in our interview on AIG a.org
will post the exact link to the show notes special thanks to
John Snowden for his help in recording.
This episode designed future now is a production of aiga the
professional association for design.
I'm Li Shan Huang until next time.