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I had a very narrow 
understanding of what graphic 

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design is. 
And now I'm like super 

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passionate about what designers 
do because I feel like we do so 

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much. 
There's like a lot of research 

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the process of getting to the 
end result is much more than 

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just diagrams and drawings 
you're listening to design 

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feature now a show about 
designing the future and the 

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future of design in this 
episode. 

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We hear from an educator and a 
student who are researching 

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teaching. 
King and practicing design in an

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age of data and disruption 
Professor Deb Little John 

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demystifies the buzzword big 
data and explains why she has 

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her students tell lies with data
MFA student Run 2 Hotty opens up

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about her hopes and fears for 
the design profession and how 

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she's facing the future head-on 
all this and more on design 

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feature now from aiga the 
professional association for 

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design. 
I'm Li Shan Huang. 

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So, my name is Doug Littlejohn 
and I am an associate professor 

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here in the College of design at
NC State and I teach graphic 

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design. 
My research interests are around

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how we use digital tools for 
Learning and when I say learning

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I'm talking about the novice or 
the non-expert, especially with 

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regards to data. 
No, I'm not looking at data 

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scientists. 
They already have their methods 

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in their tools and they know 
what they're doing. 

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But these days everybody's a 
data user. 

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Right and we have to understand 
how to use data and get some 

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sort of basic data literacy. 
And so that drives my research 

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topic who I am researching as 
well as the classes that I'm 

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teaching welcome Deb and thank 
you for coming on the show. 

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Could you give us an example of 
a project that you've worked on 

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related to helping people learn 
better or make more informed 

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decisions with data one of the 
data focused projects that I 

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have worked on involves 
collaboration with a plant 

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epidemiologist who studies plant
diseases, especially in tomato 

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plants, right? 
So what causes something called 

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stem rot? 
And so what she does in her? 

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Work is she inserts sensors into
plants and then purposefully 

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stresses them whether that is 
like a water too much light too 

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much something too much minerals
fertilizer Etc. 

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And so what's happening with 
these sensors is that it's 

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collecting data on the plant at 
a constant rate 24/7 7 days a 

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week all year and my role in 
this project is how do we 

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visualize that data in a way 
that Someone who's not a data 

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expert who needs that to know 
that data and understand it. 

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For example, a farmer can take 
care of their crops. 

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So when should I when do I need 
to water because the plan is 

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getting stressed without so much
water. 

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And so the tools the 
visualization tools that data 

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scientists might come up with 
are your typical bar charts pie 

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charts, maybe heat Maps things 
like that. 

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Whereas the tool I'm Come up 
with is something that tells 

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more of a story of the data and 
becomes something that's 

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glanceable. 
And so this idea of glance 

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ability and data visualization 
is important. 

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It gives you the big picture the
big snapshot the bird's eye view

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of the data and then for 
example, let's say the 

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visualization is a image of a 
plant that looks stressed in 

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some areas. 
It might have nodules and other 

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areas showing Health versus 
stress. 

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So the farmer could dive into 
certain points like a stress 

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point and understand what's 
happening with this crops to 

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kind of mend the situation. 
So again that the farmers using 

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the data in real time 24/7 as 
well at the data is being 

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collected 24/7 over the last few
years. 

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We've seen the tools for 
Gathering and processing data 

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become cheaper and more 
accessible than ever. 

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We've also been hearing this 
term this buzzword big data. 

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On what does that term mean to 
you and your work? 

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So I have an analogy about big 
data and I think it is a 

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buzzword. 
Absolutely. 

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I think that companies are 
collecting data because they can

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write the computer processing 
speed is able to keep up with 

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collecting data 24/7, but they 
are not collecting it for 

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specific purposes. 
They're just collecting it 

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because they can and my analogy 
here is that Nat Geo show called

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hoarders buried alive. 
So initially the Some starts out

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with this hobby or this thing 
that they do, right? 

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If you've seen the show, you 
understand that this hobby of 

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theirs is taken over their life 
and what made them happy has now

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made the miserable and kind of 
destroy their lives and their 

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families lives. 
So not having a reason for 

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collecting data means that you 
just collect everything without 

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having a undergirding question 
for why you're doing it and I 

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think this is the difference 
between how data is collected 

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from a structured standpoint. 
So in Academia, we don't go out 

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and collect data just because we
can we have a research question 

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that is guiding what data we 
collect and when and why and 

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from whom versus in the 
corporate world. 

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They're just collecting it 
because they have it and they 

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think they must That's a very 
broad generalization. 

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I'm sure that is not the case 
throughout but the problem is 

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not having specific research 
questions for why you're 

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collecting data. 
What kind of questions can I ask

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of data? 
And what questions can I not ask

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of data? 
And what do I do? 

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I need to collect to answer a 
certain question. 

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So those are kind of two camps 
that I'm coming from trying to 

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understand, you know a 
corporation. 

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Let's say our company. 
Or a farmer who is their own 

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self? 
They're self-employed. 

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They're collecting data, right 
because they can and it does 

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help them understand their 
crops. 

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But having that research 
question guides what you're 

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collecting so that it doesn't 
overwhelm you. 

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Listening to you talk about your
work Deb has me thinking that 

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doing data data analysis is like
kind of like doing design you're

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designing the process and making
choices along the way making 

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meaning with the data you 
collect what do you think of 

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that data analysis and research 
in general is a design you 

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design your research project you
design the methods and you make 

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a plan for what you're going to 
do. 

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So it does have overlaps with 
Line in that aspect. 

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So we as humans, we as designers
are designing data. 

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We are giving it structure we're
making choices, but we're also 

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imperfect were also biased right
and so how do we deal with this 

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bias? 
Any data that's collected is 

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going to have a bias just by the
very nature that is a person 

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making the decision or a group 
of people making the decision to

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collect it. 
So He's tell my students that 

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the most work you're doing and 
data analysis is the cleaning of

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the data. 
The representation of it takes 

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no time. 
Once it's clean. 

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So 90% of your time is spent 
cleaning data 10% visualizing 

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and analyzing it so through that
cleaning of the data through the

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process of cleaning data is 
where the biases can come in, 

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but bias could be interpreted as
a bad. 

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It's not it's just inherent to 
data collection that data is 

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biased and here I think as we're
in the design world, when we you

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know, release the visualization 
of the data, the thing that is 

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not told that the data analyst 
in the The Specialist the data 

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technicians do that. 
We don't we do not make 

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transparent the methods by which
we collect data and the why of 

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why we're collecting the data, 
it's just a matter of kind of 

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out in your us because that is 
how other data readers can 

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understand where those biases 
are. 

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If you look at any kind of data 
set coming from the Census 

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Bureau or you know, what's 
published often in newspapers. 

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Let's say there's a little 
paragraph that talks about where

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the data set came from and how 
they collected it. 

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So another bias that Creeps in 
as you know, unless you have a 

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situation which the Big Data 
proponents suggest that Equal 

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all so you are actually taking 
data from every single 

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representative of a sample, 
right? 

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That's kind of the Valhalla. 
I think of data collection. 

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If you're able to represent 
Reality by collecting data from 

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everybody in that group, that's 
a kind of an impossibility in 

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some depending upon the 
question. 

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So from that initial step of who
you're collecting from and 

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determining a sample. 
Is it a representative sample if

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It's not then the data is 
already not going to be 

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generalizable. 
Anyway, and I work a lot in the 

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world of qualitative data, which
is very different. 

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It's unstructured and it's 
multi-channel multimodal data. 

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It's not all numbers in other 
words. 

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So how when you are collecting 
this kind of data, how do you 

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analyze different types of data 
becomes very important most data

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from a traditional sense is 
numbers. 

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I swear that Excel spreadsheets 
come in and where we can so 

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easily hit that button to make 
me a pie chart and it just spits

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one out, right? 
You can't do that. 

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Unless all of your data is in 
the same mode so numbers time X 

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and Y axes, you know time versus
the timing years versus amounts 

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of money, you know, and you're 
comparing two variables. 

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The big data problem comes in 
when the data is unstructured. 

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Third right? 
So you have many different 

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channels of data are variables 
that you're collecting. 

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How how can you compare them? 
Deb what's inspiring you and 

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motivating you and your work 
right now this idea of data 

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literacy if data is, you know, 
becoming one of those Futures 

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topics and designers need to 
understand data and what it is 

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they need to learn how to work 
with it and they need to 

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understand how it is collected 
and why it's collected some very

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interesting projects can be 
developed around that idea. 

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I have one project that I give 
the grad students called lies 

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damn lies and statistics. 
Sticks it's a project where they

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are they are taking data and 
purposefully lying with it and 

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lying is a harsh word here. 
Right? 

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But it's some Mark Twain kind of
quote, right? 

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He said there's three types of 
lies lies damned lies and 

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statistics, right? 
And there's also a wonderful 

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book called How to lie with 
Statistics you can manipulate 

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data to say what you want it to 
say and show what you want it to

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show that is not common 
practice. 

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Is right for you and here's the 
perfect example right where you 

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get you collect some data and 
you have a couple of outliers 

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things that aren't matching with
the pattern. 

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So you just delete them. 
You delete the outliers. 

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Well outliers can have some very
interesting information for you.

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So studying those outliers is 
important, but what we see in 

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the newspapers and on TV, and 
the election data visualizations

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is the Rid of those outliers and
why that happened. 

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So anyway back to the 
assignment. 

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It's I have the students take 
two data sets that are collected

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for different reasons different 
purposes and it's different 

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types of data with one 
overlapping variable like time 

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and they are they break apart 
the data and try to understand 

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the two different sets and see a
pattern of a correlation. 

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So we all heard correlation is 
not causation. right So let's 

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say you have two data sets that 
are both trending upwards. 

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That would be your correlation. 
But because the data are not 

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collecting are not collected for
the same purpose. 

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You cannot claim there's a 
correlation but I have the 

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students make a plausible 
argument for that correlation. 

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For example data on the number 
of motorcycle registrations and 

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the births of babies, right? 
So both of these data's for a 

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certain year. 
We're going up the trend was 

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upward so you could make a Is 
correlation that the more babies

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are born the more motorcycles 
the more motorcycle riders are 

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out there or vice versa, right? 
And so what does that look like 

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and it teaches the students how 
easy it is, I guess to 

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manipulate data and get it to 
behave how you want it to behave

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and a little and you get to see 
a little bit of the behind the 

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scenes of how a data scientist 
works with data tab Little John.

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Thank you for giving us a behind
the scenes look at your work and

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your research designing with 
data design future now we'll be 

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back with our next guest. 
My name is Rhonda Heidi. 

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I'm from Kuwait. 
I did my undergrad in 

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architecture at the University 
of Miami and I'm currently 

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getting my masters in graphic 
design at NC State and I'm 

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almost done welcome Rhonda and 
thanks for joining us. 

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On design feature. 
Now you're in your last year of 

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a three-year MFA program. 
Can you share some of your hopes

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and fears for the design 
profession as you get closer and

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closer to graduation? 
I do have a lot of hopes and 

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fears in terms of the design 
world right now. 

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We had talked about this in 
studio. 

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This was like one of our 
activities was to kind of write 

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down a list of hopes and fears 
that we had and I think my my 

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fear is that you know with 
design We have a you know, we 

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have a lot of like agency when 
it comes to creativity and I 

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fear that that might be 
something that's kind of slowly 

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going away with the rise of AI 
and that's also something 

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something else that I'm fearful 
of is just machine learning and 

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artificial intelligence kind of 
taking over this like design 

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world and questioning whether or
not graphic design will exist in

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the future. 
So kind of preparing ourselves 

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for that. 
This might be a little 

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pessimistic at me, but I feel 
like I More fears than hopes in 

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terms of you know, not 
necessarily just designed but 

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just you know, the world have a 
lot of fears towards people and 

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just emotions. 
I feel like with the rise of you

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know, like social media a lot of
like digital interfaces Ai and 

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machine learning people are kind
of becoming less empathetic and 

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kind of that emotional wall is 
kind of like shut off right now.

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And so how can we as designers 
create spaces for? 

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for people to be vulnerable and 
show empathy, you know, maybe 

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still using digital interfaces, 
but just I don't know how do we 

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create those spaces and make 
people comfortable with showing 

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those emotions because I feel 
like right now people are 

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constantly on their phones on 
their laptops, and we're not 

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having face-to-face 
conversations, but I'm also 

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hopeful that maybe with the rise
of and machine learning that our

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jobs will kind of In two more 
important spaces that we are 

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we're not necessarily sure of 
exist right now. 

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So we'll the rise of AI and 
machine learning create new jobs

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and opportunities for designers.
You've been helping us out and 

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sitting in on this small 
gathering of design thought 

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leaders convened by aiga over 
the last few days, are there any

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particular themes or topics that
are of particular interest to 

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you or resonate with you theme 
song? 

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The last few days or a year MFA 
program in general one of the 

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themes was designed Futures and 
I've heard a lot of people talk 

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about that. 
We had Stewart candy who's a 

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design futurist come in and give
us a weekend workshop. 

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And I guess the way that I would
Define design futures or just 

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for site is looking into the 
future and kind of speculating 

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what you know, what design would
be like or what things would 

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emerge in the future and how We 
hope people see that or how can 

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we prepare people for certain 
things that we're speculating 

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might happen. 
So, you know, you could do that 

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through artifacts or just you 
know Holter and workshops that 

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you could you know, introduce 
people to these new ideas with 

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AI and machine learning. 
I feel like there's a lot of 

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fears around that and it's 
because people are not 

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necessarily understanding or 
like they don't really know 

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what's happening. and in terms 
of That kind of technology so 

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kind of bringing people into the
table and making sure they 

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understand what that means. 
Are there any misconceptions 

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about design that you would like
to dispel honestly when I first 

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came to this program 
specifically I had a very narrow

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understanding of what graphic 
design is and now I'm like super

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passionate about what designers 
do because I feel like we do so 

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much. 
There's like a lot of research 

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the process of getting to the 
And result is much more than 

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just diagrams and drawings. 
I guess my understanding of it 

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was more visual but there's a 
lot more that goes into and I 

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guess like the conversations 
that I've heard. 

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I when I first started I didn't 
realize that these were 

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conversations that designers 
were having like how do we think

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of you know the future of 
design? 

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What can we do as designers to 
help other designers think about

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the future and so like reading 
the a aiga Trends was really 

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interesting to me because as a 
studio we had we had to read all

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of those and create workshops 
surrounding those different 

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Trends and that's not something 
that I thought we would do as a 

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studio because you think of 
studio is just creating things 

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that you know, I think people 
still think that graphic design 

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is just creating things that 
look pretty right and that's 

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conversations that I've had with
my friends. 

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They're constantly asking me 
like, what are you doing? 

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I'm like, oh we're this what 
we're doing and they are like, 

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oh, I didn't realize that that's
what you did as a graphic 

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designer and I'm like, yeah, so 
now it's important to me to kind

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of make sure that other people 
realize that graphic design is 

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not just this small bubble. 
It's it's really this space that

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a lot of people can do really 
whatever they want. 

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And there's a there's a lot more
to it. 

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So making sure that I spread the
word about what we're doing and 

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Ali are there things that are 
inspiring you giving you life 

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right now? 
I think that there's a lot to 

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look forward to right because 
it's the space is constantly 

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changing and it's I'm looking 
forward to what it can become 

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for both for me and for my peers
and for people who you know are 

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going into this field. 
I'm interested in this just like

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this transitional space and what
what it could mean for us. 

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Thank you so much Rhonda for 
joining us on design feature now

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and for opening up about your 
hopes and fears for the future. 

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We wish you the best of luck as 
you move forward in your career 

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and thank you for listening to 
design future. 

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Now remember to rate US leave a 
review that helps like-minded 

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00:20:40,300 --> 00:20:43,900
listeners find our show and you 
can always email us what you 

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00:20:43,900 --> 00:20:48,800
think at podcast at AIG a.org, 
or you can leave us a voicemail 

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00:20:48,800 --> 00:20:53,300
on our anchor dot f m Pei Page 
you can find the design Futures 

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00:20:53,300 --> 00:20:58,400
papers that Randall referred to 
in our interview on AIG a.org 

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will post the exact link to the 
show notes special thanks to 

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John Snowden for his help in 
recording. 

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This episode designed future now
is a production of aiga the 

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00:21:11,100 --> 00:21:13,200
professional association for 
design. 

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I'm Li Shan Huang until next 
time.