Data Visualization with Ann Dzuranin – New Perspectives on Accounting and Finance


(upbeat music) – I started to get interested
in data visualization when I started teaching data
analytics and accounting in our accounting program. And employers were really
interested in getting new hires that knew how to create good data visualizations,
also use visualization to analyze large sets of data. So to teach it, I had to be
able to really understand some of the best practices. So I started researching it. I Started going to
seminars and just worked on improving my skills in
it so that I could then relay that to my students. I think that probably the most interesting data visualization gone wrong story that I heard, or maybe the
funniest one that I heard, was another colleague was
including data visualizations in an exam and she thought
that she had created this great visualization for the students to be able to analyze and
completely didn’t realize that she used colors that people that were color blind couldn’t see. So she had several students
that came up to her and they’re like, I don’t see anything in this visualization. So I tell that story
when I do presentations on best practices because
even though I think only eight percent of the population
actually is color blind, you still have to keep that in mind. One of the reasons we’ve
seen data visualization increase so much being used in business is because there’s so much data and it’s hard to make
sense of the data often by just looking at the numbers. So using data visualization,
you can quickly understand really large sets of data. And that helps you to get
a better understanding of what’s happening within your business, within your organization,
whatever it is you’re analyzing. So in that respect, it’s helped
to make very quick decisions because you don’t have to spend
hours analyzing the numbers. You can just visualize the data. Yeah, I conduct behavioral
research and I chose that type of research because
how people make decisions fascinates me. So a lot of my research
is in decision-making and how people use business
data to make decisions. I’ve also done some research in incentives and one of the most interesting results that we found, and it’s
actually in an article that the IMA published in
their Management Accounting quarterly journal, was an incentive study where we were testing to see
if people would work harder for money or for something non-monetary. And instinctively, we
also say we want money. And in fact, when we
asked the participants which type of compensation they prefer, do they prefer cash or a
non-monetary, but tangible, so maybe a gift card, maybe coffee, maybe in a business situation,
Fridays you can wear jeans. And we found that even
though participants told us they wanted the cash, they
actually worked harder for the tangible non-monetary benefit. And if you added intangible to that, you added recognition to that,
then they worked even harder. So I thought that that was fascinating because even what they
told us they wanted, they actually ended up
performing differently than what they said they would. A good data visualization is one that gets the point across that
you’re looking to get across using the least amount
of space on the page and creates a sense of understanding that can then lead to a decision. So it sounds like it’s pretty simple, but narrowing down to the
exact data and information that you need to translate to the person looking at that visualization,
that’s the hard part. Research has shown that
we remember stories. We can relate to stories. It makes the experience memorable for us. If you think back to even
when you first started, when you were a baby, we
read stories to children. You read stories to learn the language. You read stories to
understand a situation. And so telling a story with your data, when you’re trying to get
across the analysis you’ve done is really important so that you can bring the reader along with you. So if you tell a story,
you keep their interest and you’re able to move
them through what would be a lot of complex data and
potentially complex visualizations to one cohesive story. So it’s an importation
aspect of data visualization. One of the things you
have to really think about when you’re preparing your visualizations for you presentation or whatever it is. Whether you’re presenting
to a coworker, a boss, the C-suite, you have
to really think about who the audience is
going to be and make sure that they can understand the
visualizations that are there. So you have to to ride
that line of not making it overly difficult if your
audience doesn’t really understand all the details. On the other side, you don’t
want to make it so simple that the people you’re presenting to feel maybe even insulted
that you haven’t been… That you assume that they don’t understand the level that you have. It’s more impactful if
you really are presenting to the audience that’s going to be there to understand what they know. So you know how much detail to put in and how much detail to leave out. Ethics and visualizations
I think are really related because we see so any visualizations in the real world, everyday life. You see visualizations on
line, you see them on TV, you see them in news media, magazines, all trying to tell a story. And if we don’t really think about what that visualization is telling us, and really think critically
about how accurate it is. We can be really misled
by the information. So I think it’s importation
when you’re preparing visualizations that the
preparer keeps that in mind. You shouldn’t be putting
together a visualization that deliberately
influences someone to think in a certain way that
isn’t true for the data. So one of the examples that I use is, sometimes we’ll see a visualization where they don’t start the scale at zero. So, if you start it at,
if you have a variety of data, let’s say your data
points go from 45 to 60, but you start the axis at 40.
It looks much more dramatic, your increase from 40 to 60
then if you start it from zero and go to 100. So you are either inadvertently,
or maybe deliberately making the viewer see the
visualization as maybe more significant than it really is. And I think that that’s a big issue. I think it’s an issue in
what we see in public media and I think it’s an issue
in what we see people do in organizations as well. One of the things that I have
been really passionate about over the last five years
is how important it is that accountants embrace,
things like data visualization. Tools to do it and the responsibility for performing analyses using it. If the accounting
profusion doesn’t step up and join this technology
change that’s been happening, whether it’s the analytics piece
or the visualization piece. I don’t think we remain
relevant as a profession. And I think that we’re
in a great position. We already know how to do the analysis. We understand that data. We understand the story,
we just have to get better at telling it. I think once we do that, I think we become ann even more valued business partner. (upbeat music)

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