"Hi, thanks for coming."
I'm Sruti Srinivasa Ragavan from
Microsoft Research and this talk is about
how people comprehend spreadsheets.
Millions of spreadsheet users must
read and comprehend spreadsheets as
part of various activities -- to make
"decisions, to debug their spreadsheets,"
"for reviewing, and even to enter data."
But studies show that understanding
spreadsheets can be tedious and error prone.
"In fact, a study with managers showed"
that misinterpreting spreadsheets
is one of the top five most frequent
kinds of errors.
Erroneous comprehension could lead not
"just to poor decisions and financial losses,"
they could even be the root cause for
"other kinds of spreadsheet errors,"
such as data errors and formula errors.
"Unfortunately,"
we do not know a lot about
spreadsheet comprehension and how
these comprehension errors occur.
We don't even know a whole lot about how
people comprehend spreadsheets today.
"If we do not know what happens today,"
we cannot build tools to support
those activities better.
And so we set out to answer the question:
how do people comprehend spreadsheets today?
What information do they need?
What activities do they engage in?
What strategies do they adopt to
comprehend their spreadsheets?
Where are the pain points and where
"do these errors come from, In order"
"to answer these questions,"
we conducted a user study.
We recruited 15 participants.
Each of these participants had received
a spreadsheet from a colleague
and now they had to understand and
work with that spreadsheet in order
to accomplish a job task.
We simply observed participants
working on these job tasks;
we also asked them to think out aloud.
We then qualitatively analyzed participants
task videos in small 20-second segments.
We looked at the activities they engaged
"in, the information they were seeking,"
the tactics and strategies they
"adopted, their pain points and so on."
"and so we ended up with a lot of qualitative,"
rich and fine grained data from the real world.
"Coming to the results,"
here are the top few things that we
want you to take away from this talk.
The first thing is that spreadsheet
comprehension is a lot more than
just formula comprehension.
"Now, prior work has taken the"
"view that spreadsheets are code,"
and so when it comes to spreadsheet
"comprehension,"
they've mostly looked at difficulties
around understanding formulas. In our study
"also,"
participants had to understand formulas
and face difficulties doing so.
But we also saw spreadsheets where
"there were no formulas at all,"
and even then participants had a hard
time understanding those spreadsheets:
they had difficulty understanding
"the data,"
they had difficulties understanding
"and interpreting and comparing charts,"
there were a whole lot of other
"things that were hidden away,"
"so there were data validation rules,"
"conditional formatting rules,"
and even just all the formatting
that was applied to a cell.
So when we think about spreadsheet
"comprehension,"
we need to think about all these
activities and not just about formulas.
The second thing is that participants
spent 40% of their time seeking
additional information needed
to understand this
spreadsheet. A lot of the time people needed
"extra things to understand their spreadsheet,"
but that information was not available
in context and so they had to navigate
away from their current context to a
different part of the spreadsheet or
"even outside such as to the web or to emails, "
gather those pieces of information
and then come back and continue.
"Now, such context switches are not"
"just productivity loss, but we saw that"
they were also sources of errors.
The third thing is about guessing
and going back to the author.
A lot of the time when people
were looking for information
"to understand the spreadsheet,"
"they did not always find them,"
and so they resorted to guessing
how the spreadsheet worked.
Sometimes they framed hypothesis and they
were able to test them in some form.
"For example,"
"""I think this is how it is"
"calculated"" and then they would try something out,"
see if the numbers match and
then that would work.
But other times they were not
able to do that and so they just
went on with unverified guesses.
"Such guesses are a problem,"
they could be incorrect and then
result in incorrect comprehension.
"Another thing was that, when"
"people did not have the information,"
and they were not even able to
"make the right kinds of guesses,"
they decided to go back to
the spreadsheet's author.
Now authors were not always
available in time and so that was
another case of productivity loss.
So we need to think about these missing
information problems in order to help
users with spreadsheet comprehension.
"Finally,"
while it's very tempting to say that
all these comprehension difficulties
are because the spreadsheets
"were not built very well,"
it was not the case. In all
"the spreadsheets that we saw,"
"there were some form of good layout,"
"colors, documentation,"
all the best practices that are
recommended to build good spreadsheets.
"So, participants' comprehension difficulties"
"were not because of the author's efforts,"
but despite all that the author
did to make the spreadsheets more
"readable. In the paper, "
we make some arguments for why
"this might be the case,"
"what we could do about it,"
including building tools to make some of
"the things like layouts and colors easy,"
so that users could focus on other
kinds of things while writing
their spreadsheets.
Some of the other results that
"we discussed in the paper are listed here (information needs, strategies, difficulties, examples of hypotheses and comprehension errors, open research problems)."
Thanks for listening.