The document discusses various storytelling techniques for data analysis, emphasizing the importance of context, comparisons, and trends. It outlines strategies for visualizing data effectively and understanding how individual metrics interact within broader systems. The narrative highlights the need for a structured approach in presenting data insights to ensure clarity and audience engagement.

1. # D a t a 1 7

2. Welcome

3. Once Upon A Time... Seven Story
Types (and When to Use Them)
Jewel Loree
Product Manager, Storytelling
Tableau
# D a t a 1 7

4. Jewel Loree
jloree@tableau.com
@jeweloree
# D a t a 1 7

10. • Looking at how a particular factor has done
against a date measure
• Looking for trends, outliers, etc.

11. • What happened last year?
• When were we successful?
• Why does this happen at this
time?
• When should we do x?

12. • The team prepares for a certain thing to
happen at a certain time
• The team chooses when the best time to do
something is

13. • Overlay known events to see what their
effect was
• Min, max, outliers Inflection points are all
good things to dig into to look for stories
• Divide long time periods into consumable
chunks (decades, years, quarters, months)
• Compare different chunks of time to ask what
the difference was

15. • Looking at hierarchical data to see how
one particular dimension affects the
system as a whole
• Can start broad and go deeper or start
deep and go more broad

16. • What’s having the biggest effect on the
thing we care about? (drilling down)
• How does the thing we care about affect
the whole system? (drilling up)
• What is the context we need to be
cognizant of when making decisions about
a particular factor?

17. • The team has a better understanding of
how one particular measure affects the
overall system
• The team takes the whole system into
context when making decisions about a
particular measure

18. • When choosing whether to
go up versus down, think of
what your audience will
have more context for.
• Are they on the ground focused
on one particular factor? Then drill
up.
• Are they overseeing the broader
picture and need their attention
drawn to a particular area? Drill
down

20. • Focusing your attention on one
particular area of the data and
comparing it to the rest of the data
• You can start zoomed in and look
at the rest of the context, or you
can start broadly and focus on a
particular area of interest

21. • How is this doing in relation to
all the others?
• What is the baseline we
should be measuring success
by?
• Why are some
years/regions/categories more
successful than others?

22. • Context is given on how one
section of the data is doing
compared to others
• If using maps, geographical
context can also be important

23. • This technique is particularly
powerful when comparing
regions on a map
• Can be done on a time series:
zoom in to a particular point of
the time series that your
audience inherently
knows/understands and then
out to put it into the general
context

25. • Showing how things are
different between
different categories
• Comparing progress of
one group/category/item
over another

26. • What accounts for
these differences?
• How do we align these
things more?
• How do we make one
category perform more
like the other?

27. • The team gains clarity on
what potential
externalities may be
affecting the contrasted
items
• The team learns from the
success/failure from the
contrasted data to
emulate success or avoid
pitfalls

28. • Don’t just show the main
success metrics for each
group, show related metrics
to see where they really
differ
• Comparing all
groups/items/etc. to what
the averages are across all
of them is another way to
orient your audience to what
success vs. challenges look
like

30. • Pointing out shifts of when
one category overtakes
another

31. • What caused these shifts?
• Were these shifts good or
bad?
• How did these shifts affect our
overall goals?
• Should our strategy change
based on this intersection?

32. • The team examines the
sources of these shifts
• The team prevents or
promotes shifts like this in
the future

33. • Show your data a little at a
time leading up to the
intersection point, so that
your audience has a
thorough grasp of what the
baseline was
• Use other story types in
combination to dissect what
caused that intersection

35. • You have one main metric
and you are trying to show
what factors influence it the
most
• You are looking for
correlation or causation
between metrics

36. • How much do these things
affect the metric we care
about?
• Which thing affects the metric
we care about the most?
• Can we use one of these
factors to predict or control the
metric that we care about?

37. • The team has a better
understanding of which factors to
prioritize when trying to
predict/control a metric
• The team has a sense of where
to focus future investigations
• The team understands which
factors are not important and
therefore do not need to be
monitored as closely

38. • A common structure for these
stories is the “Goldilocks”
formula- show the factors that
don’t quite fit and then the one
that is “just right”
• If a factor that everyone
assumes is important isn’t as
important as they think, show
that one first and then the one
that is a better fit

40. • Pointing out specific areas
where things are
substantially different

41. • What happened here?
• What makes this data point
different from the rest?
• Was this outlier good or bad?
• How do we promote/mitigate
outliers like this in the future?

42. • The team has an idea of
areas to investigate to
understand the causes of the
outlier
• The team comes up with
ideas on how to
mitigate/promote outliers like
this in the future

43. • The farther out the outlier, the
more impactful this story will
be
• Use other story types to dig
into what makes that outlier
different

44. • Remember your high school English composition class
• Have a clear thesis, statement of premises, arguments and conclusion
• Start your story with orienting information
• Starting your audience in a place they are familiar with ensures that they have a frame
of reference
• Keep your points in consumable chunks
• Don’t try to say too much in one point.
• When in doubt, ask yourself how you came to this conclusion
• When you did your data analysis, you probably followed a particular line of thinking.
Starting there as a way of explaining it is a good baseline story to iterate off of
• Exploratory at the end
• Throwing an exploratory dashboard at the end of your story is like a self-service Q&A
session for your story

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47. Thursday, October 12
Once Upon a Time…Seven Story Types
12pm – 1pm | Bayside B
S E S S I O N R E P E A T S