My #Data17 talk going over 7 basic data stories, what kinds of discussions they start, and what kind of actions they drive.

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

46. Please complete
the session survey
from the Session
Details screen in
your TC17 app

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