1. Data Visualisation
Harvinder Atwal

2. Agenda
Warm-Up 5 mins
Data Visualisation: Why it matters 5 mins
The Rules 10 mins
Seven Common Quantitative Relationships 5 mins
The best means to encode quantitative data in charts 5 mins
Step by Step Guide 10 mins
Test 5 mins
2

3. Agenda
Warm-Up 5 mins
Data Visualisation: Why it matters 5 mins
The Rules 10 mins
Seven Common Quantitative Relationships 5 mins
The best means to encode quantitative data in charts 5 mins
Step by Step Guide 10 mins
Test 5 mins
3

4. Even the best information is useless, if its story is
poorly told
Most presentations of quantitative business data are poorly designed –
painfully so, often to the point of misinformation.
Anyone can start drawing charts in Excel and use PowerPoint but
hardly anyone is trained to do so effectively.
The effective display of
quantitative information
involves two fundamental
challenges
Selecting the right medium Designing the individual visual
of display (for example, a components of the selected
table or a graph, and the and medium to display the
appropriate kind of either) information and its message as
clearly as possible
4

5. Bad Data Visualisation can have tragic consequences
In Jan 1986 NASA had to Morton Thiokol engineers
decide whether to launch produced a chart and
the Challenger shuttle in recommended that
a ―100-year cold‖ shuttles not be flown
below 53F because of
potential damage to the
O-Rings in the booster
rockets
Morton Thiokol managers
Morton Thiokol managers accepted the
agree to the flight recommendation and
passed it on to NASA
NASA asks for the
recommendation to be
reconsidered
5

6. The engineers are Morton Thiokol came up with this chart
Looking at the O-Ring damage over the previous 24 shuttle missions, the data was
presented in chronological order showing the location and extent of the damage
sustained to the left and right boosters and the temperature at launch time.
6

7. The Morton Thiokol engineers failed to convince their
management and NASA with fatal consequences
7

8. Would this chart have been more convincing?
If instead we remove all the extraneous data and do a simple plot of
temperature vs damage then the pattern becomes much clearer.
ALWAYS
damage below
66F
Never damage
above 76F
8

9. WTF!? How many hours of valuable management time have been
wasted trying to understand a badly drawn chart?
How many £billions have been wasted on
incorrect decisions because someone
has misinterpreted a chart message?
9

10. To communicate effectively visually you need to understand
visual perception and cognition.
Present your message in a way that takes advantage of the
strengths of visual perception while avoiding its
weaknesses - matching the human thought process.
You can develop a simple set of skills (graphicacy) based on
this knowledge.
, based on clear-cut
This is principles about what
mostly Not
works and what
doesn’t
10

11. Agenda
Warm-Up 5 mins
Data Visualisation: Why it matters 5 mins
The Rules 10 mins
Seven Common Quantitative Relationships 5 mins
The best means to encode quantitative data in charts 3 mins
Step by Step Guide 10 mins
Test 5 mins
11

12. 12
Research Finding: Communication is most
effective when you say neither more nor
less than what is relevant to your message.
Principle #1: Display neither more nor less
than what is relevant to your message.

13. Tufte’s data-ink ratio is the single most important
concept in data visualisation
Data-ink ratio =
data-ink / total ink used to print the graphic
= proportion of a graphic’s ink devoted to the
non-redundant display of data-information
= 1.0 − proportion of a graphic that can be erased
without loss of data-information.
(The Visual Display of Quantitative Information, Edward R.
Tufte, Graphics Press, Cheshire CT, 1983, p.93)
13

14. Eliminate all redundant visual information!
 You wouldn’t write a document like this using multiple
fonts, gratuitous formatting, redundant
 excessive highlighting, variable colours, difficult to
read italics, pointless underlining, desperate shadows in
multiple sizes.
● Yet everyday you see the graphical equivalent as people
try to make their charts “interesting” instead of useful!.
14

15. How many items of redundant visual information can you
see in this chart?
Grey Underlining
Sales and Appointments by Region
Background
Border
200
183
180
Legend Key
Vertical
Excessive 160 150 Lines
tick marks 3-D Effect
140 Data Labels
120 112
100 97
Volume 100 91 Appointments
85
Sales
75
80
60
Border on
40 Legend
Border on
20
Bars
0
Wales and West London And South East Scotland and North Midlands
Floor
Region
Highlighting for
no reason
15

16. Less is more; the same chart de-junked…
Volumes Sales and Appointments by Region
200
Appointments Sales
180
160
140
120
100
80
60
40
20
0
Wales and West London And South East Scotland and North Midlands
Region

17. Research Finding: People perceive visual
differences in an information display as
differences in meaning.
Principle #2: Do not include visual
differences in a graph that do not correspond
to actual differences in the data.
17

18. What is the meaning of the different colours that appear on the
bars? The answer is “nothing.”
Don’t confuse
people and waste
their time by
including visual
differences that are
meaningless.
18

19. Research Finding: The visual properties that work
best for representing quantitative values are the
length or 2-D location of objects.
Principle #3: Use the lengths or 2-D locations of
objects to encode quantitative values in graphs
unless they have already been used for other
variables.
19

20. #1 How much taller is bar B than A?
A B
20

21. #2 How much higher is point A than B?
A B
21

22. #3 How much bigger is the area of B than A?
A
B
22

23. #4 How much darker is circle B than A?
A
B
23

24. Answers
#1 5x #2
4x
A B A B
#3 A
10x #4 A
B
5x
B

25. How much taller is bar B than A?
A B
25

26. Bar B is actually only 10% bigger than A, not 100%
560
550
540
530
520
510
500
490
480
470
A B
26

27. Research Finding: People perceive differences in the
lengths or 2-D locations of objects fairly accurately
and interpret them as differences in the actual values
that they represent.
Principle #4: Differences in the visual properties that
represent values (that is, differences in their lengths
or 2-D locations) should accurately correspond to
the actual differences in the values they represent.
27

28. Research Finding: People perceive things that
appear connected as wholes and things that appear
disconnected as discrete.
Principle #5: Do not visually connect values that are
discrete, thereby suggesting a relationship that does
not exist in the data.
28

29. The regions are discrete, so values that measure something going on in
these regions should be displayed as discrete.
Connecting discrete
items with a line is
misleading. Doing so
forms a pattern of
upwards and
downwards slopes
that are utterly
meaningless.
29

30. Research Finding: People pay most attention to and
consider most important those parts of a visual
display that are most salient.
Principle #6: Make the information that is most
important to your message more visually salient in a
graph than information that is less important.
30

31. Some information is more important to your
message than others
You can communicate this
fact in a graph by making
those items that are most
important more visually
dominant (salient).
It is your job to direct
people’s eyes to the most
important parts of the
display, so they
adequately focus on them.
31

32. Research Finding: Short-term memory is limited to
about four chunks of information at a time.
Principle #7: Augment people’s short-term memory
by combining multiple facts into a single visual
pattern that can be stored as a chunk of memory and
by presenting all the information they need to
compare within eye span.
32

33. By presenting quantitative information visually as
patterns, more information can be simultaneously stored in
short-term memory,
Each of the two lines in
this line graph combines
12 different sales
figures, one per
month, into a single
pattern of upward and
downward sloping line
segments.
When encoded in a visual
pattern such as this, these
12 numbers can be stored
together as a single chunk
of information in short-
term memory
33

34. Agenda
Warm-Up 5 mins
Data Visualisation: Why it matters 5 mins
The Rules 10 mins
Seven Common Quantitative Relationships 5 mins
The best means to encode quantitative data in charts 5 mins
Step by Step Guide 10 mins
Test 5 mins
34

35. Seven common quantitative relationships in graphs and
how to display them
Meaningful quantitative information always
involves relationships. With rare exceptions in
business graphs, these relationships always boil
down to one or more of the seven relationships
described on the following slides.
35

36. Time Series
Expresses the rise and fall of
values through time.
– Use lines to emphasize
overall pattern.
– Use bars to emphasize
individual values.
– Use points connected by
lines to slightly emphasize
individual values while still
highlighting the overall
pattern.
– Always place time on the
horizontal axis.
36

37. Ranking
Expresses values in order by
size.
Use bars only (horizontal or
vertical).
– To highlight high
values, sort in descending
order.
– To highlight low values, sort
in ascending order.
37

38. Part-to-Whole
Expresses the portion of each
part relative to the whole.
– Use bars only (horizontal
or vertical).
– Use stacked bars only
when you must display
measures of the whole
38

39. Deviation
Expresses how and the degree to
which one or more things differ from
another.
– Use lines to emphasize the overall
pattern only when displaying
deviation and timeseries
relationships together.
– Use points connected by lines to
slightly emphasize individual data
points while also highlighting the
overall pattern when displaying
deviation and time-series
relationships together.
– Use bars to emphasize individual
values, but limit to vertical bars
when a time series relationship is
included.
– Always include a reference line to
compare the measures of deviation
against.
39

40. Distribution
Expresses a range of values as well as the
shape of the distribution across that range.
Single distribution:
– Use vertical bars to emphasize individual
values
– Use lines to emphasize the overall shape.
Multiples distributions:
– Use vertical or horizontal bars (a.k.a.
range bars or boxes) to encode the full
range from the low value to the high
value, or some meaningful portion of the
range (for example, 90% of the values).
– Use points or lines together to encode
measures of centre (for example, the
median).
40

41. Correlation
Expresses how two paired
sets of values vary in relation
to one another.
– Use points and a trend
line in the form of a
scatter plot.
41

42. Nominal Comparison
Simply expresses the
comparative sizes of multiple
related but discrete values in no
particular order.
– Use bars only (horizontal or
vertical).
42

43. Agenda
Warm-Up 5 mins
Data Visualisation: Why it matters 5 mins
The Rules 10 mins
Seven Common Quantitative Relationships 5 mins
The best means to encode quantitative data in charts 5 mins
Step by Step Guide 10 mins
Test 5 mins
43

44. Four types of objects work best for encoding quantitative
values in graphs: points, lines, bars, and boxes.
Bars
Points
Boxes
Lines
44

45. Points and Lines
Points are the smallest of the objects that are used to encode values in graphs. They can take
the shape of dots, squares, triangles, Xs, dashes, and other simple objects. They have two
primary strengths:
(1) they can be used to encode quantitative values along two quantitative
scales simultaneously, as in a scatter plot, and
(2) they can be used to in place of bars when the quantitative scale does not
begin at zero. Unlike lines, points emphasize individual values, rather
than the shape of those values as they move up and down.
Lines connect the individual values in a series, emphasizing the shape of the data
as it moves from value to value. As such, they are superb for showing the shape
of data as it moves and changes through time. Trends, patterns, and exceptions
stand out clearly.
You should only use lines to encode data along an interval scale.
45

46. Do not use lines for Nominal or Ordinal scales!
Sales
160 Wrong Wrong
Sales
140 120
120 100
100
80
80
60
60
40
40
20 Nominal Scale 20
0 0
Extra-Value Standard Branded Finest
Wales and West London And South East Scotland and North
In nominal and ordinal scales, the individual items are not related closely enough to be linked with lines, so
you should use bars or points instead. Lines suggest change from one item to the next, but change isn’t
happening if the items aren’t closely related as sequential subdivisions of a continuous range of values. For
instance, it is appropriate to use lines to display change from one day to the next or from one price range to
the next, but not from one community bank to the next.
46

47. Use lines only for Interval scales
Sales
120 Right If, however, you want to emphasize individual
items, such as individual months, or to
100 support discrete comparisons of multiple
values at the same location along the interval
80 scale, such as revenues and expenses for
individual months, then bars or points work
60 best.
40
Sales
20
Interval Scale 120
100
0
Q1 Q2 Q3 Q4 80
60
With interval scales, you are not forced in all
cases to use lines; you can use bars and points 40
as well. If you want to emphasize the overall
shape of the data or changes from one item to 20
the next, lines work best.
0
Q1 Q2 Q3 Q4
47

48. Bars encode data in a way that emphasizes individual
values powerfully
This ability is due in part to the fact that bars encode quantitative values in two ways:
(1) the 2-D position of the bar’s endpoint in relation to the quantitative scale, and
(2) the length of the bar.
You probably recognize that these two characteristics correspond precisely to the two visual attributes that can be used
to encode data in graphs. When you want to draw focus to individual values or to support the comparison of individual
values to one another (see figure 19), bars are an ideal choice. They don’t, however, do as well as lines in revealing the
overall shape of the data. Bars may be oriented vertically or horizontally.
100 Budget Actual
90
80
70
60
50
40
30
20
10
0
Rewards Exchange
48

49. Whenever you use bars, your quantitative scale must
include zero
The lengths of the bars encode their values, but won’t When you would normally use bars, but
do so accurately if those values don’t begin at zero. wish to narrow the quantitative scale to
Notice what happens when you narrow the show differences between the values in
quantitative scale and use bars below. Actual sales
appear to be half of planned sales, but in fact they are greater detail, you should switch from bars
90% of the plan. to points, because points encode values
merely as 2-D location in relation to the
quantitative scale, which eliminates the
need to begin the scale at zero.
100 Budget Actual 560
550
540
90 530
520
510
80 500
490
480
70 470
Rewards Exchange A B
49

50. Boxes
Boxes are a lot like bars, except that
both ends encode quantitative values.
When bars are used in this way, they are
sometimes called range bars. They are
used to encode a range of
values, usually from the highest to the
lowest, rather than a single value.
In the 1970s John Tukey invented a
method of using rectangles (bars with or
without fill colors) in combination with
individual data points (often a short line)
and thin bars to encode several facts
about a distribution of values, including
the median (middle value), middle
50%, etc.
He called his invention a box plot (a.k.a.
box-and-whisker plot).
50

51. Agenda
Warm-Up 5 mins
Data Visualisation: Why it matters 5 mins
The Rules 10 mins
Seven Common Quantitative Relationships 5 mins
The best means to encode quantitative data in charts 5 mins
Step by Step Guide 10 mins
Test 5 mins
51

52. Step 1: Determine your message
Will the data be used to look up
and compare individual values, or
will the data need to be precise?
Determine your message. If so, you should display it in a
Don’t just turn your data table.
into a chart!
Think about what your Or, do both.
data means, what you
want to communicate Is the message contained in the
and most importantly shape of the data—in
your audiences’ trends, patterns, exceptions, or
needs. comparisons that involve more
than a few values? If so, you
should display it in a graph.
52

53. Step 2: Determine the best means to encode the values
Nominal comparison. Bars (horizontal or vertical). Points (if the quantitative scale does
not include zero).
Time Series. Lines to emphasize the overall shape of the data
Bars to emphasize and support comparisons between individual values
Points connected by lines to slightly emphasize individual values while still highlighting
the overall shape of the data
Ranking. Bars (horizontal or vertical). Points (if the quantitative scale does not include
zero)
What am I Part-to-Whole. Bars (horizontal or vertical) Note: Pie charts are commonly used to
display part-to-whole relationships, but they don’t work nearly as well as bar
trying to graphs because it is much harder to compare the sizes of slices than the length of
bars. Use stacked bars only when you must display measures of the whole as well
represent? as the parts
Deviation. Lines to emphasize the overall shape of the data (only when displaying
deviation and time-series relationships together)
Points connected by lines to slightly emphasize individual data points while also
highlighting the overall shape (only when displaying deviation and time-series
relationships together)
Frequency Distribution. Bars (vertical only) to emphasize individual values. This kind
of graph is called a histogram
Lines to emphasize the overall shape of the data. This kind of graph is called a
frequency polygon.
Correlation. Points and a trend line in the form of a scatter plot
53

54. Step 3: Determine where to display each variable – One
Variable
Place the categorical variable on the x-axis if your graph will include ONE categorical variable and any one of
the following is true:
• The categorical scale is an interval scale
• You are using lines to encode the data
• You are using bars to encode the data and the labels are not long or many
If you are using bars place the categorical variable on the Y-axis when either of these two conditions exist:
• The text labels associated with the bars are long
• There are many bars. 120
0 20 40 60 80 100 120
100
Beef
Fresh pork 80
Lamb
Bacon 60
Sausage
40
Beef fillet jnt
Beef sirloin joint Is better than 20
Pork roulades
Fresh pork mince 0
Fresh poultry gravy
Be avy
ge
rs
s
b
nt
es
s
rk
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ce
f
rlo nt
k
e
er
er
oc
m
co
oi
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po
tj
Be
ad
in
sa
rg
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La
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Po in j
Ba
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Beef stock
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An bur
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fil
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Sa
es
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Be
be
po
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st
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Be
8 beef steak burgers
4
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es
Fr
be
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Angus burgers
8
54

55. Step 3: Determine where to display each variable – Two or
three variables
If the graph involves two or three variables, you must decide which to display along the
axes and which to encode using distinct versions of another visual attribute, such as
colour.
200
With a line graph, place the variable that is
180 Appointments
most important to your message along the X
160 Sales
axis.
140
With a bar graph, encode the variable whose 120
items you want to make it easiest to 100
compare using a method other than 80
association with an axis. Notice how much
60
easier it is to compare appointments and
40
sales than the regions, because they are
positioned next to one another. 20
0
Wales and West London And South Scotland and North Midlands
East
55

56. Step 3: Determine where to display each variable - the
problem of the fourth variable
This solution involves a series of small graphs, arranged in the same way as a graph with three
variables, all arranged together in a way that can be seen simultaneously. Each graph is
alike, including consistent scales, differing only in that each features a different item of a
categorical variable. Each graph varies according to a fourth variable, which is sales channel
(e.g. product).
Using small multiples to support an additional variable is a powerful technique. Graphs can be
arranged horizontally, vertically, or even in a matrix of columns and rows. If you need to display
one more variable than you can fit into a single graph, select this approach.
Face-Value Rewards Big Exchange
Midlands
2010
Sales
Scotland and North
Appointments
2011
London And South East
Wales and West
0 50 100 150 200 0 50 100 150 200 0 50 100 150 200
56

57. Step 4: Determine the best design for the remaining
objects - Scale
It’s now time to make a series of design decisions that remain, including the scales and
text. These decisions are concerned with the placement and visual appearance of
items.
Sales
If the graph will be used for analysis purposes 1800
that require seeing the differences between
1600
values in as much detail as possible, narrowing
the scale can be useful. Generally, you should
1400
adjust the scale so that it extends a little below
the lowest data value and a little above the 1200
highest.
1000
800
560
Q1 Q2 Q3 Q4
550
540
530
520
If you are using bars to encode the
510
data, but your message could be better
communicated by narrowing the
500
scale, Remember to switch from bars
490
to points!
480
470
A B
57

58. Step 4: Determine the best design for the remaining
objects - Legend
Sales
1800
If a Legend Is Required, and London and South East
you are using lines, label the 1600
lines directly Wales and West
1400
1200
1000
100 Budget Actual 800
90 Q1 Q2 Q3 Q4
80
70
60 If you are using bars, place the
50
40
legend above the plot area with
30
the labels arranged side-by-side
20 in the same order as the bars
10
0
Rewards Exchange
58

59. Step 4: Determine the best design for the remaining
objects – Tick Marks and Scales
Tick marks are only necessary on quantitative scales, for they serve no real purpose
on categorical scales. A number between 5 and 10 tick marks usually does the job;
too many clutters the graph and too few fail to give the level of detail needed to
interpret the values.
If the graph can be read with the scale in only one place (left, right, top
bottom) place it nearest the data you want to emphasise or make easiest to
read.
If the graph is so large it cannot be read with only one scale, place it in both
positions ( top and bottom, left and right).
59

60. Step 4: Determine the best design for the remaining
objects – Gridlines
Unless they are necessary to understand your message or divide a scatter plot into sections leave them
off, and when used subdue them visually. Bear in mind graphs display patterns and relationships. If
you want to communicate data with a high degree of quantitative accuracy use a table.
Sales
1800
1600
1400
1200
1000
800
Q1 Q2 Q3 Q4
60

61. Step 4: Determine the best design for the remaining
objects – Descriptive Text
Although the primary message of a graph is carried in the picture it provides, text is
always required to some degree to clarify the meaning of that picture. Some text if often
needed, including:
– A descriptive title
– Axis titles (unless the nature of the scale and its unit of measure are already clear)
Numbers in the form of text Widget Sales by Region and
along quantitative scales are Sales Calendar Quarter (2007)
always necessary and 1800
legends often are. It is often London and South East
useful to include one or 1600
more notes to describe what Wales and West
is going on in the 1400
graph, what ought to be Widget sales in
examined in particular, or London and South
1200
how to read the East have been
ahead of Wales
graph, whenever these bits 1000 and West with the
of important information are exception of Q3
not otherwise obvious.
800
Q1 Q2 Q3 Q4
61

62. Step 5: Determine if particular data should be featured, and
if so, how
The final major stage in the process involves highlighting particular data if some data is more important
than the rest.
Whatever the reason, you have a number of possible ways to make selected data stand out.
One of the best and simplest ways is to encode those items using bright or dark colours, which will stand
out clearly if you’ve used soft colours for everything else. Other methods include:
–When bars are used, place borders only around those bars that should be highlighted.
–When lines are used, make the lines that must stand out thicker.
–When points are used, make the featured points larger or include fill colour in them alone.
Sales Sales
1800
120
100 1600
80
1400
60
1200
40
20 1000
0 800
A B
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
62

63. Remember to follow this process for graph selection and design in
order to communicate your information in the most effective manner
Determine your message and identify your data
Determine if a table, graph, or combination of both is
needed to communicate your message
Determine the best means to encode the values
Determine where to display each variable
The best means to encode quantitative data in charts
Determine the best design for the remaining objects
Determine if particular data should be featured, and
if so, how
63

64. Summary
Whenever you create a graph, you have a choice to
make — to communicate or not. That’s what it all comes
down to. If you have something important to say, then
say it clearly and accurately. These guidelines are
designed to help you do just that.

65. Agenda
Warm-Up 5 mins
Data Visualisation: Why it matters 5 mins
The Rules 10 mins
Seven Common Quantitative Relationships 5 mins
The best means to encode quantitative data in charts 5 mins
Step by Step Guide 10 mins
Test 5 mins
65

66. Which graph makes it easier to determine whether Mid-Cap US
stocks or Small-Cap US stocks have a greater share?
A B
66

67. Which of these line graphs is easier to read?
A B
67

68. Which of these tables is easier to read?
A
B
68

69. Which graph makes it easier to focus on the pattern of change
through time, instead of the individual values?
A
B
69

70. Only one of these graphs accurately encodes the values. The other skews the
values in a misleading manner. Which graph presents the data accurately?
A B
70

71. Which map makes it easier to find all of the counties with
positive growth rates?
A B
71

72. Which graph makes it easier to determine R&D’s travel
expense?
A
B
72

73. In which graph are the labels easier to read?
A B
73

74. Which graph is easier to look at?
A
B
74

75. Which table allows you to see the areas of poor
performance more quickly?
A
B
75

76. What percentage of the population is colour-blind?