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Sele
ctin
g a
Vis
ual A
naly
tics A
pp
licatio
n
AU
TH
OR
S:
Pro
fes
so
r Pa
t Ha
nra
ha
n
Sta
nfo
rd U
niv
ers
ity
CT
O, T
ab
lea
u S
oft
wa
re
Dr. C
hris
Sto
lte
VP, E
ng
ine
erin
g
Ta
ble
au
So
ftw
are
Dr. J
oc
k M
ac
kin
lay
Dire
cto
r, Vis
ua
l An
aly
tics
Ta
ble
au
So
ftw
are
Na
me
Infla
tion
: Vis
ua
l An
aly
tics
Vis
ua
l an
aly
tics is
be
co
min
g th
e fa
ste
st w
ay fo
r pe
op
le to
ex
plo
re
an
d u
nd
ers
tan
d d
ata
of a
ny s
ize. M
an
y c
om
pa
nie
s to
ok n
otic
e w
he
n
Ga
rtne
r cite
d in
tera
ctiv
e d
ata
vis
ua
lizatio
n a
s o
ne o
f the
top
fiv
e
tren
ds tra
nsfo
rmin
g b
usin
ess in
tellig
en
ce
. Ne
w c
on
fere
nce
s h
av
e
em
erg
ed
to p
rom
ote
rese
arc
h a
nd
be
st p
ractic
es in
the a
rea
,
inclu
din
g V
AS
T (V
isu
al A
na
lytic
s S
cie
nce &
Te
ch
no
log
y), o
rga
nize
d
by th
e 1
00,0
00 m
em
be
r IEE
E. T
ech
no
log
ies b
ase
d o
n v
isu
al
an
aly
tics h
av
e m
ov
ed
from
rese
arc
h in
to w
ide
sp
rea
d u
se in
the la
st
fiv
e y
ea
rs, d
rive
n b
y th
e in
cre
ase
d p
ow
er o
f an
aly
tica
l da
tab
ase
s
an
d c
om
pu
ter h
ard
wa
re. T
he IT
de
pa
rtme
nts
of le
ad
ing
co
mp
an
ies
are
incre
asin
gly
reco
gn
izing
the n
ee
d fo
r a v
isu
al a
na
lytic
s s
tan
da
rd.
No
t su
rpris
ing
ly, e
ve
ryw
he
re y
ou
loo
k, s
oftw
are
co
mp
an
ies a
re
ad
op
ting
the te
rms “
vis
ua
l an
aly
tics”
an
d “
inte
ractiv
e d
ata
vis
ua
lizatio
n.”
To
ols
tha
t do
little m
ore
tha
n p
rod
uce c
ha
rts a
nd
da
sh
bo
ard
s a
re n
ow
lay
ing
cla
im to
the la
be
l.
Ho
w c
an
yo
u te
ll the c
lev
erly
na
me
d fro
m th
e g
en
uin
e? W
ha
t sh
ou
ld
yo
u lo
ok fo
r? It’s
imp
orta
nt to
kn
ow
the
de
fin
ing
ch
ara
cte
ristic
s o
f
vis
ua
l an
aly
tics b
efo
re y
ou
sh
op
. Th
is p
ap
er in
trod
uce
s y
ou
to th
e
se
ve
n e
sse
ntia
l ele
me
nts
of tru
e v
isu
al a
na
lytic
s a
pp
lica
tion
s.
Fig
ure
1: T
he
re a
re s
eve
n e
sse
ntia
l ele
me
nts
of a
vis
ual a
naly
tics a
pp
licatio
n.
Do
es a
true v
isu
al a
na
lytic
s a
pp
lica
tion
als
o in
clu
de s
tan
da
rd
an
aly
tica
l fea
ture
s lik
e p
ivo
t-tab
les, d
ash
bo
ard
s a
nd
sta
tistic
s? O
f
co
urs
e—
all g
oo
d a
na
lytic
s a
pp
lica
tion
s d
o. B
ut n
on
e o
f tho
se
fea
ture
s c
ap
ture
s th
e e
sse
nce
of w
ha
t vis
ua
l an
aly
tics is
brin
gin
g to
the w
orld
’s le
ad
ing
co
mp
an
ies. V
isu
al a
na
lytic
s is
a n
ew
are
a o
f
tech
no
log
y, a
nd
it ad
ds s
om
eth
ing
sp
ecia
l to y
ou
r bu
sin
ess
inte
llige
nce to
olk
it.
© 2
00
9 T
ab
lea
u S
oftw
are
1
De
finin
g V
isu
al A
na
lytic
s: F
irst, W
ha
t It’s
No
t
Le
t’s s
tart w
ith w
ha
t vis
ua
l an
aly
sis
is n
ot: A
gra
ph
ica
l de
pic
tion
of
da
ta. V
irtua
lly a
ny s
oftw
are
ap
plic
atio
n c
an
pro
du
ce a
ch
art, g
au
ge
or d
ash
bo
ard
. Vis
ua
l an
aly
tics o
ffers
so
me
thin
g m
uch
mo
re
pro
fou
nd
. Vis
ua
l an
aly
tics is
the p
roce
ss o
f an
aly
tica
l rea
so
nin
g
facilita
ted
by in
tera
ctiv
e v
isu
al in
terfa
ce
s.
Pe
op
le w
ho
un
de
rsta
nd
vis
ua
l an
aly
tics k
no
w th
ere
’s s
om
eth
ing
mis
sin
g in
the s
ha
llow
ch
artin
g w
izard
s a
nd
da
sh
bo
ard
s fo
un
d in
mo
st b
usin
ess in
tellig
en
ce p
acka
ge
s a
nd
sp
rea
dsh
ee
ts. W
hile
ch
arts
an
d d
ash
bo
ard
s a
re in
de
ed
“v
isu
aliza
tion
s,”
the
y le
av
e o
ut
thre
e c
ritica
l ste
ps: e
xp
lora
tion
, an
aly
sis
an
d c
olla
bo
ratio
n. A
ch
art,
for in
sta
nce
, sh
ow
s c
on
clu
sio
ns, b
ut n
ot th
e th
ou
gh
ts b
eh
ind
it. No
r
ca
n u
se
rs u
se a
ch
art to
ask q
ue
stio
ns a
nd
thin
k fu
rthe
r. In a
ch
art,
the th
inkin
g h
as ta
ke
n p
lace a
lrea
dy
an
d th
e re
su
lting
vis
ua
lizatio
ns
are
little m
ore
tha
n a
sh
ow
.
Vis
ua
l an
aly
tics is
a m
ea
ns o
f ex
plo
ring
an
d u
nd
ers
tan
din
g d
ata
. It
su
pp
orts
an
d a
cce
lera
tes th
e a
na
lysis
pro
ce
ss its
elf. Y
ou
ca
n a
sk a
qu
estio
n, g
et th
e a
nsw
er, a
nd
ask fo
llow
-up
qu
estio
ns—
all w
ithin
vis
ua
l inte
rface
s. A
sto
ry u
nfo
lds fro
m o
ne
vis
ua
l su
mm
ary
to
an
oth
er. Y
ou
ma
inta
in y
ou
r train
of th
ou
gh
t with
ou
t takin
g y
ou
r
ey
es o
ff the d
ata
. La
ter, y
ou
ca
n re
trace th
e s
tory
to re
thin
k, e
xp
lore
furth
er a
nd
sh
are
. In s
ho
rt, vis
ua
l an
aly
tics a
llow
s y
ou
to g
o in
an
y
dire
ctio
n w
ith y
ou
r tho
ug
hts
wh
ile le
ve
rag
ing
yo
ur v
isu
al p
erc
ep
tua
l
sy
ste
m to
gu
ide y
ou
do
wn
the m
ost u
se
ful p
ath
s.
Wh
o is
Ad
op
ting
Vis
ua
l An
aly
tics?
Vis
ua
l an
aly
tics is
be
ing
ad
op
ted
by th
e w
orld
’s le
ad
ing
co
mp
an
ies,
un
ive
rsitie
s a
nd
go
ve
rnm
en
t ag
en
cie
s. F
rom
the w
orld
’s la
rge
st a
nd
mo
st in
no
va
tive o
rga
niza
tion
s –
Pro
cto
r & G
am
ble
, Ap
ple
, Pfi
zer,
Mic
roso
ft, Co
ca C
ola
, Go
og
le, C
orn
ell U
niv
ers
ity, P
rog
ressiv
e
Insu
ran
ce
, Am
azo
n, G
eo
rge
tow
n U
niv
ers
ity, th
e V
A (V
ete
ran
’s
Ad
min
istra
tion
), Blu
e C
ross B
lue S
hie
ld –
to o
ne
-pe
rso
n c
on
su
lting
sh
op
s, v
isu
al a
na
lysis
too
ls a
re n
ow
ma
instre
am
.
Wh
ere
vis
ua
l an
aly
tics w
as o
nce th
ou
gh
t to b
e in
the d
om
ain
of
scie
ntis
ts a
nd
en
gin
ee
rs, p
eo
ple
no
w re
co
gn
ize th
at v
isu
al a
na
lysis
acce
lera
tes b
usin
ess a
na
lytic
s. E
ve
n p
eo
ple
wh
o h
av
e m
ad
e d
o w
ith
Exce
l an
d w
izard
-driv
en
ch
artin
g c
an
be
ne
fit fro
m to
da
y’s
vis
ua
l
an
aly
tics a
pp
lica
tion
s –
the a
pp
lica
tion
s h
av
e b
eco
me
ea
sy e
no
ug
h
tha
t an
yo
ne c
an
use th
em
.
2©
20
09 T
ab
lea
u S
oftw
are
As
se
ss
ing
Fe
atu
res o
f Vis
ua
l An
aly
tics
Ap
plic
atio
ns fo
r Bu
sin
es
s D
ec
isio
n M
ak
ing
If vis
ua
l an
aly
tics is
mo
re th
an
just p
rese
ntin
g d
ata
vis
ua
lly, th
en
asse
ssin
g v
isu
al a
na
lysis
ap
plic
atio
ns is
mo
re th
an
just lo
okin
g fo
r
so
ftwa
re th
at c
an
pro
du
ce c
oo
l vis
ua
lizatio
ns. T
o d
istin
gu
ish
go
od
vis
ua
l an
aly
tics to
ols
from
ba
d, it’s
critic
al to
focu
s o
n th
e s
ev
en
esse
ntia
l ch
ara
cte
ristic
s th
at d
istin
gu
ish
a v
isu
al a
na
lytic
s
ap
plic
atio
n.
Vis
ua
l Ex
plo
ratio
n
Th
e fi
rst c
ha
racte
ristic
of a
vis
ua
l an
aly
tics a
pp
lica
tion
is th
e m
ost
imp
orta
nt: T
he a
pp
lica
tion
un
ifie
s th
e s
tep
s o
f qu
ery
ing
, ex
plo
ring
an
d v
isu
aliza
tion
da
ta in
to a
sin
gle
pro
ce
ss. D
o th
e d
ata
an
d th
e
vis
ua
lizatio
n w
ork
in ta
nd
em
? W
he
n th
e u
se
r pu
lls o
n th
e
vis
ua
lizatio
n th
e d
ata
sh
ou
ld c
om
e a
lon
g. W
ha
t do
es th
is m
ea
n? It
me
an
s p
eo
ple
ca
n g
o in
an
y d
irectio
n w
ith th
eir th
ou
gh
ts, w
itho
ut
ev
er lo
sin
g th
eir tra
in-o
f-tho
ug
ht. T
he
y m
ay n
ot e
ve
n h
av
e a
sp
ecifi
c q
ue
stio
n, b
ut a
s th
ey m
ov
e th
rou
gh
the d
ata
vis
ua
lly, th
ey
no
tice s
om
eth
ing
an
d th
at p
rom
pts
a q
ue
stio
n a
nd
the
n a
follo
w u
p
an
d s
o o
n, e
ve
ntu
ally
lea
din
g to
insig
ht. It m
ea
ns th
e v
isu
aliza
tion
s
in a
vis
ua
l an
aly
tics a
pp
lica
tion
allo
w p
eo
ple
to s
top
an
d ta
ke a
clo
se
r loo
k. F
ilterin
g, g
rou
pin
g, s
ortin
g a
nd
drillin
g a
ll take p
lace
with
in th
e v
isu
aliza
tion
itse
lf, with
a c
lick. A
use
r ma
y s
tart w
ith
ba
sic
qu
estio
ns a
nd
the
n, b
ase
d o
n v
isu
al c
ue
s a
nd
insig
ht, d
ee
pe
n
the in
qu
iry. T
he q
ue
stio
ns m
ay m
ea
n e
limin
atin
g s
om
e d
ata
,
va
lida
ting
it, or re
ach
ing
for a
ne
w s
et o
f da
ta a
ltog
eth
er. A
vis
ua
l
an
aly
tics a
pp
lica
tion
he
lps p
eo
ple
do
all o
f this
vis
ua
lly, a
nd
on
-the
-
fly. It’s
like th
e e
arly
da
ys o
f the w
eb
an
d M
osa
ic, w
he
n p
eo
ple
firs
t
sta
rting
ge
tting
the id
ea o
f stre
am
of c
on
scio
usn
ess e
xp
lora
tion
via
hy
pe
rlinks. It w
as a
ga
me c
ha
ng
er.
3©
20
09 T
ab
lea
u S
oftw
are
Fig
ure
2: V
isu
al a
naly
sis
ap
plic
atio
ns s
up
po
rt the c
ycle
of v
isu
al a
naly
sis
. Wh
en
pe
op
le a
re e
xp
lorin
g d
ata
, they e
nte
r into
a c
ycle
wh
ere
at a
ny p
oin
t they m
ay
ne
ed to
skip
ste
ps, b
ack-u
p, s
ee
k a
dd
ition
al d
ata
, or e
ve
n s
tart o
ve
r. Vis
ual
an
aly
sis
ap
plic
atio
ns s
up
po
rt this
pro
ce
ss o
f vis
ual d
ata
exp
lora
tion
.
Ho
w d
o y
ou
kn
ow
if an
an
aly
tics a
pp
lica
tion
is a
pp
rop
riate
ly
de
sig
ne
d to
su
pp
ort v
isu
al e
xp
lora
tion
? H
ere
are
a fe
w te
sts
:
da
ta? U
sin
g th
e s
oftw
are
sh
ou
ld b
e s
o e
asy th
at p
eo
ple
do
n’t
ev
en
thin
k a
bo
ut th
e m
ech
an
ics o
f cre
atin
g a
vis
ua
lizatio
n.
from
the v
isu
aliza
tion
? F
or in
sta
nce
, pe
op
le s
ho
uld
be
ab
le to
lasso
item
s a
nd
exclu
de th
em
with
a c
lick. T
he
y s
ho
uld
be a
ble
to p
erfo
rm d
rag
-an
d-d
rop
cu
lling
. Filte
rs s
ho
uld
als
o b
e
ap
plic
ab
le, w
he
n n
ece
ssa
ry, to
a c
olle
ctio
n o
f inte
ractiv
e
vis
ua
lizatio
ns b
ein
g v
iew
ed
sim
ulta
ne
ou
sly
.
with
in th
e v
isu
aliza
tion
? T
his
inclu
de
s d
rag
-an
d-d
rop
gro
up
ing
an
d o
n-th
e-fl
y b
inn
ing
.
4©
20
09 T
ab
lea
u S
oftw
are
vis
ua
lizatio
n—
for in
sta
nce
, by c
ha
ng
ing
ag
gre
ga
tion
s o
r cre
atin
g
ne
w c
alc
ula
tion
s (e
.g., Y
ea
r ov
er Y
ea
r ch
an
ge) in
sta
ntly
?
reco
rds w
itho
ut a
ny s
pe
cia
l co
nfi
gu
ratio
n? V
isu
al a
na
lytic
s
mo
ve
s a
t the s
pe
ed
of th
ou
gh
t. It req
uire
s to
ols
to s
tay o
ut o
f
the w
ay a
nd
rea
ct in
sta
ntly
.
Vis
ua
l an
aly
tics p
aid
off d
ram
atic
ally
at
Cle
ve
lan
d C
linic
. Th
ere
, the
acco
un
ts
rece
iva
ble
sta
ff sim
ply
co
uld
n’t a
na
lyze
reje
cte
d c
laim
s fa
st e
no
ug
h. E
ach
ye
ar, th
e o
rga
niza
tion
lost
su
bsta
ntia
l am
ou
nts
from
un
fulfi
lled
insu
ran
ce re
imb
urs
em
en
ts
for s
om
e o
f the 5
0,0
00
-plu
s p
atie
nts
the h
osp
ital s
erv
es. C
laim
s
an
aly
sts
co
uld
no
t ex
am
ine
cla
ims re
co
rds ite
rativ
ely
to u
nco
ve
r
wh
y th
e c
laim
wa
s re
jecte
d. T
he
re w
as n
o w
ay
the
y c
ou
ld lo
ok a
t
a s
et o
f cla
ims a
nd
follo
w m
ultip
le lin
es o
f rea
so
nin
g to
ide
ntify
wh
at c
ou
ld b
e c
au
sin
g th
e p
rob
lem
.
Co
nve
ntio
na
l rep
ortin
g s
olu
tion
s d
idn
’t allo
w th
em
to g
rou
p c
laim
s
into
diffe
ren
t se
gm
en
ts, d
rill into
de
tails
or fi
lter in
tera
ctiv
ely
. A
vis
ua
l an
aly
tics a
pp
lica
tion
from
Ta
ble
au
ga
ve th
em
the u
se
r-
dire
cte
d e
nviro
nm
en
t the
y re
ally
ne
ed
ed
. Ru
nn
ing
on
top
of a
n
ex
istin
g d
ata
wa
reh
ou
se, T
ab
lea
u e
na
ble
d C
linic
sta
ff to a
na
lyze
un
pa
id a
nd
reje
cte
d c
laim
s a
s th
ey ro
lled
in, e
ve
n th
ou
gh
insp
ectio
n re
qu
irem
en
ts v
arie
d fro
m c
ase to
ca
se
. Re
paym
en
ts ro
se
dra
ma
tica
lly. In
on
e y
ea
r, the h
osp
ital a
ttribu
ted
ove
r $2
0 m
illion
in
reco
ve
red
paym
en
ts to
the
ir use o
f vis
ua
l an
aly
tics.
Au
gm
en
tatio
n o
f Hu
ma
n P
erc
ep
tion
Ge
nu
ine v
isu
al a
na
lytic
s a
pp
lica
tion
s e
nco
ura
ge v
isu
al th
inkin
g b
y
lev
era
gin
g th
e p
ow
ers
of h
um
an
pe
rce
ptio
n. T
he h
um
an
bra
in
po
sse
sse
s a
n a
ma
zing
ca
pa
city
to p
roce
ss g
rap
hic
s fa
ste
r tha
n it
ca
n p
roce
ss ta
ble
s o
f nu
mb
ers
. Un
fortu
na
tely
, mo
st b
usin
ess
inte
llige
nce p
acka
ge
s a
nd
sp
rea
dsh
ee
ts d
o n
ot ta
ke fu
ll ad
va
nta
ge
of th
e b
rain
’s p
erc
ep
tive c
ap
ab
ilities. F
or in
sta
nce
, the
y u
se c
olo
r
an
d v
isu
al e
ffects
irresp
on
sib
ly, a
nd
the
y ig
no
re p
rov
en
rese
arc
h
on
dis
pla
yin
g d
ata
with
ou
t bia
s.
In a
vis
ua
l an
aly
tics a
pp
lica
tion
, pro
pe
rly v
isu
alize
d d
ata
“p
op
s.”
Fo
r ex
am
ple
, rela
tion
sh
ips, tre
nd
s a
nd
ou
tliers
sh
ow
up
brig
ht a
nd
cle
ar—
aid
ing
bo
th th
e u
se
r an
d th
e a
ud
ien
ce
alik
e.
5©
20
09 T
ab
lea
u S
oftw
are
Fig
ure
3: V
isu
al a
naly
tics le
ve
rag
es a
n im
po
rtant a
nd p
ow
erfu
l truth
ab
ou
t the
hu
man b
rain
: a p
ictu
re re
ally
is w
orth
mo
re th
an a
ll the w
ord
s a
nd n
um
be
rs
de
scrib
ing it. In
the to
p illu
stra
tion
, so
me d
ata
are
sh
ow
n in
a ty
pic
al re
po
rt or
sp
read
sh
eet. E
ve
n w
ith th
e u
se o
f co
lor (a
vis
ual c
ue), it’s
hard
to u
nd
ers
tan
d
mu
ch a
bo
ut s
ale
s a
nd p
rofi
t. Bu
t the im
ag
e b
elo
w, b
ase
d o
n th
e e
xact s
am
e d
ata
,
make
s th
e p
atte
rns a
nd m
ag
nitu
de
s c
lear a
t a g
lan
ce
.
Ho
w d
o y
ou
kn
ow
if yo
u a
re lo
okin
g a
t a tru
e v
isu
al a
na
lytic
s
ap
plic
atio
n? H
ere
are
so
me te
sts
:
vis
ua
l pro
pe
rties?
Pre
cis
e u
se o
f size
, co
lor, s
ha
pe a
nd
tex
t, for
ex
am
ple
, ma
ke a
diffe
ren
ce
. Wh
en
ha
nd
led
we
ll, the
y a
id
inte
rpre
tatio
n; w
he
n d
on
e p
oo
rly, th
ey d
istra
ct a
nd
mis
lea
d. T
he
ap
plic
atio
n s
ho
uld
co
nta
in e
ffectiv
e v
isu
al d
efa
ults
tha
t wo
uld
ma
ke v
isu
aliza
tion
ex
pe
rts lik
e E
dw
ard
Tu
fte a
nd
Ste
ph
en
Fe
w
pro
ud
.
vis
ua
l pe
rce
ptio
n; fo
r insta
nce th
ey e
mp
loy
cu
te, w
acky o
r
irresp
on
sib
le m
eta
ph
ors
like fra
cta
ls, 3
D o
bje
cts
, dia
ls a
nd
sp
ee
do
me
ters
. Vis
ua
l an
aly
tics s
oftw
are
he
lps a
mp
lify a
pe
rso
n’s
thin
kin
g in
the m
ost e
leg
an
t, co
mm
un
ica
tive a
nd
pro
ve
n
wa
ys.
6©
20
09 T
ab
lea
u S
oftw
are
for in
form
atio
n v
isu
aliza
tion
? A
co
mm
on
mis
take is
to e
mp
loy
free In
tern
et m
ap
pin
g s
erv
ice
s d
esig
ne
d fo
r ge
ne
ratin
g o
nlin
e
driv
ing
dire
ctio
ns. S
uch
ma
ps a
re in
effe
ctiv
e fo
r mo
st d
ata
vis
ua
lizatio
n n
ee
ds b
eca
use th
ey
vis
ua
lly o
ve
rwh
elm
wh
at y
ou
mo
st c
are
ab
ou
t (yo
ur d
ata
), ov
ere
mp
ha
size
roa
ds a
nd
lan
dm
ark
s, a
nd
are
ge
ne
rally
infl
ex
ible
.
Co
rne
ll Un
ive
rsity
fou
nd
ou
t wh
at a
diffe
ren
ce p
erc
ep
tion
ma
ke
s. T
he
y trie
d m
an
ag
ing
an
d m
on
itorin
g K
PIs
with
a
trad
ition
al b
usin
ess in
tellig
en
ce to
ol—
bu
t in n
ine m
on
ths
bu
sin
ess o
ffice
rs c
ou
ldn
’t ma
ke
se
nse o
f the d
ash
bo
ard
s o
r
rep
orts
tha
t the s
yste
m p
rod
uce
d. T
he
n th
ey d
ep
loye
d a
vis
ua
l
an
aly
tics s
uite
from
Ta
ble
au
. Th
ey e
na
ble
d C
orn
ell to
cre
ate
ea
sy-to
-un
de
rsta
nd
da
sh
bo
ard
s.
Vis
ua
l Ex
pre
ss
ive
ne
ss
No
asp
iring
pa
inte
r wo
uld
pu
t up
with
a p
ain
t-by
-nu
mb
ers
ca
nva
s.
Bu
t tha
t’s w
ha
t ma
ny p
rog
ram
s fo
rce
on
pe
op
le w
he
n th
ey u
se
ch
artin
g w
izard
s a
nd
da
sh
bo
ard
s. G
oo
d v
isu
al a
na
lytic
s to
ols
acco
mm
od
ate
pe
op
le’s
ne
ed
for d
ep
th, fl
ex
ibility
an
d
ex
pre
ssiv
en
ess in
the v
isu
al d
isp
lay
s.
Th
is is
esp
ecia
lly im
po
rtan
t wh
en
pe
op
le n
ee
d to
loo
k a
t mo
re th
an
two
or th
ree d
ime
nsio
ns o
f a p
rob
lem
sim
ulta
ne
ou
sly
. Ima
gin
e
pu
tting
fiv
e d
ime
nsio
ns o
f a p
rob
lem
(e.g
., Ye
ar, M
on
th, R
eg
ion
,
Pro
du
ct F
am
ily a
nd
Un
its S
old
) into
a c
ha
rting
wiza
rd: th
e re
su
lt just
do
esn
’t co
me o
ut w
ell. V
isu
al a
na
lytic
s a
pp
lica
tion
s le
t pe
op
le
vis
ua
lize m
ultip
le d
ime
nsio
ns o
f a p
rob
lem
effo
rtlessly
, in fo
rma
ts
tha
t are
ea
sy to
un
de
rsta
nd
. Wh
ere
cro
ss-ta
bs a
nd
piv
ot-ta
ble
s
ofte
n c
on
fuse a
nd
ov
erw
he
lm, m
ulti-d
ime
nsio
na
l vis
ua
lizatio
ns
cla
rify. V
isu
al a
na
lytic
s a
pp
lica
tion
s d
isp
lay c
om
ple
x p
rob
lem
s w
ith
ele
ga
nt s
imp
licity
.
Mu
ltidim
en
sio
na
l ex
pre
ssiv
en
ess is
pa
rticu
larly
imp
orta
nt w
he
n
time a
nd
ge
og
rap
hy a
re in
vo
lve
d. L
et’s
face it, tim
e a
nd
sp
ace
are
sp
ecia
l. Da
ta u
nd
er g
eo
gra
ph
ic a
na
lysis
ma
y n
ot h
on
or g
eo
gra
ph
ic
bo
un
da
ries, a
nd
the in
terfa
ce s
ho
uld
let th
e u
se
r follo
w a
ny lin
e o
f
ge
og
rap
hic
inq
uiry
. Do
es th
e a
pp
lica
tion
co
nta
in b
uilt-in
ge
og
rap
hic
inte
llige
nce th
at e
ffectiv
ely
dis
pla
ys m
ultip
le le
ve
ls o
f ge
og
rap
hic
de
tail?
Eq
ua
lly im
po
rtan
t is th
e tre
atm
en
t of tim
e d
ime
nsio
ns. H
an
dlin
g
time a
pp
rop
riate
ly is
n’t a
s s
imp
le a
s a
dd
ing
tren
d lin
es. P
eo
ple
sh
ou
ld b
e a
ble
to v
isu
ally
dis
pla
y d
ate
s a
nd
da
te/tim
es a
t mu
ltiple
lev
els
of d
eta
il sim
ulta
ne
ou
sly
. Fo
r ex
am
ple
, ex
am
inin
g s
ale
s tre
nd
s
by w
ee
kd
ay (e
.g. M
on
da
ys) a
nd
by
mo
nth
(e.g
., Ja
nu
ary
, Fe
bru
ary
,
Ma
rch
) sim
ulta
ne
ou
sly
ma
y b
e c
ritica
l to d
isco
ve
ring
an
up
tick in
ca
ffein
ate
d b
ev
era
ge s
ale
s o
n w
inte
r Mo
nd
ay m
orn
ing
s. M
an
y
sy
ste
ms d
o n
ot h
av
e th
e c
ap
ab
ility to
loo
k a
t time in
fle
xib
le, u
se
ful
wa
ys.
7©
20
09 T
ab
lea
u S
oftw
are
Fig
ure
4 G
oo
d v
isu
al e
xp
ressiv
en
ess c
ap
ab
ilities a
cco
mm
od
ate
pe
op
le’s
ne
ed
s fo
r
de
pth
an
d fl
exib
ility in
vis
ual d
isp
lays.
He
re a
re s
om
e te
sts
of a
n a
pp
lica
tion
’s m
ultid
ime
nsio
na
l
ex
pre
ssiv
en
ess:
mu
ltiple
s,”
as e
sp
ou
se
d b
y E
dw
ard
Tu
fte? O
r do
es th
e s
oftw
are
prim
arily
pro
du
ce th
e s
am
e b
asic
gra
ph
s fo
un
d in
Exce
l’s
ch
artin
g w
izard
? M
ost b
usin
ess in
tellig
en
ce a
pp
lica
tion
s
de
libe
rate
ly c
op
ied
the c
ho
ice
s fo
un
d in
Exce
l, no
t un
de
rsta
nd
ing
the lim
itatio
ns o
f tem
pla
te-d
rive
n g
rap
hic
s.
dim
en
sio
ns o
f a p
rob
lem
sim
ulta
ne
ou
sly
? U
se
rs o
ften
ne
ed
to
vis
ua
lize m
an
y d
ime
nsio
ns o
f a p
rob
lem
at o
nce
.
as te
mp
late
s th
at c
an
be a
pp
lied
to o
the
r da
ta? C
an
the
y e
asily
sh
are
the
ir vis
ua
l cu
sto
miza
tion
s?
ge
og
rap
hic
lay
ers?
Wo
rldw
ide m
ap
pin
g a
t mu
ltiple
lev
els
of
ge
og
rap
hy s
ho
uld
be p
rov
ide
d b
y d
efa
ult a
nd
req
uire
no
sp
ecia
l
da
ta p
rep
ara
tion
or g
eo
co
din
g.
sim
ulta
ne
ou
sly
?
ov
er tim
e a
t sp
ecifi
c lo
ca
tion
s?
© 2
00
9 T
ab
lea
u S
oftw
are
Qu
est D
iag
no
stic
s, w
hic
h p
rovid
es m
ed
ica
l testin
g to
larg
e h
ea
lthca
re fa
cilitie
s, fa
ce
d th
e lo
ss o
f a m
ulti-
millio
n d
olla
r clie
nt. T
est re
su
lts w
ere
co
min
g in
too
slo
wly
, the c
lien
t sa
id. T
ho
ug
h th
e Q
ue
st d
irecto
r sa
w
the s
tatis
tics d
iffere
ntly
, rep
orts
an
d c
ha
rts p
rod
uce
d
co
nve
ntio
na
lly w
ere
no
t co
nvin
cin
g. A
nd
wh
en
the
clie
nt h
ad
ne
w q
ue
stio
ns, Q
ue
st c
ou
ldn
’t resp
on
d fa
st e
no
ug
h w
ith
cle
ar, m
ea
nin
gfu
l an
aly
tics.
Ta
ble
au
, a v
isu
al a
na
lytic
s a
pp
lica
tion
, ch
an
ge
d a
ll tha
t. Th
e d
irecto
r
fle
w th
rou
gh
the v
isu
alize
d d
ata
inste
ad
of tru
dg
ing
thro
ug
h ro
ws a
nd
co
lum
ns—
an
d c
rea
ted
ne
w v
iew
s o
f clie
nt d
ata
, askin
g d
iffere
nt
qu
estio
ns a
nd
ge
tting
ne
w p
ers
pe
ctiv
es o
n Q
ue
st’s
pe
rform
an
ce
.
Eve
n a
s h
e p
rese
nte
d h
is fi
nd
ing
s to
his
clie
nt, h
is c
lien
t be
ga
n a
skin
g
ad
ditio
na
l qu
estio
ns a
nd
askin
g fo
r diffe
ren
t vis
ua
lizatio
ns. T
og
eth
er,
he a
nd
his
clie
nt v
isu
alize
d th
e te
stin
g tim
elin
e b
y te
st ty
pe, p
riority
an
d fa
cility
typ
e. T
he
y d
isco
ve
red
tha
t mu
ch
of th
e p
rob
lem
wa
s
ca
use
d b
y th
e c
lien
ts’ s
taff n
ot c
on
sis
ten
tly fo
llow
ing
pro
ce
du
res
acro
ss d
iffere
nt te
sts
, diffe
ren
t prio
rity s
tatu
se
s a
nd
facility
typ
e.
Ultim
ate
ly, th
e c
lien
t no
t on
ly s
taye
d w
ith Q
ue
st b
ut a
ye
ar la
ter
bu
cke
d th
e c
orp
ora
te m
an
da
te to
sw
itch
pro
vid
ers
.
Au
tom
atic
Vis
ua
liza
tion
Ima
gin
e a
n a
pp
lica
tion
tha
t tells
yo
u h
ow
yo
u s
ho
uld
loo
k a
t the
sp
ecifi
c p
rob
lem
yo
u h
av
e. F
or to
o lo
ng
, an
aly
sts
ha
ve b
ee
n ta
ug
ht
to th
ink in
nu
mb
ers
alo
ne
. A v
isu
al a
na
lytic
s a
pp
lica
tion
jum
psta
rts
the a
na
lysis
pro
ce
ss its
elf. T
his
inclu
de
s a
uto
ma
tica
lly s
ug
ge
stin
g
effe
ctiv
e v
isu
aliza
tion
s.
Fig
ure
5: A
uto
matic
vis
ualiza
tion n
ot o
nly
he
lps p
eo
ple
fin
d p
atte
rns e
asily
bu
t
als
o tra
ins th
em
to th
ink v
isu
ally
an
d m
ore
rap
idly.
9©
20
09 T
ab
lea
u S
oftw
are
10
© 2
00
9 T
ab
lea
u S
oftw
are
A k
ey b
en
efi
t of a
uto
ma
tic v
isu
aliza
tion
is n
ot ju
st th
at it re
du
ce
s
wo
rk tim
e. It a
lso
he
lps p
eo
ple
lea
rn to
thin
k v
isu
ally
. If the
y c
an
thin
k in
pic
ture
s, th
ey c
an
wo
rk fa
ste
r an
d re
ca
ll tren
ds a
nd
pa
ttern
s
mo
re e
asily
. He
re is
the e
sse
ntia
l thin
g to
loo
k fo
r:
mo
st a
pp
rop
riate
gra
ph
ica
l de
pic
tion
ba
se
d o
n th
e s
ele
cte
d d
ata
ele
me
nts?
In o
the
r wo
rds, th
e a
uto
ma
tic v
isu
aliza
tion
sh
ou
ld n
ot
just b
e a
ran
do
m s
ele
ctio
n b
ut s
ho
uld
be
ba
se
d o
n h
eu
ristic
s.
Sa
nD
ieg
o.c
om
is a
n o
nlin
e g
uid
e to
Sa
n D
ieg
o’s
ne
igh
bo
rho
od
s, e
ve
nts
an
d a
ttractio
ns. T
he v
olu
me
s o
f
traffi
c d
ata
we
nt u
nu
se
d b
eca
use th
e C
EO
did
n’t h
ave tim
e
or e
xp
ertis
e to
an
aly
ze it. W
ith T
ab
lea
u’s
au
tom
atic
vis
ua
lizatio
n c
ap
ab
ilities—
ca
lled
“S
ho
w M
e”—
the C
EO
wa
s
ab
le to
ma
ke im
me
dia
te d
isco
ve
ries a
bo
ut th
e s
ite’s
traffi
c.
Vis
ua
l Pe
rsp
ec
tive
-Sh
ifting
Th
ere
is n
ev
er a
sin
gle
vis
ua
lizatio
n th
at o
ffers
the b
est s
um
ma
ry o
f
ev
ery
fin
din
g in
yo
ur d
ata
. Ty
pic
ally
pe
op
le n
ee
d to
loo
k a
t a v
arie
ty
of v
isu
aliza
tion
s, d
ep
en
din
g o
n th
e ta
sks y
ou
wa
nt to
ach
iev
e.
Effe
ctiv
e v
isu
al a
na
lytic
s a
pp
lica
tion
s s
ho
uld
su
gg
est a
se
ries o
f
alte
rna
tive v
isu
aliza
tion
s w
hic
h c
an
be e
ffortle
ssly
flip
pe
d th
rou
gh
.
Fo
r ex
am
ple
, if yo
u’re
tryin
g to
fin
d o
utlie
rs, lo
ok a
t a s
ca
tterp
lot.
Try
ing
to u
nd
ers
tan
d tim
e-b
ase
d tre
nd
s in
the
da
ta? T
he
n a
line
or
Ga
ntt c
ha
rt mig
ht b
e id
ea
l. Try
ing
to u
nd
ers
tan
d m
ulti-d
ime
nsio
na
l
ge
og
rap
hic
va
riatio
n? T
ry a
sm
all m
ultip
le o
f ma
ps. N
o o
ne v
iew
ca
n
an
sw
er a
ll qu
estio
ns.
His
tory
ha
s s
ho
wn
tha
t loo
kin
g a
t da
ta fro
m th
e rig
ht p
ers
pe
ctiv
e is
as im
po
rtan
t as lo
okin
g a
t the rig
ht d
ata
in th
e fi
rst p
lace
. Ph
ysic
ist
Ric
ha
rd F
ey
nm
an
fam
ou
sly
sh
ow
ed
tha
t en
gin
ee
rs w
ork
ing
on
the
Sp
ace S
hu
ttle C
ha
llen
ge
r ha
d in
fron
t of th
em
all o
f the d
ata
ne
ed
ed
to c
on
clu
de th
at la
un
ch
ing
the
Sh
uttle
at lo
w te
mp
era
ture
s p
ose
d
un
pre
ce
de
nte
d ris
ks. O
ne o
f the
ir failin
gs w
as th
at th
ey n
ev
er
plo
tted
the d
ata
in th
e m
ost re
ve
alin
g w
ay.
Th
is w
as w
ell d
ocu
me
nte
d b
y E
dw
ard
Tu
fte in
his
bo
ok V
isu
al
Ex
pla
na
tion
s: Im
ag
es a
nd
Qu
an
tities, E
vid
en
ce a
nd
Na
rrativ
e.
Sh
ifting
vis
ua
l pe
rsp
ectiv
es o
n a
pro
ble
m is
als
o a
gre
at w
ay to
ge
ne
rate
ne
w q
ue
stio
ns. It jo
stle
s th
e b
rain
a little
an
d m
ake
s y
ou
mo
re c
urio
us a
bo
ut w
ha
t is a
ctu
ally
go
ing
on
in th
e d
ata
.
Fig
ure
6: D
iffere
nt v
isu
aliza
tion
s a
nsw
er d
iffere
nt q
ue
stio
ns a
nd s
olv
e d
iffere
nt
issu
es. A
bove w
e s
ee th
e s
am
e d
ata
set v
isu
alize
d in
mu
ltiple
ways. E
ach
pe
rsp
ectiv
e a
nsw
ers
diffe
rent q
ue
stio
ns a
nd h
igh
lights
diffe
rent p
atte
rns. V
isu
al
an
aly
tics le
ts y
ou m
ove fro
m o
ne p
ers
pe
ctiv
e to
an
oth
er w
ith a
clic
k.
Fo
r the
se re
aso
ns, v
isu
al a
na
lytic
s a
pp
lica
tion
s c
on
tain
stro
ng
su
pp
ort fo
r pe
rsp
ectiv
e s
hiftin
g. H
ere
are
so
me
qu
alitie
s to
loo
k fo
r:
insta
ntly
? In
just a
few
clic
ks, a
pe
rso
n s
ho
uld
be
ab
le to
mo
ve
from
vie
w to
vie
w u
ntil s
he
fin
ds e
xa
ctly
the rig
ht v
iew
for th
e
qu
estio
n a
t ha
nd
.
da
ta w
ith a
clic
k?
sim
ulta
ne
ou
sly
?
Pe
op
le o
n th
e lo
oko
ut fo
r use
ful in
form
atio
n v
isu
aliza
tion
s lik
e to
fora
ge fre
ely
in d
ata
. So
the la
st th
ing
pe
op
le n
ee
d is
a to
ol th
at
co
nfi
ne
s th
em
to a
sin
gle
, line
ar p
ath
. An
infl
ex
ible
too
l cre
ate
s a
da
tase
t an
d a
ch
art a
nd
tries to
stic
k w
ith it. A
vis
ua
l an
aly
tics
ap
plic
atio
n in
ste
ad
offe
rs d
irect a
cce
ss to
a m
yria
d o
f vis
ua
lizatio
ns,
with
no
bo
un
da
ries.
An
aly
sts
at T
he M
artin
Ag
en
cy
’s In
ge
nu
ity M
ed
ia G
rou
p
sifte
d th
rou
gh
mo
un
tain
s o
f da
ta fro
m in
tera
ctiv
e m
ed
ia
ca
mp
aig
ns to
pro
du
ce p
erio
dic
rep
orts
an
d d
ash
bo
ard
s
for c
lien
ts. P
rod
uctio
n to
ok h
ou
rs. A
na
lysts
on
ly h
ad
time
to c
rea
te w
ere
sta
tic, s
tan
da
rd c
ha
rts, w
ith n
o tim
e to
fin
d n
ew
insig
hts
. Bu
t with
Ta
ble
au
’s p
ers
pe
ctiv
e s
hiftin
g fe
atu
res, a
na
lysts
co
uld
loo
k a
t mu
ltiple
pe
rsp
ectiv
es o
f the
ir clie
nts
’ da
ta a
nd
rap
idly
fin
d th
e m
ost m
ea
nin
gfu
l on
e. It o
pe
ne
d a
ne
w w
orld
of e
xp
lora
tion
an
d d
isco
ve
ry.
11
© 2
00
9 T
ab
lea
u S
oftw
are
Vis
ua
l Pe
rsp
ec
tive
Lin
kin
g
A lo
gic
al b
ut p
ow
erfu
l ad
ditio
n to
pe
rsp
ectiv
e s
hiftin
g is
pe
rsp
ectiv
e
linkin
g. A
ltho
ug
h th
e tw
o to
pic
s a
re re
late
d, lin
kin
g e
nta
ils a
diffe
ren
t se
t of c
ap
ab
ilities th
an
pe
rsp
ectiv
e s
hiftin
g. In
sh
ort, it is
n’t
en
ou
gh
to lo
ok a
t mu
ltiple
pe
rsp
ectiv
es o
n a
pro
ble
m in
rap
id
su
cce
ssio
n –
or e
ve
n s
imu
ltan
eo
usly
. So
me
time
s th
e p
ers
pe
ctiv
es
ne
ed
to b
e in
tima
tely
linke
d. O
ne v
isu
aliza
tion
ma
y d
isp
lay a
se
t of
ou
tliers
, for in
sta
nce
. Ca
n a
pe
rso
n s
ele
ct a
n o
utlie
r an
d in
sta
ntly
se
e a
no
the
r vis
ua
lizatio
n th
at d
isp
lay
s g
rea
ter d
eta
il? A
s a
n
ex
am
ple
, a p
ers
on
ma
y n
otic
e th
at s
ale
s fo
r a p
artic
ula
r sta
te s
ee
ms
to b
e d
om
ina
ting
. By c
lickin
g o
n th
e m
ark
rep
rese
ntin
g th
at s
tate
’s
sa
les, h
e c
an
insta
ntly
up
da
te a
vis
ua
lizatio
n re
ga
rdin
g s
ale
s
am
ou
nt b
y c
om
pa
ny a
nd
se
e a
no
the
r vis
ua
lizatio
n re
dra
w a
line
ch
art s
ho
win
g s
ale
s b
y d
ate
usin
g d
ata
for ju
st th
at s
tate
. Th
at
inte
ractio
n m
ay s
he
d lig
ht o
n w
ha
t is d
rivin
g s
ale
s in
tha
t sta
te to
be
do
min
an
t.
Fig
ure
7 In
a v
isu
al a
naly
tics a
pp
licatio
n, y
ou c
an u
se p
ers
pe
ctiv
e lin
kin
g to
dis
cove
r rela
tion
sh
ips in
data
or to
un
cove
r hid
de
n s
torie
s. In
this
exam
ple
, a u
se
r
se
lects
a s
tate
on th
e m
ap (C
alifo
rnia
). Tab
leau th
en in
sta
ntly
reve
als
wh
ich d
ata
in th
e o
the
r vis
ualiza
tion
s a
re re
late
d.
12
© 2
00
9 T
ab
lea
u S
oftw
are
Ge
nu
ine v
isu
al a
na
lytic
s a
pp
lica
tion
s s
up
po
rt pe
rsp
ectiv
e lin
kin
g in
se
ve
ral w
ay
s. H
ere
are
so
me fe
atu
res to
eva
lua
te:
vis
ua
lizatio
ns?
Fo
r insta
nce
, ca
n u
se
rs s
ele
ct a
tren
d lin
e in
a
firs
t vie
w a
nd
se
e th
e g
eo
gra
ph
ic e
ntitie
s re
late
d to
tha
t line in
a
se
co
nd
vie
w?
ca
n p
eo
ple
use a
firs
t vis
ua
lizatio
n to
se
arc
h fo
r da
ta o
f inte
rest,
an
d th
en
au
tom
atic
ally
ap
ply
the s
ele
cte
d d
ata
as a
filte
r on
a
se
co
nd
vis
ua
lizatio
n?
ho
ve
ring
ov
er d
ata
?
pro
gra
mm
ing
? In
a v
isu
al a
na
lytic
s a
pp
lica
tion
, rich
da
ta
asso
cia
tion
s s
ho
uld
be d
isco
ve
red
au
tom
atic
ally
.
Co
llab
ora
tive
Vis
ua
liza
tion
An
oth
er d
efi
nin
g c
ap
ab
ility o
f effe
ctiv
e v
isu
al a
na
lytic
s a
pp
lica
tion
s
is th
e a
bility
to ite
rativ
ely
cre
ate
use
ful in
form
atio
n v
isu
aliza
tion
s in
a te
am
se
tting
. Th
is p
roce
ss u
su
ally
sta
rts w
ith a
“h
ey, lo
ok a
t this
”
mo
me
nt. B
ut th
e re
al q
ue
stio
n is
: Do
es th
e s
oftw
are
su
pp
ort th
e
invo
lve
d c
olla
bo
rativ
e p
roce
ss th
at s
ho
uld
follo
w th
ese m
om
en
ts?
Sh
are
d fi
nd
ing
s le
ad
to s
olu
tion
s, a
ctio
n, a
nd
resu
lts. In
fact, in
mo
st o
rga
niza
tion
s u
nsh
are
d d
isco
ve
ries a
re u
se
less. S
om
e
so
ftwa
re p
acka
ge
s, w
hile
me
etin
g o
the
r crite
ria, fa
il he
re. E
ffectiv
e
vis
ua
l an
aly
tics s
oftw
are
en
co
ura
ge
s c
olla
bo
ratio
n b
y le
tting
resu
lts
be s
ha
red
in w
ha
tev
er fo
rm th
e u
se
r pre
fers
. Th
e a
pp
lica
tion
’s
arc
hite
ctu
re s
ho
uld
be b
uilt e
xp
licitly
for c
olla
bo
ratio
n.
He
re a
re s
om
e o
f so
me s
pe
cifi
c fe
atu
res to
ev
alu
ate
:
se
co
nd
s? V
isu
aliza
tion
s s
ho
uld
be a
va
ilab
le v
ia a
bro
wse
r. Th
ey
sh
ou
ld b
e liv
e a
nd
ch
an
ge a
s d
ata
ch
an
ge
s.
role
or g
rou
p o
f the p
artic
ula
r info
rma
tion
co
nsu
me
rs? In
ad
ditio
n, w
ill pe
op
le w
ho
are
vie
win
g o
r inte
ractin
g w
ith th
e
vis
ua
lizatio
ns h
av
e th
e a
bility
to c
usto
mize
the
ir pa
rticu
lar
ch
an
ge
s to
it an
d s
av
e th
at v
iew
?
with
it all?
Th
e a
pp
lica
tion
sh
ou
ld s
up
po
rt Go
og
le-s
tyle
se
arc
h
tha
t en
ab
le g
rou
ps to
loca
te v
isu
aliza
tion
s q
uic
kly
.
cu
sto
mizin
g a
nd
sh
arin
g v
isu
aliza
tion
s a
nd
da
sh
bo
ard
s?
13
© 2
00
9 T
ab
lea
u S
oftw
are
an
d d
ata
mo
de
ls? W
ho
le d
ata
mo
de
ls (o
r ind
ivid
ua
l ca
lcu
latio
ns,
de
fin
ed
gro
up
s, e
tc.) s
ho
uld
be
ea
sily
sh
are
d a
nd
ve
rsio
n
co
ntro
lled
.
into
oth
er a
pp
lica
tion
s a
nd
po
rtals?
Fig
ure
8: In
this
illustra
tion
, pe
op
le s
hare
key fi
nd
ing
s fro
m th
e v
isu
al a
naly
tics
syste
m w
ith c
olle
ag
ue
s u
sin
g S
hare
po
int. E
ve
n th
ou
gh th
e re
su
lts a
re e
mb
ed
de
d,
pe
op
le c
an s
lice a
nd d
ice th
e d
ata
usin
g c
he
ck-b
ox fi
lters
, so
rt, vie
w u
nd
erly
ing
data
, zoo
m in
to s
pe
cifi
c p
oin
ts a
nd e
ve
n v
iew
the u
nd
erly
ing fi
nd
ing
s.
14
© 2
00
9 T
ab
lea
u S
oftw
are
De
plo
ym
en
t Co
ns
ide
ratio
ns
Th
is p
ap
er h
as d
escrib
ed
the s
ev
en
de
fin
ing
ele
me
nts
of v
isu
al
an
aly
tics a
pp
lica
tion
s. Y
et th
ere
are
oth
er fa
cto
rs to
co
nsid
er b
efo
re
ma
kin
g a
ch
oic
e o
n a
vis
ua
l an
aly
tics s
tan
da
rd. F
or in
sta
nce
, if the
vis
ua
l an
aly
tics a
pp
lica
tion
is g
oin
g to
be u
se
d in
a c
orp
ora
te
en
viro
nm
en
t, the
re a
re te
ch
nic
al a
nd
infra
stru
ctu
re c
on
sid
era
tion
s
as w
ell.
Un
ive
rsa
l Da
ta A
cce
ss. D
ata
co
me
s fro
m a
ll dire
ctio
ns. D
oe
s th
e
so
ftwa
re c
on
ne
ct to
virtu
ally
an
y s
ou
rce
, from
da
ta w
are
ho
use
s to
Exce
l or te
xt fi
les?
Do
es it c
on
ne
ct to
all m
ajo
r da
ta fo
rma
ts,
inclu
din
g re
latio
na
l da
tab
ase
s, O
LA
P d
ata
cu
be
s a
nd
fla
t file
s?
Fu
rthe
rmo
re, is
co
nn
ectin
g to
ne
w d
ata
so
urc
es e
asily
do
ne?
Sca
lab
ility. D
oe
s th
e s
oftw
are
su
pp
ort re
al-tim
e in
tera
ctiv
e
vis
ua
lizatio
n o
f da
ta o
f ne
arly
an
y s
ize—
ev
en
millio
ns o
r billio
ns o
f
reco
rds?
Th
e a
pp
lica
tion
sh
ou
ld b
e a
ble
to h
an
dle
larg
e a
mo
un
ts o
f
da
ta a
nd
pro
vid
e s
olid
pe
rform
an
ce
.
Ge
ne
rate
s E
fficie
nt S
QL fo
r DB
MS
. Re
po
rts s
ho
uld
ge
ne
rate
qu
ickly
.
Pe
op
le s
ho
uld
ha
ve th
e o
ptio
n to
take
, mo
dify
an
d ru
n S
QL fro
m th
e
rep
orts
an
d re
pro
du
ce th
e re
su
lts. T
he
y s
ho
uld
als
o b
e a
ble
co
nn
ect
to S
QL s
tate
me
nts
rath
er th
an
actu
al d
ata
tab
les a
nd
vie
ws.
Ab
ility to
Jo
in T
ab
les. P
eo
ple
sh
ou
ld b
e a
ble
to jo
in ta
ble
s e
asily
in
wa
ys th
at g
uid
e th
em
to a
pp
rop
riate
an
d w
ell-d
esig
ne
d jo
ins. T
ab
les
ca
n b
e jo
ine
d a
uto
ma
tica
lly o
r ba
se
d o
n u
se
r’s d
irectio
n.
Se
cu
rity. D
oe
s th
e a
pp
lica
tion
ha
ve a
full s
ecu
rity m
od
ule
to s
up
po
rt
co
llab
ora
tion
with
in e
xis
ting
pe
rmis
sio
n s
yste
ms?
Th
is s
ho
uld
inclu
de o
ptio
na
l inte
gra
tion
with
oth
er s
ecu
rity m
eth
od
s lik
e A
ctiv
e
Dire
cto
ry.
Min
ima
l IT S
up
po
rt. Go
od
so
ftwa
re fre
es IT
from
the s
ma
ll stu
ff.
Go
od
so
ftwa
re in
sta
lls e
asily
, ge
ts th
e a
ve
rag
e u
se
r up
in m
inu
tes
with
ou
t he
lp, a
nd
pro
vid
es fre
e tra
inin
g o
n d
em
an
d.
Mic
roso
ft Offi
ce C
om
pa
tibility
. MS
Offi
ce is
ub
iqu
itou
s o
n th
e
bu
sin
ess p
ers
on
’s d
eskto
p. D
oe
s th
e s
oftw
are
pro
vid
e g
ate
wa
ys in
an
d o
ut o
f MS
Offi
ce? C
an
use
rs n
ativ
ely
acce
ss d
ata
from
Offi
ce
ap
plic
atio
ns?
Ca
n th
ey o
utp
ut im
ag
es, ta
ble
s, d
ata
lists
, cro
ss-ta
bs
ea
sily
an
d d
irectly
to O
ffice?
Da
ta M
od
elin
g a
nd
Ma
na
ge
me
nt. C
an
use
rs s
ha
re e
ntire
da
ta
mo
de
ls u
se
d to
co
nstru
ct v
isu
aliza
tion
s—
alo
ng
with
ind
ivid
ua
l
15
© 2
00
9 T
ab
lea
u S
oftw
are
ca
lcu
latio
ns, g
rou
ps, e
tc? U
se
rs s
ho
uld
be a
ble
to re
ly o
n
dim
en
sio
ns a
nd
me
asu
res th
at a
re c
on
sis
ten
tly d
efi
ne
d a
nd
ha
ve
da
ta in
teg
rity. C
an
the s
oftw
are
allo
w th
e u
se
r to m
od
el d
ata
–- s
uch
as to
mo
dify
va
riab
le ty
pe
s, re
plic
ate
va
riab
les, c
ha
ng
e fi
eld
na
me
s,
sta
nd
ard
ize d
ime
nsio
ns -- w
itho
ut d
ev
elo
pe
r su
pp
ort?
Th
is is
critic
al
for g
ivin
g u
se
rs th
e a
na
lytic
al p
ow
er w
hile
als
o re
du
cin
g th
e IT
bu
rde
n.
Fig
ure
9 T
ab
leau D
eskto
p a
nd T
ab
leau S
erv
er c
an b
oth
be d
ep
loye
d in
min
ute
s.
Tab
leau’s
softw
are
arc
hite
ctu
re is
bu
ilt to m
ake c
olla
bo
rativ
e a
naly
tics e
asy to
de
plo
y a
nd s
up
po
rt. Au
tho
rs p
ub
lish liv
e, in
tera
ctiv
e d
ata
vis
ualiza
tion
s, re
po
rts
an
d d
ash
bo
ard
s to
Tab
leau S
erv
er w
he
re p
eo
ple
use w
eb b
row
se
rs a
nd w
eb
ap
plic
atio
n.
Tra
inin
g. H
ow
mu
ch
is n
ee
de
d? Id
ea
lly p
eo
ple
ca
n g
et s
tarte
d
with
ou
t an
y tra
inin
g –
the u
se
r inte
rface s
ho
uld
be o
bv
iou
s a
nd
ea
sy
to u
se
. Is th
ere
free o
nlin
e tra
inin
g a
va
ilab
le a
ny tim
e fo
r mu
ltiple
lev
els?
As p
eo
ple
ad
va
nce in
the
ir vis
ua
l an
aly
tic s
kills
, mo
re
so
ph
istic
ate
d tra
inin
g s
ho
uld
be a
va
ilab
le o
n th
eir tim
e s
ch
ed
ule
ba
se
d o
n th
eir n
ee
ds.
Lic
en
sin
g. L
ice
nsin
g m
od
els
sh
ou
ld b
e fl
ex
ible
ba
se
d o
n u
se
r ne
ed
s
with
ou
t min
imu
m c
on
fig
ura
tion
s: b
uy
1 lic
en
se
, bu
y 1
0, b
uy 1
00,
bu
y 1
00
0.
16
© 2
00
9 T
ab
lea
u S
oftw
are
Fu
rth
er R
ea
din
g o
n V
isu
al A
na
lytic
s1. J
.J. T
ho
ma
s a
nd
K.A
. Co
ok, Illu
min
atin
g th
e P
ath
, IEE
E C
S P
ress,
20
05
.
2. S
tua
rt Ca
rd, J
ock M
ackin
lay, B
en
Sch
ne
ide
rma
n, R
ea
din
gs in
Info
rma
tion
Vis
ua
lizatio
n: U
sin
g V
isio
n to
Th
ink, M
org
an
Ka
ufm
an
n, 1
99
9.
3. T
ufte
, E. R
., Th
e V
isu
al D
isp
lay o
f Qu
an
titativ
e In
form
atio
n,
Gra
ph
ics P
ress, 2
001.
4. S
tep
he
n F
ew
, Pe
rce
ptu
al E
dg
e, Im
pro
ve
Yo
ur V
isio
n a
nd
Ex
pa
nd
Yo
ur M
ind
with
Vis
ua
l An
aly
tics, w
hite
pa
pe
r.
5. S
tep
he
n F
ew
, Sh
ow
Me th
e N
um
be
rs: D
esig
nin
g T
ab
les a
nd
Gra
ph
s to
En
ligh
ten
, An
aly
tics P
ress, 2
00
4.
6. J
acq
ue
s B
ertin
, Th
e S
em
iolo
gy o
f Gra
ph
ics, O
ut o
f prin
t, 19
67.
7. D
on
ald
A. N
orm
an
, Th
e P
sy
ch
olo
gy o
f Ev
ery
da
y T
hin
gs, B
asic
Mo
rga
n K
au
fma
nn
Pu
blis
he
rs, 2
00
0.
An
aly
sis
, an
d V
isu
aliza
tion
of M
ultid
ime
nsio
na
l Da
tab
ase
s, C
AC
M,
11. C
ha
bo
t, C., D
em
ystify
ing
Vis
ua
l An
aly
tics, IE
EE
/VA
ST
ba
se
d o
n
“P
ractic
al A
pp
lica
tion
s o
f Vis
ua
l An
aly
tics: O
n th
e C
usp
of
Vis
ua
l An
aly
tics S
cie
nce a
nd
Te
ch
no
log
y.
12
. Jo
ck D
. Ma
ckin
lay, P
at H
an
rah
an
, an
d C
hris
Sto
lte, S
ho
w M
e:
Au
tom
atic
Pre
se
nta
tion
for V
isu
al A
na
lysis
, wh
itep
ap
er.
13
. Pa
t Ha
nra
ha
n, C
hris
Sto
lte a
nd
Jo
ck M
ackin
lay, V
isu
al A
na
lysis
for E
ve
ryo
ne - U
nd
ers
tan
din
g D
ata
Ex
plo
ratio
n a
nd
Vis
ua
lizatio
n, w
hite
pa
pe
r.
17
“T
ab
lea
u” a
nd
“T
ab
lea
u S
oftw
are
,” a
re re
gis
tere
d tra
de
ma
rks o
f Ta
ble
au
So
ftwa
re.
Re
fere
nce
s to
oth
er c
om
pa
nie
s a
nd
the
ir pro
du
cts
use tra
de
ma
rks o
wn
ed
by th
e re
sp
ectiv
e
co
mp
an
ies a
nd
are
for re
fere
nce p
urp
ose
s o
nly
.C
op
yrig
ht ©
20
09 T
ab
lea
u S
oftw
are
, Inco
rpo
rate
d a
nd
its lic
en
so
rs. A
ll righ
ts re
se
rve
d.
