Embed Size (px)
Citation preview
Sav
ing $
$$ for
Continued
H
IPAA C
om
plia
nce
with
Dat
a M
art
Conso
lidat
ion
and A
ctiv
e D
ata
War
ehousi
ng
6th
National
HIP
AA S
um
mit
Mar
ch 2
8,
2003
Jeri
Ost
lin
gJe
ri O
stlin
gSen
ior
Hea
lth C
are
Consu
ltan
tTer
adat
a D
ivis
ion,
NCR
Ag
en
da
•G
art
ner�
s A
rch
itect
ure
for
HIP
AA
Co
mp
lian
ce
•C
on
cep
ts>
Data
Mart
Co
nso
lid
ati
on
>A
ctiv
e D
ata
Ware
ho
usi
ng
•A
pp
lica
tion
of
these
con
cep
ts t
o C
on
tin
ued
HIP
AA
C
om
plian
ce
•A
Healt
hca
re C
ust
om
er
•Q
uest
ion
s
Gart
ner�
s W
rap
, M
ap
&
Hack
Str
ate
gy
•�W
rap-M
ap-H
ack�
tec
hniq
ue
�lim
its
the
exte
nt
to w
hic
h
applic
ation
s nee
d t
o be
renova
ted�
•This
mod
el p
rovi
des
�sh
ort-
term
res
ults
for
a re
lative
ly
mod
erat
e le
vel of
inve
stm
ent,
while
kee
pin
g futu
re o
ption
s op
en�
•The
appro
ach m
ost
HCO
are
taki
ng
•Key
com
pon
ents
incl
ude
tech
nol
ogy
to m
ap (
transl
ato
r)
inte
gra
te (
applic
atio
n inte
gra
tor)
and p
rovi
de
an o
per
atio
nal
data
sto
re (
data
base
)
Gart
ner�
s W
rap
, M
ap
&
Hack
Str
ate
gy
Arc
hit
ect
ure
s fo
r H
IPA
A C
om
plian
ceb
y G
art
ner
Arc
hit
ect
ure
s fo
r H
IPA
A C
om
plian
ceb
y G
art
ner
Act
ive
Dat
aW
areh
ouse
Ag
en
da
•D
ata
Mart
Co
nso
lid
ati
on
�A s
et
of
data
mart
s ca
nn
ot
deliver
the
sam
e b
en
efi
ts a
s a d
ata
ware
ho
use
, an
d
will b
e m
ore
co
stly
, m
ore
dif
ficu
lt t
o
man
ag
e a
nd
less
fle
xib
le.�
Gar
tner
Gro
up:
�The
Pac
kage
d D
ata
War
ehou
se M
yth�
, R
esea
rch
Not
e, 2
May
200
1.
Th
e p
rob
lem
...
�En
terp
rise
s in
itia
tin
g d
ata
mart
con
solid
ati
on
eff
ort
s in
20
02
th
at
use
a q
uality
meth
od
olo
gy
an
d a
pp
lica
tion
-neu
tral d
ata
ware
hou
se
imp
lem
en
tati
on
s w
ill exp
eri
en
ce:
-a d
ecl
ine in
sp
en
din
gof
at
least
50
%an
d-
an
in
crease
in
bu
sin
ess
valu
eof
at
least
50
0%
by 2
00
4 (
0.7
pro
bab
ilit
y).
1�
-K
evin
Str
an
ge,
VP
, G
art
ner
Gro
up
Data
Mart
Co
nso
lid
ati
on
1�D
ata
Mart
Con
solid
ati
on
: S
tren
gth
en
ing
Tre
nd
of
20
02
, M
arc
h 2
00
2�
•Str
ength
enin
g T
rend o
f 2002
Data
Ware
ho
usi
ng
Ope
ratio
nal D
ata
Dat
a Tr
ansf
orm
atio
n
DW/D
SS U
sers
Ente
rpris
eW
areh
ouse
&M
anag
emen
t
CU
STO
MER
CU
STO
MER
NU
MBE
RC
UST
OM
ER N
AME
CU
STO
MER
CIT
YC
UST
OM
ER P
OST
CU
STO
MER
ST
CU
STO
MER
AD
DR
CU
STO
MER
PH
ON
EC
UST
OM
ER F
AX
OR
DER
OR
DER
NU
MBE
RO
RD
ER D
ATE
STAT
US
OR
DER
ITEM
BAC
KOR
DER
EDQ
UAN
TITY
ITEM
ITEM
NU
MBE
RQ
UAN
TITY
DES
CR
IPTI
ON
OR
DER
ITEM
SH
IPPE
DQ
UAN
TITY
SHIP
DAT
E
Det
ail,
Nor
mal
ized
D
ata
Laye
r
Logi
cal D
ata
Mar
t La
yer
Dat
a Tr
ansf
orm
atio
n La
yer
Insi
de T
he D
W
=
DM
Support
Cost
s1+
++
. .
.
$$
$ B
ig D
ollars
DM
Support
Cost
s2D
MSupport
Cost
s3D
MSupport
Cost
sn
Co
st D
rivers
fo
r R
etu
rn o
n I
nvest
men
t
Real V
alu
e t
hro
ug
h D
MC
$
Tim
e
Bu
sin
ess
Ben
efi
tC
ost
Red
uct
ion
Key
Th
e R
eal
Valu
ein
Con
soli
dati
ng
Data
Mart
s
New
Busi
nes
sCap
abili
ties
Cost
Red
uct
ion
Har
dwar
eLi
cens
e fe
es, U
pgra
des,
Mai
nten
ance
agr
eem
ents
, Ex
tern
al c
usto
mer
sup
port,
Dep
reci
atio
n, #
of F
TEs,
D
ata
Cen
ter,
Dep
artm
ent
Softw
are
Lice
nse
fees
, Upg
rade
s, M
aint
enan
ce a
gree
men
ts,
Exte
rnal
cus
tom
er s
uppo
rt, #
of F
TEs,
Dat
a C
ente
r, D
epar
tmen
t N
etw
ork
Har
dwar
e, S
oftw
are,
# o
f FTE
s, D
ata
Cen
ter,
Dep
artm
ent
Extr
act/T
rans
form
/Loa
d (F
TEs)
Exec
ute/
mon
itor,
Mai
ntai
n/m
odify
for b
oth
Ope
ratio
nal t
o D
SS a
nd D
SS to
ana
lysi
s pl
atfo
rmD
atab
ase
Supp
ort (
FTEs
)ET
L D
BAs,
Acc
ss/Q
ry T
unin
g D
BAs,
Bus
ines
s R
equi
rem
ents
(DBA
s)B
usin
ess
Supp
ort (
FTEs
)R
equi
rem
ents
sup
port
-Def
initi
on,
Syst
em M
odifi
catio
ns, D
ata
qual
ity/c
lean
sing
Ana
lytic
al T
ool S
uppo
rtSo
ftwar
e Pu
rcha
se, S
oftw
are
Lice
nse
Fees
, Sup
port
Non
-dire
ct c
osts
Rea
l Est
ate
Allo
catio
ns, T
rain
ing
Syst
ems
Dat
a Sy
nchr
oniz
atio
n
Th
ink a
bo
ut
the c
ost
s...
Bu
sin
ess
Im
pact
Mo
del
Str
ate
gy
Drive
cre
atio
n o
f th
e busi
nes
s ca
se t
o
show
dec
isio
n m
aker
s th
at d
ata
mar
t co
nso
lidat
ion f
or
continued
HIP
AA
com
plia
nce
mak
es
finan
cial
sen
se.
The
focu
s is
prim
arily
on t
he
num
ber
s.
•D
ata
red
undancy
•D
ata
lat
ency
•D
ata
inco
nsi
sten
cy•
Busi
nes
s ru
les
are
difficu
lt t
o m
anag
e•
No
com
mon
met
adat
a•
Cos
t of
data
mov
emen
t•
Cos
t of
data
syn
chro
niz
ation
•Cost
of sy
stem
adm
inis
trat
ion
•Cost
of sy
stem
mai
nte
nan
ce
Focu
s
Bu
sin
ess
Im
pact
Mo
del -
Data
Mart
C
on
solid
ati
on
Iden
tifi
cati
on
of
Ineff
icie
nci
es
Bu
sin
ess
Im
pact
Mo
del -
Data
Mart
C
on
solid
ati
on
Today
�s I
nef
fici
ency
Vs.
Tom
orr
ow
�s P
rom
ise
The
DM
C b
usi
nes
s ca
se
iden
tifies
and c
ompar
es
IT c
ost
s as
they
exi
st
today
...
and w
hat
they
are
pro
ject
ed
to b
e to
morr
ow..
.
incl
usi
ve o
f th
e id
entifica
tion
and m
itig
ation
of th
e ri
sks
asso
ciat
ed w
ith d
eplo
ymen
t.
Exam
ple
of
Data
Mart
Co
nso
lid
ati
on
Co
nso
lid
ati
ng
22
Data
Mart
sT
imefr
am
e =
8 M
on
ths
Sta
rted
Mid
No
vem
ber
�01
Cost
to c
onso
lidat
e =
$2
5 M
illio
nSavi
ngs
within
Sam
e Per
iod =
$2
4 M
illi
on
Suppor
t Cost
s:$
9.0
Mper
Quar
ter
(22 D
ata
Mar
ts)
$3
.0M
per
Quar
ter
(New
Con
solid
ated
Sys
tem
)$
6.0
M S
avin
gs
per
Qu
art
er
$6
.0M
Savin
gs
per
Qu
art
er
56
Mo
re
Data
Mart
s P
oss
ible
...
Ban
k o
f A
meri
ca
�Sin
ce t
ransf
orm
ing t
o a s
ingle
data
ware
hou
se p
latf
orm
, Bank
of Am
eric
a has
dro
pped
its
oper
atin
g c
ost
s fr
om
$1
1
million
to
$4
milli
on
per
year�
�In t
he
past
, yo
u c
ould
ask
fou
r peo
ple
one
ques
tion
and g
et
four
diffe
rent
answ
ers
bec
ause
all
wer
e usi
ng d
iffe
rent
sets
of
data
.�
-R
uss
Vau
gh
nSen
ior
VP,
Tec
hnolo
gy
and O
per
atio
ns
Ban
k of
Am
eric
a
64
% R
ed
uct
ion
in
co
sts
64
% R
ed
uct
ion
in
co
sts
Gen
uit
y
•Con
solid
ate
2 D
Ms
>Re-
host
13 s
ubje
ct a
reas
=
600K
•3 Y
ear
NPV S
avin
gs
=
appro
x. $
4M
•Pa
ybac
k in
8.5
Mon
ths
Dat
a M
art
Con
solid
atio
n:
Bes
t P
ract
ices
of
the
An
alyt
ical
ly M
atu
re;
Ch
arlie
Gar
ry, M
ETA
Gro
up
GIG
A I
T Tr
ends
20
03
: D
ata
War
ehou
sin
g -
Incl
ude
s D
ata
Mar
t
Con
solid
atio
n
An
alys
t R
esea
rch
: Fi
ve H
igh
-Val
ue
Infr
astr
uctu
re P
roje
cts
for
the
20
03
Bu
dget
An
alys
t R
esea
rch
: M
edia
's T
ech
nol
ogy
Pri
orit
ies
Gig
a In
form
atio
n G
rou
p: "
Dat
a M
art
Con
solid
atio
n D
rive
rs"
Gig
a In
form
atio
n G
rou
p: "
Dat
a M
art
Con
solid
atio
n D
efin
ed"
Gig
a In
form
atio
n G
rou
p: "
Dat
a M
art
Con
solid
atio
n C
atch
-22
s"
Gar
tner
Gro
up
Rev
isit
ing
the
Myt
hs
of D
ata
Mar
ts.d
oc
Met
abit
s: F
inan
cial
Dat
a A
bou
t Te
rada
ta:
Dat
a M
art
Con
solid
atio
n
Val
uati
on
Gar
tner
Dat
a M
art
Con
solid
atio
n:
Stre
ngt
hen
ing
Tren
d of
20
02
DM
C -
An
aly
sts�
No
tes
(20
02
)
Data
Mart
Co
nso
lid
ati
on
•W
hit
e P
aper
: �D
ata
Mar
t C
onso
lidat
ion
: R
epen
tin
g fo
r Si
ns
of t
he
Pas
t,�
>W
illia
mM
cKni
ght,
McK
nigh
tAs
soci
ates
, In
c.
Fo
r M
ore
In
form
ati
on
on
DM
C (
Exte
rnal)
http
://w
ww
.tera
data
.com
/sol
utio
ns/d
w_d
mc.
asp
Art
icle
Rep
rin
tsC
ust
om
er
Su
ccess
Sto
ries
An
aly
sts�
Rep
ort
sW
hit
e P
ap
ers
�an
d m
ore
!!!
Ag
en
da
•A
ctiv
e D
ata
Ware
hou
sin
g
Healt
hca
re B
usi
ness
Ch
allen
ges
CEO
s� s
trat
egic
iss
ues
fac
ing t
hei
r co
mpan
ies
ove
r th
e nex
t five
yea
rs a
re:
•M
edic
al m
anagem
ent
incl
udin
g R
x dru
gs
•Pr
ovid
er
rela
tion
s/co
ntr
act
iss
ues
•C
hangin
g m
ark
et/
cust
om
er
dem
ands
•Le
gal/
legis
lative
/regula
tory
iss
ues
•E-b
usi
ness
/info
rmation
and t
ech
nolo
gy
managem
ent
Sou
rce:
Ti
llinghas
t-Tow
ers
Perr
in C
EO
Surv
ey 2
001
STA
GE 1
REP
OR
TIN
G
WH
AT
hap
pen
ed
?
Pri
mari
ly B
atc
hw
ith
Pre
-d
efi
ned
Q
ueri
es
STA
GE 2
AN
ALY
ZIN
G
WH
Yd
id it
hap
pen
?
Incr
ease
in
Ad
Ho
c Q
ueri
es
STA
GE 3
PR
ED
ICTIN
G
WH
AT
will h
ap
pen
?
An
aly
tica
l M
od
elin
gG
row
s
STA
GE 4
OP
ER
ATIO
NA
LIZ
ING
Wh
at
IS h
ap
pen
ing
?
Con
tin
uou
s U
pd
ate
&
Tim
e S
en
siti
ve Q
ueri
es
Gain
Im
po
rtan
ce
STA
GE 5
AC
TIV
E
WA
REH
OU
SIN
G
Wh
at
do
I W
AN
T t
o h
ap
pen
?
Even
t B
ase
d
Tri
gg
eri
ng
takes
hold
Batc
hC
on
tin
uo
us
Up
date
/
Sh
ort
Qu
eri
es
Even
t-B
ase
d
Tri
gg
eri
ng
Ad
Ho
cA
naly
tics
Info
rmati
on
Evo
luti
on
in
Data
W
are
ho
usi
ng
�An
ope
ratio
naliz
ed, b
usin
ess-
criti
cal c
ompo
nent
of t
he e
nter
pris
e sy
stem
.�
Cro
ss-d
epar
tmen
t, cr
oss-
chan
nel;
supp
orts
ent
erpr
ise
leve
l bu
sine
ss o
bjec
tives
.�
Res
ults
of d
ata
anal
ysis
are
tran
slat
ed in
to a
ctio
nabl
e de
cisi
ons.
�Sh
orte
ns th
e tim
e be
twee
n so
urce
dat
a an
d bu
sine
ss a
ctio
ns
take
n as
a re
sult
of a
naly
zing
that
dat
a.�
Und
erly
ing
philo
soph
y is
to in
crea
se s
peed
and
accu
racy
of
busi
ness
dec
isio
ns.
�It�
s br
oade
r tha
n �re
al-ti
me�
or �
clos
ed-lo
op� d
ata
war
ehou
sing
�Ea
ch s
tep
can
be a
s ne
ar re
al-ti
me
as it
nee
ds to
be
to s
uppo
rt th
e bu
sine
ss o
bjec
tives
.�
Ther
e is
a v
ery
mix
ed w
orkl
oad.
Eac
h ty
pe o
f wor
k ha
s its
ow
n se
rvic
e le
vel r
equi
rem
ents
.
Wh
at
is a
n A
ctiv
e D
ata
Ware
ho
use
?
�Pe
rform
ance
�W
ithin
sec
onds
for c
erta
in c
lass
es o
f wor
kloa
d�
Avai
labi
lity
�7
X24
X36
5�
Dat
a Fr
eshn
ess
�Ac
cura
te, n
ear r
eal-t
ime
data
�Sc
alab
ility
�Su
ppor
t for
larg
e da
ta v
olum
es, m
ixed
wor
kloa
ds
and
conc
urre
nt u
sers
Th
e B
usi
ness
Req
uir
em
en
ts f
or
an
Act
ive D
ata
W
are
ho
use
•O
ne
sourc
e of
data
:
>H
IPAA P
riva
cy &
Sec
uri
ty �
Less
$$ t
o m
ainta
in
acce
ss &
auditin
g a
pplic
atio
ns.
In
crea
se s
ecuri
ty
>Le
ss $
$ t
o m
ain
tain
sep
ara
te O
DS�s
and d
ata
mar
ts
>Con
sist
ency
of
repor
ts t
hro
ughou
t or
ganiz
ation
-ie:
Em
plo
yer
Gro
ups
rece
ive
matc
hin
g info
rmation
fro
m
diffe
rent
dep
art
men
ts
•Rea
l-Tim
e In
terv
ention
s fo
r im
pro
ved c
omplia
ncy
&
effe
ctiv
enes
s of hea
lth m
anagem
ent
pro
gra
ms
•Rea
l-Tim
e O
ppor
tunitie
s fo
r cu
stom
er-f
aci
ng
applic
atio
ns
ie:
Def
ined
Con
trib
ution
, Acq
uis
itio
n &
Ret
ention
Pro
gra
ms
Activ
e W
areh
ousi
ng fo
r HIP
AA C
ompl
ianc
e
�Fro
nt-
lin
e D
eci
sio
nin
g�
•Q
UESTIO
N:
>H
ow
do y
ou g
et t
he
right
info
rmat
ion t
o t
he
right
indiv
idual
in t
he
right
amount
of tim
e in
ord
er t
o m
ake
the
right
dec
isio
n?
•AN
SW
ER:
>Act
ive
Data
War
ehousi
ng (
AD
W)
and E
nte
rpri
se A
pplic
ation
Inte
gra
tion (
EAI)
•As
org
aniz
atio
ns
mov
e to
war
d g
reat
er lev
erag
e of in
form
atio
n a
sset
s by
dep
loyi
ng a
ctiv
e dat
a w
areh
ousi
ng for
tac
tica
l dec
isio
n s
uppor
t, E
AI
bec
omes
an im
por
tant
par
t of
the
arch
itec
tura
l fr
amew
ork
for
a busi
nes
s in
telli
gen
ce s
olution
enab
ling f
ront-
line
dec
isio
nin
g.
AD
W is
...
EAI
Cal
l C
ente
rW
EB
Activ
e D
ata
War
ehou
se
Hist
ory
Stat
usC
usto
mer
Web
TRX
AD
W m
akes
the c
on
nect
ion
!
•A
n A
DW
is
a m
atu
re e
xam
ple
of
a lon
g-s
tan
din
g in
teg
rati
on
ap
pro
ach
: ce
ntr
alizi
ng
data
fro
m
div
ers
e s
ub
ject
are
as
on
to a
si
ng
le d
ata
base
pla
tform
>Red
uci
ng t
he
leve
l of
dat
a m
ovem
ent
and t
ransf
orm
atio
ns
requir
ed t
o �c
onnec
t� t
he
ente
rprise
•B
eca
use
all d
ata
beh
ind b
usi
ness
d
eci
sion
s, b
oth
tact
ical an
d
stra
tegic
, is
in
teg
rate
d w
ith
in
the s
am
e s
ou
rce,
the v
iew
of
the
bu
sin
ess
th
rou
gh
th
e
ware
hou
se,
from
an
y
pers
pect
ive, is
con
sist
en
t an
d
acc
ura
te
Subj
ect E
Inte
grat
ion
of D
ata
and
its
Rep
rese
ntat
ion
Mar
tSu
bjec
t ASu
bjec
t C
OD
S
Subj
ect D
Activ
e D
ata
War
ehou
se
Subj
ect B
Mar
tOD
S
EA
I en
ab
les
the c
on
vers
ati
on
!
•E
AI
en
ab
les
ap
pli
cati
on
s ru
nn
ing
on
dif
fere
nt
pla
tfo
rms
to
�co
mm
un
icate
� even
th
ou
gh
th
ey w
ere
no
t o
rig
inall
y d
esi
gn
ed
to
in
tera
ct
•EA
I all
ow
s even
ts a
nd
in
form
ati
on
ab
ou
t th
ose
even
ts t
o b
e p
ub
lici
zed
acr
oss
an
en
terp
rise
w
hen
th
ey h
ap
pen
OLT
P
Lega
cyAp
plic
atio
ns
Pack
aged
Appl
icat
ions
Ente
rpris
e Ap
plic
atio
n In
tegr
atio
n
Dat
a W
areh
ouse
Tran
sfor
m
Tran
sfor
m
Tran
sfor
m
Tran
sfor
m
Inte
grat
ion
of E
vent
s an
d In
form
atio
n
EA
I co
mes
in m
an
y f
orm
s
•D
ata
Inte
gra
tion
>D
eals
with m
ovi
ng d
ata
among m
ultip
le d
ata
stor
es u
sing E
TL/
ELT
te
chniq
ues
. R
elat
ivel
y st
raig
htf
orw
ard:
no a
pplic
atio
n inte
ract
ion o
r in
tegra
tion r
equired
.
•M
essa
ge
Pass
ing
>M
anag
es t
he
exch
ange
of m
essa
ges
am
ong m
ultip
le a
pplic
atio
ns:
•D
iffe
rs fro
m d
ata-
leve
l EAI
in t
hat
the
applic
atio
ns
them
selv
es
per
form
the
mes
sagin
g•
More
inva
sive
bec
ause
it
requires
modific
atio
ns
to a
pplic
atio
ns
to
crea
te inte
rfac
es for
sendin
g a
nd r
ecei
ving m
essa
ges
.
•Busi
nes
s Pr
oce
ss M
anag
emen
t>
Provi
des
a fra
mew
ork
for
build
ing e
nte
rprise
-wid
e busi
nes
s pro
cess
es
and inco
rpora
ting e
xist
ing a
pplic
atio
ns:
•Ext
ensi
on o
f m
essa
ge-
leve
l EAI,
dat
a ex
chan
ge
still
occ
urs
via
m
essa
gin
g,
but
EAI
mid
dle
war
e ac
ts a
s a
�work
flo
w�
engin
e.
EA
I is
th
e �
glu
e�
that
allo
ws
the
inte
gra
tio
n t
o t
ake p
lace
Eco
syst
em
Eco
syst
em
App
licat
ions
Web
Ser
vice
s
Part
ners
hips
(Sup
plie
r, Se
rvic
e,M
arke
t, et
c.)
Peop
le(E
mpl
oyee
s, C
usto
mer
s,Pa
rtne
rs)
Proc
esse
s(O
pera
tions
, Res
pons
e,M
arke
t, et
c.)
Act
ive
Dat
a W
areh
ouse
Eco
syst
em
Eco
syst
emE
cosy
stem
Ecos
yste
m �
Logi
cal M
odel
Bill
ing
Mem
bers
hip
En
try
Cla
ims
Entr
yPr
ovid
erM
gmt.
Net
wor
kM
gmt.
Cus
tom
erSe
rvic
e
Activ
e D
ata
Activ
e D
ata
War
ehou
seW
areh
ouse
Fina
ncia
lA
naly
sis
Th
e R
ole
of
EA
I in
Act
ive D
ata
W
are
ho
usi
ng
EAI
EAI
Sale
s An
alys
is&
Com
pens
atio
n
Fina
ncia
lA
naly
sis
Cla
ims
Mgm
t
Cha
nnel
Ana
lysi
s
EA
I w
ith
ou
t A
DW
EAI Dat
aD
ata
War
ehou
seW
areh
ouse
EAI
Activ
e D
ata
Activ
e D
ata
War
ehou
seW
areh
ouse
EA
I w
ith
AD
W
Ben
efi
ts o
f ad
din
g A
DW
in
to a
n E
AI
Arc
hit
ect
ure
•A
DW
off
ers
in
teg
rati
on
at
the d
ata
level
•A
DW
reig
ns
in o
r eli
min
ate
s d
ata
red
un
dan
cy a
nd
ob
ject
p
roli
fera
tion
•A
DW
makes
info
rmati
on
late
ncy
less
of
an
iss
ue
•A
DW
makes
secu
rity
an
d a
ud
itab
ilit
y e
asi
er
•A
DW
red
uce
s th
e n
um
ber
of
pla
tform
s in
volv
ed
an
d a
dd
s u
nif
orm
ity t
o d
ata
availab
ilit
y
•A
DW
ease
s th
e d
ata
fre
shn
ess
co
nfl
ict
an
d c
on
fusi
on
•A
DW
su
pp
lies
even
t in
terp
reta
tio
n f
or
the w
idest
po
ssib
le
bu
sin
ess
use
s
EA
I R
efe
ren
ce A
rch
itect
ure
fo
r A
DW
•EAI
Ref
eren
ce A
rchitec
ture
for
dat
a st
ream
ing t
o a
nd fro
m a
n
Act
ive
Dat
a W
areh
ouse
>Sec
ure
, re
liable
, sc
alab
le m
essa
gin
g/e
vents
>�E
nte
rpri
se c
onnec
tivi
ty�
via
Intr
anet
, Ext
ranet
, an
d/o
r In
tern
et
>Route
and T
ransf
orm
cap
abili
ty
>Pu
blis
h/S
ubsc
ribe
(work
flow
) or
Poin
t-to
-Poin
t m
essa
gin
g
model
>N
on-i
ntr
usi
ve a
pplic
atio
ns
adap
ters
>Busi
nes
s pro
cess
man
agem
ent
capab
ility
EA
I +
AD
W =
�Fro
nt-
lin
e D
eci
sio
nin
g�
•W
irele
ss c
om
pan
y>
EAI
enable
s th
e ca
ll ce
nte
r agen
t to
com
mu
nic
ate
with t
he m
ark
eting s
yst
em
in
ord
er
to m
ake
the o
ptim
al pro
mot
ional of
fer
to r
eta
in t
he c
ust
om
er.
>AD
Wco
nn
ect
sth
e ca
ll ce
nte
r agent
with t
he a
ppro
pri
ate
and t
imely
in
form
ation
ab
out
the c
ust
om
er
in o
rder
to faci
litat
e r
ete
ntion
. >
Bu
sin
ess
Valu
e:
Avo
id �
chu
rn�
•A
irli
ne
>EAI
enable
s th
e g
ate
agent
toco
mm
un
icate
with t
he r
ese
rvation
sys
tem
al
low
ing t
he p
lace
ment
of all
20 p
ass
engers
on t
he n
ext
ava
ilable
flig
hts
. >
AD
W c
on
nect
sth
e g
ate
agent
with t
he a
ccum
ula
ted a
nd c
urr
ent
info
rmation
abou
t th
e c
ust
om
ers
in o
rder
to p
rior
itiz
e s
eat
ass
ignm
ent.
>B
usi
ness
Valu
e:
Pro
vid
e o
pti
mal
cust
om
er
serv
ice
•Fin
an
cial
inst
itu
tio
n>
EAI
enable
s th
e ca
ll ce
nte
r agen
t to
co
mm
un
icate
with t
he o
nlin
e w
eb s
yste
m in
ord
er
to p
rovid
e k
now
ledgeable
cust
om
er
serv
ice.
>AD
W c
on
nect
sth
e c
all
cente
r ag
ent
with t
he m
ost
rece
nt
and d
eta
iled
info
rmation a
bout
the c
ust
om
er
in o
rder
to e
xpand t
heir
por
tfolio
with a
new
pro
duct
. >
Bu
sin
ess
Valu
e:
In
crease
reven
ue b
y f
aci
lita
tin
g c
ross
-sell
Ag
en
da
•M
ed
co H
ealt
h S
olu
tio
ns
Med
co H
ealt
h S
olu
tio
ns
•C
lien
t B
ase
>Ret
ail/
Hom
e D
eliv
ery
Phar
mac
y ben
efit t
o 65 m
illio
n m
ember
s>
Man
aged
$30 b
illio
n in d
rug s
pen
d in 2
001
>Pa
rtner
with c
lose
to
hal
f of th
e Fo
rtune
100 a
nd a
third o
f th
eFo
rtune
200 c
om
pan
ies
as w
ell as
Man
aged
Car
e O
rgan
izat
ions,
Insu
rance
ca
rrie
rs,
and v
ario
us
leve
ls o
f gove
rnm
ent
and u
nio
ns.
(1600+
clie
nts
)
•A
ccess
>
2000 +
Act
ive
use
rs
>1 M
illio
n Q
uer
ies
/ M
onth
>2 b
illio
n c
laim
s>
40 +
applic
atio
n (
10 m
ajor)
•In
frast
ruct
ure
>2 s
tate
-of-
the-
art
fully
auto
mat
ed p
har
mac
ies
dis
pen
sing 1
.5M
RX/w
eek
>10 p
resc
ription p
roce
ssin
g c
ente
rs>
7 c
ust
om
er s
ervi
ce c
all ce
nte
rs w
ith 2
4/7
acc
ess
>2,5
00 p
har
mac
ists
, phys
icia
ns,
and n
urs
es o
n s
taff
>W
orld�s
lar
ges
t an
d m
ost
exp
erie
nce
d o
nlin
e phar
mac
y
Tech
no
log
y a
t M
ed
co H
ealt
h S
olu
tio
ns
•En
han
ce t
heir
cu
sto
mers
� exp
eri
en
ce,
earn
ing
an
d k
eep
ing
th
eir
lo
yalt
y
•U
se b
usi
ness
in
tell
igen
ce i
nfo
rmati
on
to
d
iffe
ren
tiate
th
em
selv
es
fro
m t
he c
om
peti
tio
n
>Rai
se t
he
bar
for
them
to m
atch
thei
r se
rvic
e to
cl
ients
and m
ember
s
•U
se e
ffect
ive i
nfo
rmati
on
man
ag
em
en
t to
gain
an
en
terp
rise
-wid
e s
ing
le v
iew
of
their
cu
sto
mers
Alo
ng
co
mes
HIP
AA
•H
ealt
h I
nsu
ran
ce P
ort
ab
ilit
y a
nd
Acc
ou
nta
bil
ity A
ct o
f 1
99
6 (
HIP
AA
) as
exte
rnal
dri
ver
>Le
gis
lative
req
uir
emen
t fo
r au
dit t
rail
of al
l pat
ient
hea
lthca
re
data
>M
edco
Hea
lth d
ata
res
ided
on n
earl
y 50 leg
acy
sys
tem
s
>Tim
e an
d r
esourc
e dem
ands
for
auditin
g d
ata
for
these
sys
tem
s ex
cess
ive
•D
eci
sio
n
>D
evel
op a
n A
ctiv
e D
ata
War
ehousi
ng S
trat
egy
>M
igra
te t
o a
n A
ctiv
e D
ata
War
ehouse
Earl
y M
ed
co A
DW
Milest
on
es
•M
ed
co d
eci
sio
n t
o b
uild
�tw
o f
en
ces�
aro
un
d
the m
ain
fram
e a
nd
aro
un
d t
heir
data
w
are
ho
use
an
d e
lim
inate
main
fram
e c
op
ies
fen
cin
g i
n e
very
data
sto
re w
ou
ld b
an
kru
pt
the c
om
pan
y>
not
com
ply
ing w
ould
subje
ct M
edco
to
fines
for
non
-co
mplia
nce
of $100 p
er f
ield
per
inst
ance
per
day
(luck
ily
capped
at
$250,0
00 p
er d
ay)
sin
ce t
hey
have
2,0
00,0
00,0
00 R
OW
S)
>M
edco
is
the
big
gor
illa in t
he
indust
ry a
nd w
ould
be
stri
ctly
scr
utiniz
ed b
y th
e fe
der
al g
over
nm
ent
for
com
plia
nce
>ki
ll al
l Syb
ase,
Ora
cle,
Info
rmix
dat
a st
ore
s
Med
co H
ealt
h S
olu
tio
ns-
-a s
ing
le v
iew
•A
DW
Str
ate
gy P
rovid
es
Inte
gra
ted
Pati
en
t V
iew
>Tim
e an
d R
esourc
e ef
fici
enci
es>
One
sourc
e of in
form
atio
n,
gat
her
ed fro
m n
um
erous
touch
poin
ts,
use
d for
multip
le,
ente
rpri
se-w
ide
purp
ose
s>
Sim
plif
ied c
om
plia
nce
with H
IPAA a
cces
s co
ntr
ols
>O
ld inef
fici
enci
es g
one
>N
ew e
ffic
ienci
es h
ere
to s
tay
•B
efo
re>
Bat
ch q
uer
ies
yiel
ded
data
up t
o f
our
wee
ks o
ld>
Bat
ch q
uer
ies
took
days
to p
roce
ss>
Duplic
ated
dat
a on m
ultip
le s
yste
ms
•A
fter
>AD
W y
ield
s dat
a only
sec
onds
min
ute
s or
hours
old
>AD
W q
uer
ies
take
sec
onds
or,
at
most
, m
inute
s to
pro
cess
>Tru
ly c
onso
lidat
ed s
ourc
e of dat
a on o
ne
au
dit
ab
leEW
sys
tem
Info
rmat
ion
War
ehou
se
(Ter
adat
a)
POS
POS
Cyc
le fi
les
(VSA
M)
POST
rans
File
s (V
SAM
)
IBSa
sync
IBS
Dat
abas
e(D
B2)
Bill
ing
2040
Cyc
le
file
(VSA
M)
OC
D
Bat
chIn
foPU
MP
Dat
a C
lean
sing
Ope
ratio
nal C
laim
sD
atab
ase
(DB
2)
Med
co H
ealth
Tra
nsac
tion
Flow
Pre
-HIP
AA
Med
co H
ealth
Tra
nsac
tion
Flow
Pos
t-H
IPA
A
Sour
ce o
f cla
im
data
for a
ll do
wns
tream
app
licat
ions
POS
TPU
MP
(via
MQ
Serie
s)
Info
rmat
ion
War
ehou
se
(3 m
illio
n cl
aim
s/da
y)
Med
co H
IPA
A A
rch
itect
ure
HIP
AA
Com
plia
nce
Syst
em (
Com
mon
Sec
urity
Ser
vice
s- P
rote
cted
Sto
re w
ith C
ripto
API
)A
ctiv
e D
irect
ory
Serv
ices
- K
erbe
ros
- Pub
lic_k
ey_b
ased
- X5
09 -S
mar
t Car
d - S
SL3/
TLS
MD
A (U
ML
- XM
I) : M
etad
ata
for H
eath
Car
e In
dust
ryB
usin
ess
Proc
ess
Map
per,
App
licat
ion
desi
gn a
nd in
tegr
atio
nXM
L w
rapp
er fo
r HIP
AA
inte
grat
ion
EAI (
Biz
talk
Ser
ver,
SeeB
eyon
d ...
)H
IPA
A S
ecur
ity a
nd P
roce
dure
s
t e x t
t e x t
Cla
ims/
Encn
trPr
ovid
erM
gmt
Enro
llmen
tM
edic
alM
gmt
Bill
ing/
Paym
ntC
ust
Serv
ice
Aud
itC
ontr
olSe
curit
yLo
ggin
gPr
ivac
yte
xt
Tera
data
- A
ctiv
e D
ata
War
ehou
se
Bro
kers
Prov
ider
sPa
tient
s/M
embe
rsC
onsu
mer
sEm
ploy
ers
Web
Inte
rfac
eIn
tran
et
Mas
ter
Prof
ile
Paye
rs
XML
Supp
ort f
or
Tera
data
(R)
Met
a In
tegr
atio
n Te
chno
logy
, Inc
.
Act
ive D
ata
Ware
hou
se
Less
on
s Learn
ed
•If
we h
ad
to
do
it
all
ag
ain
�C
hall
en
ges
>Com
ple
xity
and r
ichnes
s of data
cannot
be
under
estim
ate
d w
hen
w
ork
ing w
ith s
uch
a lar
ge
volu
me
>In
tern
al T
rain
ing -
nec
essa
ry d
eman
d o
n t
ime
and m
oney
>Sta
rt e
arlie
r
•They
com
bin
ed a
tech
nolo
gy
jum
p t
o AD
W w
ith a
n im
mov
able
date
(H
IPAA)
•D
esi
gn d
eci
sions
forc
ed b
y d
ate
are
bein
g r
evis
ited.
Th
eir
Bu
sin
ess
Use
r Lik
e I
t!
•S
ho
rten
ed
cycl
e t
ime,
data
is
act
ion
ab
le s
oo
ner
•R
ich
ness
of
data
-m
an
y m
ore
fie
lds
avail
ab
le o
n
com
mo
n t
ime s
cale
s, s
um
mari
zati
on
levels
, w
ith
co
nsi
sten
t ed
itin
g
•C
on
sist
en
cy o
f d
ata
am
on
g b
illi
ng
, cu
sto
mer
serv
ice,
rep
ort
ing
, m
em
ber
com
mu
nic
ati
on
s
•D
efi
nit
ive s
ou
rce f
or
dir
ect
co
mm
un
icati
on
of
matt
ers
o
f in
tere
st
Qu
est
ion
s?
Th
an
k Y
ou
Ro
se C
intr
on
Ro
se C
intr
on
-- All
en
All
en
Sen
ior
Hea
lth C
are
Con
sultant
RC200002@
tera
dat
a-ncr
.com
856-5
82-1
245
Ken
K
en
Ku
bis
tyK
ub
isty
Sen
ior
Hea
lth C
are
Con
sultant
KK180005@
tera
dat
a-ncr
.com
708-4
29-0
392
