Download - Snp T bone-dpm_overview
SN Opt
P T-Bon
timized D
ne - Dat
Data for T
ta Provi
Test, Co
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mpliance
g and M
e and Qu
Masking
uality Maanageme
Page 1 of 15
ent
Tran
units
techn
proce
much
comp
(clou
enter
Cons
simp
a sys
A cru
optim
objec
Com
withi
and i
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chall
acce
softw
sformations
s within a c
nical chang
esses (mer
h faster, tim
panies are
ud services
rprise agen
sequently, c
ple renovatio
stem enviro
ucial factor
mize the pr
ctive being
mpanies are
n their IT s
is a factor t
mpanies app
enges facin
eptance and
ware users.
s are now a
company fa
ge—their IT
rgers and a
me-to-marke
coming fa
or SaaS
da.
companies
on and opti
onment that
for achievin
rocesses w
to significan
e becoming
system land
hat contribu
ply quality m
ng them, s
d driving a
a regular pa
acing increa
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acquisitions
et is increas
ace-to-face
models, fo
seeking to d
mization me
t allows for
ng this is to
ithin the co
ntly improve
g increasing
dscapes. It
utes signific
managemen
such as err
more effic
art of every
ased levels
ations are
s, optimizing
singly shorte
with new
or instance)
define the m
easures alo
ever-shorte
o systematic
ompany itse
e business
gly aware
is a corners
cantly towar
t and testin
ror-free bus
cient way o
yday busine
of pressur
also feelin
g organizat
ened for pro
technologi
)—these ar
market goin
one. Modern
ening transf
cally restruc
elf and imp
performanc
of the imp
stone of a
rd its profita
g measures
siness proc
of working
ess. It is no
re due to ra
ng the stra
tional struct
oducts and
es and op
re now all
g forward c
n IT organiz
formation cy
cture the co
prove the q
ce.
portance of
company’s
ability and p
s in order to
cess suppo
from the p
t only the b
apid econo
ain. Transfo
tures) now
services an
perational c
part of t
cannot rely o
zations mus
ycles to tak
ompany’s pl
quality of da
quality as
entire IT o
erformance
o address im
rt, higher l
perspective
Page 2 of 15
business
mic and
ormation
happen
nd many
concepts
he daily
on using
st create
ke place.
latforms,
ata—the
ssurance
operation
e results.
mportant
evels of
of their
If we
appli
an ev
comp
that
deve
supp
Prov
as po
inclu
To o
a so
prod
using
third-
and R
Howe
proce
envir
and
whic
grow
Furth
TKG
signi
envir
inform
prope
e focus on
ication and
ver-growing
panies nee
is both ap
elopment pr
port when in
visioning for
ossible. It m
ding prepro
btain test d
ource. Seve
uctive. It is
g technical
-party provi
Remote Cli
ever, the di
essing effo
ronment ou
roles, for e
h in turn in
wing amount
hermore, du
, 95/46/EG
ficant need
ronment in
mation suc
erty (particu
n business
system lan
g number o
d to provid
ppropriate a
rocesses, a
ntroducing n
r both initial
must be per
ocessing an
ata that is r
eral differen
crucial, reg
tools prov
iders. For e
ent Copy) a
isadvantage
ort—particul
ut of a prod
example). In
creases the
ts of data av
ue to reason
, HIPAA, et
d to “anon
order to p
ch as busin
ularly acces
software a
ndscapes th
f datasets.
de their dev
and realistic
automate q
new busines
and recurr
rformed bot
nd post-proc
realistic, co
nt procedur
gardless of
vided by ha
example, the
also allow u
e of these a
larly post-p
uction envi
n addition,
e demand f
vailable wit
ns connecte
tc.) and inte
nymize” and
prevent un
ness partne
ss by extern
applications
hat are bec
In order to
velopment,
c. By doing
quality assu
ss functions
ring test dat
h quickly an
cessing acti
mpanies ca
res are ava
what appro
ardware an
e basic too
sers to crea
approaches
processing—
ironment (a
a significan
for storage
hin the prod
ed with stat
ernal guide
d deperson
authorized
er address
nal parties).
s, we see
coming incr
ensure tha
test and tr
g so, they
urance pro
s.
ta for these
nd with as l
vities.
an turn to th
ailable for b
oach is used
nd database
ls available
ate a copy o
s is that a co
—is require
adjusting au
nt level of r
capacity ne
duction syst
tutory data
elines regard
nalize data
access to
ses, salarie
that ERP
reasingly co
at these sys
raining syst
can then s
cesses and
e purposes
little manua
heir product
building sys
d, to create
e manufact
e within SAP
of entire sys
onsiderable
ed in orde
uthorizations
redundancy
eeded in or
tem.
protection r
ding data s
copied fro
sensitive
es, conditio
users are
omplex and
stems run o
tems with te
speed up s
d provide e
must be as
al effort as p
tion environ
stems that
e a 1:1 copy
turers or s
P® (such as
stems and c
e amount of
r to create
s, interface
y is also ge
rder to repli
regulations
security, the
om the pro
or busines
ns and int
Page 3 of 15
running
d feature
ptimally,
est data
software
effective
s flexible
possible,
nment as
are not
y (clone)
specialist
s R3load
clients.
f manual
e a test
es, users
nerated,
cate the
(BDSG,
ere is an
oduction
s-critical
tellectual
The
SNP
“SNP
deve
mask
traini
As p
test d
T-Bo
funct
a sol
mana
will e
autom
mean
SNP
the fo
By p
have
cost
syste
main
Solution
T-Bone D
P Data Dis
eloped, sop
king live da
ing.
part of the o
data anony
one Data P
tionality by
lution that m
agement op
enable com
mated or fu
ns of a defin
T-Bone Da
ollowing iss
Building
Refreshi
Provision
Provision
Anonym
providing us
e a tool that
and with m
em environ
ntenance ac
ata Provis
stillery” that
phisticated,
ata from th
overall SNP
ymization ha
Provisionin
drawing on
makes it eas
perations, f
mpanies to
ully automat
ned scenar
ata Provisi
sues, for exa
sandbox sy
ng QA syst
ning test da
ning selecte
ization of da
sers with a
t allows the
minimum e
nments are
ctivities are
ioning and
t was intro
efficient an
heir ERP s
P T-Bone t
as been dev
g and Mas
our years o
sy and effic
rom both a
determine
ted approac
io.
ioning and
ample:
ystems and
tems regula
ata for deve
ed test data
ata that nee
function fo
m to guara
effort. Any
e shortene
supported e
d Masking
duced bac
nd agile too
systems, fo
ransformati
veloped fur
sking. SNP
of experien
cient to integ
logical and
the data
ch, and will
Masking c
project sys
arly
lopers and
a for process
eds protecti
or reducing
ntee the qu
transformat
ed conside
extremely fl
(DPM)—as
k in 2006—
ol for perfo
or use in te
ion platform
rther as a s
P has devel
ce gained i
grate into te
d technical
needed fro
allow them
can help co
stems
colleagues
sing suppor
on in non-p
test data i
uality of the
tion cycles
erably and
lexibly.
a further e
—provides
orming migr
esting, qua
m, test data
eparate sol
oped the m
n this area,
est manage
perspective
om test ca
to make th
ompanies ta
working in
rt incidents
production s
n a highly
ir software
that are p
both inte
enhanceme
users with
rations and
lity assuran
a managem
lution modu
module’s un
, paving the
ement and t
e. In the fut
ses using
is data ava
ackle and ov
support
systems
flexible wa
and project
present in c
ernal projec
Page 4 of 15
nt to the
a well-
reliably
nce and
ment and
ule: SNP
nderlying
e way for
test data
ture, this
a semi-
ilable by
vercome
y, users
ts at low
complex
cts and
Figur A ho
flexib
Appl
proce
The
Unit
Inte
re 1 - SNP T
ost of selec
bility. It als
ication Life
ess tests, w
DPM modu
U
t tests / dev
egration test
T-Bone Da
ction option
so enables
ecycle Mana
with suitable
ule differenti
Use Case
veloper tests
ts / interface
ata Provisio
ns allow us
s them to
agement, s
e test data.
iates betwe
s
e tests
oning and M
sers to resp
supply th
such as bug
en the follo
- P
d
- O
p
- T
- C
Masking - A
pond to pro
e various
g fixing, un
wing use ca
Provisioning
developmen
Object-depe
processes
Time-based
Complete cl
Architecture
oject requir
different t
nit tests, int
ases:
Process
g up-to-date
nt systems
endent selec
data reduc
ient transfe
e
rements wi
test phases
tegration te
s
e master dat
ction of bus
ction
r
Page 5 of 15
ith great
s within
ests and
ta for
siness
Sup
Tra
Com
The
reduc
them
data
Neve
reduc
cons
By a
met
are f
defin
the fo
SNP
deve
from
data
disad
U
pport incide
ining system
mpliance re
DPM modu
ce the effo
m to compen
made ava
ertheless, t
ction thank
sideration.
pplying a s
and, in part
fulfilled. The
ned in the d
ollowing:
Ensuring
Repeate
Retainin
Ability to
T-Bone D
elopment an
within you
from no
dvantages:
A system
amount
Use Case
nts / bug fix
m
quirements
ule supports
ort required
nsate for a
ailable for
he test da
ks to taking
series of an
ticular, requ
e set of rul
ata scenari
g referential
ed anonymiz
g the chara
o activate or
Data Provis
nd training
ur productio
on-productio
m copy ca
of storage c
s
s periodic da
(along wit
ny adverse
testing in t
ta still sus
g the depen
onymization
uirements r
les used by
os. It takes
l integrities
zation and i
acteristics of
r deactivate
sioning an
systems w
on system.
on ERP
n only crea
capacity for
- O
- Inptrre
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tr
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d
ata refreshe
h the need
e time-relate
test system
stains a gre
ndencies th
n rules flex
egarding th
y the DPM
into accou
irreversible
f production
e anonymiza
nd Masking
ith consiste
Standard m
systems b
ate clones;
r data that y
Object-depe
ndividual coprocesses inraining meaeduction Post ante”
ransferred d
Post ex”
data/system
es; this ena
d) for post-
ed effects t
ms is able
eat level of
hat exist w
ibly, individ
he issues th
module is
nt the most
anonymiza
n data
ation for eac
g enables y
ent data ac
methods th
bring with
you there
you do not a
Process
endent selec
ombination on order to coasures while
anonymiza
during migra
anonym
ms
bles compa
processing
that may ar
to be red
f consisten
within busine
ual complia
hat surround
based on
t crucial req
ation
ch scenario
you to sup
curately ch
hat are ava
them se
efore have
actually nee
s
ction
of various rover all relee maximizin
ation of the
ation
ization o
anies to sign
activities,
rise. The vo
uced consi
ncy in spite
ess proces
ance guidel
d offshore a
the tables
quirements,
o
ply your E
hosen and o
ailable for p
everal rec
to reserve
ed. Furtherm
Page 6 of 15
reduction evant ng data
e test data
f existing
nificantly
allowing
olume of
iderably.
e of this
ses into
ines are
activities
that are
such as
RP test,
obtained
providing
cognized
a huge
more, an
a
g
SNP
proce
offers
Figur
extensiv
for interf
which yo
Similarly
demand
the volum
copy is
require is
T-Bone Da
esses and
s several ke
Both the
storage s
Your dev
with new
re 2 - SNP T
e amount o
faces, users
ou may requ
y, client cop
a lot of sto
me of data
simply far
s not includ
ata Provisi
objects tha
ey IT produ
e time nee
space requ
velopment
w production
T-Bone Da
of manual p
s, printer c
uire—is not
pies also do
orage space
involved is
too long. A
ded as part
ioning and
at you actua
ction and m
ded to fill
ired for them
and trainin
n data at sh
ata Provisio
post-proces
onfiguration
included as
o not offer
e for unnece
s over 200
Again, the
of a client c
Masking a
ally require
managemen
your non-p
m are reduc
g systems
horter interv
oning and M
sing is nee
n and so on
s part of a s
as much s
essary data
GB, the tim
process of
copy either.
allows you t
, along with
nt advantage
production
ced conside
become m
als and with
Masking - E
eded in the
n). Anonym
system copy
election po
a volumes.
me required
f anonymiza
to restrict d
h their und
es:
systems an
erably.
more efficien
h shorter do
Efficiency
target syste
mization—a
y.
ower, and th
Furthermor
to perform
ation that y
ata to the b
erlying tabl
nd the amo
nt and are s
owntimes.
Page 7 of 15
em (e.g.
process
hey also
re, when
m a client
you may
business
es. This
ounts of
supplied
The
Prov
be k
relate
sophisticat
visioning a
kept out of
ed test data
Guarante
regulatio
Enables
Allows y
Avoids th
ted anonym
and Maskin
the reach
a in this way
ees all bus
ons into acc
you to cond
ou to use liv
he time-con
mization fu
ng guarante
of any un
y:
siness proc
count
duct perform
ve processe
nsuming pro
unctions in
ee that any
nauthorized
cesses can
mance tests
es in a reali
ocess of hav
cluded as
sensitive d
parties. U
n be tested
s using real
istic test en
ving to crea
part of S
data you ma
Using anony
d while taki
listic data vo
vironment d
ate synthetic
SNP T-Bon
ay have wil
ymized pro
ing data pr
olumes
during testin
c test data.
Page 8 of 15
ne Data
l always
oduction-
rotection
ng
Func
SNP
effici
extra
Bone
signi
It us
desc
detai
entiti
and
Figur
ctionalities
T-Bone Da
ent data ex
action rules
e Data Prov
ficantly.
ses busines
cribe depen
il: all you h
ies are to be
Masking so
re 3 - The S
s
ata Provisi
xtraction too
(such as se
visioning a
ss-object-re
dencies tha
have to do
e tested. M
olution then
SNP T-Bone
oning and
ol that goes
election par
and Maskin
elated scen
at exist betw
o is simply
echanisms
n perform th
e Data Pro
Masking p
s far beyon
rameters or
ng helps to
narios to en
ween busin
select whi
included w
he following
visioning a
provides use
nd standard
r table depe
simplify the
ncapsulate
ess entities
ich process
ithin the SN
system-ba
and Maskin
ers with a h
d ERP tools
endencies) a
e process of
a complex
s and datab
ses, transa
NP T-Bone
sed proces
ng - Data M
ighly intellig
s. Using pre
as its basis,
f selecting t
x set of ru
base tables
actions or b
Data Provi
sing steps.
Model
Page 9 of 15
gent and
edefined
, SNP T-
test data
ules that
in great
business
isioning
Poss
SNP
with
Exam
sible Scena
T-Bone D
support for
mples of po
Transfer
Reducin
Transfer
Selecting
business
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IS
F
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Performior in full)
arios
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conducting
ssible scen
rring master
g transactio
rring master
g master d
s-object-rela
ERP System
S-U / IS-T
FS-CD
FI-CA
CML
CFM
Retail
ing “post ex
ing individu)
sioning and
g training eff
arios includ
r data and t
on data acc
r data witho
data and tr
ated approa
m
x” anonymiz
al migration
d Masking
ficiently or f
de:
transaction
cording to tim
out any trans
ransaction
ach:
zation of exi
ns of data /
is a univer
for building
data of a cl
me-based r
saction data
data from
isting client
table conte
rsal solution
extensive t
ient without
estrictions
a
the follow
s
ent for test s
P
n that provi
est scenario
t reduction
wing areas
systems (red
Page 10 of 15
ides you
os.
using a
duced
Data
In or
prod
data
Anon
Anon
are d
the fo
Exam
a Extraction
rder to sup
uction syst
extraction m
“Intellige
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(proc
Only
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Amo
comp
nymization
nymization
defined usin
orm of a fun
mples of an
Address
part of e
Using a
custome
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n Modes
pport the di
ems, SNP
modes, dep
ent Data Ext
bset of obje
cts being
cess/transac
selected o
ass Extract
bjects availa
ain objects
unt of data
pany code 1
n Function
is performe
ng simple ru
nction modu
onymization
and contac
nsuring refe
constant v
ers or compa
xed values t
fferent sce
T-Bone Da
pending on
traction” mo
ects are ext
extracted
ction/entity)
bjects along
ion” mode:
able within
are exclude
a can be re
1000)
ed at field le
ules as par
ule.
n rules inclu
ct data relat
erential inte
value to ov
anies
to overwrite
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the scenari
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racted alon
d are se
)
g with their
the system
ed (e.g. use
duced by a
evel during
rt of custom
ude:
ting to custo
egrity
verwrite ba
e cost cente
issues tha
ioning and
o chosen:
g with their
elected at
dependent
are extract
er data, IDoc
applying va
the data tra
mizing. Com
omers, ven
ank details
er managers
at exist wh
d Masking
related dat
t business
objects are
ted
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rious differe
ansfer. Any
mplex rules a
dors and bu
to avoid b
s and cost c
P
en setting
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ta environm
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ent selectio
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usiness par
back-refere
center texts
Page 11 of 15
up non-
ariety of
ment
explicitly
ons (e.g.
needed
ented in
rtners as
ncing to
Fea
The
apart
Data
atures
features lis
t from other
a Provision
No restri
Customi
Custome
Support
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Develop
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User-frie
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Converti
Setting u
Compen
Migrating
sted below
r solutions:
ning
iction to cer
zing, maste
er developm
provided f
s part of the
ing separat
mensional d
endly user in
data contai
ing names o
up and trans
nsating for s
g across dif
are what s
rtain ERP m
er data and
ments are su
for Industry
e standard c
te customer
ata distinct
nterface
ners” in flat
of logical sy
sferring mu
structure diff
fferent relea
set SNP T-
modules only
transaction
upported
y ERP Solu
content
r scenarios
ion
t files to res
ystems on-t
ltiple clients
ferences
ases
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y
n data are a
utions (IS-U
on top of th
tore fixed s
he-fly
s in parallel
a Provision
ll taken into
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he standard
ystem statu
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ning and M
o considerat
S-CD, Reta
content
uses
Page 12 of 15
Masking
tion
ail, CML,
Data
The
SNP
withi
data
Sum
SNP
Data
tool f
quali
provi
poss
a Masking
High-per
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same wa
Compreh
Uniform
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data involv
T-Bone D
n your prod
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mmary
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for perform
ity assuranc
iding intuiti
sible.
Scenario
Complie
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ad-ho
Data
rformance d
zed custom
calable due
fields that
ent anonym
ay during ea
hensive sup
anonymiza
ized anonym
ved remains
Data Provis
duction sys
nsistent.
Data Provis
—provides
ing migratio
ce and train
ve user gu
os can be e
s with statu
king rules a
oc masking
objects be
data transfe
izing enable
e to parallel
need protec
mization, me
ach refresh
pport for nu
ation of redu
mization log
s protected
sioning an
stems. Optio
sioning and
users with
ons and rel
ning. By foc
uidance, us
xtended fle
utory data p
are applied
g is used for
ing transfer
er / anonym
es settings
data proces
cting at data
eaning name
merous lea
undant data
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at all times
d Masking
onal deletio
d Masking—
a well-dev
iably mask
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sers can a
xibly with th
rotection an
before any
r existing te
rred can by
ization as s
to be reuse
ssing and o
a record lev
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ading ERP c
within the s
r all systems
s, as the d
g only have
on runs can
—as a furth
eloped, sop
ing their liv
rly on the ap
achieve the
he user-frien
nd data eco
data leave
st environm
restricted e
standard sof
ed
optimizing h
vel
dresses are
components
system
s within the
ata extracto
e read acce
n be used t
her enhanc
phisticated,
e ERP data
pplication a
e highest l
ndly custom
onomy regul
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ments
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ftware
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anonymize
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