Datavard, SLO BW war stories and how data management can improve BW environments tremendously

2. # 2
Frank Gundlich
Business Development Manager
frank.gundlich@datavard.com
Mobile: +49 151 43251206
Welcome

3. # 3
Agenda
• Who is DataVard
• What is the value of your data
• Lean Data Management
• Key take a ways

4. # 4
Who is
DataVard?

5. # 5
DataVard
− Helping customers improve their SAP Landscape since 1998
− Fortune 1000 and DAX30 (e.g. Allianz, BASF, KPMG, Roche, Nestle)
− SAP & DataVard, a partnership since 1999
− Development partner of SAP® Landscape Transformation Suite (LT) and
Information Lifecycle Management (ILM)
− Gartner 2015 positioned DataVard the furthest for completeness of vision in
the Niche Players Quadrant for Structured Data Archiving
− Global reaching with locations in Germany (HQ), Italy, Slovakia, United
Kingdom and the US
− BW/BI optimization, innovative and platform-dependent data management,
− Data management across your SAP Landscape
− Automated system housekeeping
− Test Data Automation
− HANA
Growth gives Credibility
Experience gives Safety
Focus gives Strength

6. # 6
• System Analysis and data
classification
• Data Volume
Management (ADK, ILM
NLS)
• Automated Housekeeping
• Hadoop integration into
SAP
• ILM
• Data cleansing
• Data migration
• Performance analysisand
tuning
• System monitoring
• Automated system
administration and
housekeeping
• Selective System copies
DataVard Solution Suite
Manage your data in line with
the data’s value
Intelligent system landscape
management
Lean Data
Management
System Monitoring &
Management
OutBoard™ Suite CanaryCode & ReLine
• Test administration
• Test data creation
• Automated testing
• Performance testing
• Test monitoring
Automated SAP testing
Lean Test Automation
& Selective System
Copy
Selective SystemCopy
• Mergers & Acquisitions
• Data harmonization and
standardization
• Company reorganization
• Divestiture, carve-outand
spin-offs
• System decommissioning
• System Landscape
Optimization (SLO)
• HANA Deployments
Re-organize your data to reflect
business change
Business
Transformation
ReLine Suite

7. # 7
Data Value in
the age of Big
Data

8. # 8
vs.
Smart Data Management is paramount
How much VALUE
do your data
generate?
How much COST
do your data
generate?

9. # 9
5%
15%
15%
9%
11%
32%
5%
5% 3%
Master data
Temporary data
Other data
PSA data
Changelog data
ODS data
Cube E data
Cube F data
Cube D data
Data distribution in SAP BW*
Comments:
§ 13-17% of system size is
reporting data
§ Quick check on
housekeeping potential
(size of BALDAT,
RS*DONE, ...)
§ HANA sizing report gives
a 1st indication (OSS
note 1736976)
“Only 12% of all data in BW is actually used.”
Forrester research
* Source: DataVard BW Fitness Test

10. # 10
How to shrink your Database – 5 common practices
Housekeeping of
temporary data
Selective Copy of
PROD to non-
PROD
Transactional &
Analytical Data
Documents /
Attachments
Selective Copy
of PROD to
non-PROD
Move non-
active data
Delete
unnecessary
or unused data
Move
documents to
separate store
§ Protocols, Logs,
Statistics, other
Temp. data
§ Standard &
automated
procedure
§ Selection e.g.
based on time-
slice
§ Maintaining
integrity of data for
testing
§ HANA: Active /
non-active data
concept
§ ERP: SARA
Archiving
§ BW: NLS, SDA,
DT
§ Using ArchiveLink
interface
§ E.g. incoming
invoices as PDF
or email
§ Attached to
Business process
Master,
Transactional &
Analytical Data
Avoid data
creation
§ Based on
changing user
behavior data or
apps may be
obsolete
?% 25% 35% 65% 11%
Typicalbeneift

11. # 11
Lean Data
Management-
Architecture

12. # 12
Central Governance, Management Functions
Incorporation into an existing operations model, Automation
Requirements definition & goals per data class
(temperature based)
Achieving - Lean Data Management
Define
Improve
Data Profiling, Growth & Cost analysis (e.g. BW Fitness Test™)
Data Classification based on usage (HeatMap) in Reporting & ETLAnalyze
Control
Measure
Data handling via simple rules derived from external/legal, internal
rules and classification

13. # 13
BW Fitness Test™ – Sample
Check
Here
„The BW Fitness Test™ prepared us perfectly to make our SAP BW fit
for the future. Now we manage our aged data with Nearline Storage
and improved our Load Performance.“
Steffen Muesel, Randstad

14. # 14
DataVard HeatMap
• Cost / benefit analysis
• Cost is usually associated
with volume and storage
• Benefit is measured by
number of queries executed
• Other important KPIs are
users, number of loads,
duration of loads, etc.
How does it work?
1: Size map of the SAP system
2: Determining KPIs
3: Correlating KPIs
4: Know hot and cold spots

15. # 15
Turning usage statistics into Operational Intelligence
DataVard HeatMap
Leadingchemical
company: 8,1 TB of
data which was
queried less than 5
times over a 6
months period.
Use Case
8,1 TB non-active
data moved to NLS

16. # 16
IQ RDBMSVertica Hadoop File Recycle Bin
DATAVARDMulti-tierStorage Management
OutBoard™ - Architecture overview on SAP Data
Management
Protocols, Logs,
Statistics, other Temp.
data
CUBE, ODS, PSA,
Ch.Log
Master-, Transactional
data
Scan from paper, PDF,
Email
LogicalviewApplicationviewStorageview
Analytical Data Transactional Data Temporary Data Documents
§ Identification /
analysis
§ Mass archiving
§ Automation
§ Recycle Bin prep
§ Cross System
§ HeatMap Analysis
§ Selection of data
§ Mass archiving
§ Automation
§ NLS Writer
§ HeatMap Analysis
§ Compliantarchiving
§ Automation
§ Browse & search via
NLS-like interface
§ OCR
§ Categorization
§ Automatic assignment
Data
Toolkit/
Features
Interface NLS SDA DT ILM “NLS”ADK ADK Deletion ArchiveLink

17. # 17
OutBoard™ - Storage Layer Concept
Manage the cost of storage inline with the value of information.
Data can be transferred to other layers, managing various aging
thresholds using Aging Profiles.
Example:
− Up to 2 years in SAP HANA
− 2-7 years in IQ
− 8-10 years in files
− 11+ will be deleted

18. # 18
OutBoard™ - Housekeeping
Scope of Housekeeping
n Unused customers
n Unused vendors
n Phantom changedocuments
n Phantom texts
n Application log
n Batch log
n IDoc tables (EDI40, EDIDS)
n qRFC, tRFC
n Job-Tables (TBTCO, TBTCP etc.)
n Change & Transportsystem
n Spool data (TST03)
n Table Change Protocols
n Batch Input Folders
n Alert Management Data (SALRT*)
n Old short dumps
n Batch input data
n …
ERP and SAP NetWeaver®
n PSAs & Change Logs
n Request logs & tables (RSMON* and
RS*DONE)
n Unused dimension entries
n Unused master data
n Cube & Aggregate compression
n Temporary database objects
n NRIV buffering
n Table buffering
n BI-Statistics
n Process Chain Log
n Errorlogs
n Unused Queries
n Empty partitions
n BI Background processes
n Bookmarks
n Web templates
n …
Business Warehouse
§ Housekeeping
addresses data which
is not relevant for
business
§ Housekeeping should
be automated to avoid
manual work
§ Housekeeping should
be done centrally for
the complete SAP
landscape.

19. # 19
Housekeeping Cockpit
“With the help of OutBoard™ and ERNA™ we were able to
reduce the system size by 35% in the initial wave of archiving.”
Jens Graef, Kion Group IT

20. # 20
The effect! A real case from automotive customer.
• Cost per GB per month
range from USD 1.13 to
USD 2.33 and approx. 40-
45% for backups
• Data gets moved to NLS
after 2 years
• Data growth is 35-40%
p.a.
• One-time effect is 43%!
• 20% of the data is
removed with
Housekeeping “ERNA”
System growth in TB Assumptions

21. # 21
ü The cost of storage needs to match the
business value of your data.
ü Separate Data Management from Storage
Technology. An open architecture secures your
current and future investments.
ü Automation and central rules for ease of Data
Management.
ü Iterate through the DMAIC cycle several times.
Refine rules based on actual data usage
statistics.
ü Start reducing data volumes from bottom
(staging) to top (reporting).
5 Key Principles of Lean Data Management

22. # 22
Thank
You

23. # 23
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24. # 24
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