Lean Data Management in SAP® BW
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Transcript of Lean Data Management in SAP® BW
Lean Data Management in SAP®BW
# 2
27 analyses from the following areas:- General system information- Data volume analysis- System performance- Data quality- System activity
Results:- Analysis report in form of offline HTML- Benchmarking information for each analysis- Workshop with result interpretation by experts- Recommendations to unleash areas with
improvements potential
BW Fitness Test
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BW Fitness Test Project Phases
Customizable parallelization
Collection of KPIs Consulting
Duration : 5 days
Collection of results Comparison with best practice / benchmark Customer requirement Building recommendations
Duration : 2 days
Presentation HTML, PPT or PDF
Output
Duration : 3 hours with customer
SAP transport Abap based Authorization
Functional
ExecutionImport of BW FT Analyze of Results Delivery and presentation
SAP BW
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BW Fitness Test
System Performance- Which are the biggest bottlenecks?- Which reports have critical
performance?- Which ETL consumes most runtime?
Data Volume- How is data distributed across the data
model layers?- How old is your data?- How frequently is data being used?- Where does data growth come from?
System Activity- Which are the outdated users?- What are the most frequent system
short dumps?
Data Quality- What are the consistency issues?- Are there any unused DIMID & SID
entries?- What are the most frequent RSRV
errors?
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Use
r ha
ppin
ess
TCO
&
data
ac
cess
TCO
Lean Data Management with OutBoard™
Performance optimization, Tuning
In-memory Ensure SLAs are met
GOALS TACTICS
Set up central policies Use appropriate
storage: Archiving, NLS, Smart data access
Set up central policies Perform housekeeping Automation
Information “at your fingertips”
Speed and high availability is key
Keep & storeReduce cost
Purge, delete & housekeeping
Hot DataBusiness critical dataData required for reporting and planning
Cold Data / Old DataAged data, historyInfrequent, rare useNeed to keep (external/legal, internal)
Dead DataTechnical data (e.g. logs, protocols, PSA)Redundant data
Future Planning and Roadmap
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1. The cost of storage needs to match the business value of your data.
2. Separate Data Management from Storage Technology. An open architecture secures your current and future investments.
3. Automation and central rules ease Data Management.
4. Iterate through the DMAIC cycle several times. Refine rules based on actual data usage statistics.
5. Start reducing data volumes from bottom (staging) to top (reporting).
5 Key principles of Lean Data Management
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Speed of Access Lower TCO
Business Warehouse
OutBoard™
OutBoard™ – Architecture overview
HANADB or
Smart Data Access*
SAP cluster tables
IQ RDBMSHadoop
For NON-HANA only
File / Cloud Deletion
INTE
RN
AL
EX
TER
NA
L
NLSInterface
Data Archiving DeletionHousekeepingDynamic
Tiering*
* under evaluation, currently not recommended
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OutBoard™ - Storage Layer ConceptEnables you to manage 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 HANA- 2-7 years in IQ- 8-20 years in files- 21+ will be deleted
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OutBoard™ - Scope of Housekeeping (ERNA)Scope of Housekeeping
Unused customers Unused vendors Phantom change documents Phantom texts Application log Batch log IDoc tables (EDI40, EDIDS) qRFC, tRFC Job-Tables (TBTCO, TBTCP etc.) Change & Transportsystem Spool data (TST03) Table Change Protocols Batch Input Folders Alert Management Data (SALRT*) Old short dumps Batch input data …
ERP and Netweaver PSAs & Change Logs Request logs & tables (RSMON* and
RS*DONE) Unused dimension entries Unused master data Cube & Aggregate compression Temporary database objects NRIV buffering Table buffering BI-Statistics Process Chain Log Errorlogs Unused Queries Empty partitions BI Background processes Bookmarks Web templates …
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.
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The Recycle Bin adds an important Safety Layer similar to Windows or Mac.
Instead of just deleting data, you can move it to a highly compressed Recycle Bin (ratio 10:1), from which it can be automatically deleted or retrieved.
ERNA - Recycle Bin
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Housekeeping – Central automation is key Housekeeping
addresses data which is not relevant for business and which cannot be archived
Housekeeping should be automated to avoid manual work
Housekeeping should be done centrally for the complete SAP landscape.
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DataVard presents DataVard- Specialized in helping you to run your SAP system landscape smarter and
better since 1998- More than 200 projects delivered p.a.- Customers range from SMEs (60 users) to Fortune 500 (e.g. Allianz, BASF,
KPMG, Roche, Nestle)
- SAP & DataVard, a partnership we are 100% committed towards- SAP preferred vendor since 1999- Development partner of SAP® Landscape Transformation Suite (LT) and
Information Lifecycle Management (ILM)
- Gartner Cool Vendor 2013, 2015 Magic Quadrant for Data Archiving- Privately held- 7 locations in Germany (HQ), Italy, Slovakia, United Kingdom and the US
Growth gives Credibility
Experience gives Safety
Focus gives Strength
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No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of DataVard GmbH. The information contained herein may be changed without prior notice.DataVard, OutBoard, ERNA, CanaryCode, BW Fitness Test and ERP Fitness Test are trademarks or registered trademarks of DataVard GmbH and its affiliated companies. SAP, R/3, SAP NetWeaver, SAP BusinessObjects, SAP MaxDB, SAP HANA and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and other countries.All other product and service names mentioned are the
trademarks of their respective companies. Data contained in this document serves informational purposes only. National product specifications may vary.
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CR Copyright DataVard GmbH. All rights reserved.CR Copyright DataVard GmbH. All rights reserved.