The document discusses 4 secrets for maintaining fitness in an SAP BW system. It shows that most data in a typical BW system is not report data but rather temporary, master, or cube data. It identifies 4 categories of BW fitness to focus on: system robustness, data quality, query performance, and information lifecycle management. It provides tips in each category, such as regularly checking for unused IDs, building indexes and aggregates to improve query performance, and using nearline storage to archive cold data and reduce data volumes over time.

1. 4 secrets for SAP BW fitness

2. Typical distribution of data in a BW system
Master data
5%
Temporary data
5%3% 5%
15%
Other data
PSA data
Changelog data
ODS data
15%
32%
Cube E data
Cube F data
Cube D data
9%
11%
Data you report on is only 13-17% of the system size
Temporary data is subject to housekeeping (BALDAT, RS*DONE, ...)
© 2013 DataVard GmbH
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3. 4 categories of BW fitness
Based on an in-depth analysis (BW
Fitness Test) of 150+ BW systems we
identified:
1. System robustness
2. Data quality
3. Performance: load and query
4. Information lifecycle management:
managing data from cradle to grave
© 2013 DataVard GmbH
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4. 1. System Robustness
Security
§
ABAP code
§
Authorizations
§
Basis parameters
BW Batch processing
§
Analysis of critical path with
monitoring
Dumps
§
Minimize number of dumps per
month with monitoring
© 2013 DataVard GmbH
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5. 2. Data Quality
Technical data quality
§ Regularly check and remove unused
SIDs, DIMIDs, Master data
Duplicates
§ Root cause analysis instead of band
aiding
Semantical data quality
Manage data quality in source systems
© 2013 DataVard GmbH
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6. 3. Query performance
• Use BIA or go directly to HANA (?)
YOU HAVE
ANOTHER
OPTION
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7. 3. Query performance
1. Avoid outdated indexes and database statistics.
2. Build secondary indexes on DSOs to speed up the data selections.
3. Build aggregates to improve the query performance and check
size & utilization.
4. Compress InfoCubes regularly.
u
Initial compression may hurt, but is worth it!
5. Consider line-item dimensions in case of large dimensions
u
Initial dimensional remodeling may hurt, but is worth it!
6. Use partitioning for InfoProviders based on time characteristics to
reduce the data volume in each InfoProvider.
u
u
As of BW 7.3 the Semantically Partitioned Objects can be used.
Before 7.3 “SPO” can be implemented manually
7. Consider the Near-Line archiving of rarely used (“cold”) data to
reduce data volume.
© 2013 DataVard GmbH
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8. 4. SAP BW Information Lifecycle Management
The art of managing your data in line with its business value
USER
current
0-2 years
>2 years
© 2013 DataVard GmbH
HOT
BW Accelerator /
SAP HANA
WARM
Nearline Storage
§ Data stored in a cost
optimized way
§ 95% compression
§ Data remains readily
available
COLD
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9. How the best manage data growth
From cradle to grave
Manage cold and old data using Nearline
Storage
Biggest potential in DSOs, but also helpful
in Cubes
Build a detailed housekeeping plan and
adhere to it. If possible automate.
© 2013 DataVard GmbH
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10. Wanna know how?
www.bwft.datavard.com
bwft@datavard.com

11. Copyright
Copyright DataVard GmbH. All rights reserved.
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 and OutBoard 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.
These materials are provided by DataVard GmbH and its affiliated companies (“DataVard") for informational
purposes only, without representation or warranty of any kind, and DataVard shall not be liable for errors or
omissions with respect to the materials. The only warranties for DataVard products and services are those
that are set forth in the express warranty statements accompanying such products and services, if any.
Nothing herein should be construed as constituting an additional warranty.
© 2013 DataVard GmbH
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