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

# 3

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

# 4

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System Activity- Which are the outdated users?- What are the most frequent system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?

# 5

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

# 7

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

# 8

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

# 9

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

# 10

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.

# 11

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

# 12

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.

# 13

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

# 14

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

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