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TammyPowlas
Active Contributor


Source: SAP


Figure 1
So much data, and so little time available.

Do you have a large system that you want to convert to SAP S/4HANA? Do you have too much data and too little downtime available to move?

In addition to being vital in the battle to maintain a manageable system size during business operations, SAP Data Volume Management is an integral part of your SAP S/4HANA conversion project to help you reduce the existing volume of data in your system as well as growth rates, both of which are crucial to ensuring a manageable downtime during the actual migration.

 

Source:  <https://www.asug.com/events/manage-time-and-data-volume-during-sap-s-4hana-conversion>


Source: SAP


Figure 2

Agenda is shown above


Source: SAP


Figure 3

Purpose of data volume management is to control your growth rates in your SAP system

How to reduce volume

Data avoidance - do not write data unnecessary data to database

Turn on summarization in FICO

Housekeeping - deletion reports available

Archiving will have a positive impact if you have an old system

Can use DVM in Solution Manager

If using SAP HANA, option to use DVM dashboard in one support launchpad

 

Why?



Source: SAP


Figure 4

Only bring over the data you require to S/4HANA

Benefits include less hardware, cost is smaller with less data, shorter conversion times


Source: SAP


Figure 5

Scenarios to move to S/4HANA

What to highlight when run conversion


Source: SAP


Figure 6

Early watch services

Statistics on the age footprint of data

After 2 years, value of data is less; not accessed frequently, think of data archiving


Source: SAP


Figure 7

What are the challenges?

Why do we do this?

Old data to migrate to S/4HANA and where the data structures are checked, old data may fail checks, and you may see data quality issues

Archive vs. delete; decision to make with the users

Future reporting requirements; S/4HANA has some built-in analytics

Data tiering concepts - what data to focus on

Custom code adjustments for archiving or data aging

New models and applications

After migration, adjust technology

  • Clustered tables before S/4HANA, declustered after


 

How to prioritize?



Source: SAP


Figure 8

Data quality - run a test conversion

You can run a financial data quality service via SAP Support

Do as early as you can

Archive as much as possible

Conversion run times - reduce size of clustered tables will help reduce run times

Check the sizing report

DVM is in readiness check


Source: SAP


Figure 9

Sizing report will tell you what to expect and what to pay for

DVM is not a must if you are OK with sizing report results


Source: SAP


Figure 10

Clean up results, some calculations based on data aging

Obsolete data can be deleted


Source: SAP


Figure 11

Largest tables

Look at deletion or archiving before S/4HANA conversion


Source: SAP


Figure 12

Readiness check sizing has a DVM area

Reduction based on 24 or 12 months


Source: SAP


Figure 13

Sizing and archiving potential


Source: SAP


Figure 14

SolMan Launchpad

Reorg and compression can be simulated

Forecasting and simulation - how big system will be

Archive or delete data

If not migrate for some time - stop data going into the system


Source: SAP


Figure 15

Guided self service, one of the HANA quick wins


Source: SAP


Figure 16

Analyze data from ECC system

Might see data outside of your retention policy, which could be a "quick win"


Source: SAP


Figure 17

DVM available on launchpad, must be on HANA

See tiles - top left, memory statistic - used and free

2 upper right- disk stats - used and free

3 (middle) memory optimization statistics

4 - click into it to see archiving/deletion object

5 - additional scripts that can be run on data collected

6 - age of data in system (bottom middle)

 

How access archived data in S/4HANA?



Source: SAP


Figure 18

See changes in data for accounting

Archived data stays in old data structure


Source: SAP


Figure 19

Example archive access

Can't access classic FI archived files


Source: SAP


Figure 20


Source: SAP


Figure 21

Define info structure on your own; see SAP note to see fields necessary


Source: SAP


Figure 22

More examples

CO_ITEM is replaced by CO_TRANS


Source: SAP


Figure 23

Changes as a result of S/4HANA


Source: SAP


Figure 24

Best practice guide

https://wiki.scn.sap.com/wiki/display/TechOps/Data+Volume+Management

Have you started archiving as part of your journey to S/4HANA?
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