Proceed data deep-dive in action: Halving the database size for cloud and S/4HANA in just 6 months 

Recently, we embarked on a transformative project with a large utilities company located in the northern United States. Utilities companies often grapple with more complex business processes than those in other sectors. This particular client was no exception, with many customers having a unique payment setup, resulting in the generation of an overwhelming number of records—ranging into the millions and billions—each month. Handling customer data brings with the need to adhere to strict legal and industry regulations, which adds to the complexity. 

Over 13 years of SAP use, the client’s database had grown to a staggering 40 terabytes by the end of May 2022, and it was continuing to grow organically at a rate of 1.5 terabytes monthly. Despite ongoing archiving efforts, the database size continued to climb, prompting an urgent need for a solution. 

The challenge at hand

Our client had their sights set on a cloud migration and a future transition to S/4HANA. The objective was to roll out this transition in stages over the coming 2-3 years. The most significant hurdle in their path was the sheer size of their database, which had grown beyond 40TB. The risks, costs, and dependencies involved in migrating such a massive database to the cloud and subsequently to S/4HANA were monumental. 

The critical challenge was to downsize the database size by approximately 50% within a 12-18 month timeframe. This reduction was a prerequisite before any further migration strategies could be considered. Recognising the gravity of the task, the client approached us for a robust and effective plan to manage this undertaking. 

The solution and its impact: Proceed’s data deep-dive

Utilising the data deep-dive, we were quickly able to identify the potential for substantial data reduction. Remarkably, we expedited our data analysis by 80%, sidestepping the extensive costs and time of traditional consultancy. With this accelerated insight, we provided the roadmap to reduce the database to 25TB in just six months. 

An analysis of existing archiving procedures and objects identified an immediate opportunity to eliminate 2TB of data. Additionally, the introduction of three new archiving objects enabled the removal of a further 1TB. A substantial 2.5TB was liberated by migrating unstructured data, such as documents and attachments, to an external content server. 

The most formidable challenge, however, was aligning business processes and encouraging business users to embrace a more aggressive archiving approach—particularly concerning the ‘residency period’ for critical documents like billing, invoices, and financial postings. After several developments, configuration changes, and extensive testing, we received the green light to decrease the residency period from six years to three. This step alone contributed to a reduction of 10TB after archiving an additional three years’ worth of historical data. 

Recommendations for further enhancements

Not stopping there, we proposed additional measures to reduce the database to 15TB by the third or fourth quarter of 2023. The strategy included database segregation, splitting the 15TB into 11TB of table space and 4TB of indices. This reorganisation would facilitate a smoother transition to a 10TB HANA system upon migrating to S/4HANA, given that only tables are moved during such migrations—HANA has its mechanisms for index management. 

Our projections showed that the migrated 11TB of tables would require 6-7TB of HANA memory, plus an additional 3-4TB for headroom, comfortably fitting into a 10TB HANA environment. 

Additional enhancements included: 

• Taking advantage our Proceed Archiving Service to manage data and archiving schedules to ensure the database size remains low 
• Reducing the residency period for billing and print documents from three years to two. 
• Configuring archive-link for OpenText VIM Z Table to store documents directly in OpenText. 
• Implementing Index Reorganisation for further potential reduction. 

The project not only illustrated the prowess of the data deep-dive but also showcased the benefits of strategic data management and the importance of a collaborative approach in tackling such large-scale IT challenges.