AI in SAP Smooths Transition to S/4HANA

PCC's Article 2 of 4 in the series: Using AI in the SAP archiving reporting solution

By Jim Paschke, CEO, PCC Archiving Solutions

AI faces the co-challenges of having sufficient data volumes from Machine Learning and sufficient data processing resources to support AI.

Glenn Hopper, CFO of Oracle NetSuite, states in his Business Guide, Demystifying AI,?"AI systems, particularly machine learning models, rely heavily on large volumes of data to learn patterns and make predictions. In the 1980s and 1990s, the availability of digital data was limited, as the internet was still in its infancy, and many sources of information were not yet digitized. This lack of data hindered the ability of AI algorithms to learn effectively and achieve high levels of accuracy. Further, AI models . . . require significant computational resources to process and analyze data."

Meanwhile, the journey to SAP S/4HANA and HANA in the cloud is fueling the ubiquitous need for effective data archiving and, with it, significantly increased demands on reporting for that archived data. Companies are archiving their data faster than ever to keep their databases small and performant, which means more valuable data is stored in the archives.?

So, in our new AI-enabled world and S/4HANA in-memory processing, how do we apply AI and Machine Learning to all these S/4HANA journeys to keep them performant, cost-effective, and successful? Mr. Hopper thus begs the question, where do we get enough archive-centric and customer-relevant read performance data to allow our AI algorithms to effectively learn and then get enough computational resources to analyze this data quickly while reading archive data?

Solutions: using customer-specific data tracking to power Machine Learning and AI

Issue 1:? unique to PCC, we monitor and track the results and characteristics of every archive read in order to create each customer's own data lake based on empirical data from the customer's own unique instance. We never rely on inferences from how things worked at another customer or in another instance. We always rely only on how things are actually working for you, the customer, in your unique instance.?

Issue 2:? we created unique, proprietary pre-processing and post-processing routines that ingest the volumes of analytical archive read results in asynchronous tasks, both summing up and further characterizing the prior reads while getting ready for the next reads that we can anticipate once we identify your archive data consumption patterns.

These unique solutions to what Mr. Hopper identified as the historical challenges to effective AI are how PCC helps you derive more value from your archive store, conjoin your archive and database data, and help drive you to a successful S/4HANA implementation and continued business growth!