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Author's profile photo Robert Heer III

Move lean to SAP S/4HANA

Considering the ever-increasing growth in the volume of data, and the costs and operating expenses attached, it is important to consider what your intentions are, and what you aim to achieve with your data, before beginning your SAP S/4HANA conversion project.

Moving along with data no longer in use is hardly profitable. It’s a clean digital core which will offer more opportune innovation. When working with high volumes of data, thinking lean, and asking the questions that your data can speak to is the key to running a healthy data volume management strategy and effectively utilizing your data.

With the help of our SAP Enterprise Support value map for data volume management, an exclusive offering of the SAP Enterprise Support Academy, you are able to significantly reduce your database size and apply strategies that help you to reduce the growth rate of your system going forward.

Join the value map on data volume management to make sure your systems are lean and clean before your move to SAP S/4HANA.

Note: To register for the value map, you may require access to SAP Learning Hub, edition for SAP Enterprise Support. Already-registered members can access the value map directly via the SAP Enterprise Support Value Maps learning room

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      Author's profile photo Jelena Perfiljeva
      Jelena Perfiljeva

      Sorry but this is an ad or a press release, at best, not a blog post. It's classified as "technical article" but there is absolutely no technical information. No personal insights either.

      I could at least understand posting an ad like this on Twitter or LI but from an SCN blogs the readers usually expect more detailed information that can't be easily found in Google. Very disappointing.

      Author's profile photo Robert Heer III
      Robert Heer III
      Blog Post Author

      Thank you for your comments regarding this post. I agree that the classifications may be debatable. The intention of course is to inspire a healthy perspective when looking into system size and growth, as removing residual data to gain more efficiencies, will release more innovative opportunities.