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The 9th of March 2018 SAP held a platform summit at the SAP HQ in the Netherlands. The worlds of Datawarehouse Experts, Cloud Platform Design Thinkers and System Architects clashed with each other, and everyone learned a thing or two. Also, these worlds will collide more often in the Platform Architecture strategy of SAP.

Intelligent enterprise session

The platform strategy was further outlined in the Intelligent Enterprise session, fueled by SAP Leonardo. This platform  presents itself as a machine learning and analytics base with flexible building blocks, which adheres to the general Cloud Platform setup. This is a vast innovation enabler: you can connect new services and products to the data core like Lego’s on a base plate. This notion is also strengthened by the consumption pricing model.

Having a flexible platform enables Rapid Application Development. Additionally, this flexibility reduces the need of a long lifespan for applications: if the development time fits in a couple of weeks, why not build an app which is only needed for three months?  This changes the game from data- and process providing applications to user oriented applications, which will improve experience and quality of data. Together with the  notion to drive continuous innovation, these were the key aspects to take home for me.

Big data and analytics session

This session started with the notion that we are going from a central relational data warehouse architecture towards distributed platform based architecture. Data will be less structured and come in large volumes, while accessibility is more important. In other words, it is the case to be able to quickly refine and orchestrate your data towards the appropriate temperature.

This can be done with SAP Datahub. This product takes care of analysis, refinement and orchestration of data in various forms, on-premise or cloud and supports multiple vendos. What followed were different scenarios of data warehousing for IoT and financial cases for example. It was a bit overwhelming but the key notion was loud and clear: we want to leave the data at its place as much as possible, we just want to expose it in our processes and applications.  And I agree with this: migration of data sources for the sake of availability is not a strong strategy.

To conclude

The Big Data and Analytics session got me to realize that there is still a lot of complexity in defining the landscape until rapid application prototyping really can be fruitful. Before this, the Data Warehouse Expert and Architect need to setup a solid landscape and expose the required data sources. It shows an continuous collaboration between these roles in a Platform Architecture strategy in order to drive innovation, a complex yet exciting process for which I cannot wait to be involved in the future!

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