Last month, tobias.koebler and I participated in an SAP Community Call and Q&A that was sharing the latest and greatest updates on SAP’s comprehensive data management solution SAP Data Intelligence. We conceived this call as a great chance to not only share our thoughts on what we’re aspiring to achieve but also to gather worthwhile feedback from the community. As a matter of fact, there were many great questions ranging from the strategical ones to the rather technical ones and we aim to use this blog to provide a quick recap of the call.
As already outlined in this blog post by Gerrit Simon Kazmaier, there is a crucial difference between collecting data and utilizing data. The latter, in particular, implies to be capable of leveraging the very potential of the data that has been gathered from various sources across the connected enterprise landscape. However, numerous enterprises are struggling to achieve a satisfying data utilization ratio due to various reasons.
Combing the invaluable customer feedback with the latest state of research it turned out that four key pillars are heavily influencing the data utilization ratio: Span, Amount, Quality, Usage. Incorporating these findings into a holistic architectural approach we can state that the SAP HANA & Analytics portfolio is providing the basis to streamline the data utilization ratio in the enterprise context.
As mentioned above, the aim of the community call was to give an update on the strategy of the solution SAP Data Intelligence (be it the Cloud Edition or the on-premise offering) which at the same time represents a main pillar of the above architecture.
SAP Data Intelligence itself represents a comprehensive data orchestration and data management solution which is running on a Kubernetes environment. It moreover can be utilized to leverage open source technologies and to operationalize selected Machine Learning scenarios (for more information we refer to the blog post by Marc Hartz).
In the proposed architecture from above, SAP Data Intelligence takes over the responsibility to establish connections to a tremendous network of data sources whose ratio of change might highly vary.
SAP Data Intelligence: A new focus for 2020 going forward
There were many questions around the new focus of SAP Data Intelligence that has been rolled out for the year 2020 going forward.
First of all, a final renaming of SAP Data Hub to SAP Data Intelligence has taken place in order to unify the naming of the offering independent of the chosen deployment approach. Precisely, when SAP Data Intelligence 3.0 is applied, SAP Data Hub’s existing customers can benefit from additional tooling as the integrated Jupyter Lab environment and there is no additional fee to be charged or any migration required.
Moreover, the key topic for SAP Data Intelligence throughout this year is set on data management and data integration. As a consequence, a huge investment will be made to both enhance the already existing integration features to both SAP as well as Non-SAP and to offer new integration capabilities going forward.
This endeavor goes together with the goal to intensify and streamline the integration to the overall SAP Enterprise Information Management (EIM) portfolio. As a matter of fact, when taking a close look at the solutions being part of the EIM portfolio like SAP Data Services, SAP Information Steward, SAP LT Replication Server, SAP Smart Data Integration etc. one quickly realizes that there are certain overlaps with SAP Data Intelligence with respect to positioning as well as functionalities offered.
To simplify our future portfolio in the area of Enterprise Information Management we aim to gradually harmonize the existing offerings to streamline the customer experience in this very important area. However, to be transparent in this regard, this is nothing which will be done from today until tomorrow and that is why this kind of simplification is planned to happen medium to long-term.
In the meantime, in order to protect existing customer investments in the EIM area, we first aim to intensify the (technical) integration of selected components of the EIM portfolio into SAP Data Intelligence. To give two examples, it is
So, short and medium term, the goal is to complement the SAP EIM solution portfolio with SAP Data Intelligence to gradually modernize the existing customers’ landscapes whenever this makes sense from a future architectural perspective.
Last but not least, we have received a couple of questions with respect to the positioning of the Machine Learning tooling included in SAP Data Intelligence. As a matter of fact, we have experienced that the vast majority of our customers is interested in accomplishing Machine Learning related use cases in combination with Data Management & Data Integration challenges. As a consequence, we are going to concentrate on embedding functionalities to operationalize ML related scenarios into overarching platform related features of SAP Data Intelligence.
At the end of this blog, Tobias and I would like to take the opportunity to thank everyone that has participated at the SAP Community Call about SAP Data Intelligence. We are already looking forward to getting the chance to have another SAP Community Call again soon. If you are eager to learn more until then you can for instance
Please let us know what you think!
Best regards from Walldorf! Stay safe and healthy!
Christian & Tobias
As already outlined in this blog post by Gerrit Simon Kazmaier, there is a crucial difference between collecting data and utilizing data. The latter, in particular, implies to be capable of leveraging the very potential of the data that has been gathered from various sources across the connected enterprise landscape. However, numerous enterprises are struggling to achieve a satisfying data utilization ratio due to various reasons.
Combing the invaluable customer feedback with the latest state of research it turned out that four key pillars are heavily influencing the data utilization ratio: Span, Amount, Quality, Usage. Incorporating these findings into a holistic architectural approach we can state that the SAP HANA & Analytics portfolio is providing the basis to streamline the data utilization ratio in the enterprise context.
The SAP HANA & Analytics portfolio at a glance
As mentioned above, the aim of the community call was to give an update on the strategy of the solution SAP Data Intelligence (be it the Cloud Edition or the on-premise offering) which at the same time represents a main pillar of the above architecture.
SAP Data Intelligence itself represents a comprehensive data orchestration and data management solution which is running on a Kubernetes environment. It moreover can be utilized to leverage open source technologies and to operationalize selected Machine Learning scenarios (for more information we refer to the blog post by Marc Hartz).
In the proposed architecture from above, SAP Data Intelligence takes over the responsibility to establish connections to a tremendous network of data sources whose ratio of change might highly vary.
SAP Data Intelligence: A new focus for 2020 going forward
There were many questions around the new focus of SAP Data Intelligence that has been rolled out for the year 2020 going forward.
SAP Data Intelligence: new focus for the year 2020 going forward
First of all, a final renaming of SAP Data Hub to SAP Data Intelligence has taken place in order to unify the naming of the offering independent of the chosen deployment approach. Precisely, when SAP Data Intelligence 3.0 is applied, SAP Data Hub’s existing customers can benefit from additional tooling as the integrated Jupyter Lab environment and there is no additional fee to be charged or any migration required.
Moreover, the key topic for SAP Data Intelligence throughout this year is set on data management and data integration. As a consequence, a huge investment will be made to both enhance the already existing integration features to both SAP as well as Non-SAP and to offer new integration capabilities going forward.
This endeavor goes together with the goal to intensify and streamline the integration to the overall SAP Enterprise Information Management (EIM) portfolio. As a matter of fact, when taking a close look at the solutions being part of the EIM portfolio like SAP Data Services, SAP Information Steward, SAP LT Replication Server, SAP Smart Data Integration etc. one quickly realizes that there are certain overlaps with SAP Data Intelligence with respect to positioning as well as functionalities offered.
To simplify our future portfolio in the area of Enterprise Information Management we aim to gradually harmonize the existing offerings to streamline the customer experience in this very important area. However, to be transparent in this regard, this is nothing which will be done from today until tomorrow and that is why this kind of simplification is planned to happen medium to long-term.
In the meantime, in order to protect existing customer investments in the EIM area, we first aim to intensify the (technical) integration of selected components of the EIM portfolio into SAP Data Intelligence. To give two examples, it is
- possible to use SAP LT Replication Server to write data into SAP Data Intelligence. This, in particular, is crucial when it comes to questions around the integration of ABAP -based systems in SAP Data Intelligence (in this context we refer to this blog post).
- planned with the next release of SAP Data Intelligence to support the import of SAP Information Steward Metapedia terms into the SAP Data Intelligence Business Glossary.
So, short and medium term, the goal is to complement the SAP EIM solution portfolio with SAP Data Intelligence to gradually modernize the existing customers’ landscapes whenever this makes sense from a future architectural perspective.
Last but not least, we have received a couple of questions with respect to the positioning of the Machine Learning tooling included in SAP Data Intelligence. As a matter of fact, we have experienced that the vast majority of our customers is interested in accomplishing Machine Learning related use cases in combination with Data Management & Data Integration challenges. As a consequence, we are going to concentrate on embedding functionalities to operationalize ML related scenarios into overarching platform related features of SAP Data Intelligence.
At the end of this blog, Tobias and I would like to take the opportunity to thank everyone that has participated at the SAP Community Call about SAP Data Intelligence. We are already looking forward to getting the chance to have another SAP Community Call again soon. If you are eager to learn more until then you can for instance
- Check out the blogs in the SAP Data Intelligence community: https://blogs.sap.com/tags/73555000100800000791/
- Register for the interactive demo:
https://www.sap.com/cmp/td/sap-data-intelligence-demo.html - Request a code jam in your area: https://community.sap.com/events/codejam
- Access white papers, blogs, articles via our product page: https://www.sap.com/dataintelligence
Please let us know what you think!
Best regards from Walldorf! Stay safe and healthy!
Christian & Tobias
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