SAP Business Technology Platform brings together application development, automation, data and analytics, integration, and AI capabilities into one unified environment optimized for SAP applications. Learn more: sap.com/btp
We are back with 3 top innovation highlights for SAP Business Technology Platform. Thanks to the newly released features for the Data & Analytics and Artificial Intelligence capabilities, you can easily access and enhance your data even if it’s stored “anywhere and everywhere”, stop staring endlessly at a loading screen AND leverage machine learning capabilities for specific uses cases, like completing fields in a sales order document. You might be thinking: “Are you sure that’s all covered in these 3 innovations?”. The answer is: “Yes, of course.” Such is the power of SAP Business Technology Platform.😊
For those avid readers out there, we’ve also got the “News in Print”
Data & Analytics Innovations on SAP BTP
SAP Datasphere - Catalog
I’m sure several of you have already heard about SAP Datasphere, the next generation of SAP Data Warehouse Cloud. But just in case, let’s start with the basics: SAP Datasphere is a comprehensive data service that enables every data professional to deliver seamless and scalable access to mission-critical business data. And yes, it runs on SAP Business Technology Platform.
As a data professional, you know that keeping track of data across multiple sources can prove to be difficult and time consuming. The new SAP Datasphere Catalog on SAP BTP can help by not only simplifying your landscape but also enhancing your data.
In the Catalog, you can search for data across multiple sources. Once you’ve found what you’re looking for, you can drill into an asset to learn more about it.
By looking at its lineage, you can learn more about where your data came from.
You can also link your assets to glossary terms, tags or KPIs to further define, categorize, or measure your data.
SAP Datasphere - Glossary
SAP Datasphere - KPI
To learn more about harmonizing and enriching your data in the Catalog, check out SAP Datasphere.
SAP Analytics Cloud - New Features for Data Analyzer
Waiting for tables to load while exploring your data is not the best way to spend your time. Thanks to some of the latest innovations released on SAP Analytics Cloud running on SAP BTP, you won’t have to worry about that.
We’ve added new features that will optimize performance and improve the end user experience.
In Data Analyzer, we have added:
Lazy loading on the table for scrolling, which improves the perceived performance and enables smooth scrolling through large datasets
Drag and drop of dimensions and measures to rearrange them within the table directly,
An adaptive layout with automatic sizing of the table to fit the number of columns
New sort icons to provide more flexibility when looking at your data
Usability enhancements also include:
A new and central filter bar (similar experience as within stories)
A one-dimension filter
A new filter dialog with copy-paste
Also, the navigation panel has been optimized and now supports BW 2-structure-queries.
You can either access the new Data Analyzer from the main menu or open it from the table context menu in an optimized story.
Applying machine learning capabilities to existing business processes can be complex and time consuming, especially if you need to build them from scratch.
SAP AI Business Services - Architecture
With Data Attribute Recommendation, one of the SAP AI Business Services running on SAP BTP, you can make use of a cloud-based machine learning service that is capable of processing classification tasks by using business blueprints with a dedicated pre-processing pipeline, tailored to specific use cases.
The latest addition is the newly available business blueprint for completing sales order documents.
Sales Order Completion Business Blueprint
This new functionality not only provides the possibility to predict missing fields in sales order documents but also qualitative metrics for each field you want to predict.
The business blueprint also recognizes and takes care of imbalanced datasets so that the performance feedback of each label properly reflects the reality.
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