SAP Mentor Spotlight Interview: SAP HANA, Database & Analytics with Irfan Khan and Frank Schuler
|The SAP Mentor Spotlight Interview Series highlights key strategic topics, such as emerging technologies, learning, and other topics, and provides insights from Mentors and SAP leaders on turning ideas into innovative approaches that impact people, process, and technology.|
Ensuring a single source of truth for all data across applications, both on-premise and in the cloud, is of paramount importance to most organizations.
This data needs to be accessible to people, algorithms, and data-driven applications with online access. There are many advantages including scalability, growth, high availability, resilience, and artificial intelligence-based automation leading to data insights across the enterprise.
Recently, we had the opportunity to catch up with two leaders:
- Irfan Khan, President, SAP HANA Database & Analytics
- Frank Schuler, Senior Technology Architect, Correla, SAP Mentor
Together they shared a variety of observations on achieving data value with data intelligence.
Among the topics discussed:
- How managing data intelligence can turn data into actionable business insights.
- Simplifying the landscape so that data services can be provided quicker and more reliably to the market.
- Enabling near real-time SAP HANA data replication with SAP Data Intelligence.
- The ability of data and algorithms to make informed decisions with artificial intelligence.
- Application of machine learning to predict and help with automated incident/issue resolution.
- Innovating with the SAP Data Marketplace as part of Data Warehouse Cloud.
- Why next-gen students and recent graduates should join the field as data scientists.
During the session, Irfan shared, “I think it’s very clear that software architectures and landscapes themselves have to evolve in order to be able to deal with specific use case requirements and ultimately consistency.”
SAP and the SAP Community provide many SAP HANA, database, and analytics resources to support continuous learning around these topics.
How do you address data value and data intelligence? Let us know in the comments below!