Do you know why most enterprise AI projects fail, not because of bad algorithms, but because of the exact data problem sitting quietly underneath them? This session steps back from AI models and orchestration entirely to cover something more foundational: how SAP HANA and SAP Datasphere build the trusted data foundation every AI initiative depends on. You'll see why "IBM," "International Business Machines," and "I.B.M." can confuse an AI model into treating one customer as three, and exactly how entities, KPIs, and spaces solve that problem at enterprise scale.
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Chapters:
00:00 Introduction & Data Foundation Overview
07:58 SAP HANA Overview
16:05 HANA Performance
25:53 Built-In Predictive Analytics
38:53 SAP Datasphere Overview
44:30 Business Data Fabric
58:30 Data Modeling
01:15:57 Datasphere UI Demo
01:25:02 Data Quality & Governance
01:36:12 Cross-Space Collaboration
01:46:26 Real-Time vs Batch Processing
In this video, you'll learn:
Why AI is only as good as the data it learns from, and why most AI projects fail due to fragmented data and poor governance, not weak algorithms
How SAP HANA's in-memory processing enables real-time analytics, risk scoring, and dynamic pricing instead of slow batch reporting
What SAP Datasphere is as a business data fabric, unifying data from HANA, SuccessFactors, Salesforce, Microsoft Dynamics, and other third-party systems
How semantic modeling turns confusing technical database fields into meaningful business entities like customer, product, and sales order
The difference between entities, metric models, and KPIs, and why well-defined KPIs directly improve the quality of AI-driven predictions
Why data lineage matters: being able to trace exactly which source, formula, and currency conversion produced a specific KPI value
How Datasphere's "space" concept creates secure, independent workspaces (finance, marketing, etc.) with their own roles, while still connecting to a shared enterprise data model
This is the unglamorous but essential layer that makes every AI feature from earlier sessions actually trustworthy in production. Skip this, and even the best-configured model will confidently give you wrong answers. The next session returns to hands-on AI development, now grounded in this proper data foundation.
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