Most SAP teams do not have one data pipeline. They have five, six, or more. Finance needs extracts in Excel. The BI team needs the same data in Power BI. The data engineering team loads it into Snowflake. Data science works in Databricks. And somewhere along the way, someone is manually archiving files to a data lake. Each pipeline is built and maintained separately. Each one pulls from SAP independently. And each one introduces its own risk of inconsistency, delay, and manual error. The DVW Flow Tool eliminates this problem. One visual workflow extracts data from SAP once, transforms it once, and delivers it to every destination in parallel. No duplication. No scripting.  Every AI model depends on the data it is built on. For enterprise AI projects, that data almost always includes data from SAP. And getting SAP data into a state that an AI workflow can use is rarely straightforward.
This post covers the data preparation problem specifically: what it involves, why SAP makes it harder than most other data sources, and how DVW Analytics products address it as part of a broader AI architecture. |
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