Practical Data Science with SAP

Chapter 3. SAP for Data Scientists

Note

If you’re an SAP professional, you may not need much of the information in this chapter. We’re trying to get data scientists up to speed on things that you probably already know.

At Big Bonanza Warehouse, Greg and Paul¹ make up the entire data science team. They’re surrounded by delicious data everywhere they look: plant automation systems, transportation records for customer shipments, marketing campaign data, and the copious spreadsheets and Microsoft Access databases that seem to sprout up everywhere at big enterprises. They can’t get up to get coffee without hearing about another fascinating data opportunity. They’re simultaneously overjoyed and swamped: they get to come in and work on interesting problems every day, but there’s no way they can ever catch up to the insane backlog of data requests.

Well, of course, there’s one way they could catch up. They could dive in and learn SAP.

Because SAP is the leviathan that continues to swallow other Big Bonanza Warehouse systems whole. As Big Bonanza moves to consolidate its enterprise software resources into the SAP portfolio, more of that delicious data disappears into the belly of the beast. Greg and Paul know the amount of data—and therefore opportunity—in SAP boggles the mind. They just have no idea how to go poking around to get it. Talking to SAP end users doesn’t reveal the true data model, and talking to SAP administrators hardly goes anywhere, because they’re ...