Greg Foss - Practical Data Science with SAP: Machine Learning Techniques for Enterprise Data

by Greg Foss and Paul Modderman

Copyright 2019 Greg Foss and Paul Modderman. All rights reserved.

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The future of data science and artificial intelligence has never looked brighter. AI now beats humans at games ranging from twitchy, reflexive Pong to deep, contemplative Go. Deep learning models recognize objects nearly as well as humans. Some even say self-driving cars perform better than their distracted human counterparts. The past decades massive gains in data volume, storage capacity, and computing power have enabled rapid advances in data science.

And of course technology has spread into every facet of your business (from finance and sales to production and logistics). However, is each part of your business turbocharged by data science and AI? Likely not. As wonderful as they might be, if you are not designing a self-driving car or predicting customer behavior, you are probably not using these technologies.

Many organizations may have access to business data from an enterprise resource planning (ERP) system such as SAP, and yours is likely no different. Data coming from a business system such as SAP is largely perfect as often validations and checks are in place before it is allowed to save to the database (and, one of the most essential and least rewarding tasks of a data scientist is cleaning the data). This means ERP data in SAP is ripe for the picking, and data science is here to do the harvesting!

Lets take a hypothetical scenario. The SAP Team at Big Bonanza Warehouse is in a constant state of process improvement. They know how to configure their SAP system to do the tasks their users want, and they play that system like a fiddle, dutifully taking requests and delivering solutions. However, there is a bit of a problem with reporting and analytics; they have a data warehouse and a business intelligence system, but developing reports is a multimonth process. The team often resorts to using standard ALV (ABAP List Viewer) reports, which are quite limited in power because they require a developer to code; in addition, it is very hard to harness the wealth of public data that could be used in conjunction with SAP. Just like at countless other enterprises, SAP data at Big Bonanza Warehouse is an island, siloed within its own system. Teams that dont work with SAP have no idea whats in there, and the teams that do work with it spend so much time maintaining the systems that they dont get the chance to look outside them.

SAP data shouldnt be an island, though. The team knows their data, how to find it, and what they want to do with it. However, when it comes to analyzing that data, everyones hands are tied by that multimonth report development process.

Sound familiar? Its the story at nearly every SAP shop with whom weve ever worked. And thats a lot in our combined 30+ years of experience.

We want to give that SAP team (and yours!) some modern insighttools and techniques they can use without defining data cubes, data warehouse objects, or learning complex frontend reports. In this book, well present simple scenarios such as dumping data straight out of SAP into a flat file and into a reporting tool. This is useful for ad-hoc reporting and investigations. Well also consider more complex scenarios, including using extractor tools and neural network models in the cloud to analyze data in ways not possible within SAP or contemporary data warehouses.