Practical Data Science with SAP

Chapter 10. Conclusion

With this chapter our journey comes to an end. We bid farewell and wish you the best in your continued travels with data science and SAP. As a conclusion, we’d like to revisit the original mission, recap what has been covered in the previous nine chapters, give you some tips and recommendations, and finally provide ways we can keep in touch.

Original Mission

We have been promised everything from self-driving cars (which, despite advancements, have not yet been widely manufactured) to AI we fall in love with (as depicted in the films Ex Machina and Her). We are also warned of a grim and desolate future in which we are replaced in the workforce by our own creations. These juxtaposing visions undermine the practical value of data science. The field of AI and data science has encountered a number of winters in its history. These were periods of marked hype followed by disappointment and a loss of interest. There is unfortunate speculation that we are entering, or even currently in, another downturn of interest. We hope to have shown in this book the immediate value simple machine learning methods can provide to enterprise data. When used with SAP data in particular, data science and AI aren’t overhyped—they’re underdelivered.

What we wanted most to do in this book was to build a bridge between business analysts and data scientists. Business analysts often have a clear understanding of their company’s data and business processes. However, they lack ...