We have covered a broad range of topics thus far. We have looked at technologies used for big data, for data science, and for machine learning. We have learned about how companies are implementing their big data corporate strategies. We have also developed a handful of real-world applications along the way.

This chapter discusses the practical considerations of big data or data science initiatives at corporations. The field is continually evolving, with the introduction of newer technologies, newer open source tools, and new concepts in data mining. Due to this, organizations of all sizes share common challenges.

Data science success stories are everywhere in the media. In fact, most, if not all, of the investment happening in technology today has some connection to aspects of data science. Indeed, it has become an indispensable and integral aspect of IT development.

In this chapter, we will discuss a few of the common themes of implementing data science, the shared challenges, and what you can do to make your initiative successful. Further, we’ll look at major successes in data science as well as examples where data science failed to live up to its promise. We’ll also provide a set of links to resources where you can go to learn more about the relevant topics.

The following subjects will be covered in this chapter:

• Corporate big data and data science strategy
• Ethical considerations
• Silicon Valley and data science
• The human factor
• Links for further reading