Getting Your Data Ready for AI

Abstract

This report briefly discusses an aspect of artificial intelligence (AI) and data science that is critical but rarely addressed: data preparation. The report first provides a brief overview of the disciplines of AI and data science. It then outlines a typical AI workflow, with a focus on the phases involved in data wrangling, before defining the challenges associated with those phases. Finally, it presents various solutions to these challenges, with special emphasis on IBM Watson Studio. Along the way, you will gain insights into the various types of data used for AI, the different flavors of AI (including machine learning), and possible long-term ramifications of the growing use of these technologies.

The State of Artificial Intelligence

The development of ever-faster computers, improvements in AI algorithms, and the exponential increase in available data—most notably unstructured data like audio, video, and photos, often referred to as big data—has led to an increased interest in AI, particularly in the business sphere. Indeed, according to a 2017 report issued by The Boston Consulting Group and MIT Sloan Management Review, “three-quarters of executives believe AI will enable their companies to move into new business” and “almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage.”

This same report reveals something else, however: as interesting as executives find AI—and its various subdisciplines, ...