Smarter Data Science

CHAPTER 3Framing Part II: Considerations for Working with Data and AI

“I've learned that if you are not failing a lot, you're probably not being as creative as you could be—you aren't stretching your imagination.”

—John Backus

In Chapter 1, ”Climbing the AI Ladder,” we mentioned that “not all data can be considered equal.” Inequality is not just restricted to data. In many ways, the fact that the employees of the organizations we work for all have different jobs implies that we invariably will have different needs—we all don't have the same identical needs. Our unique needs define what data we use and how best we wish to consume that data. This chapter will lay the foundation as to why a modern environment for data analytics and artificial intelligence (AI) should not fixate on a single version of the truth paradigm.

AI can now be considered a necessary proxy for applying human knowledge, and the ripple effect of using AI will mean that the traditional ways that people work are going to be altered. The type of data that you'll need will change too. Beyond big data, specialized data that is far more precise in describing a real-world object (such as a person) or an event (such as a political rally or a business transaction) will be required, and so we will begin to examine how knowledge of ethnography can be blended with big data.

For AI to be of assistance, organizations must encapsulate knowledge in an ontology. An ontology is the result of organizing relationships or axioms ...