AI-Enabled Analytics for Business

CHAPTER 3Myths and Misconceptions About Analytics

Myths that are believed tend to become true.

—George Orwell¹

It is natural to accept “common knowledge,” particularly when our knowledge of a subject is limited, regardless of whether that knowledge is in fact true. For example, most of us have heard of a 97% consensus among scientists that humans are causing global warming or that inhaling secondhand smoke causes cancer. However, there are many peer-reviewed publications from sources ranging from Houston University to the Journal of the National Cancer Institute that dispute these claims, yet most of us have not heard of these contradictory findings.^2,3

Similarly, there is the need to dispel some common knowledge about implementing AI-enabled analytics. One example is that implementing analytics is complex and expensive. However, as we discuss in detail in the chapters that follow, the Roadmap to AI-enabled analytics is not hard, long, or expensive—it is simply disciplined.

Executives must take heed to avoid the “myths and misconceptions” about an Analytics Culture that include (i) data scientist misconception and myth, (ii) shot in the dark, (iii) bass-ackward, (iv) AI is not IT, (v) big is not better, and (vi) not now. As we will explain, data scientists, consultants, and IT all have roles in AI and analytics; but when implementing an analytic culture, the user is the primary player, with all others playing contributing roles. Too often, the roles are reversed, and the ...