#BT Column – Data, and more data, everywhere

By Adrian Sobers

Harvard Business Review’s essay collection HBR at 100 is an absolute gem. One does not have to be in the C-Suite to benefit from this collection; we are all managing something if only ourselves/households. The first
chapter, Managing Oneself by Peter F. Drucker, is the perfect place to start and reinforces an important point that sometimes gets lost.

“Manners are the lubricating oil of an organization. It is a law of nature that two moving bodies in contact with each other create friction. This is as true for human beings as it is for inanimate objects. Manners—simple things like saying “please” and “thank you” and knowing a person’s name or asking after her family—enable two people to work together whether they like each other or not.”

“Bright people,” says Drucker, “especially bright young people, often do not understand this.”

Other stand-out essays, from an already stand-out collection include: Leading Change: Why Transformation Efforts Fail; The Small Wins; Harnessing the Science Power of of Persuasion; Barriers and Gateways to Communication, and one of their best-selling reprints, originally published in 1974, Management Time: Who’s Got the Monkey?

One essay, for which an updated version was published this year, warrants our attention here: Data Scientist: The Sexiest Job of the 21st Century by Thomas H. Davenport and D.J. Patil.

Originally published in 2012 the essay detailed the role of “people who can coax treasure out of messy, unstructured data.” The authors penned a follow-up in July to see if their analysis and claims from a decade ago still stands: Is Data Scientist Still the Sexiest Job of the 21st Century?

The title of Data Scientist was coined by one of the authors, D.J. Patil, in 2008. What on earth do they do? “Make discoveries while swimming in data. It’s their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible.”

In 2012 the role was relatively new and there were no formal Data Science degree programs or training options. In their follow-up article, the authors note the abundance of formal degree programs and other less formal options. There is now a segregation of roles that companies previously tried to squeeze out of one individual. These include “machine learning engineer, data engineer, AI specialist, analytics and AI translators, and data oriented product managers.”

Contrary to the science-fiction stories in popular culture, the authors of The Business of Artificial Intelligence (2017) make the point that “machine learning systems hardly ever replace the entire job, process, or business model. Most often they complement human activities, which can make their work ever more valuable.”

One of the authors of this article, Erik J. Larson, wrote the book The Myth of Artificial Intelligence; a long overdue counter to the tiring exuberance that often accompanies AI in general and robotics in particular. (Highly recommended.)

In the introduction to his book Larson further explains that, “Data science (the application of AI to “big data”) is at best a prosthetic for human ingenuity, which if used correctly can help us deal with our modern “data deluge.” If used as a replacement for individual intelligence, it tends to chew up investment without delivering results.”

Readers might be familiar with companies like Statista that provide information on: The Most Profitable Companies in the World; The World’s Top Super Computers; The Countries Most in Debt to China; The Richest People in Africa; The World’s Oldest Constitutions, and so on.

While the usual absolute, point-in-time numbers will always be with us, “X” number of widgets sold or customers served is increasingly becoming a relic of the past. Modern organizations increasingly rely on data in motion that provides them with a real-time comprehensive view of how people are using their products and services.

A comment on the title of Jordan Morrow’s Be Data Literate doubles as a good place to end and an action item. Morrow’s basic point is that while everyone does not need to be a data scientist or statistician, we all need to work on being more data literate. Data literacy is not an “IT”/technical thing, it is a basic requirement for working in the modern world thing.

The authors of another HBR essay, The Digitally Literate Organization, mention the need to develop “a digital mindset”, namely, “a set of attitudes and behaviours that enable people and organizations to see how data, algorithms, and AI open up new possibilities and to chart a path for success in an increasingly technology-intensive world.”

AI, and algorithms won’t replace people and companies, but the people and companies that use AI, algorithms (and improve their data literacy), will replace those that don’t.

Adrian Sobers is prolific letter writer and contributor on issues of national interest.