Читать книгу Becoming a Data Head - Alex J. Gutman - Страница 26

As established at the beginning of this book, data touches the lives of many of today's corporate workers. We came up with the following avatars to represent people who will benefit from becoming a Data Head:

 Michelle is a marketing professional who works side-by-side with a data analyst. She develops the marketing initiatives and her data coworker collects data and measures the initiatives’ impact. Michelle thinks they can do more innovative work, but she can't articulate her data and analysis needs effectively to her data coworker. Communication between the two is a challenge. She's Googled some of the buzzwords floating around (machine learning and predictive analytics), but most of the articles she found used overly technical definitions, contained indecipherable computer code, or were advertisements for analytical software or consultation services. Her search left her feeling more anxious and confused than before.

 Doug has a Ph.D. in the life sciences and works for a large corporation in its Research & Development division. Skeptical by nature, he wonders if these latest data trends are akin to snake oil. But Doug mutes his skepticism in the workplace, especially around his new director who wears a “Data is the New Bacon” t-shirt; he doesn't want to be viewed as a data luddite. At the same time, he's feeling left behind and decides to learn what all the fuss is about.

 Regina is a C-level executive who is well-aware of the latest trends in data science. She oversees her company's new Data Science Division and interacts with senior data scientists on a regular basis. Regina trusts her data scientists and champions their work, but she'd like to have a deeper understanding of what they do because she's frequently presenting and defending her team's work to the company's board of directors. Regina is also tasked with vetting new technology software for the company. She suspects some of the vendors’ claims about “artificial intelligence” are too good to be true and wants to arm herself with more technical knowledge to separate marketing claims from reality.

 Nelson manages three data scientists in his new role. A computer scientist by training, Nelson knows how to write software and work with data, but he's new to statistics (other than one class he took in college) and machine learning. Given his somewhat related technical background, he's willing and able to learn the details, but simply can't find time. His management has also been pushing his team to “do more machine learning,” but at this point, it all seems like a magic black box. Nelson is searching for material to help him build credibility within his team and recognize what problems can and cannot be solved with machine learning.

Hopefully, you can identify with one or more of these personas. The common thread among them, and likely you, is the desire to become a better “consumer” of the data and analytics you come across.

We also created an avatar to represent people who should read this book but probably won't (because every story needs a villain):

 George: A mid-level manager, George reads the latest business articles about artificial intelligence and forwards his favorites up and down his management chain as evidence of his technical trendiness. But in the boardroom, he prides himself on “going with his gut.” George likes his data scientists to spoon-feed him the numbers in one or two slides, max. When the analysis agrees with what he (and his gut) decided before he commissioned the study, he moves it up the chain and boasts to his peers about enabling an “Artificial Intelligence Enterprise.” If the analysis disagrees with his gut feeling, he interrogates his data scientists with a series of nebulous questions and sends them on a wild goose chase until they find the “evidence” he needs to push his project forward.

Don't be like George. If you know a “George,” recommend this book and say they reminded you of “Regina.”