Talking Points at Association for Institutional Researchers + Planning Officers

The items I discussed in a keynote earlier this month

9 min readJun 28

Earlier this month I delivered the annual keynote address for New York state’s Association for Institutional Researchers & Planning Officers (AIRPO). The conference theme was “Institutional Research Meets Data Science.” Here are a selection of the main points I discussed there.

Three Questions About Being a Data Scientist

The first point I discussed revolved around these three questions.

Who can be a data scientist?
Who decides who can be a data scientist?
And, why?

And related, I connected these rhetorical questions to Ada Lovelace (because I always talk about Ada). Many consider her to be the world’s first computer programmer. However, I consider her to be the world’s first data scientist.

A slide that shows a portrait of Ada Lovelace and a quotation from her journals that reads: “Supposing, for instance, that the fundamental relations of pitched sounds in the science of harmony and of musical composition were susceptible of such expression and adaptations, the engine might compose elaborate and scientific pieces of music of any degree of complexity or extent.” She lived from 1815 to 1852. — Image Credit: Author’s Illstration made in Canva. Portrait Adapted from Canavan & Stoney (2020).

Given that Ada wrote about how the computer could analyze for, analyze, and then essentially extrapolate from mathematical patterns in music — all with the aim to produce new music — I argue she was writing about generative artificial intelligence (Generative AI).

The lesson we take from Ada is that you can be a data scientist if you have the knowledge and skills to be a data scientist. If Ada were with us today I wager she wouldn’t call herself an aspiring data scientist. Instead, she would call herself a data scientist.

Scroll to the bottom of this article to read the answers to these rhetorical questions.

Data Analysts + Scientists, More Alike Than Not

This argument is significant for those involved in data-centric professions, including but not limited to, data analytics, data visualization, and data engineering. As professionals in these fields, we need to consider the potentially detrimental impacts of the ongoing debate between what defines the “analyst” role and what defines the “scientist” role. By engaging in this discourse, they are not only wasting intellectual time and resources that would be better spent on, well — SCIENCE. By engaging in this debate we are also…