It goes without saying that for the past few years, Artificial Intelligence (AI) has been the hot topic in the world of technology, and it’s hard to spend even a few minutes on LinkedIn or at a conference without encountering conversations on how it is going to change the world.
NB: This is an article from Revenue Generation
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However, for all of the times I’ve encountered the term “AI” in passing, mentions of “Citizen Data Science” pale in comparison. I believe that understanding and embracing Citizen Data Science will be essential for industries like hospitality as they work to successfully integrate AI and leverage it for effective business decision-making.
We at Revenue Generation have spent the past year engaged with a portfolio of five independent hotels, assisting them with their Citizen Data Science journey. We’ve developed fully automated tools to organize and synthesize their data, recreate manual reports, and develop analysis that quickly delivers insights in a repeatable fashion. We’ve empowered team members within their commercial organization to be data “citizens,” pairing their business expertise with these analytical tools to address their most important problems. Crucially, they’ve been able to leverage their current technological and statistical skillsets to accomplish this, without the need for more training. While its journey is still ongoing, this organization has focused on incorporating data-driven decision making into its culture, and it shows in the results.
Citizen Data Science Defined
So what is Citizen Data Science, or similarly, a Citizen Data Scientist? There are various definitions floating out there, but the one I like the best comes from Gartner, the global research and advisory firm: “a person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside the field of statistics and analytics.” This person likely still needs to possess a high comfort level with technology, a decent understanding of mathematical and statistical concepts, and certainly (and arguably, most importantly) the appropriate subject-matter expertise. An advanced degree in a quantitative field is not required, and this distinction may be what sets the Citizen Data Scientist apart from their “professional” counterparts.
The Citizen Data Scientist sits at the important intersection between information technology and operations, which has historically been occupied by business managers, analysts, engineers, product designers, “professional” data scientists, or some combination of the above. Many industries face the ever-growing challenge of keeping pace with rapidly expanding data, while making business decisions at an exponentially faster rate and greater scale; remember the “three V’s” of Big Data from that conference you attended ten years ago – volume, velocity, and variety.
