How to Be Curious about Wine + Data: Questions from the Next Generation
What piques the curiosity of the next generation of wine professionals, when it comes to data?
I was curious myself, for answers to this question as I make a micro-tour of virtual classrooms where the topic of wine + data is on the agenda.
Last week it was CESSA Universidad in Mexico City, speaking with students of gastronomy and tourism. This week it was Cornell University in Ithaca, New York, presenting to a class on wine marketing. And next week it will be the Bologna Business School in Italy, as I lead a course for students in the MBA program in Food and Wine Management.
The instructors, students and I all arrive to the classroom knowing that wine + data is the general topic. Beyond that, though, I invite the students to steer the conversation. What do they want to know? What makes them curious about this topic?
Let me highlight four patterns of curiosity that I’ve noticed so far:
Artificial Intelligence and Machine Learning. They’re cool catchphrases of the moment, which is the entry point of the conversation. How we apply them to wine, and how they’ll make these students more effective at their jobs, is when they listen most intently. Specific applications of interest are churn rates and identifying members at risk of leaving a winery’s club, and identifying chronic underperforming wines within a portfolio.
“How did you get that data?” I love hearing this question, because it points directly to the need for relationships in a tech-heavy environment. A fundamental belief of Enolytics is that there’s no shortage of data in the wine world but there is a shortage of access, analysis and application of that data. Relationships, cultivated over time, bridges that gap.
Visualizations that persuade. Obviously this generation of digitally native students are well aware of the influential power of visuals. But they’re more curious about the mechanics of the visuals – the software we use and the process of creating the graphics – than the beauty or appearance of them.
Data Literacy as a place to start. I’m cheating with this point because I’m the one who introduces the topic, as an initiative that’s become an important part of our work. The students “read” data literacy as a friendly introduction to the potential impact of analysis and application. Which it is.
One final observation that caught my attention: many students I’m seeing are not twenty years old and just at the very beginning of their professional journeys. Some are career changers, and some bring a decade or two of industry experience to the table and are looking to expand their skill set.
I see them as the momentum carriers, who move our current phase of wine + data forward into something more adopted, more robust and more impactful. We’ll get there through curiosity and education, in the classroom formally and also through more casual “101” platforms like this.
Thank you for your own curiosity, and for being part of the journey.