First let me say Thank You, very much, for your podcast recommendations last week. I love that there are so many diverse options out there (wine and otherwise!), and that so many of you are enthusiastic listeners. It’s given me ideas, and lots to think about.
This week I wanted to share some other eye-openers that have crossed our desks lately: three ways that Enolytics’ data team engages in the wine world, that we in no way anticipated when we launched the business three years ago.
These examples reflect the desire, and the need we think, for strong engineering and analysis skills applied to the wine industry. They make a difference, because they expand decision making from “gut” to “head.” We rely on them both, and we’re energized by these possibilities to expand the thinking even further.
Here goes, with three recent examples we’re working on.
Data, Wine Tourism, and Boosting Domestic Consumption
An emerging wine region wants to boost domestic consumption from 30% to 50%, and they want to further develop their wine tourism initiatives as a strategy to achieving this goal. An early step is to add winery and wine information to a central database. From there the region’s app, which already exists, can populate via the database’s API. They can also choose to invest in analysis and documentation of their existing vineyards, whose geolocation can also be integrated with the app. Tourists, then, can access several kinds of information from a single source: winery location and mapping, visitor essentials like opening hours and directions, and wine availability and expectations. It adds up to a better consumer experience which, expectedly, can boost sales and move toward domestic consumption goals.
Analyze Investment Risk
We’re forging a partnership with a climate analysis company for whom agriculture (including vineyards) is an important category. They’ve developed algorithms to assess risk by considering factors as diverse as wildfire exposure to Employee Impact from natural disasters. My appreciation, as someone who is not a engineer, centers on the humanity of the algorithm. Certainly we’re talking about risk and investment and bottom line here, but the people in the equation matter too.
Normalizing Consumer Data
One of the most dynamic types projects we work on involve millions of wine consumer data records. Which sounds big and sexy and it is, but the bigness of it can be hard to handle. A variable we need to consider is the relative usage of consumer-facing wine apps over time, from which the raw data comes. Our team needs to “normalize” the records — which in this case is ridiculously hard to do — but otherwise the results wouldn’t be optimized for accuracy. Which we think is pretty much the whole point.
Thank you, as always, for reading. As always also, I’d love to hear your thoughts and feedback.
Please note: There won’t be an Enolytics 101 post next week, as we’ll be celebrating Mother’s Day!