Do I Taste Data Science in My Soup?
How data science infiltrates everyday life — from food to consumer products.
title: "Do I Taste Data Science in My Soup?" slug: "do-i-taste-data-science-my-soup" description: "How data science infiltrates everyday life — from food to consumer products." datePublished: "2012-11-30" dateModified: "2026-03-15" category: "Data Strategy" tags: ["data science", "consumer", "everyday life", "analytics"] tier: 3 originalUrl: "http://www.applieddatalabs.com/content/do-i-taste-data-science-my-soup" waybackUrl: "https://web.archive.org/web/20121130233855/http://www.applieddatalabs.com:80/content/do-i-taste-data-science-my-soup"
Do I Taste Data Science in My Soup?
"Have you noticed the service getting better at your favorite restaurant? How about the menu?" That's how we opened this piece back in 2012. We were writing about Slingshot, a startup that was bringing analytics to the restaurant industry. Their pitch was that restaurants were stuck in the dark ages -- paper-based systems, no data analysis, zero insight into server performance. Slingshot tracked every item sold, every tip received, every moment of the dining experience. One manager could now tell a server: "You sell 40 percent less red wine than your peers and you work in a steakhouse."
It was a cool product for 2012. A curiosity, really. Today, data science isn't in your soup. Data science is the restaurant.
Where Restaurants Were in 2012
We described the restaurant industry as "stuck in the twilight zone when it comes to data science." Most restaurants tracked nothing beyond basic sales figures. The industry lagged behind almost every other sector. The financial crisis had just forced many restaurants to close, and the survivors were starting to look at data out of desperation more than vision.
Slingshot and a few competitors represented the first wave. But in 2012, the idea of a restaurant using people analytics to evaluate server performance was novel enough to write about. The concept of data-driven menu optimization barely existed outside of the biggest chains.
The Data-Driven Restaurant in 2026
Toast went public in 2021 and now powers over 100,000 restaurants with a platform that does everything Slingshot dreamed of and about fifty things it didn't. Square (now Block) expanded from payment processing into full restaurant analytics. Every major POS system now includes analytics dashboards that would have been cutting-edge enterprise software in 2012.
In 2012, we wrote about restaurants discovering data science. In 2026, a restaurant without AI-driven inventory management is like one without a refrigerator -- technically possible, but you won't last long.
But the real transformation came from outside the restaurant walls. DoorDash, Uber Eats, and Grubhub didn't just change how people order food -- they created massive datasets about eating behavior. DoorDash knows what neighborhoods order Thai food on rainy Tuesdays. Uber Eats knows which menu items photograph well enough to drive orders. This data flows back to restaurants (sometimes) and shapes menus, pricing, and operating hours.
Ghost kitchens -- restaurants with no dining room, built solely for delivery -- emerged as a pure data play. Companies like CloudKitchens (founded by former Uber CEO Travis Kalanick) used delivery data to decide which cuisines to offer in which neighborhoods. The entire business model is driven by algorithms. No ambiance, no servers, no guesswork.
AI menu optimization has become its own category. Companies analyze sales data, food cost fluctuations, seasonal trends, and competitor pricing to recommend exactly which items should be on a menu and at what price point. Some restaurants use AI to generate menu descriptions that increase order rates. McDonald's acquired an AI company called Dynamic Yield in 2019 for $300 million to personalize drive-through menu boards based on weather, time of day, and trending items.
The Hidden Data Layer
What's really interesting is the data layer that customers never see. Predictive inventory systems now tell restaurants how much of each ingredient to order, reducing food waste by 30-40% in some implementations. AI scheduling tools optimize staff levels based on predicted traffic patterns, weather forecasts, and local event calendars. Even food safety got a data upgrade: IoT sensors monitor refrigerator temperatures continuously and alert managers before food spoils.
This is operational AI in its most literal sense: AI running the actual operations of a business, not sitting in a dashboard waiting for someone to look at it. The restaurants that have embraced this approach aren't just more profitable. They waste less food, schedule staff more fairly, and serve more consistent quality.
Back in 2012, we thought data science in restaurants was a charming trend piece. The server analytics from Slingshot were interesting but felt niche. Fourteen years later, AI touches every part of the restaurant operation, from what goes on the menu to who works the Friday night shift. That server selling 40% less wine? An AI system would have flagged that on day one.