Since its founding by two pharmacists in 1945, Alcon Labs has become a global leader in eye care. Now serving patients in 140 countries, Alcon Labs is at the cutting edge of research into new and innovative eye treatments, including their famous LASIK surgery.
Robby Dally, Director of Global Insights and Analytics for Alcon, recently spoke with us about technology’s impact on their business and the importance of making great use of data and AI.
Dally, a self-described “data wrangler,” is a huge fan of data and analytics. He likes being on the forefront of automation, democratizing AI usage throughout the organization, and coming up with new AI use cases. “I love analytics,” he said. “We want to make decisions based on data, not on gut feelings or what we’re hearing from the eye surgeons we talk with.”
But Dally is not the usual kind of data scientist. He’s an autodidact who earned his data spurs building SQL databases many years ago. He admits to a few gaps in his software expertise. “That’s where a tool like DataRobot liberates me,” he says. “I don’t have the bandwidth to go learn Python, so I can use DataRobot instead. I use Alteryx to get my datasets right, then run them through DataRobot and let DataRobot do its magic.”
Alcon Labs has two core areas of business. One is focused on eye surgery, such as tools to help surgeons perform LASIK surgery or cataract repair. The other is focused on producing medications to correct problems, such as dry eye, and on the manufacture and distribution of contact lenses.
Managing a complex supply chain of high-tech products isn’t easy. To help with sales forecasting, managing finances, and gaining a better understanding of industry trends, Alcon Labs relies on the DataRobot platform.
Making sense of trend lines is one example. It’s one thing to add data to an Excel spreadsheet and model it. But to contend with a wide range of variables, Alcon also develops time-series models that consist of data points ordered by time. These models are designed to help forecast events influenced by factors such as time of day, seasonality, or autocorrelated target variables.
