Mar 15–18, 2020

A study of bees: Using AI and art to tell a data story

Nancy Rausch (SAS)
1:45pm2:25pm Tuesday, March 17, 2020
Location: LL21A
Secondary topics:  Culture and Organization

Who is this presentation for?

Data scientists or analysts




Analytics and AI are powerful methods for extracting insights hidden in data. However, these methods by themselves cannot convey insights. Visualization is a key requirement to explain analytical findings. But visualizations such as graphs and charts are not always enough to explain data, especially to a nontechnical audience. You may need to use a different approach to help them connect with the data.

Nancy Rausch shares the experience of a case study SAS undertook to combine machine learning and art to tell a big data story. She explains how the company collected and prepared IoT streaming data from local beehives, applied computer visualization to capture bee activity, and created an analytical model to forecast hive health. The results were visualized for general audiences in an interactive sculpture and used artificial intelligence and natural language processing to allow visitors to interact with the art. The project brought the beehives to life in a way that was able to engage and delight visitors of all ages and backgrounds.

Prerequisite knowledge

  • Experience with statistics

What you'll learn

  • Learn about novel visualizations for telling a data story, AI methods for forecasting, how beehives work and what's important to their health, and a fun case study for combining machine learning and art
Photo of Nancy Rausch

Nancy Rausch


Nancy Rausch is a senior manager at SAS. Nancy has been involved for many years in the design and development of SAS’s data warehouse and data management products, working closely with customers and authoring a number of papers on SAS data management products and best practice design principals for data management solutions. She holds an MS in computer engineering from Duke University, where she specialized in statistical signal processing, and a BS in electrical engineering from Michigan Technological University. She has recently returned to college and is pursuing an MS in analytics from Capella University.

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