Science Fairs Are Not Just for Students
Science fairs have long been a place for students to think creatively and execute hypotheses they wanted to challenge. Many ideas stemmed from professors inspiring students to take risks, test their hypotheses, create solutions, and promote a new way of thinking that could solve problems. Having our own Data Science Fair at AGCO to better serve our customers spurred conversations, generated new ideas, and created a forum to discuss data in a way that any person whether, “tech-savvy” or not, could understand.
Science Fairs Are Not Just for StudentsScience fairs have long been a place for students to think creatively and execute hypotheses they wanted to challenge. Many ideas stemmed from professors inspiring students to take risks, test their hypotheses, create solutions, and promote a new way of thinking that could solve problems. Having our own Data Science Fair at AGCO to better serve our customers spurred conversations, generated new ideas, and created a forum to discuss data in a way that any person whether, “tech-savvy” or not, could understand.
The buzz words “big data,” “industrial revolution 4.0,” and “Internet of Things (IoT)” have been replaced by new trends: data science and data analytics. While the underlying objectives are the same, the shift represents a change from companies talking about it at conferences to experimentation and practicing.
We recently decided to host the first ever Data Science Fair here at AGCO Corporation, headquartered in Duluth, Georgia, for our employees. Instead of baking soda volcanoes and potato batteries, different functions, including market intelligence, IT, aftersales, and smart farming solutions (Fuse®), presented their existing data analytics projects using different data sources and tools. Amazon Web Services (AWS) also had a booth at the Data Science Fair to share other real-life examples of using their platform.
Nearly half of the campus stopped by during lunch hours to learn more about how data is being used at AGCO. Solving problems are not new to us or any company, but the availability of data from digital sources, inexpensive storage, and the proliferation of tools have made data science as attainable as studying chemistry in high school. Not only did the event raise awareness of AGCO’s data analytic effort, but it also initiated a ton of dialogue, interest, and ideas for new projects and collaboration.
Those that want to create their own data science fair may ask, “Where do I start?” Here are three steps to spark data science in any company.
- Pick up some tools. Just as you may need some beakers, graduated cylinders, and a microscope for a science experiment, learning some data analytic tools, such as Python™, Tableau®, and RStudio®, are essentials. These tools can be presented and introduced to employees at the fair to expand their skill base. With inexpensive online courses widely available, anyone can learn enough to start exploring.
- Instead of scoping out the highest value or transformational project, start with a problem or process that people already work on regularly. Think of ways to improve those with incremental data and insights. For example, if you forecast based on historical sales, find ways to incorporate IoT and external factors to enhance your forecast by just a little. Repeat and test the next hypothesis.
- You may need external partners and consultants occasionally, but encourage participation from different levels and functions to practice data science collaboratively. Data analytics cannot be built once, as it requires continuous learning, experimentation, and improvement to solve problems. Let everyone in the lab.
Increasing data awareness and literacy via an internal data science fair can be a great way to jump-start momentum inside your company. I know it certainly did for us, and we are now working toward an immersion training with AWS and an internal problem-solving session. By creating these sessions and fairs, we are able to provide more solutions to help our customers be more profitable and efficient. Ideas for projects are great, but creating more data scientists is even better.
Written by: Matt Wong