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What's the Cropster Data Project?

Roasting coffee is an art. It is the variety of roasting styles we see all over the world which is responsible for the fascination for specialty coffee. It is fair to say that all specialty coffee roasters work in a unique way, where their experience, their affection and their style shape the taste of the coffee more than everything else.

At Cropster we believe that this love of coffee is worth preserving. We also think consistency is a key component of a quality coffee and that the key to reliable quality is the ability to reproduce results.

As techies we support a data driven view on coffee roasting and we are convinced that we can help coffee professionals achieve better results if we combine numbers and facts with their knowledge and expertise. In our business, the phrase "better result" means delivering roasts of a consistently high quality. We know that some customers enjoy tinkering and experimenting with their profiles and once they find the profile they are looking for, they want to be able to consistently roast batch after batch. After all, a roast is a key component of a company's signature, it reflects the hard work of all the people in the supply chain and is the essence of what is valued by their customers. Their customers expect consistent quality, and our goal is to support roasters so they can get their roasts right every time. 

By collecting data during all stages of a coffee's journey, we aim to turn measurements into facts that help roasters make informed decisions. The Cropster Data Project seeks to analyze the rich dataset roasting creates to increase our shared understanding and incorporate what we learn into actionable insights that support our customers in their strive for excellence.

Understanding what produced a result and knowing how to repeat that result is at the core of the Cropster Data Project.

The Background of the Data Project

If you are reading this you know that coffee roasting is a complex chemical and physical process, where many factors influence the outcome. The characteristics of the green coffee beans, roast machine used (type and manufacturer), environmental factors, the roast profile and of course the craftsmanship of the roaster as well as other factors all contribute to the final product. There is a lot of information to consider, collect and analyze in a modern roastery.

Although all the details of the roasting process are still not fully understood, we know that our understanding of certain measurements and parameters allows roasters to achieve a consistent quality. With so many factors in play, an important goal for us is to minimize the complexity for coffee professionals while maximizing the benefit and quality of their work

How do we do that? We connect data from different stages of the process(es) and we present it in a way that makes all the information useful and informative. One way is with visualizations of the various analyses we perform in the background.

By pulling together information and presenting it simply the Data Project aims to deliver new tools to help coffee professionals deliver consistently excellent results.

Producing actionable information requires regular engineering tasks, where we fine-tune our platform to improve its performance across multiple data points from physical analysis through roasting. How do we know where to start? After many conversations over the years with roasters and industry experts, we found the same questions coming up over and over: "What other information is hidden in the roasting data? How can it help the coffee community?"

As techies and data people at heart we strongly felt that in order to dig deep and find answers to these questions, we would need to launch a dedicated project. One where we could perform research in close collaboration with our customers. This was the beginning of the Cropster Data Project. So far we have over 25 partners in the project and it's growing as are the insights we are seeing. 

In our next blog post (coming in two weeks) we'll dig into the details of how we work with our participants' data, privacy and the beginning of our collaboration with the University of Innsbruck in our search for better 'data driven' coffee.

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