Vaughn Tan’s The Uncertainty Mindset is one of the most fascinating books I’ve read this year. It’s an exploration of how to build R&D teams drawing on lessons learned in high-end kitchens around the world. I love cooking and I’m interested in creative R&D and what makes high-performing teams work well. I’d strongly recommend it if you’re interested in any of these topics.
I’m also a sucker for a good intellectual framework that helps me think about things in different ways. I did that recently with the BASEDEF framework.
Tan introduces a nice framework in Chapter 4 of the book which looks at four broad types of innovation around food. These are presented as a way to help the reader understand how and where innovation creates impact in restaurants. The four categories are:
- New dishes – new arrangements of ingredients, where innovation might be incremental refinements to existing dishes, combining ingredients together in new ways, or using ingredients from different contexts (think “fusion”)
- New ingredients – coming up with new things to be cooked
- New cooking methods – new ways of cooking things, like spherification or sous vide
- New cooking processes – new ways of organising the processes of cooking, e.g. to help kitchen staff prepare a dish more efficiently and consistently
The categories are the top are more evident to the consumer, those lower down less so. But the impacts of new methods and processes are greater as they apply in a variety of contexts.
Somewhat inevitably, I found myself thinking about how these categories work in the context of data:
dishesanalyses – New derived datasets made from existing primary sources. Or new ways of combining datasets to create insights. I’ve used the metaphor of cooking to describe data analysis before, those recipes for data-informed problem solving help to document this stage to make it reproducible
ingredientsdatasets and data sources – Finding and using new sources of data, like turning image, text or audio libraries into datasets, using cheaper sensors, finding a way to extract data from non-traditional sources, or using phone sensors for earthquake detection
cookingmethods for cleaning, managing or analysing data – which includes things like Jupyter notebooks, machine learning or differential privacy
cookingprocesses for organising the collection, preparation and analysis of data – e.g. collaborative maintenance, developing open standards for data or approaches to data governance and collective consent?
The breakdown isn’t perfect, but I found the exercise useful to think through the types of innovation around data. I’ve been conscious recently that I’m often using the word “innovation” without really digging into what that means, how that innovation happens and what exactly is being done differently or produced as a result.
The categories are also useful, I think, in reflecting on the possible impacts of breakthroughs of different types. Or perhaps where investment in R&D might be prioritised and where ensuring the translation of innovative approaches into the mainstream might have most impact?
What do you think?