the Data Science Shop project and the need to codify Data Science
what French cuisine and aerial combat can teach us about transforming the way we think about and practice Data Science
TL;DR
Innovation often comes from systematizing existing knowledge - as shown by Escoffier's "Le Guide Culinaire" (1903) and Boyd's "Aerial Attack Study" (1959), which transformed their fields by codifying collective wisdom into teachable frameworks.
These historical examples share a common pattern: they took disciplines that operated as crafts, created comprehensive guides with clear methodologies, and established common languages for their respective professions.
Data Science today mirrors the pre-codification state of these fields - despite its importance, it remains heavily focused on tools rather than outputs, lacks standardized approaches to building Data Products, and relies on informal knowledge transfer.
The Data Science Shop project aims to do for Data Science what Escoffier and Boyd did for their fields - create a systematic framework that bridges the gap between education and practice while focusing on practical outputs rather than just tools
In the world of innovation, sometimes the greatest advancements come not from inventing something new, but from codifying collective wisdom. These watershed moments occur when someone takes the time to organize and document what practitioners already know intuitively, transforming scattered expertise into systematic knowledge.
This type of innovation—the kind that turns informal expertise into structured, teachable systems—has repeatedly revolutionized many disciplines throughout history. Two remarkable examples illustrate this pattern: the systematization of French cuisine in the early 1900s and the codification of aerial combat tactics in the 1950s. Their stories not only demonstrate the power of turning tacit knowledge into explicit frameworks but also offer valuable insights for another field standing at a similar crossroads today: Data Science.
From chaos to codification: Escoffier's kitchen blueprint
Let me tell you a story. Up until the early 1900s, French cooking was an art form with little organization. The dining experience in those days was nothing like the one you have today in any restaurant around the world.
A seasoned chef, Auguste Escoffier, decides to systematize his experience and knowledge about cooking techniques and organizing the operation of a restaurant kitchen. So, he writes Le Guide Culinaire (1903) – for all practical purposes the tactical manual for modern cooking and restaurant management that is accessible to everyone in his profession.
His book is as much a cookbook as it is a culinary textbook, and its influence continues to this day. It codified the five foundational sauces in French cuisine, relayed a hierarchical system to organize the operation of a kitchen through specialized roles (la brigade de cuisine), and put the accent on the outputs of the kitchen with the focus on menus from where to order à la carte and course-by-course service. It truly changed what was a chaotic experience for most practitioners and patrons of his time.
From instinct to strategy: Boyd's aerial combat manual
Let me tell you another story. By the 1950s and after about 40 years of existence, air-to-air combat – a.k.a. dogfighting – was considered an art form with a few tricks and maneuvers that were passed down informally between pilots.
A young US Air Force Captain, John Boyd, decides to codify his experience and knowledge about aerial combat to the benefit of other pilots. So, he writes Aerial Attack Study (1959) – for all practical purposes, the tactical manual for air-to-air combat and strategy.
His book became the reference manual for air forces around the world, and its principles continue to be in use today. Boyd managed to codify what many pilots instinctively knew: that air combat is a series of moves and counter moves, so it can be thought of as an algorithm thus subject to strategic thinking and systematic analysis. For that reason, aerial combat has a method that can be taught and studied.
The common thread
The stories of Escoffier and Boyd share a common thread. Each one of these books revolutionized fields that were previously approached as crafts, with skills passed informally between experts. The books changed their disciplines because they organized and codified techniques and best practices. In doing that, each created a comprehensive guide that serves as a foundational reference with a common language for professionals in each discipline.
More precisely, each one of these books codified the “what” of the discipline, and they also systematized “how” to carry out the day to day of the discipline, altering how they were practiced and taught.
Data Science: the current state
In many ways, we still treat Data Science as a craft - and we need to take it to the next professional level:
we are still struggling with the common misconception that Data Science is about AI, machine learning and prediction – too much focus on tools, too little focus on outputs
we have not yet converged in understanding that the practice of Data Science is about the Data Products it builds to solve a need or a problem in a business
we rarely address core elements of Data Science work: the kinds of Data Products are built in Data Science, the process to build Data Products, or the technical people and tech architectures needed to build Data Products
we underemphasize crucial questions like how to define the correct problems to solve, how to evaluate the appropriateness of data for a Data Product, or how to engage with final users and stakeholders
we have important gaps between the way we teach and the way we practice Data Science – again, too much focus on tools and not enough focus on outputs
we don’t have a systematic reference to bridge the gap between seasoned and newly minted Data Scientists, since empirical knowledge and best practices are passed down informally among practitioners
True, there are many blog posts, newsletters, podcasts, YouTube videos, GitHub repos or articles with some of this information. But there isn’t a cohesive, systematic and organized codification of the practice of Data Science that lives coherently in a single location.
The Data Science Shop project
This is where the Data Science Shop project comes in. It aims to do for Data Science what Escoffier and Boyd did for their respective fields.
The Data Science Shop project – newsletter, website and upcoming book(s) – addresses these challenges by providing a structured framework for the field. Rather than relying on scattered resources across blogs, articles, and informal knowledge sharing, practitioners now have access to a systematic guide that bridges theory and practice. The project offers clear methodologies for building Data Products, practical approaches to problem definition and stakeholder engagement, and guidance on technical architectures and team structures.
This resource serves multiple audiences:
Data Scientists and Engineers can use it to strengthen their practical skills beyond technical tools,
managers tasked with implementing Data Science or AI initiatives can understand what's involved in building effective Data Science Shops and digital solutions despite not being technical experts themselves, and
Executives can gain clarity on how Data Science and AI translate into business value.
Like Escoffier's guide for French cuisine or Boyd's manual for aerial combat, the Data Science Shop provides a common language and framework that helps align everyone involved in making Data Science work in real-world settings.
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Did we spark your interest? Then also read:
What is Data Science? to learn more about the practical way to understand Data Science: focus on its outputs (Data Products)
What is the Data Science Shop? to learn more about the roadmap for the operation of Data Science in a business
WHO are the people that make Data Science possible? to learn about the specialized technical roles that work in tandem on a Data Science Shop
WHERE are Data Products built (and maintained)? to learn about the technology backbone of Data Products
WHAT Data Products can Data Science build? to learn about the four classes of Data Products that any Data Science Shop can build
HOW are effective Data Products built in Data Science? to learn about the process to build successful Data Products: the Data Product Cycle