Everything You Need to Know About Data Products

Summary

Data Mesh encourages organizations to treat data as a product, not just as a technical asset.
To do this well, teams need a product-thinking mindset that starts with understanding user needs and the problem to solve.
Two core principles are to focus on the problem before the solution, and to think in terms of complete products rather than isolated features.
Creating a data product requires clear answers about the problem, the users, the vision, and the strategy behind it.
Data product thinking helps ensure datasets are designed to deliver real value and meet concrete user needs.

In recent years, the data management and analytics landscape has witnessed a paradigm shift with the emergence of the Data Mesh framework. Coined by Zhamak Dehghani in 2019, Data Mesh is a framework that emphasizes a decentralized and domain-oriented approach to managing data. One notable discipline in the Data Mesh architecture is to treat data as a product, introducing the concept of “data products”. However, the term “data product” is often tossed around without a clear understanding of its essence. In this article, we will shed light on everything you need to know about data products and data product thinking.

Shifting to Product Thinking

For organizations to treat data as products and transform their datasets as data products, it is essential for teams to first shift to a product-thinking mindset. According to J. Majchrzak et al. in Data Mesh in Action,

Product thinking serves as a problem-solving methodology, prioritizing the comprehensive understanding of user needs and the core problem at hand before delving into the product creation process. The primary objective is to narrow the gap between user requirements and the proposed solution.

In their book, they highlight two main principles:

Love the problem, not the solution: Before embarking on the design phase of a product, it is imperative to gain an understanding of the users and the specific problem being addressed.
Think in terms of products, not features: While there is a natural inclination to concentrate on adding new features and customizing assets, it is crucial to view data as a product that directly satisfies user needs.

Therefore, before unveiling a dataset, adhering to product thinking involves posing essential questions: