Building Data Products: The Ultimate Guide

Data is no longer a second-class citizen. With better tooling, more advanced use cases, and a clearer understanding of its potential, many organizations have come to view their data ecosystem as a fully formed element of the company tech stack.

As a result, the most forward-thinking teams are adopting a new paradigm: treating data like a product. This guide will define the buzz and dive into exactly what it means to build a data product with concrete best practices and strategies from some of today’s leading practitioners.

What does “data as a product” mean?

Data as a product or data products were most recently popularized as one of the four core principles of Zhamak Dehghani’s data mesh paradigm, although academics and industry experts have been writing about their potential for several years.

We define data products to mean developing your critical data assets–such as key data sets, dashboards, or machine learning models–with the same reliable processes and architecture that you would use to develop a product for an external customer.

You can be expansive in the type of asset that is defined as a data product (is it an A/B testing platform, a multi-layered data platform, a data set, who cares?), but you must be exacting in the criteria (which we delve into in Chapter 2).

This all sounds straightforward in theory, but consider software-as-a-service (SaaS) solutions contractually guarantee uptimes in the range of “ five 9s,” or in other words 99.999%. Also, let’s not forget the inherent resource and procedural challenges to developing a product.

Not only have product development processes evolved over decades to become incredibly sophisticated, but literal companies of people are formed to support them post-launch with entire departments organized around ensuring adoption, customer support, feature updates, and more.

But these challenges can also be an opportunity for savvy data teams. Here’s how.