I've heard this question dozens of times this past year. More when I gave myself the title "Head of Data Products" three years ago at a marketing agency.

Well, just like a product is a collection of software developed for the express purpose of use by a group of users on a continuous basis to satisfy a job they need to accomplish, data products do the same. The added layer of complexity of data is the key and why we are here.

Software as a Service (SaaS) products have the luxury of decades of development and inherently are unopinionated outside of the process. They focus on the "how" and the "what" but rarely the "why". Data necessitates a "why" and the context this carries.

Boiling this down into a useful definition might be this below:

A data product is a collection of data that is designed to meet the specific needs of a user base, providing a range of interfaces through which users can interact with it. These interfaces may include software, visualizations, direct feeds, and more. Ultimately, a data product serves to fulfill certain tasks or "jobs" that the user base requires, making it a purpose-built solution for meeting specific data-related needs.

Explain it to me like I'm 10.

A data product is like a special toolbox full of information that helps people get things done. Just like a regular toolbox has different tools for different jobs, a data product has different ways to use the information inside. It might be a computer program, a picture, or something else that helps people understand the information better.

A data product is a product that is created using data insights, domain knowledge, product thinking, and system thinking.

1. Data insights: Data insights refer to the knowledge and understanding that is gained from analyzing and interpreting data. This can include insights into customer behavior, market trends, or the performance of a business.
2. Domain knowledge: Domain knowledge is the expertise and understanding of a specific industry or field. In the context of a data product, domain knowledge is crucial for understanding the context and relevance of the data being analyzed.
3. Product thinking: Product thinking involves designing and developing a product with a specific user in mind. This includes considering the needs and pain points of the user, as well as the features and functionality that will make the product valuable and easy to use.
4. System thinking: System thinking involves considering the bigger picture and how all of the pieces of a product fit together. This includes understanding how a product will be used within a larger system, and how it will integrate with other products or processes.

Combining these four elements allows data product teams to create data products that are not only based on accurate and relevant data, but also designed to be useful and valuable to the end user.

By using data insights and domain knowledge to inform product design, and considering both the user and the larger system, organizations can create data products that drive business value and help solve real-world problems.

Is it really that simple?!

Well from the number of obtuse words used above... no. But yes it can be.

An enterprise sales dashboard?  A data product.

A direct API to Census.gov? A data product.

A personal budget? A data product.

An SAP instance for a Fortune 10 company? A data product

A family calendar or travel expense planner? A data product.

It's a special toolbox of data and ways to interact with it built to help people to get things done.

Learn More

• How to Create Data Products - Whitepaper from Tableau
• Data as a product vs. data products
• Defining data products