ANSWER

“Data as a Product” or DaaP is a construct of the data mesh paradigm and it’s a way to look at the data within the enterprise as a product offered by the various data teams to the rest of the enterprise.

Data as a product is a combination of strategy and tools which look at data as a ready to consume product by internal customers.

Consider, for example, how you, as a consumer, make a simple purchase of a pair of shoes. In your shopping process you have come to expect the following:

• Ability to search for different types of shoes
• Ability to read details about a given show model (features, colors, options)
• Ability to make customized selection when purchasing a pair of shoes (ex: size, color, width, etc.)
• Ability to order the shoes you like and be able to specify where and how it should be delivered to you
• Ability to receive the shoes in a reasonable amount of time and even expedited if you are willing to pay more

Data as a product works fairly similarly aka there is a provider of data (the seller), there is a customer looking for data (the buyer), and there is a way to acquire the data (the shopping system).  This means that DaaP has a few characteristics such as:

• Data moves one way (unidirectional) from the data provider (data engineers, data analysts, data scientists) to the consumer within the enterprise.

• Data can be found in a data catalog where it can be easily searched and acquired/ordered.

• There are different types of “data products” such as raw data, data models, ML data insights, etc.

Just with regular consumer products, a “Data as a Product” strategy needs to include both:

• Tools - these are the delivery and “shopping” systems where data is made accessible to enterprise clients.

• Mindset - is the customer-driven development of data products where the data providers work diligently to understand the needs of their customers and develop data products which are in high demand and of value to end users.

An advanced Data as a Product program will go to a lengths to establish a robust set of practices surrounding these data products including:

• Data Product Development
• Data Product Usability
• Data Product Reviews
• Data Product Support
• Data Product Safety
• Data Product Inventory
• Data Product Quality
• Data Product Marketing
• etc.

We realize, there are many more nuances and definitions of data as a product.  What is your point of view?