Data Product vs. Data as a Product
We live in a world full of data, but how can we best use it? It should come as no surprise that, when used wisely, data is the most precious thing on the planet.
Be aware that a “data product” is not the same as “data as a product”. We might hear the term “data as a product” more often these days due to the current hot trend in the data industry known as “data mesh,” which claims to be able to solve many of the problems of its predecessors. One of the principles of the data mesh paradigm is to consider data as a product. This principle is sometimes shortened to “data product,” which leads to a misunderstanding between data product and data as a product.
Let’s explore the distinctions in detail between these two notions.
What is a data product?
Any product is called a data product if data is the key enabler for its primary goal. This means that any digital product or feature that relies on data to achieve its ultimate purpose or goal may be referred to as a data product.
The former chief data scientist of the United States, DJ Patil, defined a data product in his 2012 book Data Jujitsu: The Art of Turning Data into Product as “a product that facilitates an end goal though the use of data.”
In general, a data product is any tool or application that processes data and produces outcomes. These outcomes may provide businesses with valuable insights.
Various types of data products
Typically, data products are categorized by type:
- Raw data
- Derived data
- Insights (Decision support)
- Automated decision-making
Examples of data products
Examples of data products are:
- Any online shopping page may be a data product if the featured products are dynamically displayed depending on my previous purchases and searches.
- Google analytics is a data product since the insights it presents to the end user are derived from data.
- A data warehouse - This data product is a mix of raw data, derived data and insights.
- A self-driving car - It’s of the type automated decision-making.
What value does it provide?
Data products can help organisations extract insight from their data in order to develop more accurate forecasts, reduce expenses, and increase revenue.
Data as a product
Data as a product. In other words, data as a first-class product. This implies that data is considered as a true product, as opposed to a by-product. The data being discussed is organizational analytical data generated by several domains.
The goal is to make this data discoverable, addressable, trustworthy, and secure so that other domains can make good use of it. This principle implies that there are data consumers outside of the domain. In other words, each domain team considers the other domains as internal customers of their data, and they take on additional stewardship responsibilities for their data in order to meet the needs of other domains by providing high-quality data. This principle applies a product-thinking mindset to analytic data.
Product-thinking: When it comes to identifying solutions, the design team must consider the whole picture in order to make the product effective for the user. Product-thinking places the focus on the product rather than the features.
An example of data as a product
Well, what does data as a product look like? Consider data as a product to be a microservice for analytics or for the data world. Like a microservice, data as a product comprises the code (to perform data computation), its data and metadata, and the infrastructure required for its operation.
Data product vs. data as a product
After understanding each of these concepts, it becomes clear that they all substantially depend on meticulously derived data. However, “data product” is a broad term, while “data as a product” is a subset of all possible “data products”. In other words, “data as a product” is formed from the data type “data product”.