What Is A Data Product And What Are The Key Characteristics?
Data mesh is compelling as it evolves our thinking so older approaches that might not have worked in practice actually can work today. The biggest change is how we think about data: as a product that must be managed with users and their desired outcomes in mind. Organizations are looking to apply product management practices to make their data assets consumable. The goal of a data product is to engender higher utilization of “trusted data” by making its analysis easier by a diverse set of consumers. This in turn increases an organization’s ability to rapidly extract intelligence and insights from their data assets in a low-friction manner.
The data management space has steadily been adopting well-tested software development life cycle methodologies, like DevOps and observability. Now the focus has shifted to adopting agile development practices and product management to data and analytics.
You can think of a data product as a self-contained data “container” that directly solves a business problem or is monetized. They are built for internal or external users at various levels of maturity, and some practical examples include:
• A good, old table or a view with a published data model, like a star schema or a business-friendly semantic layer. An example is a denormalized (flattened) table or a materialized view that joins employee data from diverse data sources, like HR, learning management and survey Excel files.
• A report, dashboard or an application with its own user interface (UI), an API, or command-line SQL access. An example includes a customer-360 dashboard that unifies sales, marketing and services data.
• An ML model or a metric that can be embedded into users’ workflows. For example, a model to predict customer churn or sentiment analysis. It may be available as a user-defined function for easy consumption by citizen data scientists or partners outside the organization.
How are data products different?
You might think, what’s the big deal and what’s new? Isn’t this what we have been doing for a long time?
What makes data products unique is that they focus on the people and process side. In the past, our job was done once we created and delivered the technical parts mentioned above. However, now we are addressing the entire life cycle of data—from its requirements, to its creation, usage and eventually to its end of life. This requires a different mindset—one where we prioritize business use over technology. Fundamentally, we are bringing “product thinking” to data.
What are some of the key characteristics of data products?
If we are to treat data as a product, then we should establish a data team led by a data product owner. The team should comprise analysts, data (or analytics) engineers, user experience designers and architects who would develop data products to meet the following characteristics:
One goal of data products should be reusability. For example, if an organization has invested to develop a cross-functional customer-360 data product, then it should be leveraged by various departments.
