Data is a critical component for many businesses today. It is used to drive intelligence and inform decision-making. But what exactly is a data product? In essence, a data product is a data interface that provides consistent, reliable, and superior data access for business decisions or outcomes.
A data product is created by combining data sets, domain models, and access through APIs and visualization options. While there is no specific agreement in the tech industry on what constitutes a data product, even a simple data structure like a table view can be defined as a data product if it is self-contained, combined with a semantic layer, and accessible through a self-service interface.
It is essential to note that data marts, data warehouses, data lakes, and lake houses are data management platforms rather than data products. Data products should be created using data collaboration principles, be agile, avoid the need for data integration, and support various modes of advanced analytics and query engines.
By creating data products, businesses can create foundational data assets that serve broad domain use cases, making data agreements more transparent and actionable between data producers and consumers. Data products also offer users a self-service user experience without needing to know the physical details, providing consistency in outcomes.
To build a data product, several critical steps must be taken. First, define the scope of the data product, including its intended use cases, expected outcomes, and the data sources involved. Then, design the domain model, which defines the business-friendly terms used in the data product, the metrics, and the transformation logic. Next, the data product is built by the data engineering team using data engineering practices and tested for reliability, scalability, and performance. Finally, the data product should be able to interact freely with other data and be made available directly through a self-service interface.
There is currently a sense of urgency around data products for many organizations that want to drive innovation and increase their competitive advantage. Data teams need to rethink how to delight their business counterparts, aka their customers, to be effective. A data productization approach can offer several benefits, including improved efficiency, enhanced data quality, greater business agility, and cost savings.
Data productization can help organizations unlock the full potential of their data assets, drive innovation, and gain a competitive edge in their respective markets. By creating data products, businesses can provide consistent, reliable, and superior data access for better decision-making and outcomes.