Enterprises should standardize and automate data delivery to avoid data performance and quality issues. To an extent, automated pipeline and quality tools can help. But by themselves, they cannot offer the necessary standardization because the sources, targets, users, use cases, and platforms are diverse. An innovative approach to standardizing and automating data delivery is to treat data as a product.
A data product is a modular package that data teams can create, use, curate, and reuse with no scripting. It enables data engineers to become more productive. A data product also empowers data scientists, data analysts, developers, or even data-savvy business managers to consume data without the help of data engineers.
The data product includes transformation logic, metadata, and schema, along with views of the data itself. If implemented correctly, it will automatically incorporate changes to ensure users maintain an accurate, consistent picture of the business at all times. The data product provides monitoring capabilities to keep users informed of material changes. Furthermore, a viable data product offering includes role-based access controls that authenticate users and authorize their actions on the data.
There are many ways to access and consume a data product. For example, a data scientist or data analyst might use an analytics tool to discover and query the data product to support analytics. Or employ software as a service (SaaS) application that might consume the data product through an API pull service for enabling operations or embedded analytics. These scenarios allow enterprise workloads to consume data in a standardized and automated fashion.
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