Exploring the Concept of Data Products in the Context of Data Mesh

This article aims to delve into my interpretation of data products within the framework of Data Mesh. At #InfoCepts, we have successfully delivered a data product project to a customer, and this article aims to shed light on our insights.

When confronted with statements like "Data Product is a new term for data models," "Snowflake is Data Mesh," or "Data Product can be built at the source," it's crucial to understand the reality behind these assertions. The delivery of a

Data Product involves a three-component strategy:

1) IT Strategy,

2) "Data Mesh Strategy

3) Data Governance Strategy.

Essentially, a Data Product involves standardizing technology, processes, and data models to enable the development of Data & AI Products. Achieving this requires strong business alignment, focusing on multiple technologies, standards, and delivery models. The potential for simplification and standardization arises, leading to the evolution of a data product with a hybrid model, incorporating domain-specialized marts through unifying standards and technologies. This facilitates faster integration with other systems.

The data and AI landscape presents various challenges, such as supporting numerous analytical systems within IT budget constraints, managing multiple copies of data, navigating governance difficulties across systems, adapting to new competitive business requirements, and addressing the needs for automation and modernization with AI.

Data Products within the context of Data Mesh are constructed on an integrated platform that leverages existing data management, combining raw data and business processes to enable reusable AI and enterprise insights. Features like reinforcement learning, recommendation engines, and predictive and forecasting capabilities contribute to creating enterprise insights at a rapid pace.

In contrast to a monolithic data platform, the data mesh comprises a collection of integrated data products, each overseen by its dedicated data product team. These teams maintain a level of relative independence, allowing them to remain agile in responding to emerging requirements and possessing the autonomy to deploy new data and product features independently.

While there are numerous resources, including videos, to learn about Data Mesh, here are three key takeaways:

• It represents a shift in our work approach driven by modern technology.
• It is an approach that accelerates the speed at which value is delivered.
• It aligns well with broader data priorities such as discovery, easy access, integration, and re-usability.

If you are looking for implementing Data Product, Data Architect Solution, Get in touch with me and we can help to transform your journey.