#3 - Why does a Data Product matter? - Part 2

What are the Main Characteristics of a Data Product?

In the latest post (Part 1), we have delved into the background of how a data product has been introduced in a data mesh infrastructure and the unique benefits and value it can offer to an enterprise — decentralised data ownership with dedicated domain teams, leading improved data quality, agility and scalability. Read the previous article here.

The centralised platform and data domain teams should promote the key attributes of a data product to increase efficacy of producing the intended result to business domains, as well as to successfully manage it.

🔍 Discoverable — Data products should be easy to navigate and explore by keeping centralised data catalog or registry that publishes their ownership, metadata, operations agreement and other policies. The catalog or registry serves as the “one-stop-shop” entry point for those who are keen on exploring and consuming datasets from a data product, while enabling the structural engagement to start with a data product.

🧑🏽‍💻 Addressable — Each data product is located at a permanent and unique address, which guarantees continuous use irrespective of its evolution over time and in accordance with access policies.

 🤝 Trustworthy — Data product owners must take ownership of managing data quality and adhere to an approved service level objectives through a data contract which explains how data quality dimensions can be managed.

In APRA CPG 235, dimensions typically considered in the assessment of data quality include:

1️⃣ Accuracy: the degree to which data is error free and aligns with what it represents

2️⃣ Completeness: the extent to which data is not missing and is of sufficient breadth and depth for the intended purpose

3️⃣ Consistency: the degree to which related data is in alignment with respect to dimensions such as definition, value, range, type and format, as applicable

4️⃣ Timeliness: the degree to which data is up-to-date

5️⃣ Availability: accessibility and usability of data when required

6️⃣ Fitness for use: the degree to which data is relevant, appropriate for the intended purpose and meets business specifications

🗣️ Understandable — It is important to incorporate well-defined schemas with well-described semantics and syntax. This enables data consumers to independently access and utilise the data without requiring additional support.

✍️ Interoperable (Standardised) — The metadata (e.g. headers) and data types of enterprise-wide information can be standardised. The business entities such as application and employee details can be centrally managed by the platform team as master data. This helps data consumers easily combine one domain of a data product with others.

🔐 Secure — Each data product should be inherently secure with access control that only allows the authenticated data consumers to access datasets. The measure to protect the personal identifiable information (PII) must be in place. The access control is centrally managed with the platform team, and they configure approval process to provision access in consideration with ownership and stewardship of a given data product.

Thanks for reading! The next article will cover the detailed values with examples, that a data product can bring in to an enterprise.

 See more previous article below: