Standardized Data Product Metadata Examples Based on Real-world Published Data Products
Metadata management is vital in the data economy and data product development as it enables data discovery, integration, governance, and collaboration. It improves data quality, facilitates compliance, supports data monetization, and ensures efficient data lifecycle management. By effectively managing metadata, organizations can unlock the full potential of their data assets and derive maximum value from them.
Modern metadata management doesn’t stop with just defining data and making it accessible, it also solves DataOps use cases such as workflow management, automation, observability, tool integration, and change management. That is why a common and shared metadata model of data products is so important. Decoupling data products from vendor code was also one of the main reasons to start developing Open Data Product Specification.
Standard data product metadata model — ODPS
Open Data Product Specification version 2.1. release candidate is now available and we expect to make a release in August 2023. While it contains several improvements compared to the previous version, we did not settle only to provide those. Instead, we decided to help you to understand the usage of the standard in a practical way. This will also lower the learning curve and raise the adoption rate.
Raise the understanding to lower the adoption barrier
When it comes to adopting new standards in the data economy, several barriers can impede the process. These barriers can vary depending on the specific standard and the context in which it is being introduced. While there are multiple reasons why adopting a new standard is a challenge for organizations, the lack of awareness and understanding is one of the first to tackle any new standard.
Sometimes, organizations may not be aware of the benefits and implications of new data standards. They may lack understanding about how the standard can improve their operations or enhance data interoperability. Insufficient awareness and education about the advantages of new standards can impede their adoption. In this release, we decided to add example data product descriptions next to “hello world” example in the specification details.
Ready-made data product metadata examples
Providing examples of how to adopt a new standard enhances understanding, accelerates implementation, builds confidence, and promotes collaboration. Examples offer practical guidance, clarify the standard’s application, and provide reference points for customization. By leveraging examples, organizations can navigate the adoption process more effectively, maximize the benefits of the standard, and foster stakeholder alignment.
Examples illustrate how the new standard can be implemented in real-world scenarios. They provide practical guidance and demonstrate the steps, processes, and best practices involved in adopting the standard. Examples help organizations understand the specific actions they need to take and offer a blueprint for successful implementation
Examples make the new standard easier to comprehend by showcasing its application in familiar contexts. They help organizations visualize how the standard can be integrated into their existing systems, processes, and workflows. By seeing the standard in action, organizations gain a clearer understanding of its purpose, benefits, and potential challenges.
Examples serve as reference points, enabling organizations to leverage existing templates, frameworks, or methodologies. This can significantly speed up the adoption process by reducing the time and effort required for trial and error. Organizations can learn from the experiences of others who have already implemented the standard successfully, avoiding common pitfalls and streamlining their own adoption journey.
Real-world existing data products as the source
The above reasons exemplify the importance of providing real-world data product metadata description examples with standard Open Data Product Specification. As a still have around a month for the release of ODPS version 2.1. there’s still time to suggest examples you would like to see. Our aim is to have examples from 4–5 categories each of which to contains 2–4 data product examples. The examples are built from real-world data products available in the markets.
Open source and open for suggestions
The examples will be provided via Github repository and licensed under Creative Commons. The examples are not official representations of the data products and link to original source used is always added next to the derived example.
Suggest what kind of data product you would like to see in the list! Go to Github and raise an issue.
