What are the best practices for data product management and how does it differ from software product management? Data mesh produced a fair amount of interest in treating data as a product. Treating data as a product implies some kind of product management. 

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Director of Engineering in Healthcare and Biotech, 501 - 1,000 employees
I like the idea of app portfolio management tools to create efficiencies. It's important to continually examine the most high risk areas and look to transition them out or into different solutions. 
Director of Engineering in Software, 10,001+ employees
Data is precious and it plays a critical role in creating new product. Managing data efficiently is key and important. 
Director of Customer Engineering - APAC in Software, 201 - 500 employees
Ensure the data is used properly and build a roadmap as to how to make it more useful and accessible.
Sr. Director of Engineering in Software, 51 - 200 employees
Data Generation, Structure, Release, Access, Security, Maintainability and data schema upgrade are synonym terms that match with software product lifecycle elements of requirements, design, implementation, release, maintenance and upgrades. 
The best practice for data product management is to establish and adopt optimised standard procedures in all these areas and rinse-and-repeat them in every data release cycle. This way whole data product release can be managed efficiently. 

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