Deciding Data Product Ownership: A Practical Guide
For some time, I have been wanting to crystalise my thinking around data product and to explore what it truly means to manage data as a product in practice.
My journey with data products began in 2020 (already!), surprisingly I encountered the concept while applying for a job where I was developing a use case focused on datamesh and its implications. At the time, I was working on a large cloud migration project where we encountered several challenges in rationalising the current data landscape and gaining the business buy-in for the transition to cloud. This experience prompted a realisation: by applying product thinking to the data and analytics space, we could effectively demonstrate the value of data to the business and identify underutilised datasets. With the project team, we brainstormed strategies to transition from a convention project-driven mindset to establishing product owners and cross functional teams (across data, BI, machine learning).
This shift in our approach marked the beginning of my journey with data product which aims at assisting leaders and organisations in applying product mindset and developing data products at scale, whether it is through datamesh, data product deployment at scale or infusing product thinking into cloud migration or mergers and acquisition programmes. The opportunities are truly boundless!
