Podcast: Practical tips to start a Data as a Product mindset

Curious about how to leverage data as a product but not sure how to get started?

In the latest episode of the Data Mesh Radio podcast, I discuss tips for practical application of a Data as a Product mindset with industry experts Martina Ivaničová and Xavier Gumara Rigol.

This lively panel discussion dives into the nuances of Data as a Product to provide tons of great insights from panelists’ real-world experience. Some of the topics include:

Mindset vs. Outcome

What is data as a product? What about data products and data assets? Why does it matter?

Why Adopt a Data as a Product Mindset?

What are the benefits of adopting a DaaP mindset? How can you get folks to think differently about data? Maybe most importantly, how do you articulate what’s in it for them?

Pitfalls of the “Data Service Trap”

Tired of constantly reacting to data requests? Learn how a DaaP approach keeps users and goals front and center, transitioning to a more proactive and empowered model.

Overcoming Cognitive Load

The panel acknowledges and discusses how to address the cognitive load for those outside of the “data bubble” to lead to greater alignment and understanding.

The Path to Implementation

Implementing a DaaP mindset goes beyond technology; people and process are critical aspects as well. Communication and organizational change are essential. Sometimes securing buy-in is a challenge - we talk through re-thinking traditional ROI and building momentum when starting small with initial wins.

Access the podcast episode

To learn more about how to maximize the value of your data, including tips on how to get started, check out Data Mesh Radio episode #291, “Panel: Data as a Product in Practice.”

Stream the audio here, or read the episode transcript here.

More Data as a Product resources

Check out more DaaP resources recommended by the panel:

• The State of Data as a Product in the Real World study
• Infonomics by Douglas Laney
• Xavier’s blog post on data as a product vs. data products