Panel: Data User Experience - An Introduction (Data Mesh Learning and Data Mesh Radio)

Hosted by the Data Mesh Learning Community in partnership with Data Mesh Radio.

Karen Passmore (CEO at Predictive UX) led this discussion with Alice Parker (Data Engineer at DNB) and Wannes Rosiers (Product Manager at Raito). It will help get your head around user experience in data, for consumers and producers.

Karen's LinkedIn:   / karenpassmore  
Alice's LinkedIn:   / aliceparker  
Wannes' LinkedIn:   / wannes-rosiers  

Data Mesh Radio - a vendor-independent resource for learning about data mesh and related topics in a fun and relaxed way from implementers and data leaders: https://daappod.com/data-mesh-radio/

Some key takeaways from panelist Wannes Rosiers:
DUX handles user experience using data for all your users (data producers, data engineers, data analysts, data scientists, report users, ...), hence it is not - and certainly not restricted to - data platform user experience.
Data products are typically chained from producer oriented data products up until consumer oriented data products. Downstream product and UX requirements are completely different than Upstream, yet you should be considering all of them. This friction between global UX and local UX is the biggest challenge to scale Data User Experience.
Much more than typical digital products, you have no idea how people interact with your data products. We are moving more and more to self-serve analytics, which means that while developing a data product, you don't know what will end up on the screen of your user. You can wireframe fixed dashboards, you can't for certain other data products.
As always, accessibility of your data product is a huge part of your user experience. Next to this, it is important to monitor usage to continuously evolve your product. (Not surprisingly the two key elements we work on at Raito)
Domain thinking exceeds source thinking in your product. You should abstract away the concept of data sources, users are interested in valid and bounded insights, typically domain-bounded.
And last but not least. You need top-level decisions on data product granularities. To make sure you can connect certain data products, they should be interoperable and preferably on the same granular level. Which level to pick is mainly guided by company KPIs, hence you need management support to pick these.

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