137 - Immature Data, Immature Clients: When Are Data Products the Right Approach? feat. Data Product Architect, Karen Meppen

Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)

This week, I'm chatting with Karen Meppen, a founding member of the Data Product Leadership Community and a Data Product Architect and Client Services Director at Hakkoda. Today, we're tackling the difficult topic of developing data products in situations where a product-oriented culture and data infrastructures may still be emerging or “at odds” with a human-centered approach. Karen brings extensive experience and a strong belief in how to effectively negotiate the early stages of data maturity. Together we look at the major hurdles that businesses encounter when trying to properly exploit data products, as well as the necessity of leadership support and strategy alignment in these initiatives. Karen's insights offer a roadmap for those seeking to adopt a product and UX-driven methodology when significant tech or cultural hurdles may exist.

Highlights/ Skip to:

• I Introduce Karen Meppen and the challenges of dealing with data products in places where the data and tech aren't quite there yet (00:00)
• Karen shares her thoughts on what it's like working with "immature data" (02:27)
• Karen breaks down what a data product actually is (04:20)
• Karen and I discuss why having executive buy-in is crucial for moving forward with data products (07:48)
• The sometimes fuzzy definition of "data products." (12:09)
• Karen defines “shadow data teams” and explains how they sometimes conflict with tech teams (17:35)
• How Karen identifies the nature of each team to overcome common hurdles of connecting tech teams with business units (18:47)
• How she navigates conversations with tech leaders who think they already understand the requirements of business users (22:48)
• Using design prototypes and design reviews with different teams to make sure everyone is on the same page about UX (24:00)
• Karen shares stories from earlier in her career that led her to embrace human-centered design to ensure data products actually meet user needs (28:29)
• We reflect on our chat about UX, data products, and the “producty” approach to ML and analytics solutions (42:11)

Quotes from Today’s Episode

• "It’s not really fair to get really excited about what we hear about or see on LinkedIn, at conferences, etc. We get excited about the shiny things, and then want to go straight to it when [our] organization [may not be ] ready to do that, for a lot of reasons." - Karen Meppen (03:00)

• "If you do not have support from leadership and this is not something [they are] passionate about, you probably aren’t a great candidate for pursuing data products as a way of working." - Karen Meppen (08:30)
  
• "Requirements are just friendly lies." - Karen, quoting Brian about how data teams need to interpret stakeholder requests (13:27)
  
• "The greatest challenge that we have in technology is not technology, it’s the people, and understanding how we’re using the technology to meet our needs." - Karen Meppen (24:04)

• "You can’t automate something that you haven’t defined. For example, if you don’t have clarity on your tagging approach for your PII, or just the nature of all the metadata that you’re capturing for your data assets and what it means or how it’s handled—to make it good, then how could you possibly automate any of this that hasn’t been defined?" - Karen Meppen (38:35)

• "Nothing upsets an end-user more than lifting-and-shifting an existing report with the same problems it had in a new solution that now they’ve never used before." - Karen Meppen (40:13)
  
• “Early maturity may look different in many ways depending upon the nature of business you’re doing, the structure of your data team, and how it interacts with folks.” (42:46)

Links

• Data Product Leadership Community https://designingforanalytics.com/community/
• Karen Meppen on LinkedIn: ​​https://www.linkedin.com/in/karen--m/
• Hakkōda, Karen's company, for more insights on data products and services:https://hakkoda.io/