Skills for Enhancing User Experience in Tech Products

Data Products are NOT all code, infra, and biz data. Even from a PURE technical POV, a Data Product must also have the ability to capture HUMAN Feedback. The User’s insight is technically part of the product and defines 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭’𝐬 𝐟𝐢𝐧𝐚𝐥 𝐬𝐭𝐚𝐭𝐞 & shape. This implies Human Action is an integrated part of the Data Product, and it turns out 𝐚𝐜𝐭𝐢𝐨𝐧 𝐢𝐬 𝐭𝐡𝐞 𝐩𝐫𝐞𝐥𝐢𝐦𝐢𝐧𝐚𝐫𝐲 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐛𝐥𝐨𝐜𝐤 𝐨𝐟 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤. How the user interacts with the product influences how the product develops. But what is the 𝐛𝐫𝐢𝐝𝐠𝐞 𝐛/𝐰 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 𝐚𝐧𝐝 𝐇𝐮𝐦𝐚𝐧 𝐀𝐜𝐭𝐢𝐨𝐧𝐬? It’s a 𝐆𝐎𝐎𝐃 𝐔𝐬𝐞𝐫 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞 that doesn’t just offer a read-only experience like dashboards (no action or way to capture action), but enables the user to interact actively. This bridge is entirely a user-experience (UX) problem. With the goal of how to enhance the User's Experience that encourages action, the interface/bridge between Data Products and Human Action must address the following: 𝐇𝐨𝐰 𝐭𝐨 𝐟𝐢𝐧𝐝 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐭𝐡𝐚𝐭 𝐬𝐞𝐫𝐯𝐞𝐬 𝐦𝐲 𝐧𝐞𝐞𝐝? A discovery problem addressed by UX features such as natural language search (contextual search), browsing, & product exploration features. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐈 𝐮𝐬𝐞 𝐭𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭? An accessibility problem addressed by UX features such as native integrability- interoperability with native stacks, policy granularity (and scalable management of granules), documentation, and lineage transparency. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐈 𝐮𝐬𝐞 𝐭𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐰𝐢𝐭𝐡 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞? A more deep-rooted accessibility problem. You can't use data you don't trust. Addressed by UX features such as quality/SLO overview & lineage (think contracts), downstream updates & request channels. Note that it's the data product that's enabling quality but the UI that's exposing trust features. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐈 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 & 𝐬𝐮𝐠𝐠𝐞𝐬𝐭 𝐧𝐞𝐰 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬? A data evolution problem. Addressed by UX features such as logical modelling interface, easily operable by both adept and non-technical data users. 𝐇𝐨𝐰 𝐭𝐨 𝐠𝐞𝐭 𝐚𝐧 𝐨𝐯𝐞𝐫𝐯𝐢𝐞𝐰 𝐨𝐟 𝐭𝐡𝐞 𝐠𝐨𝐚𝐥𝐬 𝐈’𝐦 𝐟𝐮𝐥𝐟𝐢𝐥𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬 𝐩𝐫𝐨𝐝𝐮𝐜𝐭? A measurement/attribution problem. Addressed by UX features such as global and local metrics trees. ...and so on. You get the picture. Note that not only the active user suggestions but also the user’s usage patterns are recorded, acting as active feedback for data product dev and managers. This UI is like a product hub for users to actively discover, understand, and leverage data products while passively enabling product development at the same time through consistent 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐥𝐨𝐨𝐩𝐬 𝐦𝐚𝐧𝐚𝐠𝐞𝐝 𝐚𝐧𝐝 𝐟𝐞𝐝 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐫𝐞𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬 by the UI. How have you been solving the UX for your Data Products?

Building AI products that customers love may be the most important skill for Product Managers in 2025. But most PMs are just sprinkling AI on top… instead of baking it into the experience. Here’s how to build AI that’s actually used and actually drives business value (with a real example): — I've written a whole blueprint here (no paywall): https://lnkd.in/eDGmsvZ5 Here's the short version. — AI SPRINKLES: FLASHY BUT FORGOTTEN You know these features when you see them: → Attention-grabbing but superficial → Sit on top of existing features → Used occasionally, if at all But it’s only a matter of time before users realise the truth. So here are some signs you can look for early on: - Features hidden in sidebars - Require new user behaviors to start - High initial curiosity, but lower retention - Marketed with fancy badges and animations - Can be removed without affecting the core product These are the "look, we have AI too!" features. Users try them once but then forget. — AI CAKE: INVISIBLE BUT INDISPENSABLE The best AI products don’t demand the spotlight. Their goal is to give the user the best experience. Here’s what makes them different: → Deeply integrated into product DNA → Work invisibly in the background → Re-imagines core experience → Solves real problems And here’s what it looks like for the user in the real world: - Self-naming workflows that adapt to what you're doing - Dynamically generated content without prompting - Insights that appear exactly when you need it - Problems solved before users notice them So the magic lies not in showcasing intelligence. But embedding it where it matters most. — THE KEY SHIFTS TO MAKE To move from AI sprinkling to baking: 1. Start with user needs first, not technology (Don't just ask where AI fits, identify real friction points) 2. Make intelligence invisible rather than showcasing it (Great AI doesn't rely on neon signs, it solves problems) 3. Enhance existing workflows instead of creating new (Take the existing user experience from 'good' to 'great') — AN EXAMPLE Take Attio for example. They didn't obsess over the fanciest AI transcription. They cared about turning call data into valuable insights. That's the difference. Because users don't say "Wow, cool AI." They think "Great, this makes my job way easier." — What AI products do you like most? Repost to share with others. P.S. If you liked this, you'll love my newsletter: https://lnkd.in/gp2cCv8K

My approach to addressing user experience challenges involves several steps ⤵️ 1. Understanding User Needs: Conduct user research to understand the goals and pain points of the target audience. Utilize interviews, surveys, and usability testing to gather insights. 2. Collaboration and Alignment: Work closely with cross-functional teams such as product management, engineering, and customer support to align on user goals and prioritize UX issues. 3. Data Analysis: Analyze user data to understand behavior patterns. Look at analytics, feedback, and support tickets to identify areas where users are struggling. 4. Ideation and Sketching: Encourage brainstorming sessions and sketching to come up with creative solutions. Focus on solving the real problems and not just the symptoms. 5. Creating User Journeys and Wireframes: Develop user flows to understand how users will interact with the product. Create wireframes to visualize the structure of the interface. 6. Prototyping and Testing: Create high-fidelity prototypes that simulate the final product. Conduct usability testing to validate design decisions and uncover issues. 7. Iterative Design: Use feedback from usability testing to refine the design. UX is an ongoing process; be prepared to iterate based on user feedback and changing needs. 8. Implementation Support: Assist the development team during implementation to ensure that the design is translated accurately into the final product. 9. Post-Release Analysis: Once the product is released, continue to monitor user feedback and analytics. Be proactive in identifying new challenges and opportunities for improvement. 10. Educate and Advocate: Constantly educate stakeholders on the importance of UX and advocate for resources and prioritization of UX initiatives. This cyclical approach helps in creating a product that not only meets user needs but also adapts to changes and continuously improves over time. #ux #strategy #userexperience #innovations