Enhancing User Experience In Scalable Web Applications

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?

One of the constant challenges in UI/UX design is creating websites that serve diverse user needs effectively. While development and research teams often aim for universal accessibility, end users arrive with vastly different objectives. Consider Apple's website - visitors might need MacOS update information, iPhone purchasing, technical support, laptop upgrades, or countless other Apple-related services. Yet their homepage prominently features only their latest phone model at the top. This one-size-fits-all approach, while efficient for high-traffic priorities, can now be fundamentally reimagined through AI-driven personalization. Large Language Models enable us to aggregate visitor context and dynamically generate user interfaces that adapt to individual needs in real-time. This shift from static layouts to Generative UI (GenUI) demonstrates a significant change in how we approach web experiences. To explore this concept, I built a demonstration using GenUI techniques - specifically implementing an LLM model to generate complete user interfaces based on user needs and context in a laptop purchasing e-commerce setting. By combining existing user information with guided conversation, the LLM is able to dynamically generate and modify webpage content to precisely match a user’s individual preferences. Rather than navigating through generic product pages, users experience interfaces explicitly tailored to their requirements at that exact moment. The technical implementation leverages several key components: 1. Real-time UI generation based on conversational context 2. Dynamic content adaptation using visitor data 3. Integration patterns that maintain responsive performance This approach fundamentally disrupts traditional UI/UX methodologies, where interfaces are often designed once for many users. Instead, GenUI enables interfaces that are generated uniquely for each user, each time. To watch how GenUI is reshaping web experiences, learn the specific techniques I used, and see this demo in action check out my latest video: https://lnkd.in/evXBq9wc

A client spent over $400,000 on an app that barely worked. We took it over about 4 months ago. REALITY CHECK: Their previous dev team left them with: • Unstable codebase • Frustrated client and users • Monthly crashes • Zero scalability • Bleeding money Most agencies would rebuild from scratch. We did something different. Step 1: Deep dive technical audit Analyzed 50,000+ lines of code Found over 100 critical bugs Identified several security vulnerabilities Step 2: Strategic stabilization Fixed core functionality Patched security holes Optimized database queries Reduced load time by 73% Step 3: UX transformation Redesigned key user flows Simplified navigation Added performance monitoring Improved accessibility score by 89% Current status: • Zero downtime in 120 days • 94% reduction in user complaints • 40% faster load times • Platform ready for scaling Building from scratch can cost more. Smart optimization saves money. What we learned: Technical debt compounds like financial debt. Early fixes prevent costly rebuilds. User experience drives retention. Speed matters more than features. We're now building their next phase. Faster. Better. More scalable. Your software should work for you, not against you. Agree? Like and share your rescue story below.