Measuring Data Product Usability and Trust

Ever looked at a UX survey and thought: “Okay… but what’s really going on here?” Same. I’ve been digging into how factor analysis can turn messy survey responses into meaningful insights. Not just to clean up the data - but to actually uncover the deeper psychological patterns underneath the numbers. Instead of just asking “Is this usable?”, we can ask: What makes it feel usable? Which moments in the experience build trust? Are we measuring the same idea in slightly different ways? These are the kinds of questions that factor analysis helps answer - by identifying latent constructs like satisfaction, ease, or emotional clarity that sit beneath the surface of our metrics. You don’t need hundreds of responses or a big-budget team to get started. With the right methods, even small UX teams can design sharper surveys and uncover deeper insights. EFA (exploratory factor analysis) helps uncover patterns you didn’t know to look for - great for new or evolving research. CFA (confirmatory factor analysis) lets you test whether your idea of a UX concept (say, trust or usability) holds up in the real data. And SEM (structural equation modeling) maps how those factors connect - like how ease of use builds trust, which in turn drives satisfaction and intent to return. What makes this even more accessible now are modern techniques like Bayesian CFA (ideal when you’re working with small datasets or want to include expert assumptions), non-linear modeling (to better capture how people actually behave), and robust estimation (to keep results stable even when the data’s messy or skewed). These methods aren’t just for academics - they’re practical, powerful tools that help UX teams design better experiences, grounded in real data.

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?

You've crafted an amazing digital world, but how do you know users are loving it?....... Have you heard about UX KPIs? Let's Unpack That, Think of them as design detectives. These KPIs spill the beans on how users feel about your creation. They tell you if your design is high-fiving users or leaving them puzzled. Behavioral UX KPIs - 💯 Task Success Rate - Your users' ability to accomplish tasks effortlessly is crucial. Task Success Rate quantifies the percentage of users who successfully complete a specific task or goal within your user experience study. It reflects how well your design supports users in achieving their objectives. ⏱️ Time on Task - Understanding how long users take to complete tasks reveals insights into the efficiency and complexity of your design. This metric gives you a clear picture of whether your design facilitates swift interactions or if users are struggling to navigate. ❌ User Error Rate - Mistakes happen, but frequent errors can be detrimental to user satisfaction. This metric measures the frequency of errors users encounter while interacting with your product. It helps you identify pain points and areas that need improvement. Attitudinal UX KPIs - 📊 System Usability Scale (SUS) - This questionnaire-based metric evaluates users' perceived usability of your product. Participants respond to statements that assess their agreement levels. SUS provides valuable insights into how user-friendly and intuitive your design is. 📣 Net Promoter Score (NPS) - NPS gauges user loyalty and satisfaction by asking a simple question: "How likely are you to recommend this product to others?" The score ranges from 0 to 10, categorizing users as promoters, passives, or detractors. It's a powerful indicator of user advocacy. 😃 Customer Satisfaction Score (CSAT) - Keeping your users content is essential. CSAT measures user satisfaction by asking them to rate their experience. This simple rating scale, often ranging from 1 to 5, helps you grasp user sentiment and pinpoint areas for improvement. .......KPIs guide our decisions, illuminate our paths, and validate our efforts. With each iteration, we refine our designs, infusing them with the pulse of user-centricity. Remember, success in UX design isn't a static destination – it's an evolving journey shaped by the insights we glean. Follow & Connect - Rohit Borachate #UXKPIs #UXMetrics #UXInsights #uxdesign #keyperformanceindicators #UserExperienceMetrics #UXAnalysis #uxstrategy

Data products play a crucial role in today’s data-driven world, providing ready-to-consume assets derived from raw, consolidated, and enhanced information. The popularity of #dataproducts continues to soar across sectors and organizations of all types, but not all of them result in #business success. Reviewing successful and failing data products, patterns emerge as to what drives business impact. It turns out that the value derived from a data product is directly proportional to its level of maturity. This maturity can be defined as the extent to which a data product is strategically established and equipped with the necessary features to maximize the extraction of value from data. Shriram (Shri) Salem proposes 10 dimensions: 💭AWARENESS. The extent to which there is awareness among existing and potential users and what it can be used for. If people don’t know it exists, they can’t use it. 🖱️USABILITY. It is easy to find, access, and then use for anyone with a use for it and the right access rights. Access is self-service and immediate. ⚙️INTEROPERABILITY. It can be coupled with other internal and external data, as well as #AI, #analytics, and visualization capabilities as needed. Data and insights are easily fed into use case applications. 🏃🏽ACTIONABILITY. The data and derived insights are directly applicable for, and connected to, well-understood use cases with precisely articulated input needs. ⏩SPEED. It enables consumers to rapidly find, access, understand, and use it to make decisions and drive actions at speed. It is immediately ready for use. 💡INNOVATION. It drives #innovation as consumers can experiment, relate it to other data, and apply #datascience to test value-driven hypotheses. ✅TRUST. It is reliable, secure, and quality-controlled. It is available for consumers in a safe experimentation environment to discover if it can be of use. 👩💻ADOPTION. It is used across critical domains and processes and referenced in socialized success stories. There is evidence demonstrating user adoption and value creation. 📈BUSINESS IMPACT. There is a demonstrable, quantified impact that the #data product has on the organization through specified use cases and impact statements. 📦PRODUCT ORIENTATION. The asset is managed as a product in that it focuses on customers and their needs, taking an iterative lifecycle approach to drive continuous improvement and value. These dimensions “multiply.” That is, maturity needs to be high (or of a minimum level) in every single dimension. If maturity is low in just one of them, this will depress the impact it can have on the enterprise. By assessing data products along these dimensions, organizations can identify areas of improvement and develop highly targeted plans to prioritize and enhance their data products. For a tactical application of this framework 👉 https://lnkd.in/eZse5z93

Most people have no clue how to effectively measure the success of their data products, let alone data more broadly. (And yes, by 'data products' I mean everything from APIs, analytics dashboards, and AI models to traditional datasets.) If you're managing data products (officially or unofficially), you've got to get serious about KPIs. Not only does it create focus and prioritization for data and governance teams, it proves the value of why you are doing a data product approach in the first place. Some quick, honest no-BS thoughts: 📈 Adoption & Usage: Is anyone actually using your data product? Track unique users, frequency, API calls, dashboard views, queries run. ⚙️ Reliability & Quality: Is your product trustworthy? Measure downtime, response times, data accuracy, freshness, and error rates. 💰 Business Impact: Are you actually making the company money or saving it? Look at revenue generated, costs reduced, efficiency gained, or strategic decisions enabled. 🎭 User Satisfaction: Do users like (or hate) your data product? Monitor NPS, satisfaction surveys, feedback loops, and direct user comments. 🛠️ Scalability & Maintainability: Can you sustain growth without headaches? Consider tech debt, scalability metrics, and cost of operations. Metrics mean nothing though if they're not driving decisions. Don't just track; ACT: ➡️ If adoption is low, reconsider product-market fit. ➡️ High error rates? Invest in quality and testing. ➡️ Positive business impact? Double down and expand. Do you have certain KPIs do YOU find most valuable for your data products? Or data investments more generally? #DataProducts #DataProductManagement #DataStrategy #BusinessValue #DataGovernance #DataLeadership #KPIs