MarraData’s Post

Why Data Science is Vital in #Decision_Making? Data science is crucial in UX design for decision-making because it provides actionable insights from user data. By analyzing user behavior, preferences, and interactions, designers can make informed choices that enhance user experience, optimize design elements, and drive better outcomes. Data-driven decisions lead to more effective designs, reducing guesswork and aligning the product with user needs. #DesignThinking #Innovation #PredictiveAnalysis #Ai #ArtificalIntelligence #BusinessSolutions #Technology #UXProcess #UXDesign #UIDesign #DataScience #DecisionMaking #GuideBook

We've recently made a major UX overhaul with Dynamic Time-Period Controls! 🦾 With just a simple click, you can now easily switch between various periods, from Daily to Yearly perspectives, allowing for an effortless shift between detailed, immediate data points and broader, long-term trends. Experience how easy it is to shift between detailed, immediate data points and broader, long-term trends with a simple click. #DashboardParalysis is the death of data adoption, and DataGPT continues to provide a single source for business users to get everything they need from their data in one place. #generativeai #dataanalytics #datademocratization #dataculture

There's no denying that the emergence of big data has tremendously altered the landscape of UX/UI design. By utilizing big data, designers can now make informed decisions driven by hard numbers, trends, and user behaviors, instead than basing solely on intuition. The fusion of big data and UX/UI design allows for a hyper-personalized user experience, reducing guesswork on user preference and enhancing overall product functionality. It eases the process of pinpointing user pain points and fine-tuning design interfaces accordingly, thus leading to improved customer satisfaction and businesses' growth. Turning big data into actionable insights could be complex, yet it's crucial in making user-centric design decisions. Breaking down the wall between data science and design, in fact, creates an opportunity, not a hindrance. In the end, successful UX/UI design is all about delivering the right experience at the right time, and big data plays a crucial role in achieving this! #UXUIDesign #BigData #DataDrivenDesign #UserExperience  

Drowning in qualitative data? Don't fret! Let's explore two popular methods that can help you make sense of it all: thematic analysis and affinity mapping. Thematic analysis offers a structured approach to uncovering key themes, while affinity mapping provides a visual and collaborative way to identify patterns. Here's a quick rundown of how each method works. Thematic analysis: 🔖 Collect your data 🧩 Break it down into manageable snippets 🏷️ Code each snippet to categorize themes 🔄 Explore relationships between themes 💡Interpret themes to uncover meaningful insights Affinity mapping: 📊 Collect qualitative data from your study 📌 Visualize data snippets on a board 🌟 Group similar elements into clusters 🔎 Analyze clusters to identify patterns and insights Ready to unlock the full potential of your research data? Dive deeper into our latest article by UX expert Michele Ronsen for insights and practical tips - https://loom.ly/Iae4HBk #UXResearch #AnalysisSynthesis #QualitativeData #ExpertInsight #usabilitytesting #userexperience #usertesting #userresearch #data #ux #lyssna #analysis #synthesis

𝑁𝑎𝑣𝑖𝑔𝑎𝑡𝑖𝑛𝑔 𝑐𝑜𝑚𝑝𝑙𝑒𝑥 𝑑𝑎𝑡𝑎 𝑡𝑜𝑜𝑙𝑠 𝑠ℎ𝑜𝑢𝑙𝑑𝑛'𝑡 𝑏𝑒 𝑎 ℎ𝑒𝑎𝑑𝑎𝑐ℎ𝑒—𝑒𝑠𝑝𝑒𝑐𝑖𝑎𝑙𝑙𝑦 𝑤ℎ𝑒𝑛 𝑒𝑥𝑝𝑙𝑎𝑖𝑛𝑖𝑛𝑔 𝑖𝑛𝑠𝑖𝑔ℎ𝑡𝑠 𝑡𝑜 𝑛𝑜𝑛-𝑒𝑥𝑝𝑒𝑟𝑡𝑠! During our product development journey, we recognized the challenge of 𝒎𝒂𝒌𝒊𝒏𝒈 𝒎𝒂𝒓𝒌𝒆𝒕𝒑𝒍𝒂𝒄𝒆 𝑲𝑷𝑰𝒔 𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅𝒂𝒃𝒍𝒆 𝒂𝒏𝒅 𝒗𝒂𝒍𝒖𝒂𝒃𝒍𝒆 𝒕𝒐 𝒏𝒐𝒏-𝒆𝒙𝒑𝒆𝒓𝒕𝒔. By integrating feedback from industry experts and leveraging our extensive experience, we've designed Studio to streamline data interpretation, making it accessible and practical for brand teams. Our decision to prioritize a simplified UX and UI in Studio was driven by the need to 𝒃𝒓𝒊𝒅𝒈𝒆 𝒕𝒉𝒆 𝒈𝒂𝒑 𝒃𝒆𝒕𝒘𝒆𝒆𝒏 𝒅𝒂𝒕𝒂-𝒉𝒆𝒂𝒗𝒚 𝒊𝒏𝒔𝒊𝒈𝒉𝒕𝒔 𝒂𝒏𝒅 𝒂𝒄𝒕𝒊𝒐𝒏𝒂𝒃𝒍𝒆 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒊𝒆𝒔 for brands. How do you currently streamline data communication within your organization? #WitailerStudio #ProductDevelopment #UXDesign #DataClarity #BrandSuccess #AmazonStrategy #StakeholderEngagement

📈 Statistical Essentials for UX Researchers: Which Test to Use? 📈 Wondering which statistical test to apply? Here’s a simple breakdown: T-Test: Ideal for comparing averages between two groups. Chi-Square Test: Best for examining relationships between categorical data. ANOVA: Useful for comparing averages across three or more groups. The right test makes your data insights clearer. Follow for more! 👍 #UXResearch #StatsForUX #DataAnalysis #DataScience #SimplifiedStats #EasyStats #StatsMadeEasy

Visualizing trends to improve UX in Power Bl ✅ Save this post for later! Want to learn more about Power Bl and how to create outstanding reports? 🔥 Then join my Design Transformation Program in September #report #analytics #powerbi

In celebration of #NationalPiDay, we are slicing up data viz by sharing tips on why many UX practitioners avoid using pie charts. • Pie charts are less effective for comparing #data. It is challenging for users to accurately compare sizes, angles, and proportions of different segments, especially when there are many segments or they are similar in size. • Pie charts require more space to display the same amount of information compared to other charts. This can be problematic for space-constrained situations like mobile interfaces or dashboards. • For folks who are #neurodivergent or have visual impairments, there may be difficulties distinguishing between different segment colors; excluding certain users from engaging with the data. Pie charts can be visually appealing but they can be less effective when conveying data. You can always opt for alternative visualization techniques that have better #usability and user comprehension: like the trusty bar.

We’ve already explored 𝚠𝚑𝚢 visualizing machine learning and analytical models is so important. Now, let’s dive into the next big question—𝚠𝚑𝚘 are we actually creating these visualizations for? When you’re crafting visualizations, knowing your audience is key. It’s all about tailoring your visuals to fit their unique needs. 🧠 In the world of data analytics, we often think about three key personas: 𝙳𝚒𝚊𝚗𝚊 𝙳𝚎𝚟𝚎𝚕𝚘𝚙𝚎𝚛, 𝙱𝚎𝚊 𝙱𝚞𝚜𝚒𝚗𝚎𝚜𝚜 𝙻𝚎𝚊𝚍𝚎𝚛, and 𝚂𝚊𝚖 𝚂𝚝𝚊𝚔𝚎𝚑𝚘𝚕𝚍𝚎𝚛. Each of them brings a different focus, level of expertise, and amount of time they can dedicate. ⏰ Over the next few posts, we’ll explore each of these personas one by one—what they care about, what they need, and how you can deliver visualizations that speak directly to them. So, which one resonates with you? Are you more of a 𝙳𝚎𝚟𝚎𝚕𝚘𝚙𝚎𝚛, a 𝙱𝚞𝚜𝚒𝚗𝚎𝚜𝚜 𝙻𝚎𝚊𝚍𝚎𝚛, or a 𝚂𝚝𝚊𝚔𝚎𝚑𝚘𝚕𝚍𝚎𝚛? #PowerBI #UX #DataAnalytics _____________________________________________ Visualising analytical data - post series - post 4

It's a head-scratcher how separated the worlds of UX and data architecture have become. UX is not only the end-state surface: it's about the value of that surface in meeting user needs through functional interactions and content. And what powers that surface and the value what you find there? It's the data strategy, structure, content. When UX is a designing that layer, there's a much bigger impact on value and making it possible for customers to achieve their outcomes. #dataengineering #userexperience #contentstrategy