Information Architecture Basics

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

"Why cognitive load (not clean code) is what really matters in coding" What truly matters in software development isn't following trendy practices - it's minimizing mental effort for other developers. I've witnessed numerous projects where brilliant developers created sophisticated architectures using cutting-edge patterns and microservices. Yet when new team members attempted modifications, they struggled for weeks just to grasp how components interconnected. This cognitive burden drastically reduced productivity and increased defects. Ironically, many of these complexity-inducing patterns were implemented pursuing "clean code." The essential goal should be reducing unnecessary mental strain. This might mean: - Fewer, deeper modules instead of many shallow ones - Keeping related logic together rather than fragmenting it - Choosing straightforward solutions over clever ones The best code isn't the most elegant - it's what future developers (including yourself) can quickly comprehend. When making architectural decisions or reviewing code, ask: "How much mental effort will others need to understand this?" Focus on minimizing cognitive load to create truly maintainable systems, not just theoretically clean ones. Remember, code is read far more often than written. #programming #softwareengineering #tech

The Brain Isn’t Actually Multitasking What we perceive as multitasking is, in neurological terms, rapid task-switching — a process that incurs significant cognitive costs. The brain doesn’t truly do two things at once; it simply toggles between tasks quickly, and that toggling has a price. It Costs You Time and Accuracy Research by Rubinstein, Meyer, and Evans found that task-switching can cost up to 40% of a person’s productive time due to the cognitive load of moving between tasks. Studies using brain-imaging technology confirm that performance scores are lower and error rates increase in multitask conditions compared to single-task conditions. It Impairs Memory and Attention Chronic multitaskers show inferior working memory performance and greater difficulty filtering out irrelevant information, leading to increased mental fatigue and stress. Frequent media multitasking is also associated with more self-reported attention lapses, mind-wandering, higher impulsiveness, and more problems with executive functions. It Hurts Academic and Professional Performance Research indicates that media multitasking interferes with attention and working memory, negatively affecting GPA, test performance, recall, reading comprehension, note-taking, self-regulation, and efficiency. Students also tend to underestimate how much it’s hurting them in the moment. The Brain Can “Disengage” Under Overload According to research, brain may “downshift” or limit additional resource allocation when cognitive load becomes excessive, rather than rising to the challenge. The Bottom Line For complex, goal-oriented work, monotasking — focused engagement with a single task — remains the superior strategy for sustainable productivity and cognitive fidelity. The research is fairly consistent: the feeling of being productive while multitasking is largely an illusion.

𝐓𝐡𝐨𝐬𝐞 𝐖𝐡𝐨 𝐓𝐫𝐲 𝐉𝐮𝐠𝐠𝐥𝐢𝐧𝐠 𝐓𝐨𝐨 𝐌𝐚𝐧𝐲 𝐁𝐚𝐥𝐥𝐬 𝐅𝐚𝐢𝐥 𝐌𝐢𝐬𝐞𝐫𝐚𝐛𝐥𝐲. You’re juggling three balls, it feels you’ve got this. Now you’re juggling four, it’s tough but you manage. Now you’re juggling five, chaos builds. Now you’re juggling six, you drop all of them! That’s exactly how cognitive load feels. When your brain is juggling too much information and too many decisions at the same time. As a psychologist, I see this all the time. People think they’re indecisive or unproductive, but the truth is, their mental bandwidth is maxed out. 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞 𝐥𝐨𝐚𝐝 - 𝐭𝐡𝐞 𝐦𝐞𝐧𝐭𝐚𝐥 𝐰𝐞𝐢𝐠𝐡𝐭 𝐨𝐟 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐭𝐨𝐨 𝐦𝐮𝐜𝐡 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐨𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐛𝐚𝐫𝐫𝐢𝐞𝐫𝐬 𝐭𝐨 𝐜𝐥𝐞𝐚𝐫, 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐭 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐦𝐚𝐤𝐢𝐧𝐠. When your brain is overwhelmed, even small decisions feel monumental. That’s why you might spend ages picking a restaurant after a day of big meetings. Your brain isn’t lazy—it’s overworked. But it’s not just about feeling tired. Cognitive load impacts the quality of your decisions. The more overwhelmed you are, the more likely you are to choose what’s easy, familiar, or convenient, not necessarily what’s best. Sounds scary. Right? I’ve worked with clients who felt stuck, unable to decide between career moves, new opportunities, or even personal goals. Most of the time, the problem wasn’t indecision. It was the sheer amount of information and options clouding their minds. 𝐒𝐨, 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐥𝐢𝐠𝐡𝐭𝐞𝐧 𝐭𝐡𝐞 𝐦𝐞𝐧𝐭𝐚𝐥 𝐥𝐨𝐚𝐝 𝐚𝐧𝐝 𝐦𝐚𝐤𝐞 𝐛𝐞𝐭𝐭𝐞𝐫 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬? → 𝐋𝐢𝐦𝐢𝐭 𝐘𝐨𝐮𝐫 𝐈𝐧𝐩𝐮𝐭𝐬: Be selective about what you consume. Your brain wasn’t designed to process infinite notifications or social feeds. Filter and focus. → 𝐁𝐚𝐭𝐜𝐡 𝐒𝐢𝐦𝐢𝐥𝐚𝐫 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬: Make decisions in clusters. Planning your week’s meals in one go is far less taxing than deciding every day. → 𝐒𝐞𝐭 𝐁𝐨𝐮𝐧𝐝𝐚𝐫𝐢𝐞𝐬: Not every choice deserves endless time. Give yourself limits. Trust your instincts and move forward. One client came to me overwhelmed by decisions, from strategic career moves to daily operations. We simplified her processes, grouped her tasks, and gave her decision-making space. Within weeks, she felt clearer, more confident, and far more in control. Cognitive load isn’t something you can escape entirely, but you can manage it. By reducing the mental clutter, you create space for clarity, confidence, and focus. If this clicks with you, I’d be delighted to share more insights into the psychology of decision-making with your team! Let’s get talking! #decisionmaking #team #mentalhealth #career #psychology #personaldevelopment

Data catalogs are widely promoted as the heart of modern data management —Yet many organizations end up with a static index rather than a dynamic governance engine. Is your Data Catalog a static index or a dynamic governance engine? I've seen many organizations treat it like a checkbox project. But for a data professional, it’s the heart of the platform. A data catalog without governance is like Google Maps with outdated roads—pretty interface, wrong directions. 𝗪𝗵𝘆 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝘀 𝗦𝗵𝗼𝘂𝗹𝗱 𝗖𝗮𝗿𝗲?  • 80% of analytics time = hunting for the right data  • Modern catalogs aren't just inventories—they're active metadata engines  • AI tools are only as smart as your catalog metadata (garbage in, garbage out) 𝗪𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗠𝗮𝘁𝘁𝗲𝗿𝘀? → Discovery & Trust  • Semantic mapping prevents "monthly active users" meaning 5 different things  • Last-mile lineage = your secret weapon for reconciling conflicting reports → The AI Connection  • Automated impact analysis for schema changes  • AI-generated data quality rules from business glossary  • Developer assistants that surface metadata inline 𝗪𝗵𝗼 𝗗𝗼𝗲𝘀 𝗪𝗵𝗮𝘁? Data Owner → Defines & approves terms Data Steward → Maintains metadata daily Data Engineer → Publishes lineage & changes Data Analyst → Validates & provides feedback 𝗖𝘂𝗿𝗶𝗼𝘂𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗸𝗶𝗹𝗹 𝘁𝗼 𝗮𝗱𝗮𝗽𝘁? Metadata management tools (Alation, Collibra, Atlan) API/event stream integration Business glossary design Change management Clean metadata does not make AI impressive. It makes it safe. 𝗪𝗵𝗮𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲?  • Define the few metrics that run the business and map everything to them  • Make ownership explicit and visible  • Treat lineage as a business artifact, not an engineering diagram  • Bring the catalog into BI tools, notebooks, and reviews Image Credits - Peter Baumann Here's the article that inspired me to talk about Data Catalog on "Data Catalog 2.0: Get value from Metadata" - https://lnkd.in/dS9FZaPW Here's how you must start: Prioritize business KPIs → assign stewards → automate lineage → measure adoption. Stop treating data catalogs as inventory. 𝗕𝘂𝗶𝗹𝗱 𝘁𝗵𝗲𝗺 𝗮𝘀 𝘁𝗿𝘂𝘀𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝘀 - 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗜.

When someone lands on your site, every extra word, button, or menu is a cognitive tax. Take this landing page comparison: Attio - keeps the load light • One navigation bar • 12 words in total for the header + sub-header • 9 clickable exits above the fold • Lots of whitespace • Sneak peak at product imagery The result = focus 🧘♀️ HubSpot - seems to have many cooks in the kitchen • Two navigation bars at the top • 50% more words (24 words in the header + subheader) • 13 clickable exits above the fold • Bigger chat widgets • Lifestyle imagery instead of whitespace The result = distraction 🐿️ With busier pages comes higher cognitive load, the paradox of choice, and decision paralysis 🧠 In real terms: if someone pauses even a split second more and doesn’t act, they’re more likely to bounce. And this isn’t just true for landing pages - it applies to pricing pages, homepages, dashboards… anywhere with competing priorities 👩🍳 👩🍳 👩🍳 It’s easy to add, hard to cut. ✂️ Good design isn’t what you add, it’s what you remove (or don't add in the first place). So ask yourself: What's the 30% you can remove from your page? 🗑️

𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝗼𝗳 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗶𝗻 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁? Cognitive overload happens when the mental effort required to use a system or process exceeds the user’s capacity. In Procurement, this happens when tools are overly complex or poorly designed. 𝗧𝗵𝗲 𝗰𝗼𝗻𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 𝗼𝗳 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗮𝗿𝗲 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 and range from a persistent operational inefficiency, more errors, low adoption of complex solutions and ultimately a risk for employee burnout. While some level of complexity is inevitable to support advanced functionality, the way tools and workflows are designed plays a crucial role for their usability, how effectively users can engage with them and the level of mental load they create. The Cognitive Load Theory (CLT), introduced by John Sweller in the 1980s, provides a framework for reducing mental strain by focusing on how users learn, process and retain information. The CLT identifies three types of cognitive load and offers insights into how Procurement Systems can be optimised for usability: 1️⃣ 𝗜𝗻𝘁𝗿𝗶𝗻𝘀𝗶𝗰 𝗟𝗼𝗮𝗱 which arises from the inherent complexity of the task or information. In Procurement, examples include multi-dimensional RFP scoring or the authoring of complex contracts and their SLAs. 𝗛𝗼𝘄 𝘁𝗼 𝗵𝗮𝗻𝗱𝗹𝗲 𝘁𝗵𝗶𝘀? Break down and simplify complex tasks into manageable steps using modular workflows, and provide pre-configured templates for common scenarios. 2️⃣ 𝗘𝘅𝘁𝗿𝗮𝗻𝗲𝗼𝘂𝘀 𝗟𝗼𝗮𝗱 stemming from poor system design, irrelevant information or inefficient processes. For example, clunky interfaces, unnecessary workflow steps or dashboards that hide insights under excessive detail. 𝗛𝗼𝘄 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲 𝘁𝗵𝗶𝘀? Minimise Extraneous Load with a functional user interface design, using smart visualisations and streamlining workflows. 3️⃣ 𝗚𝗲𝗿𝗺𝗮𝗻𝗲 𝗟𝗼𝗮𝗱 resulting from the cognitive effort that directly supports learning and mastery. Examples include tooltips, clear guidance, and onboarding processes that make systems easier to navigate. 𝗛𝗼𝘄 𝘁𝗼 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝘁𝗵𝗶𝘀? Enhance Germane Load with role-specific training, embedded tool tips & intuitive help features accelerating user learning. All three types can lead to a reduced capacity of employees to be able to operate effectively and potential negative consequences and mental stress. 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗼𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗰𝗼𝗺𝗲𝘀 𝗮𝘁 𝗮 𝗵𝗶𝗴𝗵 𝗽𝗿𝗶𝗰𝗲. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘄𝗵𝗶𝗰𝗵 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗮 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗱𝗲𝘀𝗶𝗴𝗻 and optimise their cognitive load levels by unveiling tasks step by-step, simplifying design and providing helpful learning features, 𝗵𝗮𝘃𝗲 𝗮 𝗵𝗶𝗴𝗵𝗲𝗿 𝗰𝗵𝗮𝗻𝗰𝗲 𝘁𝗼 𝘁𝘂𝗿𝗻 𝗳𝗿𝗼𝗺 𝗮 𝗵𝗲𝗮𝗱𝗮𝗰𝗵𝗲 𝘁𝗼 𝗮 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗯𝗼𝗼𝘀𝘁𝗲𝗿. ❓How do you think can solutions be humanised to reduce cognitive load. ❓What else helps to generate a good usability and user experience.

For Data Engineers, Databricks Unity Catalog is the secret to managing data at scale across teams, clouds, and projects. But what exactly is behind that term?   𝗨𝗻𝗶𝘁𝘆 𝗖𝗮𝘁𝗮𝗹𝗼𝗴 is the unified governance layer for 𝗮𝗹𝗹 your data and assets. Tables, files, notebooks, ML models, you name it.   It’s not just another feature; it’s a 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 for managing your data platform at scale, making data usable, secure, and trustworthy across your whole platform.   Here’s the core idea: - 𝗠𝗲𝘁𝗮𝘀𝘁𝗼𝗿𝗲: A single source of truth for metadata. One per region, no more per-workspace metastores. - 𝗖𝗮𝘁𝗮𝗹𝗼𝗴𝘀: Group your data by business domains → sales, marketing, operations. - 𝗦𝗰𝗵𝗲𝗺𝗮𝘀: Organize data logically → Bronze, Silver, Gold layers. - 𝗧𝗮𝗯𝗹𝗲𝘀 & 𝗩𝗶𝗲𝘄𝘀: Where your data lives. Structured, secure, discoverable. - 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Securely link cloud storage with access policies via Storage Credentials.   But it’s not just structure. Here’s what Unity Catalog really brings to the table: ➡️ 𝗖𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗮𝗰𝗰𝗲𝘀𝘀 𝗰𝗼𝗻𝘁𝗿𝗼𝗹: Manage permissions across all Databricks workspaces. No more messy, scattered permission settings. ➡️ 𝗙𝗶𝗻𝗲-𝗴𝗿𝗮𝗶𝗻𝗲𝗱 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Control access down to the column or even row level. Perfect for sensitive data (PII, anyone?). ➡️ 𝗠𝘂𝗹𝘁𝗶-𝗳𝗼𝗿𝗺𝗮𝘁 𝘀𝘂𝗽𝗽𝗼𝗿𝘁: Delta, Iceberg, Hudi. Work with the formats your team needs, no vendor lock-in. ➡️ 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗹𝗶𝗻𝗲𝗮𝗴𝗲: See exactly how data flows: from Bronze to Silver to Gold. Great for debugging, impact analysis, and compliance. ➡️ 𝗘𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗹𝗶𝗻𝗲𝗮𝗴𝗲: Trace data from ingestion to final report, automatically updated in real time. and more...   For Data Engineers, Unity Catalog means fewer headaches and more confidence: - No more separate metastores per workspace. - Clear separation of storage (where data lives) and metadata (who can access what, how, and why). - Full traceability of every transformation, whether it’s a small type cast or a complex data model change.   ***** In my Azure Databricks project, we put Unity Catalog into practice! Here, you'll: ➡️ Set up storage credentials to access Azure Data Lake securely. ➡️ Create external locations for raw and processed data. ➡️ Organize data in a 3-level namespace: catalog.schema.table → aligned with Medallion Architecture. ➡️ Control access: Business users only see Gold, engineers get access to Silver/Bronze for transformation.   This is how we make complex data systems manageable. And this is what modern Data Engineers need to build.   🎓 Want to learn how it works, step by step? Check the project link in the comments! 👇

You can’t see cognitive overload. That’s why it’s ignored. Most teams treat accessibility as contrast ratios and alt text. But cognitive accessibility is wider than that, and less forgiving when you get it wrong. Here are 5 common cognitive disabilities And what designers can actually do. 1. ADHD Challenges: • Distractibility • Difficulty prioritizing • Overwhelm from dense layouts Design for: • Clear visual hierarchy • One primary action per section • Step-based flows Avoid: • Competing primary CTAs • Auto-rotating carousels • Notification overload 2. Dyslexia Challenges: • Slower decoding • Reading fatigue • Difficulty with dense text blocks Design for: • Plain language • Left-aligned text • Generous line height (1.5+ recommended) • Clear headings and chunking Avoid: • Justified text • Long paragraphs • Low-contrast body text 3. Autism Spectrum Challenges: • Sensory sensitivity • Cognitive overload • Distress from unexpected change Design for: • Predictable layouts • Explicit labels • Warnings before context shifts • User-controlled animation and motion Avoid: • Sudden modals • Autoplay video • Reduced motion off by default • Ambiguous copy like “Try it” or “Explore.” 4. Memory Impairment Challenges: • Forgetting steps • Losing context in multi-step flows Design for: • Persistent instructions • Progress indicators • Auto-save • Clear error recovery Avoid: • Clearing form data on error • Hiding previous answers • Long forms without sectioning 5. Anxiety Disorders Challenges: • Fear of mistakes • Stress from uncertainty • Decision paralysis Design for: • Reassuring microcopy • Undo functionality • Transparent consequences • Calm error messaging Avoid: • Countdown timers • Aggressive urgency language • Vague destructive actions Ask yourself: "Does this screen reduce thinking or increase it?" 👇🏽 Are we over-indexing on visual accessibility while ignoring cognitive overload? Drop your thoughts in the comments. ♻️ Share and save this for your team. --- ✉️ Subscribe to my newsletter for accessibility and design insights here: https://lnkd.in/gZpAzWSu --- Accessibility note: Content in the post is the same as the image attached (except for a few bullets omitted for easy scanability)

This paper describes how a large pharmaceutical company adopted an ontology-based data management strategy to ensure scientific data is findable, accessible, interoperable, and reusable from the moment it is generated. 1️⃣ The approach emphasizes creating structured, high-quality data at the source to preserve context and reduce downstream processing time. 2️⃣ Standardized vocabularies and models (ontologies) are used to align data across systems and teams, supporting consistency and integration. 3️⃣ Public ontologies are adapted with organization-specific extensions while maintaining compatibility with external data standards. 4️⃣ Simplified term lists are derived from complex models to enable broader adoption across teams with varying technical backgrounds. 5️⃣ Data from different systems is integrated virtually rather than physically moved, enabling secure, real-time access without redundancy. 6️⃣ This framework enhances the performance of advanced analytics and machine learning by providing clear, semantically rich context. 7️⃣ Controlled vocabularies are delivered through interfaces like APIs and dropdowns, ensuring consistent metadata usage at scale. 8️⃣ The unified semantic structure improves enterprise search, allowing users to retrieve contextually relevant data from across domains. 9️⃣ Adoption metrics show growing usage across multiple phases of the pharmaceutical value chain, reflecting system scalability and value. 🔟 Organizational alignment—from executive support to operational implementation—has been critical, with recent advances in AI further enabling this transformation. ✍🏻 Shawn Zheng Kai Tan, Shounak Baksi, Thomas Gade Bjerregaard, Preethi Elangovan, Thrishna Kuttikattu Gopalakrishnan, Darko Hric, Joffrey Joumaa, Beidi Li, Kashif Rabbani, Santhosh Kannan Venkatesan, Joshua Daniel Valdez, Saritha Vettikunnel Kuriakose, Digital evolution: Novo Nordisk’s shift to ontology-based data management. Journal of Biomedical Semantics. 2025. DOI: 10.1186/s13326-025-00327-4