Building a Project Management Dashboard

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data. ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose key tasks and see how successful users are. It won’t be right at first, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/eUBScaHp 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush: https://lnkd.in/dUgWtwnu 👍 UKO: https://lnkd.in/eNFv2p_a 👍 Wireframing Kit: https://lnkd.in/esqRdDyi 👍 [continues in comments ↓]

After designing hundreds of business dashboards, I keep coming back to these four patterns: Tall + Scrolly Stack everything vertically, organized by metric family, and let people scroll to their level of depth. Best for mobile viewing and email delivery with basic chart types that doesn't require instructions. Where I've seen this work: New product/feature introductions where audiences are different levels (executive to operators) and functions. BANs + Decomp Big numbers that focus attention and breakdowns that show differences. For when you've identified the important metrics, but want to show segment granularity. Switch group-by dimension while maintaining familiar layout. Where I've seen this work: Operational monitoring for teams that have ownership of metric outcomes. Sankey + Wide Table Flow diagram establishes a map of the whole system and reference tables show details. For diagnosing conversion and retention patterns across nodes and segments to know where to optimize. Where I've seen this work: Growth teams figuring out behavior across complex funnels and overlapping segments. Potential Show what you could be delivering versus what you're actually delivering. Makes the gap between current performance and available capacity visible. Where I've seen this work: Operational teams that have a clear action to take, but limited time. What each of these have in common: - Establish big picture awareness, but direct small picture action (think global, act local) - Strengthened by KPI ownership - Act as a prioritization mechanism Organizations often start with one dashboard trying to serve everyone, then evolve into multiple dashboards with different patterns for different groups. The more established the business, the more discrete the problems being solved are. That means early on, you go from optic oriented communications to more optimization oriented direction. I've found that organizations lack a portfolio strategy for their analytics interfaces, they take templates from one context and try to apply them to another OR they try to combine use cases together into a singular dashboard because they only have budget for one but multiple stakeholders with different needs, so they get a flying-boat-car of compromises. Some data work and analytics are going to be a cost of doing business, like reporting that just keeps everyone informed. While other data work is a strategic bet. The challenge is that some analytics deliver hard value you can measure in dollars, while others provide soft value like better collaboration and shared understanding that's difficult to quantify. Most organizations don't think about this mix deliberately. #dataAnalytics

My client didn’t trust their Power BI dashboard Here's how I fixed it in 5 steps: ⤵️ Problem: → They had one dashboard for everyone. → Managers in one region could see data from another, which created noise, confusion, and security concerns. → Basically, there were no limits on what employees could see, and that's problematic What I did: 1- Identified user roles and grouped them by country and team. 2- Added 𝗥𝗼𝘄-𝗟𝗲𝘃𝗲𝗹 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 (𝗥𝗟𝗦) in Power BI based on UserPrincipalName() mapping to a security table. 3- Created a central Access Matrix in the database: user email → allowed country/team codes. 4- Tested RLS with sample accounts to ensure data visibility matched permissions. 5- Documented the process so new hires can be onboarded in minutes. Results: → 1 dashboard → used by 5+ countries and multiple teams. → Zero cross-country data leaks after launch. → Load times improved by 70% because filters were applied at the model level. Sometimes the biggest value in a dashboard isn’t a new chart, it’s who sees what. RLS can turn one messy report into a scalable, secure asset. ♻️ Repost if you enjoy tips in data analytics

Simon Mylius has just updated our AI Incident Tracker dashboard, which maps all (1100+) incidents in the AI Incident Database according to the MIT AI Risk Repository’s causal and domain taxonomies, and assigns each incident a harm-severity score. Using an LLM, it processes raw incident reports, providing a scalable methodology that can be applied cost-effectively across much larger datasets as numbers of reported incidents grow. The data is output in a structured dataset and a dashboard, which you can explore to identify trends and insights. For instance, you can see - distribution of incident classifications by year - distribution of incident sub-domains by year - incidents with high direct harm severity scores by year - incidents causing severe harm in more than one harm category - distribution of harm severity scores by year This update also adds new evaluation fields for each incident, including: - 5 categories of NatSec impact: Physical Security & Critical Infrastructure / Information Warfare & Intelligence Security / Sovereignty & Government Functions / Economic & Technological Security / Societal Stability & Human Rights - A Fishbone/Ishikawa diagram that presents a number of potential causes for each incident - The primary goal of the AI system involved You can read an overview of the update in the document attached, or visit our website to explore the data.

How many have been here before? You get a request for a simple overview or a new report. Then, some modification or customization. Before you know it, you’re looking at a myriad of 1,050 dashboards that you don't have a clue if they provide value. This Jurassic Park moment may hit home for data teams: building metrics with creating value get confused. Your data team isn’t just a report factory; it should be a strategic decision empowering hub. Here is a 3-step guide for stopping dashboard sprawl and building a data culture that actually looks beyond the charts. 1. The 'Nobody Cares' Audit (Evaluation) Before you build another view, audit what you have. Check the Heartbeat: When was the last time this dashboard was viewed by anyone other than the creator? If it’s been more than a month, is it a ghost? The Owner/Sponsor Mystery: Ask around for the owner of a low-usage dashboard. If stakeholders look at their feet and whisper, "I thought you owned it," it’s time to retire it. Query the Clicks: A high view count doesn’t mean engagement. Are users interacting with the tiles, or just landing on the page and immediately fleeing? Zero clicks means you are just maintaining digital wallpaper. 2. The Great Dashboard Exorcism (Improvement & Reduction) You’ve identified the digital dust-collectors. Now, what do you do? The Consolidator Approach: Stop building a new dashboard for a single metric. Fold related views into existing, higher-level dashboards. Combine 10 views into one powerful dashboard with filters. The Dashboard Graveyard: Move unused dashboards to a "Retirement" folder for 30 days. If nobody asks for them back, delete them. If someone does ask for them, require a documented business case. The 'One-In, One-Out' Policy: Implement a rule: to get a new dashboard approved, a stakeholder must suggest one existing dashboard for decommissioning. This forces prioritization. 3. Building Value-Add Dashboards (The Helpful Kind) How do we make the views people actually need? Pass the 'So What?' Test: Before adding a metric, ask: "If this number moves, what is the required action?" If the answer is "nothing," delete the metric. Narrative Over Data: A great dashboard tells a story. "We are here. This is why. This is the goal. This is what we need to do to fix it." A random collection of charts is just noise. KPIs, not PPIs: Focus on Key Performance Indicators, not Possible Performance Indicators. Don't measure everything because you can; measure the right things because you must. Stop being the team that maintains a myriad of dashboards and charts. Be the team that turns data into a competitive advantage. Be the team that builds the right stories and narratives. Stay nerdy, my friends. #data #AI #dataliteracy #AILiteracy #Datastorytelling

“Dashboards are dead”? Only the context-free ones. Most teams start with definitions. They write a KPI dictionary, argue about formulas, then stack charts. Start with relationships. Map what drives what. Use metric maps and driver trees to sketch causality. ↳ Then define formulas. ↳ Then design screens. ↳ Then pick visuals. Here’s the 4-layer model we use: 1) Maps & Drivers: – metrics maps – driver trees 2) Definitions: – cohorts – formulas – granularity – attribution model – validation checks 3) Information Architecture – filters – page flow – drill paths – segments – comparisons 4) Visuals & UX – chart patterns – color semantics – legends & labels – responsive layout – conditional formatting Why this order? Because “what moved?” is useless without “why.” Common traps this avoids: ✕ Glossary-first thinking. Clean formulas ≠ causal logic. ✕ Chart sprawl. More graphs ≠ more clarity. ✕ Mixed levels. Result, diagnostic, actionable in one pot. If your dashboard doesn’t explain change, it’s reporting, not analytics. Build the logic first. Then display it. #dashboards

Financial reporting should be about strategic decision-making, not manual data wrangling. Yet, finance teams still spend days pulling data, reconciling numbers, and formatting reports—only to find errors at the last minute. The process is time-consuming, prone to mistakes, and slows down critical business decisions. Robotic Process Automation (RPA) with tools like UI Path is transforming financial reporting. Instead of manually extracting, cleaning, and consolidating data, automation does it for you—accurately, in real time, and without delays. Here’s how it works: ✅ Data is automatically pulled from multiple sources (ERP, CRM, spreadsheets, banks). ✅ Reconciliations happen instantly, reducing errors and improving accuracy. ✅ Reports are generated in minutes—standardized, formatted, and audit-ready. Without automation, finance teams are stuck in reactive mode, spending 80% of their time on report preparation and only 20% on analysis. The result? Slower decision-making, frustrated CFOs, and outdated insights. A company that automated its reporting process cut preparation time by 60%—freeing up finance teams to focus on forecasting, strategy, and real business impact. If your team is still manually preparing reports, you’re already behind. It’s time to automate and turn your finance team into a real-time data powerhouse. 📩 Let’s talk about how RPA can transform your financial reporting. Drop a comment or send me a message if you’re ready to make the shift! #Automation #RPA #FinanceTransformation #CFO #FinancialReporting

Introducing: App Security Dashboard AI models are getting increasingly good at writing code— but that doesn’t always mean they follow security best practices. Base44 is striving to be the best end-to-end platform for building great products. But great products also mean keeping those products—and their data—secure. There’s still a lot of ground to cover in this space, but today, Base44 takes a big step forward - making it easier for builders to create and enforce their own security policies. --- The App Security Dashboard - The App Security Dashboard gives you one central place to manage all your app’s security rules. You can find it under workspace -> security. We're starting with Row-Level Security— who can see, and who can update, different types of data in your app. You now have a (relatively) simple, human-readable interface for defining exactly which users can access which data records. Take the example in the screenshot below— It’s a project management app, and the current setup defines the following logic: • Only admins can create new projects • Within projects, users can view tasks for their own department • Only the user who created a task can edit or delete it --- Our goal is to support a wide range of use cases, while keeping it crystal clear how your app’s data behaves. Here’s what’s coming next for the App Security Dashboard: • Manage login and authentication methods • Automatically detect security vulnerabilities in your code (e.g. exposed API keys) • Smart suggestions Would love to hear your thoughts and feedback!

🔒 You Just Accidentally Showed Salary Data to the Entire Company – Time to Learn RLS! Building great reports is step one. Protecting sensitive data while delivering personalized insights? That's where Row-Level Security (RLS) separates amateur dashboards from enterprise-grade solutions. What Is RLS? Imagine a magic report that shows executives everything, managers their departments, and employees only their own data – all from one single dashboard. That's Row-Level Security: intelligent data restrictions at the row level based on user identity. 🎯 Why This Is Non-Negotiable: - Data Protection: Sensitive information stays with authorized eyes only - Personalization: Each user sees relevant data automatically - Efficiency: One report replaces dozens of filtered versions 💡 Real-World Transformation: 1. Sales Report Scenario: - CEO: Sees all regions, all accounts, complete picture - Regional Head: Only their region's performance data - Sales Rep: Exclusively their own accounts and metrics One report. Three completely different views. Zero manual filtering. 🛠️ Two RLS Flavors: - Static RLS: Fixed rules like "Region = East" – simple but requires manual role creation for each group - Dynamic RLS: Smart rules using DAX functions (USERNAME(), USERPRINCIPALNAME()) that automatically adapt to logged-in users – scales beautifully ⚡ The Pro Implementation: 1. Define roles and rules in Power BI Desktop 2. Assign roles to users in Power BI Service 3. Reports automatically filter based on identity 4. Test thoroughly before deployment (critical step!) 🔥 Enterprise Use Cases That Demand RLS: - Finance: Departments see only their budgets, not competitors' - HR: Employees access only their performance data - Sales: Representatives view their accounts, managers see team totals 🚀 The Power Combination: RLS + Workspaces + Apps = Secure, scalable, enterprise-ready reporting environment that makes IT and stakeholders both happy. Without RLS, you're either building 50 versions of the same report or risking data exposure. With RLS, you build once and secure intelligently. The Trust Factor: Master RLS, and you become the person trusted with sensitive business intelligence projects. Tags : Shashank Singh 🇮🇳 | Pradeep M | Dhaval Patel | Hemanand Vadivel | Saddam Ansari | Codebasics | Indian Data Club | Munna Das | Tejas Rane | Tajamul Khan #PowerBI #DataSecurity #RLS #BusinessIntelligence #DataGovernance #Enterprise #Security #DataProtection #Analytics

📌 The Backend of a Dashboard (Dashboards are only 20% of the work) A lot of people think a dashboard project starts when you open Power BI, Tableau, or Looker Studio. But in reality? By the time you’re designing charts, 80% of the real work should already be done. The truth is: a trustworthy dashboard is just the visible tip of a much bigger process. Beneath it lies backend planning, architecture, pipelines, and governance that make or break adoption. Here’s what that journey really looks like using Kimball’s Methodology: 1️⃣ 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 Before starting, you need absolute clarity on the basics. This includes the scope, budget, timeline, and the key stakeholders involved. This is also where you go deep on the business side and capture the business requirements: → What are the highest-value use cases? → Which KPIs actually matter? → How exactly will they be defined and measured? 2️⃣ 𝐃𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 This is where strategy turns into architecture. You’re deciding the type of warehouse that will serve as your single source of truth (e.g., lakehouse, relational, or hybrid). And here’s the trap: many teams choose tools because they’re popular. Choosing the right data stack should never be based on the new/current hype. It’s about scalability, governance, integration, and how seamlessly it fits your workflow. → Fabric → Databricks → Snowflake → Etc. They all work with their pros and cons. Your choices here don’t just impact the build. They lock in your long-term flexibility, cost structure, and even your ability to adapt to future business changes. 3️⃣ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 & 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 This is where architecture becomes reality. This is where data movement, integration, and reliability become critical. You connect to source systems (ERPs, CRMs, spreadsheets, APIs…), build ingestion pipelines, and structure raw data into usable formats. Data is extracted, cleansed, and loaded through pipelines designed for scalability. On the BI side, you’re crafting an experience, not just a report: → Navigation flows logically. → Filters anticipate user behavior. → Visuals deliver answers without forcing people to dig for them. And you validate with real end users early, because adoption is earned long before the live deployment. 4️⃣ 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭, 𝐆𝐫𝐨𝐰𝐭𝐡 & 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 A dashboard isn’t “done” at launch. It should evolve: ⤷ Add new sources, refine KPIs, boost adoption ⤷ Monitor pipelines, update models, fix data issues The backend must be actively monitored: latency, data freshness, and integrity issues are fixed before they reach the business. The key takeaway? Without a strong backend, even the prettiest dashboard is just decoration. YES, aesthetics get attention, but reliability earns trust. And trust is THE metric that truly drives adoption. #BusinessIntelligence #DataStrategy