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

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

🚨 My dashboard is useless when the dataset is incorrect !!!!! I once made it to the final round of an interview for a Data Analyst role. The task? Build a dashboard in Excel or Power BI based on the company’s requirements. At that time, I was super confident in my Power BI skills. I built a beautiful dashboard with almost every feature from the meme — colorful visuals, interactive filters, drill-down magic, even a clean schema from Power Query. But… I forgot one small thing: removing duplicates. And here’s the truth: no matter how fancy your dashboard looks, stakeholders won’t care if the data feeding it is wrong. If your dataset isn’t reliable, your insights are useless. That experience taught me an important lesson: before you think about making a “wow” dashboard, make sure the dataset is correct. Here are a few expanded steps I now follow to keep my data clean: 1. Scan and understand your dataset - Start with a data audit — what kind of dataset is it? Transactional, customer, operational, or something else? - Understand the logic of rows and columns: are they events, unique IDs, or aggregated summaries? - Profile the data by running quick checks: number of rows, missing values, duplicate counts, and overall structure. - Treat duplicates carefully. Sometimes they’re errors, but sometimes they’re valid (e.g., multiple transactions from the same customer on the same day). 2. Check column types and validate formats - Classify every column: categorical (e.g., product category), numeric (e.g., sales amount), or time/date (e.g., transaction date). - Verify consistency: Categorical fields → spelling consistency (“USA” vs. “U.S.” vs. “United States”). Numeric fields → make sure they’re truly numeric and not stored as text. Dates → standardize to one format (e.g., YYYY-MM-DD) across the dataset. - Review NULL or missing values. Decide whether to impute, drop, or escalate — but never ignore them. 3. Spot anomalies and outliers - Check for extreme values that don’t make sense (e.g., negative sales, a customer age of 400). - Use descriptive statistics (mean, median, standard deviation) to highlight outliers. - Always validate with the business context before removing or adjusting. Sometimes outliers are the most important story! 4. Document every step of cleaning - Keep a “data diary” — document what transformations you applied, what errors you found, and how you handled them. - Track unresolved issues. For example: “Column X had 125 NULL values — awaiting stakeholder input.” “Customer IDs had 15 duplicates — validated as system error, removed.” - This makes your process transparent, reproducible, and easy to explain in future audits. ✅ In short: data cleaning isn’t “extra work,” it’s the foundation of reliable dashboards. A fancy front end might impress once, but clean, trustworthy data keeps stakeholders coming back. ✨ let’s connect and share ideas! #DataAnalytics #PowerBI #DataCleaning #DataStorytelling

Data quality isn’t a luxury; it’s the seatbelt in your analytics car—skip it, and the crash is inevitable. Why We Actually Care (Factors) → Business decisions: Executives trust your dashboards. Don't let them down. → ML models: Garbage in, garbage out. Your model is only as good as your data. → Pipelines: One bad field breaks everything downstream. Fix it early. → Compliance: Auditors don't accept "oops." Neither does GDPR. → Cost: Bad data means reruns, fixes, and late nights. Good data saves money. The Six Dimensions (Your Quality Checklist) → Accuracy: Does it reflect reality? → Completeness: No missing pieces. → Consistency: Same story everywhere. → Timeliness: Fresh, not yesterday’s leftovers. → Validity: Fits the rules, like a puzzle piece. → Uniqueness: No duplicates—because one identity crisis is enough! How We Actually Do It (Process) → Input validation: Stop bad data at the door. Always. → Constraints & rules: If age > 150, something's wrong. → Data profiling: Know your data before you trust it. → SLAs & SLOs: Set expectations. Measure reality. → Monitoring & alerts: Catch issues before users do. → Lineage tracking: When things break, trace it back. → Triage & RCA: Fix the bug. Fix the system. Document it. The Tools That Help (Frameworks) → Great Expectations: Write tests for your data like you test code. → Deequ: Amazon's gift to data quality. Scales beautifully. → Monte Carlo: Observability for data pipelines. Sleep better. → dbt tests: Test your transformations. Trust your models. 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀𝗻'𝘁 𝗮 𝗼𝗻𝗲-𝘁𝗶𝗺𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁. 𝗜𝘁'𝘀 𝗮 𝗱𝗮𝗶𝗹𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲. Data without quality is like coffee without beans—pointless. As data engineers, we’re not just pipeline plumbers; we’re the guardians of trust. Build systems that catch issues early and keep the flavor of truth intact. 𝘉𝘶𝘪𝘭𝘵 𝘣𝘺 𝘥𝘢𝘵𝘢 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘴, 𝘧𝘰𝘳 𝘥𝘢𝘵𝘢 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘴. 𝘕𝘰 𝘧𝘭𝘶𝘧𝘧, 𝘫𝘶𝘴𝘵 𝘳𝘦𝘢𝘭𝘪𝘵𝘺.

If your dashboard doesn’t answer these 3 questions in under 60 seconds, it’s not helping. Project managers aren’t just building reports. We’re building visibility. We’re building alignment. We’re building trust. And too often, dashboards turn into data dumps that no one actually reads. I’ve learned this the hard way: when stakeholders don’t get what they need from your dashboard, they default to side messages, follow-up meetings, or worse, silence. That's why every dashboard should focus on just three main questions: 1. What’s on track? Let them see wins at a glance. It builds confidence. Example: “Frontend 95% done, UAT still on track for Friday.” 2. What’s at risk? Call out blockers early, before they spiral. Example: “Testing delayed due to vendor handoff, patch in motion.” 3. What needs a decision? Make choices visible so momentum doesn’t stall. Example: “Scope change approval needed, will push timeline 3 days.” Dashboards are not just for project status. They’re built with stakeholders in mind, designed to match how they think, decide, and act. And when done right? They reduce status meetings. They cut back confusion. They show stakeholders exactly what they need, when they need it. Because clarity doesn’t come from more data. It comes from asking better questions. → Found this helpful? Repost ♺ and follow Jesus Romero for grounded PM frameworks that elevate clarity and trust.

Launching your dashboard is just the beginning, you need to maintain it. Here’s how to ensure your dashboards stay relevant long after its initial launch: 1. 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗣𝗲𝗿𝗶𝗼𝗱𝗶𝗰 𝗥𝗲𝘃𝗶𝗲𝘄𝘀: Business goals and data needs change over time. Establish a routine for reviewing your dashboard’s effectiveness and relevance. Is it still meeting the users' needs? Does it align with current business objectives?     2. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: Create channels for ongoing feedback and encourage users to report issues or suggest improvements. This open line of communication is crucial for iterative development.     3. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀: Use the feedback and review outcomes to make targeted improvements to dashboards. This could mean refining visualizations, adding new data points, or optimizing performance. Keep in mind, that a great dashboard is never truly finished.     4. 𝗘𝗱𝘂𝗰𝗮𝘁𝗲 𝗮𝗻𝗱 𝗘𝗻𝗮𝗯𝗹𝗲 𝗨𝘀𝗲𝗿𝘀: Offer training sessions for new features or changes. Enable users to make the most of the dashboard, ensuring it remains a valuable tool that gets regularly used.     5. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗖𝗵𝗮𝗻𝗴𝗲𝘀: Keep a changelog or documentation of updates and modifications. This transparency helps manage expectations and provides a history of the dashboard’s progression.     6. 𝗞𝗻𝗼𝘄 𝗪𝗵𝗲𝗻 𝘁𝗼 𝗦𝗮𝘆 𝗚𝗼𝗼𝗱𝗯𝘆𝗲: Not all dashboards are meant to last forever. Recognize when a dashboard no longer serves its purpose and plan for its retirement or replacement. This decision helps to ensure resources are focused on tools that deliver value. Handling the post-launch lifecycle is an important, but often overlooked task for data analysts. You need to focus on continuous improvement and alignment with ever-changing needs. What's your experience on what happens with dashboards after go-life? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #businessanalytics #dashboard #businessintelligence  #continuousimprovement

Designing the best dashboard will always be a subjective, creative and bespoke process There is no ideal template or perfect output; quality relies on the situation and the context.   But I have learned (and nicely coined) five things you need to keep in mind, all starting with an A: 1️⃣ Audience If you lose the audience, nothing else matters. You need to understand your audience. What do they do everyday? What decisions do they make? Remember to map out who your users are and what they want! 2️⃣ Automation Eliminate menial tasks and the ability to expedite a user’s path to insights. Notifications, filters, automated refreshes and integrations can all help this. Integrations are the hardest part, so check out Hugo Lu’s content on how far orchestrated automation has come between backend and front-end tools 3️⃣ Accessibility Two things. First, create access. Access to data is about building trust with your stakeholders and necessary to get them to use your tool. Second, make the design accessible. Inclusivity is important and inclusive, accessible design creates outputs that are simple and visually appealing 4️⃣ Analysis Designing your dashboard with the right analysis in mind is crucial. From calculating the right KPIs to applying analytical models on top of data to embedding AI-generated text insights, there is so much opportunity here! 5️⃣ Action One of the most important yet hardest to do. Without action, nothing gets done. This means designing with an end goal in mind. Every visualisation, KPI, and interactive element should have a clear pathway to potential business action. These five A’s are meant to bring some clarity and framework to designing your dashboard effectively. Hopefully they help! Any more to add? They don’t have to start with an A… Check out my Data Ecosystem article (link in the comments) on more in-depth dashboard building content!

“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