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 ↓]

📌 The 3 Types of Dashboards This confusion shows up constantly in BI projects, usually much later than it should when stakeholders start saying things like: “𝘚𝘰… 𝘸𝘩𝘢𝘵 𝘢𝘮 𝘐 𝘴𝘶𝘱𝘱𝘰𝘴𝘦𝘥 𝘵𝘰 𝘥𝘰 𝘸𝘪𝘵𝘩 𝘵𝘩𝘪𝘴?” At that point, the issue is rarely the data model or the visuals themselves, but the fact that the dashboard was never designed around the kind of decision it was supposed to support. Today’s a good day for a quick reminder: Dashboards are not interchangeable. This means that different roles = different priorities = different dashboards. For simplification, let's classify them into 3 major categories. 1️⃣ 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 They are built for people who need to understand where the business is going and whether it is still on track. They tend to surface a small number of signals that summarize overall health, progress against objectives, and emerging risks. Data IS updated frequently enough to stay relevant but not so often that it distracts from long-term thinking. Because of that, these dashboards usually rely on aggregated KPIs and controlled views, and they work best when they help leadership align on direction rather than debate numbers. 2️⃣ 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐚𝐥 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 These dashboards are built to be explored, filtered, sliced, and challenged, often across long historical windows, because understanding patterns, seasonality, and anomalies takes context and depth. They are typically used by analysts, data scientists, or domain experts who expect to spend time inside the data, moving back and forth between views until the story becomes clear enough to support a decision. 3️⃣ 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 They are designed for teams who need to see what is happening right now, or what just happened, and react immediately. The value of these dashboards comes from timeliness and clarity rather than completeness, which is why they often focus on live or near-real-time metrics, alerts, and very specific indicators tied to concrete actions. If looking at an operational dashboard does not clearly suggest what should be done next, it usually means it is trying to serve too many purposes at once. Now, one of the most common mistakes in BI is trying to build one single dashboard that does all of this, with the hope that filters and tabs will somehow make it work for everyone. In practice, this usually leads to frustration on all sides, because executives, analysts, and operational teams are not asking the same questions, even when they are looking at the same data. Good dashboard design starts much earlier than layout or tooling. It starts with being explicit about: → Who the dashboard is for → What kind of decision it is meant to support → How quickly that decision needs to be made. Everything else follows from there. 💾 Save this for your next BI discusion Repost ♻️ for others.

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

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.

Most teams polish visuals. Few design the thinking. That’s why dashboards often look fine — but explain nothing. Try this flow instead: 1) Structure metrics – map relationships, drivers, and shared definitions. 2) Define purpose – clarify what decisions it supports. 3) Build & format – choose charts that mirror logic. 4) Add context – if-then prompts, comparisons, slices, thresholds. 5) Maintain & evolve – track usage, prune, update. Pretty dashboards inform. Logical dashboards explain. Save this for your next redesign.

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.

Dashboards should deliver the insights to the right people at the right time. Here is how to build a dashboard for different stakeholders. 1. 𝗧𝗼𝗽 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀: Strategic, Big Picture     When designing dashboards for executives, think high-altitude view. They don’t need to know the granular details. They want clear, strategic insights that drive business decisions. Here’s what to focus on: • 𝗞𝗣𝗜𝘀, 𝗡𝗼𝘁 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: Top managers care about key performance indicators connected to business goals (revenue, profit, market share). Focus on the top 3-5 metrics that have a significant impact on them.    • 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆: Avoid cluttered boards and use clean, intuitive visuals like summary cards or high-level bar charts.    • 𝗧𝗿𝗲𝗻𝗱𝘀 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝘀: They are interested in the big picture of past trends and future forecasts.    • 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: Provide clear takeaways and recommendations. They want to know what steps to take next. 2. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗨𝘀𝗲𝗿𝘀: Actionable, In-Depth Insights     For operational teams, dashboards need to dig deeper. They’re in the weeds, and they need tools that help them drive tactical decisions and track day-to-day performance. • 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗨𝗽𝗱𝗮𝘁𝗲𝘀: Ensure your dashboard pulls from live data sources so operational teams can act quickly.    • 𝗗𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: They need to see everything like sales numbers, inventory levels, and customer response times. Drill-down options are required.    • 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆: Build custom filters to let users explore data by region, product line, or department.    • 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴 𝗙𝗼𝗰𝘂𝘀: Highlight bottlenecks or inefficiencies in real-time so they can act fast.   How do you adjust your dashboards for different stakeholders? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #dashboard #stakeholder #careergrowht

You've heard me ask this before... Are your dashboards reliable? Now, let me ask you: Are you stuck on a decision? And to follow up: Is it because of something on your dashboard? Spoiler alert: The problem lies with not having a clear question for your data. Data does not argue back.   It sits there, looking sophisticated, while teams debate stories around it. It gives you an answer to a question you might not even know to ask. Shift gears... go from decision-goal to data, not the other way around! Here's how: 1) Start with a stuck decision, not a dataset  - Instead of “What can this report show?”, ask “Which decision is frozen this quarter?”   - Then strip the analytics down to only the variables that move that choice.   - The trade-off: you lose breadth but gain speed and clarity.   - You do not need every metric. You need the few that change behavior. 2) Build a baseline that makes inaction visible  - Every model should answer: “What happens if we keep our current path?”.   - That status quo view creates a reference basket for cost, risk, and missed upside.   - Once you see the price of doing nothing, false consensus collapses.   - Disagreements become numerical choices, not arguments. 3) Force every analysis to end in ownership - No presentation is “done” without 3–5 prioritized actions, a named owner, and a time frame.   - That constraint turns analytics into a product for the business, not a report about the business.   - It also exposes gaps: if nobody wants to own the action, the insight is not trusted yet. The point is not prettier content on a dashboard.   It is a tighter loop from decision to outcome. And it hinges on what question you need to ask, Not the maze of possible answers on a dashboard. -Dr. Kruti Lehenbauer of Analytics TX, LLC #DataScience #PostitStatistics #DecisionScience #RefreshWithRyza P.S.: What is the most impressive report your company uses right now? What decisions does it actually drive?

📊 Project Progress Templates Every Engineer & Manager Must Use to Track Real Construction Performance In today’s construction and oil & gas projects, the biggest challenge is not manpower or material — it’s real-time visibility of progress. A well-structured Project Progress Dashboard gives engineers and managers the power to control timelines, costs, and risks with absolute clarity. This is where Project Progress Templates + EVMS (Earned Value Management System) become game changers. 📌 Why Engineers & Managers Must Use Progress Dashboards ✔ Present progress in a clear, visual, management-friendly format ✔ Track planned vs actual progress in real time ✔ Identify delays early through CPI, SPI, variance and trends ✔ Improve communication between Project Managers, Planning Engineers & Site Engineers ✔ Take quick and precise decisions backed by actual field data ✔ Build credibility and leadership by demonstrating analytical reporting skills 📌 How EVMS Techniques Save Projects EVMS gives you: 🔹 SPI (Schedule Performance Index) – Tells if you are ahead or behind schedule 🔹 CPI (Cost Performance Index) – Shows if project is spending right or overshooting 🔹 Variance Analysis – Identifies the exact area where loss is happening 🔹 EAC (Estimate at Completion) – Predicts future project cost or timeline With these insights, managers make faster, data-driven decisions instead of reactive ones. EVMS is the single most powerful technique to control runaway costs and schedule delays. 📌 Planning & Scheduling Strategy Behind This Template This dashboard syncs with: ✔ Primavera P6 baseline & weekly updates ✔ Site DPR (Daily Progress Reports) ✔ Material receipts, manpower logs & equipment usage ✔ Quality, HSE & commercial updates It allows planners to: ● Update progress weekly ● Recalculate critical path ● Track key milestones ● Align procurement, site works & subcontractors ● Compare planned vs actual quantities ● Feed real-time decisions into execution This is how planning becomes a living system — not a static document. 📌 Why This Template Helps Your Entire Execution Team ✔ Site teams understand what is required this week ✔ Managers get clarity on bottlenecks ✔ Finance/Commercial teams get projected costs ✔ Clients see transparent, auditable reporting ✔ Leadership teams get confidence for critical decisions A strong dashboard can literally change the project direction within one review meeting. 📣 Want This Project Progress Template? Comment “Progress Template + Email” and I will share the soft copy with you. Let’s make project reporting professional, transparent, and data-driven. #NEOM #PROJECTS #PRIMAVERA6

I'm excited to share my latest data analytics project: a comprehensive Retail Performance Analysis Dashboard. Problem: The retail company struggled with a lack of clear insights, making it difficult to track overall performance, understand customer behavior, and manage inventory efficiently. Solution: I developed and deployed an interactive, end-to-end Power BI dashboard. By connecting directly to SQL databases, the solution provides a real-time, holistic view of the business, analyzing key KPIs like sales, profit margins, customer segmentation, supplier performance, and stock health. 📊 Tools Used: Power BI | SQL | Excel | DAX | Data Modeling 💡 Key Insights & Highlights: • Total Sales: ₹5.34M • Profit Margin: 28.77% • YoY Sales Growth: 23.48% • Top Performers: The North Region (₹1.52M) and the supplier "Boat" (₹1.1M) were the primary drivers of sales. • Operational Health: Maintained a 65% delivery rate against a 9.17% return rate. • Actionable Inventory: Identified 3 critical products as "Low Stock" (Stock = Reorder Level), flagging them for immediate re-purchasing. Dashboard Link: https://lnkd.in/gHTPaTce #PowerBI #SQL #DataAnalytics #BusinessIntelligence #Dashboard #DataVisualization #RetailAnalytics #DataInsights