Building a Project Management Dashboard

You think your job is to build a dashboard. It's not. By delivering a dashboard, you are providing a tool, not an answer. You are transferring work. This abdication of duty destroys your value. A dashboard isn't a map. It's a telescope. You were hired to find the constellation, not hand over the instrument and leave your stakeholder to wander the night sky. Your real value is in simplifying the universe of data. Stop hiding behind dashboards. Your job is to tell a story. The Analyst's Playbook: 1. Your Dashboard is a Telescope: Use it for discovery. Never for delivery. Your goal is to deliver an insight, not the instrument. 2. Your Deliverable is a Star Chart: Synthesize complexity into a focused narrative. Give them the map, not the sky. 3. Your Leave-Behind is an Observatory Deck: Offer a simplified dashboard with high-level KPIs. This allows stakeholders to spot changes and brings them back to you—the astronomer with the telescope—for the real story. They don't want a tool. They want an answer. Be the storyteller they need. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

99% of dashboards are useless Dashboards that just show some metrics (however important they may seem) are the most useless dashboards in the world. What good is it if you know what your conversion rate, ROI, average order value, or sales trends are? What's the point? You won't be able to link them to business goals, you won't understand how much a decrease in the average check is critical for you and how much you will lose/earn on its fluctuations. Multichannel attribution? Okay, it can save you $200/month. The only useful dashboards are the ones that show you achieving your goals. Here's a goal you set for yourself - “sell $11 million dollars in 2025.” Then you need to define target values for more localized metrics: - number of orders -> number of orders placed -> order-to-payment conversion - average order value -> average price per item -> average number of items in a receipt And so on. In any case, you will have limit values of some metrics, within the range of which you will work. For example, it is impossible to quickly increase the average check by 100%. So, you need to not just track metrics, but understand: 1) How close you are to the required values by cumulative metrics 2) How well your processes are performing against the planned standards An even steeper level is when you track not only the planned metrics, but also those that directly affect them, which are precursors to possible deviations. If you've built your analytics in this order: Business Goals -> Primary Metrics -> Related Metrics -> Dashboards ...then dashboards become an always up-to-date source of information for you as far as your pace goes. And if there are difficulties... - here's what they are related to - how much you are losing on them - here's what you can do. In this way, checking dashboards is much more meaningful. (!) You are focused on specific metrics and their impact on the bottom line (!) You can evaluate the decrease or increase of some metric in the criteria of impact on your bottom line. Otherwise, what's the point of panicking if the average cost of a basket toss has gone up if it doesn't affect the outcome (yet)? (!) Achieving the outcome becomes more understandable because it is calculated using an understandable formula with predefined variables. Share your thoughts in comments

Executives don't have time for color legends. So in dashboards, I go with what they know: green is good and red is bad.* In the "Dash This" example, the KPI and trend comparisons all follow that red/green approach. For other color needs, I try to stick with dark colors for "this is important" and grays for "this is context." In the "Not That" example, the comparisons are orange is bad and blue is good, which isn't as easy to understand at a glance. Other color choices are jarring, like green for the current year, red for last year. Assume the executive will never look at a color legend. Does your dashboard still make sense? That's the key. For more storytelling with color tips, see this Playfair Data video tutorial from Tableau legend Ryan Sleeper: https://lnkd.in/dX5szkPm *This is for the typical U.S. business audience. And to accommodate color vision deficiency, do a second encoding such as an up arrow for good, a down arrow for bad. #dataforexecs #datavisualization #dashboards

Scrolling through Tableau Public like I usually do, I came across this killer dashboard by Waqar Ahmed Shaikh — and I couldn’t help but share because it’s loaded with smart design choices that actually make the data easier to digest. 🧠💥 A lot of people think a good dashboard is about cramming in as many charts and metrics as possible, but it’s really about guiding the viewer’s attention to what matters most. So, here’s why this one stood out: 1️⃣ Card Layout: Breaking data up into card-style visuals isn’t just about aesthetics. It’s about creating mental compartments for your audience. It segments complex information into digestible bites, making it easier for anyone — even someone completely new to the data — to follow along. If your dashboard looks like a tangled mess, it is a tangled mess. 2️⃣ Heat Map for Day & Hour Analysis: This is pure brilliance for time-based insights. Heat maps visually show the frequency of events over time, making it easy to spot trends and outliers. In this case, it highlights hot spots in patient treatment patterns. Imagine trying to sift through hundreds of rows in a spreadsheet to find these patterns—good luck with that. Instead, the visual tells you everything in seconds. 3️⃣ High-Low Dots in KPI Spark Lines: This is what I call a "shortcut to insights." It’s not just about showing trends but immediately pointing out where the highs and lows occurred. This way, you don’t waste time digging into what’s normal and what’s not. High-Low dots say: “Here’s where you should be paying attention.” This is exactly the kind of detail that separates a good dashboard from a great one. Moral of the story? Design for impact, not just for the sake of being flashy. Waqar’s dashboard does this well, and it’s a reminder that visuals are tools for clarity, not confusion. If you want to elevate your dashboards, check out his work. It’s a solid benchmark to measure your own designs against. 🔥 #DataVisualization #Tableau #DataDesign #DashboardDesign

✈️ Most dashboards are designed like airplane cockpits…when what you really need is a Control Tower. Too many BI dashboards try to show everything at once: KPIs, segments, raw data — all mashed together. It overwhelms users and kills decision speed. Instead, think about your dashboards as a Control Tower. The top of the tower offers a clear, panoramic view. You’re scanning for major movements and disruptions. When needed, you can zoom in with instrumentation or speak directly to pilots, but that's not your default. By managing your information hierarchy in layers, you can start simple and progressively reveal complexity. Here’s how it works: 📊 L1: The Tower View – high-level KPIs, trends, and alerts. What’s happening? 🔍 L2: Segment View – explore segments and categories. Where is it happening? 🧾 L3: Transaction View – detailed records and raw data. Why is it happening? Each level is built for a specific cognitive mode. Mixing them forces your brain to multitask and that’s where insight gets lost. 🧠 Rule of thumb: Dashboards should optimize for low cognitive load at entry. Users should never have to reconcile different zoom levels simultaneously. Control Tower dashboards allow users to scan, zoom, and act without overwhelming them. By designing dashboards to reflect human cognitive modes and information hierarchy, you create tools that are not just insightful but usable. #dataviz #dashboards #BI #uxdesign #analytics #productivity

“If we track everything, we’ll finally feel in control.” That’s what a founder told me before launching a 60+ KPI dashboard. It was real-time. Color-coded. Mounted on TVs across the office like a Formula 1 pit wall. And it completely backfired. Here’s what actually happened: - The founder checked it every hour… and panicked when a number dipped. - Meetings turned into 45-minute metric marathons. - The team checked out, not because they didn’t care, but because they didn’t understand what mattered. The truth? Startups don’t fail because they lack data. They fail because they track everything, and act on nothing. More metrics = more noise. More dashboards ≠ more clarity. So… how do you choose the right KPIs? Start here: 1. Anchor to a core objective. What does success look like right now? (Retention? Burn runway? Gross margin?) 2. Define decision-making needs. If a metric doesn’t inform a clear decision, cut it. 3. Limit by team, not by dashboard. Give each leader 2–3 metrics they own, understand, and drive. 4. Make it human. A KPI is only useful if your team understands it. Talk about it. Teach it. Use it to drive action, not anxiety. It’s about shifting the conversation inside the company: → From reporting to decision-making → From panic to progress → From reactive leadership to aligned execution If you’re scaling and your dashboard looks impressive but feels like noise, it’s probably time for a reset. PS: Be honest, how many KPIs are you currently tracking?

Do you want your data to make a difference? Transform your numbers into narratives that drive action—follow these five key steps: 📌 STEP 1: understand the context Before creating any visual, ask: - Who is your audience? - What do they need to know? - How will they use this information? Getting the context right ensures your message resonates. 📊 STEP 2: choose an appropriate graph Different visuals serve different purposes: - Want to compare values? Try a bar chart. - Showing trends? Use a line graph. - Need part-to-whole context? A stacked bar may work. Pick the right tool for the job! 🧹 STEP 3: declutter your graphs & slides More isn’t better. Remove unnecessary elements (gridlines, redundant labels, clutter) to let your data breathe. Less distraction = clearer communication. 🎯 STEP 4: focus attention Not all elements on your graphs and slides are equal. Use: ✔️ Color ✔️ Annotations ✔️ Positioning …to guide your audience’s eyes to what matters most. Help them know where to look and what to see. 📖 STEP 5: tell a story Numbers alone don’t inspire action—stories do. Structure your communication like a narrative: 1️⃣ Set the scene 2️⃣ Introduce the conflict (tension) 3️⃣ Lead to resolution (insight or action) Make it memorable! THAT'S the *storytelling with data* process! ✨ Following these five steps will help you create clear, compelling data stories. What's your favorite tip or strategy for great graphs and powerful presentations? Let us know in the comments!

Intentional color selection matters, particularly on dashboards. When we design dashboards, we think about more than how a color choice works for a single chart. Instead, we think about how to help our audience explore the data with ease. Sometimes that means using alerting colors like red or orange to focus attention on metrics that aren't performing well, but we're not always comparing results to a target. When I was working with the strategic information team at USAID, Aaron Chafetz, Tim Essam, Ph.D., and Karishma Srikanth did a refresh on the #dataviz style guide for the team, including revisiting colors used not just in one dashboard but across a whole suite of tools. Breaking down results and funding by agency was analytically valuable, but they didn't want to just pull the colors from logos. The colors needed to work for other data viz purposes too. So, in addition to having a dedicated set of colors (tested for contrast and other features), the guide included specific recommendations for categorical colors, like this set of colors by agency and color palettes for performing above or below goals. On charts with multiple agencies, the colors work well together. On a chart representing results for one agency with the option to filter, the color would change as the user filtered the dashboard to different slices of data, giving an added visual cue that the data had changed. The result? More consistency across dashboards and other visualizations, which can help stakeholders more quickly see patterns in information.

I know I talk about this topic a lot but anytime someone asks me "What's the first thing you look for when reviewing someone's #powerbi work?" the answer is almost always professional use of color. Why? Because this tells me instantly that the person who built the report or dashboard understands that the goal of a #businessintelligence or #datavisualization project is not to simply throw together a bunch of charts and call it a day. No, the goal is to communicate information visually. It's kind of like Fight Club, right? The first rule of Power BI is to communicate the data. The second rule is, well, the first rule. The first visual below is illustrative of novice/beginner reports that I see all the time (not that there's anything wrong with being a novice; we all start there) and perfectly highlights the trap that new developers fall into: - Color is used with reckless abandon, meaning no consideration for the user and the power that color can have in pulling our attention to the most important insights. - Colors are inconsistent, meaning that one color might be useful for one category of data but another might be used for the same category of data in another place on the same page. - Colors are garish, which is really just the word "ugly" wearing a fancy top hat a monocle. - Colors are not accessible, meaning they occasionally clash and text becomes almost impossible to read. Note the "Revenue" and "Profit" card titles near the top of the report. That yellow text on a light blue background is a HUGE no-no. - Colors are sometimes bright (looking at you, Monthly Profit line chart), which can cause eye strain for the user. I designed the cleaned-up version to be a) super boring (that was the point) and b) easy to read. Since this is sales data, I narrowed the scope of this one pager to not focus on revenue, profit, costs, and volume but only on profit since that's kind of the headline. Revenue? Costs? It's all just building to profit, the bottom line. No reason to bury the lede. By narrowing the scope, I have fewer color choices to reconcile: Profit go up? Green. Profit go down? Red. Easy. Too many newcomers to data viz spend too much time adding color in places where it's irrelevant (slicer backgrounds, title backgrounds, visual backgrounds) and not enough time learning to suppress that nagging urge to apply color. Save it for the #data, Friends! In time, with lots of practice, you might learn to incorporate more dynamic colors and color juxtaposition without loss of information or clarity but these more artistic sensibilities are only refined with years of practice. In the meantime, while you're honing your craft, remember the first and second rule: Communicate that data.

Last year, I was working with the manager of a national staffing agency during their Q1 internal review. They had a branch that was off track from the start. After just three months, the branch was significantly behind its revenue and margin targets, and they were worried. We pulled up the dashboard to analyze the situation. On the surface, everything looked decent with the recruiting performance meeting the benchmark. But when we dug deeper, we found the problem: not enough job orders were available to meet their financial targets. Even if they filled 100% of the existing orders (which, as we know, is unlikely), it simply wouldn’t be enough to hit the goal. Next, we broke down where the orders were coming from. The branch was on track with their existing client growth plan, but they didn’t have enough new clients bringing in new orders. Initially, the manager wanted to create a new sales plan that increased the number of prospects and activities the sales rep was responsible for....but this would have been the wrong decision. That’s when we turned to the Sales Activity Tracker. The numbers immediately jumped out: The in-market sales rep was already maxed out with prospecting activities, meetings, and pipeline management. Based on the first-year value of a new client, it became clear that this sales rep literally couldn’t do enough to hit the branch's goal. At that moment, the manager made the tough decision to revise the revenue target and rework the budget to maintain profitability for the year. They also placed the sales rep on a performance improvement plan to focus on improving conversion rates through the sales cycle. In the end, the sales rep didn’t work out, but here’s the silver lining: By identifying this early, they were able to pivot quickly. Rather than holding on to an underperforming sales rep and risking a loss at the end of the year, they made the tough call, restructured, and ended up with a profitable branch. The key takeaway? Know your numbers, track your metrics, and use the insights to make data-driven decisions. It’s better to adjust early than wait until it’s too late.