Data Visualization Software

This is not your regular matrix table in Power BI. It’s built to feel like a real calendar — and actually help users find what matters. ✅ You select a month ✅ You see events and KPIs directly on the calendar ✅ You highlight specific tasks instantly with one click And the best part? No custom visuals. No hacks. Just a matrix visual + a calendar table + a few smart DAX tricks. It’s clean, interactive, and easy to understand. And it instantly answers the question: "What's happening, and when?" And it gets even better: 🔹 The calendar automatically shows all days, even if no event exists, so you never lose the full month view. 🔹 You can display multiple pieces of info — like the day number, events list, and total spend — inside each calendar cell. 🔹 Users can select multiple events and highlight only what they care about. 🎯 Perfect for marketing calendars, campaign tracking, content publishing plans, or internal project timelines. Would you use a calendar view like this in your reports? 🎥 Here is the complete step-by-step tutorial: https://lnkd.in/grgAy66e #PowerBI #CalendarDashboard #DataViz #DashboardDesign

I built an AI Data Visualization AI Agent that writes its own code...🤯 And it's completely opensource. Here's what it can do: 1. Natural Language Analysis ↳ Upload any dataset ↳ Ask questions in plain English ↳ Get instant visualizations ↳ Follow up with more questions 2. Smart Viz Selection ↳ Automatically picks the right chart type ↳ Handles complex statistical plots ↳ Customizes formatting for clarity The AI agent: → Understands your question → Writes the visualization code → Creates the perfect chart → Explains what it found Choose the one that fits your needs: → Meta-Llama 3.1 405B for heavy lifting → DeepSeek V3 for deep insights → Qwen 2.5 7B for speed → Meta-Llama 3.3 70B for complex queries No more struggling with visualization libraries. No more debugging data processing code. No more switching between tools. The best part? I've included a step-by-step tutorial with 100% opensource code. Want to try it yourself? Link to the tutorial and GitHub repo in the comments. P.S. I create these tutorials and opensource them for free. Your 👍 like and ♻️ repost helps keep me going. Don't forget to follow me Shubham Saboo for daily tips and tutorials on LLMs, RAG and AI Agents.

One of the biggest challenges in data visualization is deciding 𝘸𝘩𝘪𝘤𝘩 chart to use for your data. Here’s a breakdown to guide you through choosing the perfect chart to fit your data’s story: 🟦 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 If you’re comparing different categories, consider these options: - Embedded Charts – Ideal for comparing across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴, giving you a comprehensive view of your data. - Bar Charts – Best for fewer categories where you want a clear, side-by-side comparison. - Spider Charts – Great for showing multivariate data across a few categories; perfect for visualizing strengths and weaknesses in radar-style. 📈 𝗖𝗵𝗮𝗿𝘁𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗢𝘃𝗲𝗿 𝗧𝗶𝗺𝗲 When tracking changes or trends over time, pick these charts based on your data structure: - Line Charts – Effective for showing trends across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴 over time. Line charts give a sense of continuity. - Vertical Bar Charts – Useful for tracking data over fewer categories, especially when visualizing individual data points within a time frame.    🟩 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗖𝗵𝗮𝗿𝘁𝘀 To reveal correlations or relationships between variables: - Scatterplot – Best for displaying the relationship between 𝘵𝘸𝘰 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦𝘴. Perfect for exploring potential patterns and correlations. - Bubble Chart – A go-to choice for three or more variables, giving you an extra dimension for analysis. 🟨 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Understanding data distribution is essential for statistical analysis. Use these to visualize distribution effectively: - Histogram – Best for a 𝘴𝘪𝘯𝘨𝘭𝘦 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦 with a few data points, ideal for showing the frequency distribution within a dataset. - Line Histogram – Works well when there are many data points to assess distribution over a range. - Scatterplot – Can also illustrate distribution across two variables, especially for seeing clusters or outliers. 🟪 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Show parts of a whole and breakdowns with these: - Tree Map – Ideal for illustrating hierarchical structures or showing the composition of categories as part of a total. - Waterfall Chart – Perfect for showing how individual elements contribute to a cumulative total, with additions and subtractions clearly represented. - Pie Chart – Suitable when you need to show a single share of the total; use sparingly for clarity. - Stacked Bar Chart & Area Chart – Both work well for visualizing composition over time, whether you’re tracking a few or many periods. 💡 Key Takeaways - Comparing across categories? Go for bar charts, embedded charts, or spider charts. - Tracking trends over time? Line or bar charts help capture time-based patterns. - Revealing relationships? Scatter and bubble charts make variable correlations clear. - Exploring distribution? Histograms or scatter plots can showcase data spread. - Showing composition? Use tree maps, waterfall charts, or pie charts for parts of a whole.

One map. 506,000 impressions. A masterclass in visual storytelling. Last year, I shared a visualization of train travel times across Europe by Benjamin Tran Dinh and it was one of the most wide reaching posts of the year. But the data itself wasn't new. So, why did this specific map go viral and what can we learn from it. Most data visualizations fail because they require "cognitive load." You have to read the legend. You have to interpret the scale. This map removed that friction. Here is the 3-part framework that made it successful: 1. It makes the invisible, visible. We think of distance in miles or kilometers. But we live in hours and minutes. By distorting the map based on time (isochrones), it validates our reality. 2. It tells a story of inequality. Look closely at the map. The arms of high-speed rail shoot out from Paris. But the gaps in the network are just as loud. It turns a boring infrastructure dataset into a debate about connectivity (and German train delays). 3. It invites the "me" factor. You can’t look at this map without checking your own city. "Can I get to Berlin?" "Why is the connection to Spain so bad?" Good data viz isn't about the data. It's about the user's place within the data. Stop building dashboards that require a manual to read. - Reduce the cognitive load. - Center the user's experience. - Make the aha moment instant. Complex data doesn't have to be complicated. 🌎 I'm Matt Forrest and I talk about modern GIS, earth observation, AI, and how geospatial is changing. 📬 Want more like this? Join 11k+ others learning from my daily newsletter → moderngis.com

🍱 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 ↓]

Best LLM-based Open-Source tool for Data Visualization, non-tech friendly CanvasXpress is a JavaScript library with built-in LLM and copilot features. This means users can chat with the LLM directly, with no code needed. It also works from visualizations in a web page, R, or Python. It’s funny how I came across this tool first and only later realized it was built by someone I know—Isaac Neuhaus. I called Isaac, of course: This tool was originally built internally for the company he works for and designed to analyze genomics and research data, which requires the tool to meet high-level reliability and accuracy. ➡️Link https://lnkd.in/gk5y_h7W As an open-source tool, it's very powerful and worth exploring. Here are some of its features that stand out the most to me: 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜 𝐆𝐫𝐚𝐩𝐡 𝐋𝐢𝐧𝐤𝐢𝐧𝐠: Visualizations on the same page are automatically connected. Selecting data points in one graph highlights them in other graphs. No extra code is needed. 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐓𝐨𝐨𝐥𝐬 𝐟𝐨𝐫 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: - Filtering data like in Spotfire. - An interactive data table for exploring datasets. - A detailed customizer designed for end users. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐮𝐝𝐢𝐭 𝐓𝐫𝐚𝐢𝐥: Tracks every customization and keeps a detailed record. (This feature stands out compared to other open-source tools that I've tried.) ➡️Explore it here: https://lnkd.in/gk5y_h7W Isaac's team has also published this tool in a peer-reviewed journal and is working on publishing its LLM capabilities. #datascience #datavisualization #programming #datanalysis #opensource

90% of data analysts show pretty charts. Only 10% know which chart tells the right story. But here's the brutal truth: They don't want to know what happened. They want to know what to do about it. 𝐓𝐡𝐢𝐬 𝐦𝐢𝐧𝐝𝐬𝐞𝐭 𝐬𝐡𝐢𝐟𝐭 𝐬𝐞𝐩𝐚𝐫𝐚𝐭𝐞𝐬 𝐠𝐫𝐞𝐚𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐭𝐬 𝐟𝐫𝐨𝐦 𝐠𝐨𝐨𝐝 𝐨𝐧𝐞𝐬: Good analysts report: "Sales dropped 15% last quarter." Great analysts recommend: "Sales dropped 15% in Region A. Shift budget to Region B, where conversion is 3x higher." Good analysts show: "Customer churn increased"   Great analysts advise: "Churn spiked after the pricing change. Reverting would save $2M annually." 𝐓𝐡𝐞 𝐜𝐡𝐚𝐫𝐭 𝐭𝐲𝐩𝐞 𝐬𝐭𝐢𝐥𝐥 𝐦𝐚𝐭𝐭𝐞𝐫𝐬. 𝐁𝐮𝐭 𝐨𝐧𝐥𝐲 𝐢𝐟 𝐢𝐭 𝐝𝐫𝐢𝐯𝐞𝐬 𝐚𝐜𝐭𝐢𝐨𝐧: Bar Chart → Compare performance, identify winners to scale Line Chart → Spot trends early, predict what's coming Scatter Plot → Find correlations that unlock opportunities Heatmap → Highlight problem areas that need immediate attention 𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥𝐢𝐭𝐲: Your stakeholders have 50 dashboards already. They don't need another one. 𝐓𝐡𝐞𝐲 𝐧𝐞𝐞𝐝 𝐲𝐨𝐮 𝐭𝐨 𝐭𝐞𝐥𝐥 𝐭𝐡𝐞𝐦 𝐚 𝐬𝐭𝐨𝐫𝐲: - What's broken - Why it matters - What to do next Stop being a reporter. Start being an advisor. The best visualization isn't the prettiest one. It's the one that gets a decision made. ♻️ Share this with an analyst ready to level up 𝐏.𝐒. I share data storytelling insights and career tips in my free newsletter. Join 19,000+ readers → https://lnkd.in/dUfe4Ac6

Claude for Finance: 4 Power Moves Most finance teams are using AI like a chatbot Here's the right way to use it: https://lnkd.in/dM3AS2rj Claude is basically your finance analyst that never sleeps It can build models Write and audit formulas Generate dashboards Turn messy data into clear insights Here are 4 power moves you’re probably not using 1. Install Claude directly inside Excel Stop copying data back and forth • Install the Claude add-in (Web or Desktop) • Open it from the sidebar (Control+Option+C on Mac / Control+Alt+C on Windows) • Start modeling directly inside your workbook Instant modeling Instant formula support Instant analysis 2. Build full scenario planning models in minutes Instead of spending hours structuring sheets, prompt it clearly: “I am the finance manager of a SaaS company. I need to build a scenario planning for our sales for our products. We have the personal plan at $49 per month with currently 1,500 subscribers, and with a growth of 8% per month, and a churn at 3%. And enterprise: $99 per month with 400 subscribers and 5% growth with 2% churn. Build this model for the next two years with formulas and dynamic assumptions, and a graph.” You get: • 24-month projections • Dynamic assumptions • Automated formulas • Clean MRR charts 3. Build models using voice-to-text On the train At the airport Between meetings Use voice mode to dictate your full model request Claude turns it into structured Excel logic Finance on the go 4. Create interactive dashboards for meetings Ask Claude to: “Create a dynamic interactive dashboard where I can adjust key assumptions (growth rate, churn rate, subscribers, pricing) and the outputs and charts update automatically.” Now you can: • Adjust growth live • Test churn scenarios • Change pricing in real time • Answer CFO-level questions instantly This is how you move from reporting numbers To steering the business 👉 I’m hosting a live masterclass where I show exactly how to become an AI-powered CFO 👉 Save your seat here: https://lnkd.in/dM3AS2rj 💬 Which of these 4 power moves are you not using yet? 🔁 Repost if this helps your finance team work smarter

7 Chart Types That Will TRANSFORM Your Financial Reporting 📊 Excel is my FAVORITE tool for financial data visualization...by far. After building HUNDREDS of dashboards for CFOs and finance teams, I've narrowed down the must-know chart types that I use EVERYDAY. ➡️ COLUMN CHARTS Column charts are PERFECT for showcasing information over time. I use these constantly to display cash balance trends, revenue by month, or budget vs actuals. Want to level up? Try clustered columns to compare different metrics side by side...or even better, stacked columns to show the distribution between accounts. But please...PLEASE get rid of those ugly gray boxes by hiding field buttons! And add data labels - your boss will thank you. ➡️ PIE CHARTS Pie charts get a lot of hate...but they're AMAZING when used correctly! Keep them to 6 categories or fewer and use them specifically for showing distribution - like departmental expense breakdown or revenue mix. They're instantly understandable for your stakeholders who need to grasp the big picture in 5 seconds or less. ➡️ LINE CHARTS Nothing beats a line chart for spotting trends and relationships between points... The REAL MAGIC happens when you use them to show breakeven points - that exact moment when your revenue line crosses your cost line. I'm not a fan of those pointy default lines though...smooth them out and add markers at key points. Trust me on this one! ➡️ KPI BOXES These aren't even a standard Excel chart type - but they're a GAME CHANGER for any financial dashboard! I build these custom using shapes or merged cells (I know I know...merged cells are usually a big no-no, but this is the exception). Add those up/down arrows for variances and you've got an instant performance snapshot that even your CEO can understand in seconds. ➡️ DONUT CHARTS Donut charts are EVERYWHERE - just look at your fitness app or battery meter on your phone! I LOVE using these to show budget vs actual percentages. That hollow center is PERFECT for adding key stats. My favorite trick? Make it change colors automatically when you hit targets vs miss them. Green for good, red for...well, you know. ➡️ CLUSTERED BAR CHARTS Want to show year-over-year performance? Clustered bar charts are your new best friend. I always sort mine from largest to smallest (just right-click → sort largest to smallest) to instantly highlight what matters most. This one simple trick makes complex data comparisons crystal clear to even the most non-financial stakeholders. ➡️ WATERFALL CHARTS When you need to explain what DROVE changes in your numbers, nothing beats a waterfall chart. I use these ALL THE TIME to break down ARR movements - new customers, upgrades, downgrades, and cancellations. The visualization makes complex movements crystal clear...even to the sales team! 😂 === What charts do you use most often in your financial reporting? Drop a comment below 👇 and let me know which of these you'll try in your next dashboard