Matthew Carter’s Post

Why do all these different BI tools exist in a world with #Excel? Users don't need another tool to create a line chart. Excel already does that, yet this is how they market themselves. And why is it so often that these BI tools are effectively used as a means to export data to Excel? It's because they solve the problem that has plagued Excel forever. Excel does not scale. But in doing so, they leave behind every Excel user's favorite feature, the cell grid. https://lnkd.in/d3vjgaQZ #bi #data #businessintelligence #analytics

Recently, I posited that people don't need more tools to create charts. If I come off as cynical of BI tools, that's not my intention. I enjoy working in #BI. I take a lot of satisfaction building solutions that help drive business improvement, and these BI tools help me do that. When I look at a line chart I made, I have the advantage of knowing all about the data behind it. My worldview of the data is not limited to a few spreadsheets handed to me. I often conduct my exploratory work from a database, which gives me access to all the possibilities. I've matched the data up to the application. I know of those important and less important timestamps that mark the intake funnel. I found that table that tracks the history of all the previous promotions. I have the full context surrounding the chart. Your typical BI consumer just sees the few points on the chart. That might be all they need, but they don't gain an inherent acceptance of the data behind it because they can't easily see what I can. This behavior is clear when so often your carefully crafted charts become a means to export the data to Excel. Most BI tools don't make peeking at the underlying data very intuitive, if even possible. It can be done sometimes, but often relies on us BI folks setting up some maintenance heavy configurations (which are still limited to whatever we decided to include). BI tools need to make seeing the data behind the charts a more consumer friendly experience by default. #data #analytics #businessintelligence

Understand what a paginated report is and how to use it effectively in Power BI. Learn about its role in data analysis and producing precise, print-ready reports. #PowerBI #PaginatedReports #datavisualization #businessintelligence #ReportBuilder #DataAnalytics

📊 Master Pivot Tables for Enhanced Data Analysis! 🔄 Unlock the full potential of your data with pivot tables! Our detailed guide covers everything you need to know about pivot tables, from basic concepts to advanced techniques, helping you transform raw data into insightful, actionable reports. 🌟 Key insights include: What pivot tables are and why they're essential. Step-by-step instructions for creating and customizing pivot tables. Advanced tips for analyzing and visualizing data. Real-world applications and benefits across various industries. 💡 Whether you're a data analyst, business professional, or Excel enthusiast, this guide provides the knowledge and skills to make your data work for you. 👉 Dive into the full guide and start mastering pivot tables today: https://hubs.la/Q02DGXC30 #PivotTables #DataAnalysis #ExcelTips #PolymerInsights

𝟭𝟱 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗳𝗼𝗿 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝗳𝘂𝗹 𝗮𝗻𝗱 𝗔𝘁𝘁𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 In today's data-driven world, dashboards are crucial for translating raw data into actionable insights. Designing an effective dashboard requires a blend of art and science. Here are 15 principles, inspired by sqlbi.com, to help you create dashboards that not only look good but also deliver valuable insights at a glance: 1. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗮 𝗧𝗮𝗿𝗴𝗲𝘁 Have a clear aim for your dashboard usage. Focusing on a specific area can make your dashboard stand out. 2. 𝗞𝗲𝗲𝗽 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗮𝘁 𝗮 𝗚𝗹𝗮𝗻𝗰𝗲 Avoid scroll bars. Ensure all vital information is visible without the need to scroll. 3. 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗦𝗶𝗺𝗽𝗹𝗲 Don’t add unnecessary visuals or designs. Simplicity aids in comprehension. 4. 𝗔𝗹𝗶𝗴𝗻 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀 Align elements both horizontally and vertically for a clean, organized look. 5. 𝗕𝗲 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 Maintain the same alignment and abbreviations throughout to enhance readability. 6. 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁 𝘁𝗵𝗲 𝗠𝗼𝘀𝘁 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 Highlight key data points to catch the user's eye immediately. 7. 𝗕𝗲 𝗖𝗹𝗲𝗮𝗿 Always explain abbreviations to avoid confusion. 8. 𝗦𝘁𝗮𝗿𝘁 𝗳𝗿𝗼𝗺 𝗭𝗲𝗿𝗼 Starting from zero prevents misinterpretation of data at first glance. 9. 𝗦𝗵𝗼𝗿𝘁𝗲𝗻 𝗡𝘂𝗺𝗯𝗲𝗿𝘀 Use abbreviations for large numbers (e.g., millions, billions) to make them easier to comprehend. 10. 𝗦𝗵𝗼𝘄 𝘁𝗵𝗲 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 Provide visual context through headings and descriptions to guide users. 11. 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗖𝗼𝗹𝗼𝗿 Use appropriate colors to distinguish different data points effectively. 12. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀, 𝗡𝗼𝘁 𝗥𝗲𝗽𝗼𝗿𝘁𝘀 Focus on creating dashboards with visuals rather than tables, emphasizing visual storytelling. 13. 𝗦𝗵𝗼𝘄 𝗩𝗮𝗿𝗶𝗮𝘁𝗶𝗼𝗻𝘀 Display trends to provide insights into business performance over time. 14. 𝗟𝗲𝗮𝘃𝗲 𝘁𝗵𝗲 𝗡𝗼𝗶𝘀𝗲 𝗢𝗳𝗳 Ignore irrelevant data. Only include information that adds value. 15. 𝗣𝗶𝗰𝗸 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗖𝗵𝗮𝗿𝘁 Select the appropriate chart type to convey your data most effectively. By incorporating these principles, you can design dashboards that not only look appealing but also provide powerful insights, driving better decision-making. What principles do you follow when designing dashboards? Share your thoughts and let's elevate our data visualization skills together! #datavisualization #dashboarddesign #dataanalytics #bi #sqlbi #visualstorytelling #effectivedashboards #learnings

Unlock the true potential of your Power BI reports! 📊 Avoid these top 5 common design mistakes that might be hindering your data exploration journey, with this article by ZoomCharts. Read on to discover how to create impactful reports and empower users! https://lnkd.in/g9AxqeqT #PowerBI #DataVisualization #ReportDesign #DataAnalytics

Mastering Data Visualization with the 3-30-300 Rule: I just read an eye-opening article about the "3-30-300 rule" for data visualization, and I had to share it! If you’re into making data tell a story, this rule is a game-changer: 1️⃣ 3 Seconds: Your visualization should grab attention and get the main message across in just 3 seconds. It’s all about making that first impression count! 2️⃣ 30 Seconds: In half a minute, viewers should start seeing the big picture: key patterns and trends should become clear. This helps quickly understand the main takeaways. 3️⃣ 300 Seconds (5 Minutes): If someone spends up to 5 minutes with your visualization, they should be able to dive deeper and uncover detailed insights. This is where the magic happens for those who want to dig into the data. This rule is a fantastic guide for creating visuals that are not only engaging but also easy to understand and explore. Check out this article from SQLBI for more tips and tricks to level up your data visualization skills: https://lnkd.in/eRENhWVv #DataVisualization #Analytics #DataStorytelling #DesignThinking #PowerBI

In this blog post, we'll compare three prominent BI tools. Given our extensive experience with all three, we believe sharing our insights can offer valuable perspectives for your consideration 🤓 #businessintelligence #selfserviceanalytics . https://lnkd.in/eCKSZig5

❇️ 𝐌𝐞𝐫𝐠𝐢𝐧𝐠 𝐯𝐬. 𝐀𝐩𝐩𝐞𝐧𝐝𝐢𝐧𝐠 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 ❇️   In Power BI Merging Queries and Appending Queries are two distinct operations used to combine data from different sources. Here's a brief explanation of the key differences:   ➡️ 𝐌𝐞𝐫𝐠𝐢𝐧𝐠 𝐐𝐮𝐞𝐫𝐢𝐞𝐬:   𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Combine data horizontally based on common columns (keys) between tables. 𝐇𝐨𝐰 𝐢𝐭 𝐖𝐨𝐫𝐤𝐬: Creates a new table by matching rows from different tables using a common column, offering options like inner, left, right, or full outer joins. 𝐑𝐞𝐬𝐮𝐥𝐭: Generates a unified table with columns from both tables, merging rows based on specified criteria. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Ideal when you need to unify data from different tables using a shared identifier or characteristic. 𝐂𝐨𝐥𝐮𝐦𝐧 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬: Requires at least one common column (key) between the tables being merged. ➡️ 𝐀𝐩𝐩𝐞𝐧𝐝𝐢𝐧𝐠 𝐐𝐮𝐞𝐫𝐢𝐞𝐬:   𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Place rows from one table under another, like stacking blocks on top, to make a longer table. 𝐇𝐨𝐰 𝐢𝐭 𝐖𝐨𝐫𝐤𝐬: Add rows of one table to the end of another; both tables should have the same structure (same columns in the same order). 𝐑𝐞𝐬𝐮𝐥𝐭: Produces a new table with all rows from the first table followed by all rows from the second table. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Useful when you have data in similar formats across multiple tables and want a consolidated, longer table. 𝐂𝐨𝐥𝐮𝐦𝐧 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬: Requires tables to have the same column structure for successful appending.     Understanding these distinctions is crucial for performing effective data transformations and consolidating information from various sources in Power BI.     Happy Learning 😊       #dataanalyst #powerbideveloper #powerbi #learningandgrowing

Have you ever wondered how to create beautiful, effective reports? Take a look at my recent blog series where I discuss some ticks and tricks for building impactful reports in SAS Visual Analytics. Step 1: Draft a Plan. https://lnkd.in/givYVtEZ