Data Visualization Software

Imagine you saved the last slice of your favorite chocolate cake, carefully hiding it in the fridge. You remember generally where, but now you're desperately rummaging through leftovers, expired yogurt, and questionable takeout containers. Relatable? Happens with me all the time!  That's how many data visualization teams feel when trying to find those critical, but scattered, data insights hidden in the depths of their databases. Smaller but delicious datasets might seem easy to handle, but when they're super important (think sales figures or customer behavior) analysts need fast, reliable access. Traditional tools often mean slow queries, waiting on data refreshes, and the general sense that your insights are always a few steps behind. That's where Google BigQuery's BI Engine comes in. Think of it like a super-powered engine for your data. It's an in-memory analysis service that makes working with those high-value datasets lightning fast. With BI Engine, your data visualization team gets sub-second responses on queries. Dashboards feel alive – no more awkward pauses while data refreshes! Analysts can explore and experiment at the speed of thought, uncovering those crucial insights that might otherwise stay buried. BI Engine gets even smarter with strategies: 👉 Reservations: Guarantee yourself a slice of computing power, ensuring snappy performance even during peak usage times. 👉 Acceleration: Cache frequently used data for a serious speed boost. It's like your nose leading you directly to a fresh batch of cinnamon buns in the bakery, warm and full of chocolate. BI Engine isn't just about faster insights – it makes developers happier, too. It integrates seamlessly with BigQuery and popular visualization tools. Less time tinkering, more time building awesome, insight-driven experiences. Why BI Engine over other tools? 👉 Fast: Sub-second responses even on complex queries 👉 Scalable: Handles growing data and user demands like a champ 👉 Cost-effective: Pay for what you use, avoid overprovisioning We at Google help organizations make sense of their data chaos. If you're tired of feeling like a frantic squirrel with your most important insights, reach out! Let's talk about how to power up those dashboards and give your analytics the speed boost they deserve. Coming up tomorrow in our Data Kitchen: Google BigQuery Studio Follow Omkar Sawant for more such delicious insights! #dataandanalytics #dataanalytics #businessanalytics #businessintelligence #bi #datavisualization #visualizations #data #bigquery #googlecloud #google #lifeatgoogle

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

#PowerBI has introduced new 𝐕𝐢𝐬𝐮𝐚𝐥 𝐋𝐞𝐯𝐞𝐥 𝐅𝐨𝐫𝐦𝐚𝐭 𝐒𝐭𝐫𝐢𝐧𝐠𝐬 (𝐩𝐫𝐞𝐯𝐢𝐞𝐰), offering more flexibility in formatting data🎉🎉 Originally designed to address the formatting limitations of #visual_calculations, which aren't part of the data model, this feature allows us to format calculations directly within visuals. With this update, Power BI now supports three hierarchical levels of format strings: 𝟏) 𝐌𝐨𝐝𝐞𝐥 𝐋𝐞𝐯𝐞𝐥: Applies to columns and measures 𝐚𝐜𝐫𝐨𝐬𝐬 𝐚𝐥𝐥 𝐯𝐢𝐬𝐮𝐚𝐥s unless overridden. 𝟐) 𝐕𝐢𝐬𝐮𝐚𝐥 𝐋𝐞𝐯𝐞𝐥: 𝐍𝐞𝐰𝐥𝐲 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐞𝐝 with the August 2024 version, it allows formatting for any column, measure, or visual calculation within a specific visual, 𝐨𝐯𝐞𝐫𝐫𝐢𝐝𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥-𝐥𝐞𝐯𝐞𝐥 𝐬𝐞𝐭𝐭𝐢𝐧𝐠𝐬. 𝟑) 𝐄𝐥𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐯𝐞𝐥: Used for specific visual elements like 𝐝𝐚𝐭𝐚 𝐥𝐚𝐛𝐞𝐥𝐬, 𝐨𝐯𝐞𝐫𝐫𝐢𝐝𝐢𝐧𝐠 𝐛𝐨𝐭𝐡 𝐦𝐨𝐝𝐞𝐥 𝐚𝐧𝐝 𝐯𝐢𝐬𝐮𝐚𝐥 𝐥𝐞𝐯𝐞𝐥𝐬. 📊 For example, we can format a measure as a whole number on one visual while keeping it as a decimal in the model, or apply scientific notation only to data labels. This hierarchy ensures that the most specific formatting settings are applied, offering greater control and customization. #PowerBI #DataVisualization #BusinessIntelligence #DataAnalysis

Most assume data stories will consist entirely of charts, but that’s not always the best approach. Sometimes, non-data visuals do a better job at structuring ideas, clarifying relationships, and guiding decisions. Yes, bar charts, line charts, and scatterplots help communicate key findings and insights, but data storytelling isn’t just about presenting numbers—it’s about explaining, persuading, and driving action. That’s where non-data visuals can help. They can establish the problem, clarify key concepts, and frame possible solutions in a way that is easier to grasp. Here are a few examples of how you might use non-data visuals: 𝐕𝐞𝐧𝐧 𝐝𝐢𝐚𝐠𝐫𝐚𝐦 → 𝐒𝐡𝐨𝐰𝐬 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩𝐬 𝐚𝐧𝐝 𝐨𝐯𝐞𝐫𝐥𝐚𝐩𝐬 📌 Use case: Analyzing customer behavior across two product categories. "60% of Product A users also use Product B, but 40% don’t. This suggests an opportunity for cross-selling." 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐲 𝐩𝐲𝐫𝐚𝐦𝐢𝐝 → 𝐈𝐥𝐥𝐮𝐬𝐭𝐫𝐚𝐭𝐞𝐬 𝐡𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐲 𝐚𝐧𝐝 𝐩𝐫𝐨𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 📌 Use case: Explaining the drivers of customer satisfaction. "At the base level, customers expect reliability. Moving up, customer support influences satisfaction, but at the top, personalization creates long-term loyalty." 2𝐱2 𝐦𝐚𝐭𝐫𝐢𝐱 → 𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐳𝐞𝐬 𝐚𝐧𝐝 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞𝐬 📌 Use case: Evaluating marketing strategies based on impact vs. effort. "High-impact, low-effort strategies (top-right quadrant) should be our priority—like social media campaigns. Meanwhile, high-effort, low-impact tactics like print ads should be reconsidered." Great data storytelling blends science (data) with art (visual communication). The best stories aren’t just about numbers—they help your audience understand what’s at stake and what to do next. What’s a non-data visual you’ve used (or seen) that made an impact? 🔽 🔽 🔽 🔽 🔽 📬 Craving more of my data storytelling, analytics, and data culture content? Sign up for my newsletter today: https://lnkd.in/gRNMYJQ7 📚Check out my new data storytelling masterclass: https://lnkd.in/gy5Mr5ky 🛠️ Need a virtual or onsite data storytelling workshop or speaker? Let's talk. https://lnkd.in/gNpR9g_K

Noisy data makes trends hard to identify. Being unable to see trends causes two major issues: 1. Misguided decisions (not seeing the forest through the trees) 2. Wasted Resources (variation is normal, not every change needs an answer) So, how do we shed light on patterns in noisy data? Stock prices are a great example of noisy data. Stocks generate new data every nanosecond, so, analysts use two key methods to draw out their trends over the course of days, weeks, months, & years. 1. Smoothing This is easily accomplished by placing a moving average on top of the time series data, creating a smooth line that considers a larger set of data to off set near term price fluctuations. 2. Changing Time Frequency Looking at data by the nanosecond is not feasible for a human, so we naturally compress time to hours, days, week, etc. so that we can see how the data performed in those time period. Stock prices typically use the candlestick charts below that show min, max, and the open/close prices. Smoothing and zooming out in time are some of the best ways to handle noisy data, especially in time series data. Below is a basic example you can play with in Python to get a better sense of how these work 👇 -------- import pandas as pd import numpy as np import matplotlib.pyplot as plt # Generate a hypothetical stock price dataset for 6 months (approximately 180 days) np.random.seed(0) dates = pd.date_range(start="2023-01-01", periods=180) prices = np.random.normal(0.5, 0.75, size=180).cumsum() + 100 # Create a DataFrame stock_data = pd.DataFrame(data={'Price': prices}, index=dates) # Apply a 30-day moving average for smoothing stock_data['30D_MA'] = stock_data['Price'].rolling(window=30).mean() # Reduce the time frequency to weekly, taking the last price of the week # Can change to 'M', 'Q', or 'D' for differing time frequencies weekly_data = stock_data.resample('W').last() # Plotting plt.figure(figsize=(14, 7)) plt.plot(stock_data['Price'], label='Daily Prices', alpha=0.5) plt.plot(stock_data['30D_MA'], label='30-Day Moving Average', linewidth=2) plt.plot(weekly_data.index, weekly_data['Price'], label='Weekly Prices', marker='o', linestyle='-', linewidth=2) plt.title('Stock Price with Smoothing and Time Frequency Reduction') plt.xlabel('Date') plt.ylabel('Price') plt.legend() plt.grid(True) plt.show() ------ Hi I'm Joe 👋 I work with young medtech, healthcare, & life science companies to help them understand their data and win in the market. #dataanalytics #dataanalyst #datavisualization #powerbi #tableau

Visualizing Uncertainty in Machine Learning with Gaussian Process Regression I've been reflecting on how Gaussian Process Regression (GPR) visualizations provide one of the most intuitive ways to understand uncertainty in machine learning models. What makes these visualizations so powerful is how they transform abstract statistical concepts into immediate visual insight: 🔍 Uncertainty as space: The confidence interval (typically shown as a shaded region) visually represents where the model believes the true function might lie. It's uncertainty made tangible. 📊 Data-driven confidence: Watching how uncertainty narrows precisely at locations where data exists, while remaining wide in unexplored regions, creates an immediate "aha!" moment about how models learn. 📈 Correlation intuition: Seeing how adding a single point affects predictions in neighboring regions helps build intuition about the fundamental concept of correlation in probabilistic models. 🧠 Prior knowledge visualization: GPR visualizations elegantly show how prior assumptions about smoothness and variation influence predictions in regions with sparse data. I find these visualizations particularly valuable when explaining complex concepts like Bayesian reasoning, active learning, and the exploration-exploitation tradeoff to stakeholders without technical backgrounds. What I appreciate most is how a simple curve with a shaded region conveys a sophisticated mathematical concept: that our models aren't just making predictions; they're expressing degrees of confidence that systematically decrease as we gather more evidence. Have you found other visualization approaches that make complex ML concepts more intuitive? I'd love to hear your thoughts! #MachineLearning #DataScience #Visualization #UncertaintyQuantification #GaussianProcesses #BayesianML

📊 Enhancing Power BI Reports with Custom Visuals 🚀 Unlock the full potential of your Power BI reports by incorporating custom visuals! Here are key considerations for a successful integration: Data Compatibility: Ensure your chosen custom visual aligns with your data source, handling data types effectively. Reliability and Support: Opt for well-maintained visuals with active support to avoid issues down the line. Performance: Test the visual's performance with your data to prevent slowdowns in report rendering. Accessibility: Prioritize visuals that adhere to accessibility standards for an inclusive user experience. Data Security: Confirm compliance with your organization's data security policies to safeguard sensitive information. License and Cost: Be mindful of any licensing costs associated with custom visuals, as they can add up. User Training: Provide training or documentation for users to navigate custom visuals effectively. Customization Options: Ensure visuals can be tailored to match your report's branding and requirements. Integration: Verify seamless integration with Power BI features like filters and cross-filtering. Compatibility with Updates: Keep visuals up to date with Power BI's latest versions for smooth operation. Community and Documentation: Leverage user communities and documentation for troubleshooting and best practices. Vendor Reputation: Trust reputable vendors for reliable and well-maintained custom visuals. Scalability: Confirm that visuals scale efficiently as your reports and datasets grow. Legal Compliance: Ensure compliance with legal regulations and licensing agreements. User Feedback: Continuously gather user feedback to refine and optimize custom visuals. #PowerBI

Dimensionality Reduction (DR) simplifies complex, high-dimensional datasets into more manageable lower-dimensional forms for easier interpretation and better computational efficiency while preserving key information. Modern nonlinear DR techniques, such as t-SNE and UMAP, are popular for transforming complex datasets into simpler visual representations. However, they can produce results that are difficult to interpret due to the lack of inherent meaning in the shapes and clusters, suboptimal hyperparameters, and potential distortions. DimVis is a visualization tool developed by the authors of [1] which employs supervised EBM (Explainable Boosting Machine) models (trained on user-selected data of interest) as an interpretation assistant for DR projections. The DimVis tool facilitates high-dimensional data analysis by providing an interpretation of feature relevance in visual clusters through interactive exploration of UMAP projections. Specifically, the DimVis tool utilizes a contrastive EBM model that is trained in real-time to distinguish between data points inside and outside a cluster of interest. Leveraging the inherently explainable nature of the EBM, this model is then used to interpret the cluster through single and pairwise feature comparisons, ranked according to the EBM model’s feature importance. The applicability and effectiveness of DimVis are demonstrated through a use case and a scenario involving real-world data. Their paper [1] and the DimVis #Python code [2] are available through the links provided in the comments.

This month’s Power BI update is quite exciting... 🤓 The PBI Core Visuals team is working on some pretty cool stuff lately. Let’s dive into the details. 1️⃣ Marker Enhancements: Advanced controls for markers make data points pop: ↳ Customize by Categories or Series: Control marker styles at the category or series level. ↳ Marker Visibility Toggles: Toggle markers on/off for specific categories or series. ↳ Marker Shape & Transparency Control: Personalize markers by adjusting shapes (rotations supported, except circles) and sizes. ↳ Customizable Marker Borders: adjustable color, transparency, and width. 2️⃣ Small Multiples for Card Visuals: Compare data across categories or dimensions with ease: ↳ Flexible Layout Options: Choose Single Column, Single Row, or Grid layouts. ↳ Overflow Handling: Use pagination or continuous scrolling to manage excess data. ↳ Advanced Styling Controls: Customize borders, gridlines, and background colors. Round corners for a modern look. ↳ Header & Title Customization: Control header settings and adjust titles for font, color, padding, and text wrap. Align with your report’s branding. 3️⃣ New Text Slicer: Enhance data filtering with text-based searches: ↳ Intuitive Text Filtering: Type into the input box to filter data in real-time. ↳ Comprehensive Appearance Customization: Configure the input box with placeholder text, font, color, and transparency. ↳ Enhanced Button Controls: Adjust Apply button settings for color, transparency, borders, and padding. Customize the Dismiss button for clearing filters. ↳ Focus Accent Bar & Borders: Highlight the active input field with an accent bar. Set borders around the input area. Excited about these new features? I for sure am 🚀 * Note 1: Some features might still be in development. * Note 2: All images used are from Microsoft as unfortunately I don't have the latest version of Power BI on my laptop yet * Note 3: Links to all the updates details in the comments! #data #datapears #powerbi #report #reporting #dataviz #datavisualization #news

Have you ever wondered how a Large Language Model like #ChatGPT decides what to say next? A recent visualization project “Look into the machine's mind“ offers a glimpse into this complex process, revealing the diverse paths an LLM can take to complete a sentence. Using the prompt "𝐼𝑛𝑡𝑒𝑙𝑙𝑖𝑔𝑒𝑛𝑐𝑒 𝑖𝑠", and setting a high temperature for more creative and varied responses, this project illustrates the model's many paths to generating text. The visualization is split into two parts: • 🌐 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐒𝐩𝐚𝐜𝐞 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (left): Every text completion or word sub-sequence from the model finds its place in a vast 1536-dimensional space. This space is condensed into three dimensions through the magic of Principal Components Analysis (PCA). PCA allows us to see the branching paths of thought as the AI develops its responses. • 🌳 𝐓𝐫𝐞𝐞 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (right): This side shows all the potential completions as a branching tree, highlighting the probability of each word following the last. It's a visual representation of choice and chance within the AI's workings, showing how specific paths are prefered over others based on the complexity of language and context. 𝘗𝘭𝘦𝘢𝘴𝘦 𝘯𝘰𝘵𝘦: although, in theory, each next word (or token) reflects how much some words are most likely to appear after others in the training data, the human feedback provided via Reinforcement Learning (RLHF) and the higher temperature make it stray significantly from this original distribution. By exploring this visualization, we can see the journey from "𝐼𝑛𝑡𝑒𝑙𝑙𝑖𝑔𝑒𝑛𝑐𝑒 𝑖𝑠" to the many ways the Chatbot expands on this thought, demonstrating the model's inner workings visually intuitively. This work, crafted by the creative data scientist Santiago Ortiz (@moebio), isn't just a visualization (link in comments); it's a bridge connecting us to AI's often opaque thought processes. It is a brilliant example of how #DataVisualization can illuminate the complex mechanics of #MachineLearning models. #LargeLanguageModels #GenerativeAI