Data Visualization Software

When trending more than one series in a line chart 📈, you have four different options for visualizing the information. I thought it would be helpful to review the strengths and weaknesses of each approach. SINGLE CHART Strengths: When you plot all your data points in a single line chart, it’s going to be the easiest for direct comparisons between the series. It’s also going to be the most space-efficient option. Weaknesses: If the data series have vastly different scales or units, they can become confusing or misleading (dual axes). When there are many data series, the overlapping trends can become cluttered, and key values can be obscured by other series. SIDE-BY-SIDE CHARTS Strengths: Each line chart can have a unique scale, which can reduce potential confusion. Separating the time series into separate charts makes it easier to interpret each chart individually with less clutter or overlapping data. With side-by-side line charts, the y-axis of each line chart doesn’t need to be compacted, which may be useful with certain datasets. Weaknesses: Having the time series side-by-side makes it harder to compare specific points in time across the two charts. Because the layout requires more horizontal space, both charts will need to be reduced in size (x-axes), making the labels and features of the data smaller and harder to read. STACKED CHARTS Strengths: Each line chart can have its own unique scale. With the line charts stacked on each other, it’s easier for the audience to compare the data sequentially and at specific points in time. Weaknesses: With the line charts stacked on each other, there will be less vertical space, which will compact the y-axis of each line chart. This treatment will flatten the data and make the trends harder to see. SMALL MULTIPLES Strengths: Separating the time series into individual charts makes it easier to identify and compare patterns and variations across multiple variables at a high-level. Weaknesses: To make the most sense, all multiples must have consistent scales. To gain a high-level perspective of patterns, you’re sacrificing the ability to compare specific values directly. When designing a data scene with trended data, you’ll need to consider what message you’re trying to convey to your audience. How you approach it will depend on the dataset and what you’re trying to communicate or emphasize. For example, if you have weekly sales results for several stores, you might use a small multiple to highlight the similar or diverging patterns between them. If you were comparing the impact of a change to your HR benefits across two distinct metrics, you might use a stacked chart approach to show how the change impacted one metric but not the other. What other pros or cons do you consider when choosing your line chart approach? 🔽 🔽 🔽 🔽 🔽 Craving more of my data storytelling, analytics, and data culture content? Sign up for my brand new newsletter today: https://lnkd.in/gRNMYJQ7

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

Line charts are an immensely powerful way to visualize time series data points, but comparing multiple overlapping series simultaneously within the same visual can present its own set of challenges. One way that we can visually communicate multiple volatile data time series at the same time is by tapping into the capabilities of stream charts. In this visual type, we focus less on the y-axis values and more on the fluctuations over time for each category. These diagrams illustrate what the exact same data looks like in a standard line chart versus a stream chart, which I built using the standard line and stacked area charts with Power BI. Because stream charts scale the y-axis value labels, we can completely remove them to avoid confusion about what exactly those calculated numbers represent. The nice thing about building a stream chart in a tooltip supported application (like Power BI and also like Excel and many other programming tools) is that we can place the actual numbers into this otherwise hidden easily accessible third dimension for building effective visualizations. I created a sample data table using generic fluctuating values to use in this example, but there are countless real-world data sets that we can use in stream charts, like inflation rates, exchange spot rates, stock prices, and much more! #PowerBI #DataVisualization #TimeSeriesModels

Handling real-time data? Make sure your pipeline is built for it. Unlike traditional databases, time series data plays by different rules—it’s all about constant updates, fast queries, and time-based analysis. If your pipeline isn’t built for it, you’ll hit performance bottlenecks FAST. Here’s how to set it up the right way: ✅ Pick the right DB – InfluxDB > relational DBs for time series ✅ Design for time-based queries – Use tags & fields wisely ✅ Ingest data from all sources – API + historical CSVs for full context ✅ Optimize queries – Flux makes slicing & analyzing time windows easy ✅ Visualize it – Grafana dashboards make insights interactive I built a hands-on "Storing & Visualizing Time Series Data" project that walks you through all this step by step. If you’re tired of static reports and want real-time, high-frequency insights, check it out! 🚀 ⚠️ Link to the full course in the comments!

🚀 Power BI: Adding Custom Tooltips to KPI Cards! While exploring Power BI capabilities, I discovered a creative workaround to add detailed tooltips to KPI cards - something that's not natively supported! The Challenge 🎯 Standard KPI cards in Power BI lack the ability to show comprehensive tooltip information, limiting user interaction and data exploration. My Solution 💡 I developed a custom HTML-based approach that transforms regular KPI cards into interactive, information-rich components: ✅ HTML-Wrapped DAX Measures: Converted standard measures into HTML format for enhanced styling ✅ Dual Theme Support: Implemented both light and dark modes for better user experience ✅ Dynamic Theming: Created a separate theme table with all color variations ✅ Rich Tooltips: Added detailed breakdowns including Monthly Recurring Revenue, One-time Sales, and Refunds Key Features 🌟 Responsive Design: Adapts to different screen sizes Theme Consistency: Seamless light/dark mode switching Enhanced UX: Detailed information on hover without cluttering the main view Professional Styling: Clean, modern card design with gradients and shadows Implementation Highlights 🔧 // Theme table with comprehensive color schemes Theme = DATATABLE( "ThemeMode", STRING, "ColorType", STRING, "ColorValue", STRING, // Light & Dark theme definitions... ) Important Consideration ⚠️ While this solution works effectively, be mindful of the HTML card container's width as it can overlap with other visuals. Proper positioning and sizing are crucial for optimal user experience. Results 📊 ✨ Enhanced user engagement with interactive KPI cards ✨ Better data storytelling through rich tooltips ✨ Professional, modern dashboard appearance ✨ Improved accessibility with theme options This approach opens up new possibilities for creating more engaging and informative Power BI dashboards. Sometimes the best solutions come from thinking outside the box! Share your experiences in the comments! #PowerBI #DataVisualization #BusinessIntelligence #DAX #HTML #DataAnalytics #Innovation #Dashboard #Microsoft #DataScience

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

July's Power BI update has one feature worth your attention. Everything else can wait. 𝐑𝐞𝐩𝐨𝐫𝐭-𝐰𝐢𝐝𝐞 𝐭𝐡𝐞𝐦𝐢𝐧𝐠, straight from the 𝐅𝐨𝐫𝐦𝐚𝐭 𝐩𝐚𝐧𝐞. Customise current theme now sits in the Format pane. Click an empty part of the canvas, and it's there. → Change report-wide colours with a palette, without losing your other customisations → Set defaults for borders, backgrounds, shadows, header icons, corner radius and padding → Apply page size, background and wallpaper across every page at once → Push filter pane formatting to current and new pages → Export the result as a theme and reuse it or share it with your team Every new visual you drop on the canvas inherits it. The part I care about: consistency stops being a cleanup job at the end of the build. It becomes the default. Individual page and visual styling still wins where you need it, and you can reset any setting back to the theme. Still in preview. Turn on Modern visual defaults and customise theme improvements in Options first. #PowerBI #MicrosoftFabric #DataViz #BusinessIntelligence

#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

𝗥𝗲𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗟𝗲𝗴𝗲𝗻𝗱𝘀 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜: 𝗨𝘀𝗶𝗻𝗴 𝗕𝗔𝗦𝗘64 𝗜𝗺𝗮𝗴𝗲𝘀 𝗳𝗼𝗿 𝗖𝘂𝘀𝘁𝗼𝗺 𝗩𝗶𝘀𝘂𝗮𝗹𝘀 Power BI’s default legends are functional, but not always flexible. They take up space, limit design options, and don’t always match the look you want. A simple way to take control is by using 𝗕𝗔𝗦𝗘64 𝗶𝗺𝗮𝗴𝗲𝘀 inside custom visuals. This lets you design your own legend system with icons, logos, or stylised shapes - all within Power BI. ✅ Why it’s worth trying: • 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 → swap plain text for icons or branded graphics • 𝗦𝗽𝗮𝗰𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗮𝘁𝗶𝗼𝗻 → cleaner, inline indicators instead of bulky legends • 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 → align with brand guidelines or client design systems • 𝗨𝘀𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 → faster to scan and interpret at a glance ⚙️ How to set it up: 1. Create or source your icons (PNG → BASE64 conversion works well). 2. Convert them into BASE64 strings. 3. Store those strings in a table (e.g. LegendIcons). 4. Set the column’s Data Category to “Image URL” so Power BI renders the BASE64 as an image. 5. Bind the column to an Image custom visual. 6. Place the icons wherever you need them - legends, headers, KPI cards, or even table rows. This approach lets you 𝗱𝗶𝘁𝗰𝗵 𝗿𝗶𝗴𝗶𝗱 𝗱𝗲𝗳𝗮𝘂𝗹𝘁𝘀 and treat your report more like a designed application. Small tweaks like this can transform how polished and usable your dashboards feel. #PowerBI #UIUX #DataViz

3 quick (and MUCH more effective) chart options than a stacked bar chart (when working with time series data) Take a look at the first chart below. Then go grab a coffee, and try again. What doesn’t work: - Incomplete data (2025 data is only through March) - Tiny values for OneWeb (looks awkward and distracting) - Stacked bars for time series (we expect data over time to flow left to right) So what could you do instead? Depends on your core message. —— 1) Bar chart Use when you want to emphasize how much was delivered each year and/or to compare individual years side by side. - Best for highlighting discrete totals. - Makes it easy to spot peaks, dips, or standout years. —— 2) Line chart Use when you want to emphasize the trend or rate of change across years (e.g., to show how things are increasing, decreasing, or diverging). - Ideal when the pattern over time matters most. - Instantly signals “this is time series data.” —— 3) Cumulative line chart Use when you want to show the total growth over time (how the number has added up since the starting point). - Great for highlighting progress toward a goal or the gap between categories as it compounds. - Focuses on momentum rather than individual snapshots. —— And most importantly: don’t overcomplicate things. No one’s ever said: that chart is just TOO easy to understand. 📌 Save this post for the next time you need to visualize data over time 👋🏼 I’m Morgan Depenbusch, PhD. I help analysts grow their influence through better charts, clearer stories, and more persuasive communication