Powering Up Power BI: The Top 5 Custom Visuals for 2026
As an artificial intelligence and machine learning expert, I‘m fascinated by the ever-expanding capabilities of data visualization tools like Microsoft Power BI. One of the most exciting areas of innovation in Power BI is the ability to extend its native visuals with custom visuals. These custom visuals harness cutting-edge web technologies and data science techniques to help you see and understand your data in new ways.
In this article, I‘ll share my picks for the top 5 custom visuals that every Power BI user should know about in 2024. We‘ll dive deep into what makes these visuals so valuable, look at real-world examples and use cases, and explore how they work under the hood. I‘ll also share my thoughts on the future of custom visuals and how they will evolve as AI and machine learning continues to advance.
The Rise of Custom Visuals
First, let‘s set the stage with some background on custom visuals. While Power BI includes an impressive array of built-in visualizations, its real power comes from the ability to extend those with custom visuals. These visuals are built by Microsoft and the Power BI community using standard web technologies like HTML5, CSS3, and JavaScript.
Custom visuals are more than just pretty pictures. Under the hood, they often leverage sophisticated data science concepts and machine learning models. For example, some custom visuals use clustering algorithms to automatically group related data points. Others apply sentiment analysis to unstructured text data. The end result is that custom visuals allow you to extract insights from your data that wouldn‘t be possible with the default visuals alone.
The popularity of custom visuals has skyrocketed in recent years. As of 2024, the Microsoft AppSource marketplace now hosts over 1,000 custom visuals, covering everything from specialized charts to industry-specific diagrams to AI-powered visualizations. Developers have created over 3.5 million custom visuals using the Power BI Custom Visual SDK, and major enterprises have even built their own proprietary custom visuals to fit their unique data needs.
With that backdrop in mind, let‘s count down the top 5 custom visuals that I believe are poised to have the greatest impact and adoption in 2024.
5. Tachometer
Starting off our list is the tachometer visual. This visual takes its inspiration from industrial engineering, where tachometers are used to measure and display the rotational speed of machines. In a business context, the tachometer visual is an intuitive way to display a key performance indicator compared to a target value or range.
The tachometer visual consists of a radial gauge with a needle that points to the current value. Color-coded ranges on the gauge indicate if the value is good, average, or poor compared to the target. This makes it easy to see at a glance if a metric is on track or needs attention.
One of the strengths of the tachometer is that it taps into our natural ability to process information visually and spatially. Research has shown that people can interpret and recall gauge displays more quickly and accurately than numeric displays. This is especially true in high-pressure situations where quick decisions are needed.
Some common use cases for the tachometer visual include:
• Displaying progress towards sales or revenue targets
• Monitoring customer satisfaction or net promoter scores
• Tracking on-time delivery or quality metrics in a production environment
When using the tachometer, the key is to choose metrics where there are clear targets that you want to stay within. The min, max, and target values should be carefully chosen to provide meaningful context. You‘ll also want to put some thought into the color-coding to ensure it aligns with your organization‘s standards and is easily interpretable.
4. Word Cloud
Next on our list is the word cloud visual. Word clouds (also known as tag clouds) display the most prominent or frequent words in a text dataset. Words appear larger and bolder the more often they occur. This provides a quick and engaging way to surface major themes and points of interest in unstructured data.
Behind the scenes, most word cloud visuals use natural language processing techniques to parse and tokenize the text data. Common preprocessing steps include:
• Converting all text to the same case
• Removing punctuation and special characters
• Filtering out generic stopwords like "the" and "and"
• Applying stemming algorithms to treat different forms of the same word as equivalent
The cleaned-up text is then analyzed to generate a frequency distribution of all the words. The visual plots the top N words, scaling the size proportionally to the frequency. Depending on the visual, other information like sentiment analysis may be encoded with color.
Some interesting use cases for word clouds include:
• Analyzing the most mentioned topics in customer reviews or support tickets
• Visualizing trending hashtags or keywords on social media
• Summarizing the main themes in open-ended survey responses
• Exploring the most common terms in a collection of documents or web pages
When creating word clouds, one tip is to use phrases rather than single words where possible. For example, "customer service" is more meaningful than the individual words "customer" and "service". Some word cloud visuals have options to detect and group common phrases. Also pay attention to any words that should be filtered out to avoid noise in the visual.
3. Violin Plot
In third place is the violin plot. This custom visual is an advanced version of the popular box-and-whisker plot used to visualize the distribution of a dataset. What makes violin plots special is that they show the full distribution of the data, not just summary statistics.
To create a violin plot, the data is first split into groups based on a category. Then for each group, a kernel density estimate (KDE) is calculated. The KDE is a smoothed version of a histogram, where each data point contributes a small "bump" to the curve. The result is a continuous estimation of the data distribution.
The violin plot takes the KDE curves and mirrors them vertically around a central line. The resulting shape looks similar to a violin, hence the name. The thickness of the violin at any point represents the density or frequency of data points in that region. Thinner sections indicate relatively rare values, while thicker sections show concentrations of common values.
Violin plots have several advantages over box plots and histograms for exploratory data analysis:
• They provide more nuanced detail about the shape of the distribution
• They are better at revealing multimodal distributions with multiple peaks
• They take up less space than a series of individual histograms
Some common applications of violin plots include:
• Comparing the distribution of a metric across different segments or time periods
• Identifying outliers and investigating why they occurred
• Visualizing the impact of various factors on a continuous outcome variable
One best practice when using violin plots is to overlay additional statistics like the median and interquartile range. Many violin plot visuals allow you to superimpose a mini box plot for easier interpretation. You‘ll also want to be judicious about how many violins you show at once, as too many can become hard to read.
2. Bullet Chart
The runner up on our list is the bullet chart. Conceived by data visualization expert Stephen Few, the bullet chart is a variation of a bar chart designed for contexts where there is a clear comparative measure.
A bullet chart consists of a primary bar showing a metric‘s actual value, and a secondary bar showing the comparative measure. The actual value bar is usually segmented into qualitative ranges to indicate if the metric is bad, satisfactory, or good. The comparative measure bar provides additional context on where the actual value falls.
Bullet charts are ideal for displaying metrics with specific targets, such as key performance indicators. They work especially well in dashboards and reports where someone needs to quickly assess if a metric is on track. The two-bar format draws attention to metrics that are off-target while making efficient use of space.
Some real-world examples of bullet chart applications include:
• Tracking actual vs forecasted revenue or budget
• Displaying patient‘s health metrics compared to recommended ranges
• Monitoring error rates or quality measures against targets
Compared to traditional gauges, bullet charts have some distinct advantages. They take up much less space, allowing more metrics to be displayed on a single screen. Their linear layout also makes them easier to read than circular gauges, and less prone to misinterpretation.
When designing bullet charts, it‘s important to choose meaningful qualitative ranges and comparative measures. The actual and comparative bars should use distinct colors for easy differentiation. Sorting the bullet charts from most to least important makes it easy for readers to prioritize their attention.
1. Visio Visual
Taking the top spot on the list is the Visio visual. This custom visual brings the power of Microsoft Visio‘s diagramming tools into Power BI, opening up a whole new world of visualization possibilities.
For those unfamiliar with Visio, it‘s a vector graphics application that lets you create professional diagrams and flowcharts. Visio has an extensive library of shapes and stencils for different industries and use cases. One of its key features is support for data linking, where shapes can be connected to external data sources.
The Visio visual allows you to embed an interactive Visio diagram directly in your Power BI report. But the real magic happens when you connect the diagram to your Power BI dataset. Now you can drive the colors, text, and other properties of the shapes based on your data. As your data updates, the Visio diagram automatically adjusts in real-time.
This combination of flexible diagramming and data-driven insights is extremely powerful. Some exciting use cases include:
• Visualizing an organization chart that automatically updates with personnel changes
• Diagramming a production line where each machine is color-coded by its status
• Mapping out an IT network where shapes are sized by usage or performance metrics
• Creating a floorplan where rooms are shaded based on occupancy or environmental conditions
The Visio visual opens up a new dimension of storytelling with your data. By combining Power BI‘s analytics with Visio‘s focus on process and relationships, you can create compelling visuals that instantly communicate how your business operates.
When using the Visio visual, look for opportunities to replace static images with dynamic, data-driven diagrams. Experiment with different visual treatments to draw attention to the most important data points. And don‘t forget about the Visio visual‘s built-in interactivity features like zooming, panning, and tooltips.
The Future of Power BI Custom Visuals
As we‘ve seen, custom visuals are already pushing the boundaries of what‘s possible with data visualization. But this is only the beginning. As Microsoft continues to invest in Power BI and technologies like machine learning become more accessible, I believe we‘ll see a new wave of AI-powered custom visuals emerge.
Imagine a custom visual that uses computer vision to automatically detect objects and extract insights from image data. Or a time series forecasting visual that leverages deep learning to predict future values along with confidence intervals. The possibilities are endless.
Microsoft is also making it easier than ever to develop custom visuals. The introduction of Power BI visual components will allow developers to leverage pre-built code modules and focus on the unique aspects of their visual. As the barriers to entry come down, I expect we‘ll see an explosion of new custom visuals addressing specialized domains and use cases.
However, with increased power also comes a need for increased responsibility. As custom visuals become more sophisticated, it will be important for developers to follow best practices around data privacy, security, accessibility, and performance. Organizations adopting custom visuals will need to do their due diligence and have governance processes in place.
Conclusion
Power BI custom visuals are a prime example of how data visualization is evolving to keep pace with the speed and complexity of modern business. By extending Power BI‘s native capabilities, custom visuals allow organizations to tell richer and more interactive stories with their data.
In this article, we‘ve explored my top picks for custom visuals in 2024 and beyond. Each of these visuals – the tachometer, word cloud, violin plot, bullet chart, and Visio visual – brings something unique to the table. What they all have in common is a commitment to helping users discover and communicate insights faster.
Whether you‘re just getting started with Power BI or are a seasoned pro, I encourage you to take a closer look at these custom visuals. Think about how you could apply them to your own data challenges. The beauty of Power BI is that it‘s so easy to experiment with different visual treatments until you find the right fit.
As an AI and machine learning expert, I‘m excited to see what the future holds for custom visuals. As the underlying technologies continue to evolve, I believe we‘re on the cusp of a new era in data visualization. By harnessing the power of AI, we‘ll be able to create visuals that are not only beautiful, but also intelligent and adaptive.
Of course, visuals are only one part of the equation. To truly succeed with data, organizations need to build a culture of data literacy and empower users to explore data on their own terms. But with the right tools and mindset, I believe that anyone can become a data rockstar.
So go forth and experiment with custom visuals. Flex your creativity and see what insights emerge. And above all, have fun! Because at the end of the day, the joy of data visualization comes from that magical moment when the numbers click into place and the story comes alive.
