2025 Gartner® Market Guide for Event Intelligence Solutions
Selector Recognized as a Representative Vendor.
2025 Gartner® Market Guide for Event Intelligence Solutions
Selector Recognized as a Representative Vendor.
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Making Sense of Complex Data in Observability Tools

Metrics, analytics, measurements, and parameters – can we truly see these abstractions? Data visualization helps us do just that, bridging the gap between raw information and human comprehension.

Visualizing data is like rafting down a river – dynamic, unpredictable, and full of discoveries along the way. In this guide, we’ll explore how to craft visualizations that inform, engage, and inspire. So, grab your paddle and hop aboard!

Go with the data’s flow

The fundamental principle of effective visualization is working with your data’s inherent structure rather than against it. Like water finding its path, data has natural patterns and rhythms that should guide visualization choices. Recognizing whether you’re working with numbers, categories, or time series helps your visuals flow smoothly and convey insight clearly.

Finding the perfect chart for your story

The human brain loves patterns. Clear, structured visuals make it easier to spot trends, anomalies, and insights. Here’s a brief overview of which chart types best fit different kinds of data.

Temporal data

Track changes and trends with:

  • Line plot: Monitor performance metrics and reveal progression over time.
  • Stacked area plot: Show cumulative values and how multiple series contribute to totals across time.
  • Timeline heatmap: Visualize activity levels and density patterns using color intensity across time.
  • Calendar: Display time-based data in a calendar view for quick, date-specific insights.

Category-based data

Compare groups effectively using:

  • Donut chart: Highlight parts of a whole and emphasize proportional relationships.
  • Honeycomb: Display hierarchical categories in compact hexagonal cells.
  • Matrix: Show categorical relationships in a grid for quick cross-referencing.

Single-value metrics

Display key measurements with:

  • Gauge: Show values within defined ranges using clear visual indicators.
  • Big text: Emphasize critical metrics with prominent numerical display.
  • Multi-line text: Present multiple related values in text format for quick scanning.

Event and status data

Visualize occurrences and states through:

  • Event plot: Show discrete events along a timeline to reveal patterns and clusters.
  • Stacked event plot: Layer multiple event streams to highlight overlaps and relationships.
  • Log table: Present detailed event logs with timestamps and attributes for deeper analysis.

Numerical datasets

Analyze distributions and relationships using:

  • Table: Present detailed values in rows and columns for precise comparison.
  • Correlation: Reveal relationships and dependencies between multiple variables.
  • Analysis: Examine statistical patterns and trends within numeric data.

Nested structures

Represent layered information with:

  • Sunburst: Show hierarchy as concentric rings to display proportions at each level.
  • Honeycomb: Use hexagonal cells to represent nested groupings efficiently.

Location-based data

Map spatial information through:

  • Map: Show data distribution across geographic regions.
  • Path map: Visualize routes and connections between locations.
  • Topo map: Display network topology overlaid on a geographic layout.

Network structures

Illustrate system relationships with:

  • Topology: Show nodes and their connections to represent infrastructure and dependencies.
  • Topology path: Visualize data or process flow between nodes, helping users trace communication routes, performance bottlenecks, or dependency chains across the system.

Designing for humans 

Selecting appropriate visualization types is only half the battle. Even the best-matched chart needs thoughtful UX design to communicate its story honestly. Adopting these core principles will help you transform raw data into clear insights.

  1. Simplicity first: Keep visualizations straightforward and eliminate unnecessary complexity, allowing users to grasp the data and draw conclusions instantly and intuitively.
  2. Context and meaning: Include labels, legends, and scales to make data meaningful and interpretable.
  3. Maintain consistency: Reuse design elements – colors, fonts, chart styles – across your application. Avoid creating custom components unnecessarily, as users rely on familiar patterns.
  4. Direct the user’s focus: Highlight the most important trends or data points. Use techniques such as color contrast, size variation, or annotations to direct the user’s attention exactly where it needs to be.
  5. Accessibility: Ensure your visualizations are usable by everyone. Use color-blind-friendly palettes, offer text alternatives, support different color modes, and allow seamless keyboard navigation.
  6. Responsiveness: Visualizations must fluidly adapt to different screen sizes, resolutions, and devices. Design individual widgets to be fully responsive, ensuring the data remains functional and comprehensible within any shape or layout.
  7. Enable exploration: Implement filtering and search capabilities to help users focus on relevant data subsets. “Search everywhere” functionality is invaluable in data-rich applications.

Designing for real-world data 

Simple charts rarely stay simple. A line chart that works for a few series can quickly spiral into chaos with hundreds of series. Here are some of the challenges we encountered – and the design solutions that brought order back.

1. Untangling line charts

Building a basic line chart sounds easy – two axes and a ready-made component from a library like Highcharts. But in data-heavy environments, “basic” rarely exists. The backend often delivers dozens of overlapping lines, sometimes on multiple Y-axes, and suddenly the problem shifts from building a chart to making it readable.

To bring order to the chaos, our team designed a few key improvements:

  • Interactive legends with grouping: Users can toggle individual lines or entire line groups to focus on what’s relevant.
  • Smarter tooltips: Compact hover details that can be pinned for comparison, reducing visual noise.
  • Future trend previews: We extended the chart beyond static data, adding options to display predicted trends based on historical patterns.

What started as a simple line plot evolved into an adaptive visualization tool – one that helps users explore data rather than get lost in it.

2. Handling missing data

Data is never static – it fluctuates, pauses, and sometimes disappears. Visualizations need to handle all these moments gracefully, from empty timelines to overwhelming data bursts. 

In our case, the challenge was visualizing empty states. Simply hiding a widget with no data wasn’t an option – it confused users and broke context. The solution was to differentiate between intentional emptiness and missing data. If an empty state was expected, we showed it clearly. If data was missing, we made that absence explicit. This simple distinction prevented confusion and helped users instantly understand what was happening.

3. Designing beyond color

Relying solely on color in data visualizations is risky. Not all users can distinguish hues, large datasets can overwhelm the eye, and assigning a unique color to every data point quickly creates visual clutter.

In our product, we found a more reliable approach was logical grouping and structured organization. Whenever possible, we minimized color use, relying on layout, grouping, and contrast to communicate meaning. This not only reduced clutter but also made the design more accessible and easier to interpret.

4. Looking for contrast in Stacked charts

Color alone often isn’t enough. In stacked event charts, with many thin layers, maintaining clear contrast can be extremely challenging. While WCAG guidelines ensure high contrast for text and UI elements, there are no universal rules for data visualization – especially when hundreds or thousands of points each need a distinct color. Not using standard status colors like red, green, or yellow for datasets can make differentiation even harder.

In our product, we applied several practical strategies to improve readability:

  • Tailored color selection: We limited the palette to the expected data range, avoiding too many shades that reduce contrast and make patterns hard to distinguish.
  • Intentional color ordering: Colors were arranged from warm to cool and light to dark, creating visual separation between layers.
  • Multiple display modes: We developed both a colorful mode and a monochromatic mode. The monochrome option provides higher contrast for visually impaired users and clarity for everyone when too many colors could overwhelm the chart. 

By combining careful color choice, thoughtful ordering, and adaptable display modes, we made even densely layered stacked charts clear, accessible, and easy to interpret.

5. Which red is more red

Status colors can be surprisingly tricky. Many users have some form of color blindness, cultural associations vary, and too many shades make it hard to remember what each color represents. In monitoring and observability apps, this problem is common: multiple greens, reds, and yellows often appear simultaneously, leaving users to wonder – which red is more severe? Which green indicates optimal health?

As a UX designer, I aim to simplify interfaces by reducing status colors to the essentials. One shade each for error (red), warning (orange), and healthy (green) is usually sufficient.

When we needed to indicate “almost healthy” widget cells, we faced a design challenge: brief, insignificant errors sometimes triggered a red state, frustrating operational engineers who had to investigate issues that were no longer relevant. Introducing new color shades would have increased cognitive load and emotional impact – which green indicates optimal health? Which red signals real risk?

Our solution was elegant and subtle: we kept the original green but added a dotted background. This visually communicated that the status was fundamentally healthy while hinting at minor past turbulence – all without introducing new colors or confusing the user.

Building blocks

Chart library selection

For most charts in our product, we relied on Highcharts. Its demos made it easy to test against our needs, and it covered the majority of our visualizations. 

Highcharts is powerful out of the box – a few tweaks deliver interactive, attractive charts. Custom requirements, however, can be tricky. The API is extensive and challenging to navigate, some options override others, and documentation isn’t always complete. Snippets and fiddles help, but logging is limited.

Despite these challenges, Highcharts is versatile, handles updates smoothly, and produces high-quality visualizations. A paid license is required, but the effort is well worth it.

Table management

For advanced tables, we chose AG Grid. It excels at displaying tabular data, including standard tables and logs. The documentation is clear and well-structured, with a transparent distinction between free and paid features. AG Grid handles large datasets efficiently and allows extensive customization – from sorting, filtering, and grouping to controlling how data is displayed and how users interact with it. We would definitely recommend it.

Geographic visualization

Mapbox-gl handled our mapping needs, including custom markers for nodes and edges, with the ability to cluster them as the user zooms in or out.

Getting started with Mapbox-gl can be challenging due to its powerful but complex internal API, which covers aspects like layers, sources, and, most complexly, expressions. Expressions enable dynamic calculation of data and styling.

Although the Mapbox-gl API is feature-rich, it cannot meet all our requirements on its own. Some features required additional tools, like supercluster, but the result was visually rich and fully functional. Mapbox-gl requires an API key and follows a pay-as-you-go model, with a generous free tier.

Network topologies

Besides displaying maps, we encountered the need to depict behaviors not related to latitude and longitude. For non-geographical topologies, Reactflow was perfect. It’s intuitive, easy to customize, and provides smooth, efficient interactions. Free to use, it also offers advanced integrations, like 3d-force, which add flexibility and power to our visualizations.

Custom development

Sometimes existing packages can’t meet specific requirements due to API limitations or project complexity. Building visualizations like honeycombs, correlations, sunbursts, calendars, or timeline heatmaps from scratch requires significant effort but provides customization freedom, complete control over performance and results, freeing us from the constraints of pre-built APIs.

Key takeaways

  • Align visualizations with data characteristics: Proper chart selection for time series, categorical, or spatial data ensures effective communication.
  • Apply core design principles: Focus on clarity, context, and exploration capabilities while maintaining consistency and accessibility.
  • Select tools strategically: Whether using Highcharts, AG Grid, Mapbox-gl, or custom implementations, choose based on your specific needs and be prepared to adapt.
  • Iterate continuously: Data visualization evolves constantly. Stay current with emerging practices and tools to refine your approach and maximize impact.

Stay Connected

Selector is helping organizations move beyond legacy complexity toward clarity, intelligence, and control. Stay ahead of what’s next in observability and AI for network operations: 

Ready to see what modernization should really look like? Schedule a demo with our team. 

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Making Sense of Complex Data in Observability Tools

Metrics, analytics, measurements, and parameters – can we truly see these abstractions? Data visualization helps us do just that, bridging the gap between raw information and human comprehension. Visualizing data is like rafting down a river – dynamic, unpredictable, and full of discoveries along the way. In this guide, we’ll explore how to craft visualizations that inform, engage, and inspire. So, grab your paddle and hop aboard! Go with the data’s flow The fundamental principle of effective visualization is working with your data’s inherent structure rather than against it. Like water finding its path, data has natural patterns and rhythms that should guide visualization choices. Recognizing whether you’re working with numbers, categories, or time series helps your visuals flow smoothly and convey insight clearly. Finding the perfect chart for your story The human brain loves patterns. Clear, structured visuals make it easier to spot trends, anomalies, and insights. Here’s a brief overview of which chart types best fit different kinds of data. Temporal data Track changes and trends with: Category-based data Compare groups effectively using: Single-value metrics Display key measurements with: Event and status data Visualize occurrences and states through: Numerical datasets Analyze distributions and relationships using: Nested structures Represent layered information with: Location-based data Map spatial information through: Network structures Illustrate system relationships with: Designing for humans  Selecting appropriate visualization types is only half the battle. Even the best-matched chart needs thoughtful UX design to communicate its story honestly. Adopting these core principles will help you transform raw data into clear insights. Designing for real-world data  Simple charts rarely stay simple. A line chart that works for a few series can quickly spiral into chaos with hundreds of series. Here are some of the challenges we encountered – and the design solutions that brought order back. 1. Untangling line charts Building a basic line chart sounds easy – two axes and a ready-made component from a library like Highcharts. But in data-heavy environments, “basic” rarely exists. The backend often delivers dozens of overlapping lines, sometimes on multiple Y-axes, and suddenly the problem shifts from building a chart to making it readable. To bring order to the chaos, our team designed a few key improvements: What started as a simple line plot evolved into an adaptive visualization tool – one that helps users explore data rather than get lost in it. 2. Handling missing data Data is never static – it fluctuates, pauses, and sometimes disappears. Visualizations need to handle all these moments gracefully, from empty timelines to overwhelming data bursts.  In our case, the challenge was visualizing empty states. Simply hiding a widget with no data wasn’t an option – it confused users and broke context. The solution was to differentiate between intentional emptiness and missing data. If an empty state was expected, we showed it clearly. If data was missing, we made that absence explicit. This simple distinction prevented confusion and helped users instantly understand what was happening. 3. Designing beyond color Relying solely on color in data visualizations is risky. Not all users can distinguish hues, large datasets can overwhelm the eye, and assigning a unique color to every data point quickly creates visual clutter. In our product, we found a more reliable approach was logical grouping and structured organization. Whenever possible, we minimized color use, relying on layout, grouping, and contrast to communicate meaning. This not only reduced clutter but also made the design more accessible and easier to interpret. 4. Looking for contrast in Stacked charts Color alone often isn’t enough. 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In monitoring and observability apps, this problem is common: multiple greens, reds, and yellows often appear simultaneously, leaving users to wonder – which red is more severe? Which green indicates optimal health? As a UX designer, I aim to simplify interfaces by reducing status colors to the essentials. One shade each for error (red), warning (orange), and healthy (green) is usually sufficient. When we needed to indicate “almost healthy” widget cells, we faced a design challenge: brief, insignificant errors sometimes triggered a red state, frustrating operational engineers who had to investigate issues that were no longer relevant. Introducing new color shades would have increased cognitive load and emotional impact – which green indicates optimal health? Which red signals real risk? Our solution was elegant and subtle: we kept the original green but added a dotted background. This visually communicated that the status was fundamentally healthy while hinting at minor past turbulence – all without introducing new colors or confusing the user. Building blocks Chart library selection For most charts in our product, we relied on Highcharts. Its demos made it easy to test against our needs, and it covered the majority of our visualizations.  Highcharts is powerful out of the box – a few tweaks deliver interactive, attractive charts. Custom requirements, however, can be tricky. The API is extensive and challenging to navigate, some options override others, and documentation isn’t always complete. Snippets and fiddles help, but logging is limited. Despite these challenges, Highcharts is versatile, handles updates smoothly, and produces high-quality visualizations. A paid license is required, but the effort is well worth it. Table management For advanced tables, we chose AG Grid. It excels at displaying

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