Effective Techniques for Visualising Industrial Data

Industrial environments generate vast amounts of data every day. From machine performance metrics to production line outputs, this data holds valuable insights that can improve efficiency, reduce downtime, and enhance safety. Yet, raw numbers alone rarely tell the full story. Visualising industrial data effectively transforms complex information into clear, actionable insights. This post explores practical techniques to help you present industrial data in ways that are easy to understand and use.

Real-time industrial data dashboard showing machine performance

Understand Your Audience and Their Needs

Before creating any visualisation, consider who will use it and for what purpose. Industrial data users range from plant managers and engineers to operators and maintenance teams. Each group has different priorities:

• Plant managers need high-level summaries to track overall production and spot trends.

• Engineers focus on detailed performance metrics and root cause analysis.

• Operators require real-time alerts and simple indicators to respond quickly.

• Maintenance teams look for predictive data to schedule repairs and avoid failures.

  
  

Tailoring visuals to these needs ensures the data is relevant and actionable. For example, a heat map showing machine temperature trends might be perfect for engineers but overwhelming for operators who need simple red/green status indicators.

Choose the Right Visualisation Types

Selecting the appropriate chart or graph type is crucial for clarity. Here are some common visualisations suited for industrial data:

• Line charts for tracking changes over time, such as equipment temperature or vibration levels.

• Bar charts to compare production volumes across shifts or machines.

• Scatter plots for identifying correlations, like pressure versus output quality.

• Heat maps to highlight areas of concern, such as hotspots in a manufacturing process.

• Dashboards combining multiple visual elements for a comprehensive overview.

  
  

Avoid cluttered or overly complex visuals. Each chart should communicate one clear message. For example, a line chart showing machine uptime over a month is more effective than a multi-line chart with too many variables.

Use Color Wisely to Enhance Understanding

Color can make or break a visualisation. Use it to highlight important data points, indicate status, or group related information. Follow these guidelines:

• Use consistent color codes across all visuals (e.g., green for normal, yellow for warning, red for critical).

• Avoid using too many colors, which can confuse viewers.

• Choose colors that are distinguishable by people with color vision deficiencies.

• Use muted backgrounds with bright colors for key data points to draw attention.

  
  

For example, a dashboard showing machine health might use green bars for machines running smoothly and red bars for those needing immediate attention.

Incorporate Interactivity for Deeper Insights

Interactive visualisations allow users to explore data at their own pace. Features like zooming, filtering, and tooltips help users drill down into details without overwhelming them initially. For instance:

• Clicking on a production line graph could reveal hourly breakdowns.

• Hovering over a data point might show exact values and timestamps.

• Filters can let users view data by machine, shift, or product type.

  
  

Interactive tools empower users to find answers quickly and support better decision-making on the factory floor.

Interactive touchscreen displaying detailed industrial data visualizations

Simplify Complex Data with Aggregation and Summaries

Industrial data often comes in high volumes and fine detail. Presenting every data point can overwhelm users. Instead, use aggregation techniques:

• Summarize data by hour, shift, or day to show trends.

• Calculate averages, maximums, and minimums to highlight performance ranges.

• Use thresholds to flag values outside normal limits.

  
  

For example, a weekly report might show average machine efficiency per shift rather than every minute’s reading. This approach helps users focus on meaningful patterns instead of noise.

Use Clear Labels and Annotations

Labels and annotations guide users through the data story. Make sure every chart includes:

• Descriptive titles explaining what the visual shows.

• Clear axis labels with units (e.g., temperature in °C, pressure in psi).

• Legends for color codes or symbols.

• Annotations to highlight key events or anomalies, such as maintenance dates or production spikes.

  
  

Well-labeled visuals reduce confusion and improve trust in the data.

Test Visualisations with Real Users

Before rolling out new data visuals, test them with actual users. Gather feedback on:

• Ease of understanding

• Relevance of information

• Visual appeal and clarity

• Any missing or unnecessary details

  
  

Iterate based on feedback to improve usability. This step ensures your visualisations truly support industrial teams in their daily work.

Use Tools Designed for Industrial Data

Many software tools specialize in industrial data visualisation. These tools often include features like:

• Integration with industrial control systems (SCADA, PLCs)

• Real-time data streaming

• Pre-built industrial chart templates

• Alerting and reporting functions

  
  

Examples include Tableau, Power BI with industrial connectors, and specialized platforms like Ignition by Inductive Automation. Choosing the right tool can save time and improve data accuracy.

Keep Accessibility in Mind

Industrial environments can be noisy and fast-paced. Visuals should be easy to read from a distance and under different lighting conditions. Consider:

• Using large fonts and clear icons

• High contrast between text and background

• Avoiding overly detailed visuals that require close inspection

  
  

Accessible design helps all team members quickly grasp important information and act accordingly.

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