Data visualisation: tools and best practices

How to choose the right tool and create visualisations that communicate clearly

9 min

Data without visualisation is noise. A table with 10,000 rows tells no story; a well-designed chart reveals the trend, the anomaly or the pattern that leads to a decision. Data visualisation is the bridge between technical analysis and human understanding.

This guide compares the main visualisation tools, explains which chart type to use for each kind of data and offers practical principles so your visualisations communicate with clarity, not decoration.

Looker Studio (Google Data Studio)

Looker Studio is the go-to option for teams working within the Google ecosystem. It is free, connects natively to GA4, Google Ads, BigQuery, Google Sheets and dozens of additional sources through community connectors.

Its strengths are accessibility (anyone can build a dashboard without technical training), shareability (dashboards accessible by URL) and BigQuery integration for large datasets. Its limitations appear with complex calculations, advanced visual customisation and performance with very large datasets.

Tableau

Tableau is the reference tool for analysts who need advanced visual exploration. Its calculation engine lets you create complex visualisations by dragging and dropping fields, with a moderate learning curve for basics and a steep one for advanced calculations (LOD expressions, table calculations).

Tableau excels at ad-hoc data exploration and visualisation quality. Its licensing model is more expensive than alternatives, but for organisations with dedicated analytics teams, the productivity it delivers justifies the investment.

Power BI

Power BI is Microsoft’s offering for enterprise data visualisation. Its integration with Excel, Azure and the Microsoft 365 ecosystem makes it the natural choice for organisations already using those tools.

The DAX language for calculations is powerful but has a significant learning curve. Power BI Pro has a competitive per-user cost and Power BI Premium offers dedicated capacity for large organisations. Its weak spot is web publishing: it is not as seamless as Looker Studio for sharing public dashboards.

Which chart type to use and when

Choosing the right chart is as important as the data it displays. A bar chart compares magnitudes across categories. A line chart shows temporal evolution. A scatter plot reveals correlations. A heatmap identifies patterns in data matrices.

Pie charts are the most misused: they are hard to read with more than 3–4 categories and do not allow precise comparisons. Replace them with horizontal bars when you need to compare proportions.

  • Bars: compare magnitudes across discrete categories
  • Lines: show temporal evolution or trends
  • Scatter: reveal correlations between two numeric variables
  • Heatmaps: identify patterns in matrices (cohorts, schedules)
  • KPI cards: highlight key metrics with context (vs. target, vs. previous period)

Data storytelling

An effective visualisation does more than show data: it tells a story. Start with the main conclusion, provide context with comparisons and benchmarks, and guide the reader towards the action they should take.

Use annotations to flag relevant events (launches, campaigns, incidents), reference lines to show targets, and colour with purpose (red for alerts, green for positive results). Every visual element should serve a communicative purpose, not a decorative one.

  • Start with the key finding, not the raw data
  • Use annotations to contextualise spikes and dips
  • Include benchmarks and targets as visual reference points
  • Remove decorative elements: unnecessary gridlines, borders, shadows

Common visualisation mistakes

The most serious mistake is distorting the scale. A Y-axis that does not start at zero can make a 2% difference look like 200%. 3D charts add visual distortion without adding information. Random colours hinder interpretation.

Another frequent error is information overload. A chart with 15 time series is unreadable. It is better to split it into several focused charts or use interactivity (filters, tooltips) so the user can explore without being overwhelmed.

  • Truncated axes that distort magnitudes
  • 3D charts that add visual noise without information
  • Too many series or categories in a single chart
  • Colours with no consistent meaning
  • Missing titles, labels and units on axes

Key Takeaways

  • Looker Studio is the best free option for the Google ecosystem
  • Tableau excels at advanced visual exploration; Power BI at Microsoft environments
  • Choose the chart type based on the message, not aesthetics
  • Data storytelling transforms visualisations into decisions
  • Avoid scale distortions, 3D charts and information overload

Need to visualise your data effectively?

We create dashboards and visualisations that tell the story behind your data and help your team make better decisions.