6 leading data visualization techniques for analysts

Data visualization techniques help analysts turn complex datasets into clear, engaging visuals. From bar charts to heatmaps, these tools make it easier for non-technical teams to explore trends, identify patterns, and act on insights.

What is data visualization?

Data visualization is the process of making graphical representations of data. Graphs, charts, maps, and dashboards turn complex data into easy-to-understand visuals. A visual format lets users quickly spot trends and identify patterns. The importance of data visualization lies in its ability to make data as accessible as possible.

Depending on the intended audience and the content of the data, different data visualization techniques might be more appropriate. A technique is the medium an analyst uses to represent their data, whether that’s a pie chart or a word cloud. 

Types of data visualization analysis

The number of variables in your analysis will influence the data visualization technique you choose. Here are the main three types:

• Univariate analysis: Focuses on a single variable; useful for tracking changes over time — for example, total product sales.
• Bivariate analysis: Compares two variables to explore relationships. For example, you could trace how employee engagement impacts productivity.
• Multivariate analysis: Examines three or more variables at once. This technique helps visualize complex relationships in a clear, digestible format.

6 data visualization methods and techniques

Because datasets vary widely in structure and content, analysts use a range of visualization techniques to communicate insights clearly.

Here are six common data visualization strategies you can use to depict data.

1. Charts and graphs

Charts and graphs are some of the most common visualization tools, as they’re both easy to make and understand. Graphs are especially effective when analyzing a small number of variables.

Common charts include:

• Bar chart: Compares different categories, like total revenue for each individual product.
• Line chart: Shows trends over time, clearly mapping out metric changes like website traffic per month over a year.
• Pie chart: Illustrates the total proportion of a whole that a data point represents, like how an overall budget is divided across multiple categories.
  

2. Maps

Analysts can use maps to demonstrate how data varies across geographical regions. These are the most common data visualization maps for location-based analysis:

• Choropleth map: Shades geographical regions based on data values — for example, highlighting population density across different metropolitan areas using darker shades for more populated cities.
• Heat map: Uses the intensity of a color to show the concentration or density of a statistic. For example, you could use a heat map to show which cities customers commonly buy from.
• Dot distribution map: Places a dot on a map to show where something occurs. Larger dots represent more activity in that area.
  

3. Histograms

Histograms are similar to bar charts but divide continuous data into bins. They reveal patterns and distributions within a dataset by grouping information into ranges.

Histograms can be adapted in several ways depending on the analysis goal:

• Stacked histogram: Compares multiple different distributions stacked on top of one another. For example, you could measure incoming website traffic for both paid and organic across a month.
• Cumulative histogram: Sequentially adds the running total of a data point to show a growing figure over time. You could use a cumulative histogram to show total donations to a campaign over time.
• Relative frequency histogram: The Y-axis on this histogram displays the relative proportion, rather than raw counts. Mapping out the percentage of customers that fall into different total lifetime spend categories would use this format. 

4. Box and whisker plots

Analysts can use a box plot to visualize the distribution of a dataset by quartiles. The box represents the middle 50% of the data and spans the lower and upper quartiles, while the whiskers extend to the minimum and maximum values. 

Here are a few common box and whisker plot styles:

• Standard box plot: Maps the median, quartiles, and outliers for a dataset. You could use this to compare student test scores to see things like the average grade or highest and lowest mark.
• Notched box plot: A standard box plot with an added notch around the median to indicate the confidence interval. 
• Variable-width box plot: Plots several box plots next to one another to represent both the dataset and the sample size of each group. A wider box translates to larger samples and, therefore, more reliable data.
  

5. Scatter plots

A scatter plot (aka scatter graph) uses X and Y values to plot data points where these values intersect. Analysts can draw a line of best fit to show the general trend.

• Simple scatter plot: Plots dataset information across the X and Y axes. You could identify the correlation between total monthly ad spend and total paid traffic. 
• Bubble chart: Adds an extra dimension to the scatter plot by changing the size of a bubble where X and Y intersect to provide more context. With the previous example, you could add a bubble to show revenue generated for more data on campaign efficacy. 
• Scatter plot with trend line: Adds a trend line to show the overall pattern and help spot any outliers.
  

6. Word clouds

A word cloud turns natural language data into a visual representation, with words that appear more frequently in written feedback taking up more space in the diagram.

• Categorical/grouped word cloud: Groups words based on frequency. After requesting product feedback, a word cloud will show more common feedback larger in the cloud.
• Shaped word cloud: Aligns a word cloud into a recognizable shape, often a brand logo or something similar.
• Interactive word cloud: A clickable word cloud that allows users to see the numerical data behind it, like how many times a specific word came up in feedback.

Applying visualization techniques in business dashboards

When businesses use automatic ELT pipelines like Fivetran’s, they can feed high-quality data directly into business intelligence tools. Many of these BI systems use dashboards that include any of the data visualization techniques previously described. Here are some common examples:

• Operational dashboard: Monitors day-to-day activities and metrics related to how a business runs. For example, a factory operational dashboard might show real-time inventory levels, average lead time, and so on.
• Strategic dashboard: Displays insights for teams that want to track long-term goals or KPIs. A marketing dashboard, for example, might include campaign ROI, click-through rates, and website traffic.
• Analytical dashboard: Offers more advanced filtering and drill-down capabilities, making it easier to find more specific prescriptive analytics. A marketing team might want to filter through traffic data to see which demographics interact most with their campaigns.

How to choose the right data visualization technique for you

With so many different data visualization ideas to choose from, picking the right one can seem overwhelming. These four criteria can help guide your decision:

• Define your key question: Clarify what insight you want the data to deliver. Are you highlighting trends over time, exploring relationships, or identifying outliers? Let your analytical goal guide your technique.
• Match the data type to the format: Certain visualizations are best suited to specific data types. Use bar charts for categorical data, line charts for time-series data, and scatter plots to show relationships between variables.
• Factor in data volume and complexity: Large datasets can overwhelm simple visuals. Use aggregated views, filters, or interactive dashboards to maintain clarity without sacrificing depth.

How Fivetran supports better data visualization

Effective data visualization starts with clean, reliable data. Fivetran automates data ingestion from hundreds of sources into your cloud data platform, using built-in schema management to ensure consistency and accuracy.

By eliminating manual pipelines and reducing time spent on data wrangling, Fivetran frees analysts to focus on building visualizations that uncover critical insights and drive business outcomes. Book a live demo to see how Fivetran’s fully managed ELT platform powers real-time, trustworthy data for every dashboard, chart, and report.

FAQs

What are some data visualization tool examples?

Data visualization tools are platforms that help analysts create charts, graphs, maps, and dashboards. Popular examples include Tableau, Power BI, Looker, and Qlik.

What is data analysis and visualization software?

This type of software combines analytical capabilities with data visualization, allowing users to explore metrics and trends through visual dashboards instead of raw tables or code.

What is interactive data visualization software?

Interactive tools let users manipulate visualizations — such as filtering data, changing dimensions, or drilling into specific data points — to uncover deeper insights.

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