In this part of the blog series, we’ll analyze real-world examples from annual and integrated reports, examining commonly used visualization techniques. By evaluating their strengths and weaknesses, we’ll uncover valuable insights and best practices that can help refine double materiality visualizations.
Note: The visual examples in this chapter are used solely for the purpose of education.
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In this three-part educational series, we’ll explore the interplay between double materiality and data visualization:
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Tables offer a straightforward way to present double materiality scores, allowing for detailed information for multiple topics.
Detailed information: Tables can display precise numerical scores, offering a high level of detail for readers who want to delve deeper into the data.
Organization: Organized rows and columns can clearly separate categories and their respective impact and financial materiality scores.
Ease of understanding: Tables offer a clear and straightforward way to present data, making them easily accessible to a wide audience, as most people are familiar with how to interpret them.
Machine-readable: Tables are well-structured, making them easy to export (e.g., CSV, Excel) for integration with software tools and analysis. This supports CSRD compliance by streamlining reporting and audits.
Important
Lack of comparison: A key challenge of tables in double materiality is the lack of visual comparison between impact and financial materiality scores, requiring manual interpretation, which can be time-consuming.
Important
Lack of relationship: Tables fail to illustrate the interplay between impact and financial materiality, making it harder to analyze their relationship and identify key topics across both dimensions.
Lack of visual appeal: Tables are not as visually engaging as charts, making it harder to immediately capture the audience's attention.
Limited prioritization: Tables lack visual cues like size, color, or position, requiring manual comparison to determine materiality rankings.
During our research on commonly used double materiality assessment charts, we discovered that the following chart type, often called a 'butterfly bar chart’ or ‘tornado chart’, is frequently used. This chart type displays the materiality scores for various topics using a mirrored design.
Double Materiality Assessment Results: VodafoneZiggo Integrated Annual Report 2023
Double Materiality Assessment Results: Philips Annual Report 2023
Comparison of topics: The butterfly chart’s mirror design places topics directly underneath each other, making it easy to compare topics across categories.
Comparison within a topic: The butterfly chart’s mirrored bars make it easy to compare financial and impact materiality, highlighting differences within each topic.
Color coding: Colors enhance readability by representing materiality levels. Bright or distinct colors highlight high scores, drawing attention to critical issues.
Grouping: With butterfly charts, it is possible to group the topics into environmental, social, and governance categories providing clarity and structure and allowing the audience to focus on specific areas of interest.
Data labeling: In this type of chart, the data labels are positioned next to the corresponding bars, making it simple to associate each category with its respective value.
Important
Lack of relationship: In double materiality, understanding the link between impact and financial materiality is key. The butterfly chart struggles to show this relationship, limiting its effectiveness in highlighting interconnected issues.
Potential confusion with the centerline: The centerline acts as the baseline for both sets of bars, but without a clear explanation, it may confuse viewers unfamiliar with this type of visualization.
Challenging comparison of dimensions: Although financial and impact materiality are placed side by side, their opposite orientation means they are not directly next to each other. This separation makes it harder to accurately compare their values at a glance, especially when differences are subtle.
Scatter plots are a widely used visualization method for representing double materiality assessments. They map topics as individual points on a two-axis system, with one axis representing impact materiality and the other financial materiality. This format provides an intuitive way to position topics relative to both dimensions.
Double Materiality Assessment Results: NS Annual Report 2022
Important
Clarity in relationships: The chart format is excellent for showing correlations, clusters, or outliers, providing an intuitive way to understand the interconnectedness between impact and financial materiality. Research by William Cleveland and Robert McGill in the early 1980s demonstrated that people interpret scatter plots more accurately and quickly than any other chart type.
Color coding: Using distinct colors to represent categories—such as environmental, social, and governance—can effectively differentiate data points in the chart. This approach makes it easier for viewers to identify patterns and relationships within and across categories at a glance. To enhance clarity, ensure the colors are easily distinguishable, accessible for colorblind viewers, and accompanied by a clear legend mapping each color to its corresponding category.
Flexibility for customization: Besides coloring coding, scatter plots can be customized with stroke colors, icons, and size to further enhance the viewer’s understanding of the data.
Clean design: Without zones, matrices or other elements in the background, the scatter plot remains uncluttered, focusing attention purely on the distribution of topics.
Important
Overlapping data points or labels: When many topics are plotted close together, points or labels can overlap, obscuring information and reducing the chart’s readability and effectiveness.
To address this, consider using numbers or abbreviations inside or next to the data points and placing the full data labels in a legend beside the chart.
No visual thresholds or zones: Without background zones or division lines, the scatter plot doesn’t explicitly highlight thresholds or benchmarks, such as what qualifies as "material" or "non-material". This requires additional explanation, potentially detracting from the chart’s usability.
Bubble Chart
A bubble chart is a variation of the scatter plot that adds a third dimension to the visualization by using the size of the bubbles to represent an additional variable.
Double Materiality Assessment Results: DSM Integrated Annual Report 2023
The bubble chart shares the strengths and weaknesses of the scatter plot, so we will focus only on the additional aspects unique to this chart type.
Multi-dimensional representation: A bubble chart adds a third dimension (bubble size) to the standard scatter plot, enabling the representation of additional variables, such as stakeholder importance or urgency, alongside financial and impact materiality. This provides added context and depth, enabling a more comprehensive understanding of the data.
Visual appeal: The bubble chart’s design is visually engaging, making it an attractive option for presentations and reports, which can help draw attention to key insights.
Important
Complexity for non-expert audiences: Adding a third dimension with bubble size can make the chart harder to interpret, particularly for viewers unfamiliar with data visualizations. Understanding the relative significance of bubble size may require additional explanation.
Difficult comparisons: Comparing bubble sizes accurately can be challenging, as the human eye does not naturally perceive differences in area as intuitively as differences in length for example.
Scatter Plot with a Division Line
A scatter plot or bubble chart with a division line builds on the classic scatter plot by adding a clear threshold to distinguish between material and non-material topics. A circular line running from the Y-axis to the X-axis serves as the divider: topics above the line are considered material, while those below are not.
Materiality Matrix: Colruyt Annual Report 2023/2024
The scatter plot with a division line shares the strengths and weaknesses of the scatter plot, so we will focus only on the additional aspects unique to this chart type.
Important
Clear threshold identification: The division line clearly separates material and non-material topics, improving accessibility and helping stakeholders prioritize key issues.
Flexible for various thresholds: The position and angle of the division line can be adjusted to reflect different thresholds or criteria, allowing for customization based on specific organizational needs.
Important
Subjectivity of the organic line: An organic division line may lack a clear mathematical basis, making its placement potentially subjective and harder to justify, leading to inconsistent interpretation.
Ambiguity near the line: Topics close to the division line might oversimplify nuanced topics, potentially overlooking the need for further context or consideration. Small changes in scores could shift a topic from "non-material" to "material," requiring explanation or additional data.
Assumes binary categorization: A division line enforces a binary categorization (material vs. non-material), which may overlook intermediate levels of materiality. Introducing additional “zones” can offer a more nuanced representation, allowing for a clearer distinction of varying degrees of significance.
Matrix Chart
A matrix chart is a grid-based visualization that plots topics based on two or more dimensions. Typically structured as a 2x2 or 3x3 grid, it organizes topics into clear categories based on their scores.
Double Materiality Assessment Results: H+H Annual Report 2023
Double Materiality Assessment Results: Orsted Annual Report 2023
Intuitive structure: The grid-based design provides a straightforward framework, helping viewers quickly understand the relationships between topics and their placement within the categories. Topics in specific quadrants, such as “high financial and high impact materiality,” can be easily identified and prioritized.
Simplicity for communication: The matrix format is easy to explain and interpret, making it an effective tool for communicating results to a broad audience, including non-experts.
Visual appeal: A well-designed matrix chart enhances presentations by balancing visual appeal and readability. Thoughtful alignment and the use of icons help maintain clarity, preventing clutter while improving overall aesthetics.
Important
Rigid categorization: The fixed grid structure may oversimplify relationships, as topics must fit into discrete categories that don’t fully capture their nuanced materiality levels. While increasing chart size or adding more quadrants can offer more granularity, the matrix format inherently enforces a structured classification rather than allowing for continuous variation.
Limited data points: Matrices can become cluttered and difficult to interpret if too many topics are plotted, as the space within each quadrant is limited.
Scatter Plot with Matrix
A scatterplot with matrix combines the flexibility of a scatter plot with the structured framework of a matrix. The scatter plot overlays data points onto a background grid, such as a 2x2 or 3x3 matrix, which categorizes the space into predefined areas.
The scatter plot with matrix shares the strengths and weaknesses of the scatter plot, so we will focus only on the additional aspects unique to this chart type.
Important
Combination of detail and structure: The scatter plot provides granular insight into individual topics, while the matrix adds a layer of organization, enabling both detailed analysis and a high-level overview.
Visually intuitive: The clear separation of zones in the background helps viewers quickly interpret the materiality levels of different topics, making the chart accessible even for non-experts.
Important
Ambiguity at boundaries: Topics that fall near the edges of matrix zones can create uncertainty about which category they belong to, potentially requiring additional explanation or clarification.
Design complexity: Designing a scatter plot with a matrix requires careful planning to avoid clutter, especially when adding data labels or bubble sizes. The example above shows how thoughtful design maintains clarity, balancing visual elements for easy interpretation.
Harder to automate: Automating a scatter plot with a custom grid is challenging, as many tools struggle with precise alignment and styling. Manual adjustments can be time-consuming and error-prone, while automation ensures consistency and efficiency but often requires specialized tools or workflows.
Datylon for Illustrator simplifies this by allowing the background matrix to be designed in Illustrator while the scatter plot is layered on top. Using Datylon Report Server, this template can be automated, preserving its custom design within reports.
Scatter Plot with Organic Zones
A scatterplot with organic zones overlays data points on a background divided into soft, circular zones. These zones visually differentiate levels such as low, medium, and high scores on both axes, creating a fluid framework for interpreting the data. This format combines the precision of a scatter plot with an intuitive, visually appealing way to highlight key regions within the dataset.
Telefónica’s double materiality matrix in Consolidated Annual Report 2022
The scatter plot with organic zones shares the strengths and weaknesses of the scatter plot, so we will focus only on the additional aspects unique to this chart type.
Important
Flexible categorization: Organic zones create nuanced zones that reflect real-world complexities, allowing gradual transitions between materiality levels. Their soft edges help viewers intuitively grasp relative scores without rigid boundaries.
Highlights priorities: Organic zones can be customized to emphasize specific areas of interest, such as highlighting critical thresholds or grouping topics with similar scores. Adding style attributes as color or stroke color can help group and draw attention to the material topics even more.
Visually appealing: Organic zones have a smooth, flowing design that creates a visually engaging chart, making it stand out in presentations or reports.
Important
Subjectivity of organic zones: Organic zones are inherently subjective, as they often lack a clear mathematical basis, making their placement harder to justify and potentially leading to inconsistent interpretation. Different designers might emphasize various aspects of the data when creating these zones, which can introduce bias and reduce consistency across visualizations.
Potential for overwhelm: Combining scatter plot data with organic zones can result in a visually overwhelming chart. To avoid a chaotic appearance, it’s important to limit the use of colors for both the background zones and the dots in the scatter plot.
Harder to automate: The custom, freeform shapes of organic zones are difficult to automate, as they require precise, often manual design work to match the data and visual style. This complexity can make updates cumbersome when new data is available.
Similar to the scatter plot with a matrix, Datylon for Illustrator simplifies this by allowing the background zones to be designed in Illustrator while the scatter plot is layered on top. Using Datylon Report Server, this template can be automated, preserving its custom design within reports.
Scatter Plot with Linear Zones
A scatterplot with linear zones organizes data points against a background defined by straight, diagonal lines. Unlike organic zones, which emphasize fluidity, linear zones create clear, geometric divisions that reflect a more mathematical approach to categorizing materiality levels, such as low, medium, and high. A small decrease in one dimension requires a proportional increase in the other to maintain the same materiality level.
Storebrand double materiality matrix in Materiality Analysis Report 2023
The scatter plot with linear zones shares the strengths and weaknesses of the scatter plot, so we will focus only on the additional aspects unique to this chart type.
Important
Clear, objective boundaries: Linear zones are based on straightforward mathematical principles, typically defined by linear equations. This creates clear, objective boundaries that are easy to justify, reducing ambiguity in how topics are categorized.
Consistent interpretation: Linear zones provide uniform thresholds, minimizing subjective interpretation and ensuring consistency across years. While current thresholds can be subjective, future regulations may standardize them, making clear boundaries even more valuable.
Easy to automate: The geometric simplicity of straight, diagonal lines makes them easy to implement and automate in data visualization tools. Updates can be managed with minimal manual adjustments, ensuring consistency and efficiency when new data is introduced.
Important
Rigid perception: The sharp boundaries of linear zones can create a false sense of rigid categorization, implying that topics are either inside or outside a zone with no middle ground. This may overlook topics that sit on the threshold and deserve more nuanced consideration.
Potential for overwhelm: Combining scatter plot data with linear zones can result in a visually overwhelming chart. To avoid a chaotic appearance, it’s important to limit the use of colors for both the background zones and the dots in the scatter plot.
Scatter Plot with Future Trends
A scatterplot with future trends adds an extra layer of insight by indicating how the importance of topics is expected to change over time. Arrows or other subtle markers show whether topics are likely to increase or decrease in materiality, helping to highlight evolving priorities.
Deloitte’s approach to materiality assessment
The scatter plot with future trends shares the strengths and weaknesses of the scatter plot, so we will focus only on the additional aspects unique to this chart type.
Forward-looking insights: Many sustainability reports aim to prepare stakeholders for future challenges. Highlighting whether a topic's importance is expected to increase or decrease aligns with this goal.
Strategic prioritization: Understanding which topics are expected to gain importance enables decision-makers to allocate resources more efficiently, showcasing the company’s commitment to long-term strategic planning.
Important
Data uncertainty: Predicting future importance is inherently uncertain. It requires robust data, scenario modeling, or expert judgment, which may not always be available or defensible. If predictions are wrong, they may reduce trust in the analysis.
Chart clarity: Adding future trends can make the chart more complex and harder to read, especially if there are many topics and it could distract from the core message about current materiality.
In this blog article, we explored various real-world examples of double materiality charts. We started with a simple table chart, moved on to butterfly charts, and concluded with different versions of scatter plots, bubble charts, matrices, and their combinations.
Here are the key best practices we can extract from the examples. If you need expert guidance in designing the right double materiality chart, Book a Demo and let’s discuss how Datylon can help.
It’s important to note that there is no single "perfect" solution. The best chart design will always depend on the specific data and context it represents. Consider these best practices as inspiration and guidelines to inform your work, rather than a one-size-fits-all blueprint.
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While we’ve covered key best practices, some aspects still require further exploration to reach definitive conclusions. These will be addressed in Part 3 of this educational guide through focused experimentation. Key topics for exploration include:
Through these experiments, we aim to refine the approach to double materiality visualization, creating charts that are both insightful and impactful. |
We’d love to hear your thoughts too! Send us your ideas via email or Book a Demo to explore Datylon’s tools in action. Stay tuned for the results in Part 3!
Resources & Further Readings
ESG reporting: a comprehensive guide to communicating ESG results
https://finance.ec.europa.eu/capital-markets-union-and-financial-markets/company-reporting-and-auditing/company-reporting/corporate-sustainability-reporting_en
https://www.efrag.org/en/sustainability-reporting
https://www.globalreporting.org/