RippleFilters
Chart Function
What is the purpose of your visualization?
Data Scales
What types of data are you working with?
Color Palette
Do categories convey negative or positive connotations?
core guidelines
Data Visualization
Data visualization in Ripple transforms information into insight. These guidelines help teams design visual stories that empower higher education users to make informed, data-driven decisions.
Overview
To effectively create meaningful visualizations, it's essential to grasp the concept of data visualization and its significance to Watermark. Understanding this is crucial for leveraging Watermark's capabilities to enhance decision-making and communication.
What is Data Visualization?
"There is no such thing as information overload. There is only bad design."
Edward Tufte, American data visualization designer and professor
Effective data visualization takes data and context into consideration, organizing and translating it to tell a story. Data visualizations are powerful tools in the problem-solving toolbox, enabling the breakdown of large and complex data sets and concepts into more manageable and understandable components.
Why It Matters at Watermark
Every Ripple visualization should bridge the gap between data understanding and human action. Leaders in higher education use these visualizations to gauge progress, make informed decisions, and share institutional narratives.
Examples of institutional goals and outcomes
| Goal | Outcome |
|---|---|
| Track performance of departments over time | Identify low-engagement areas |
| Monitor completion of program reviews | Highlight milestones |
| Compare outcome results across terms | Identify under-performing outcomes |
Choose the Right Visualization
Follow these guidelines to select the most effective visualization.
1
Follow Our Principles
Learn about our core Principles for creating data visualizations. These guidelines help make our visualizations meaningful by ensuring they are accurate, accessible, and actionable.
2
Understand Data Scales
Learn about the four common Data Scales: nominal, ordinal, interval, and ratio to understand the ways data visualizations present data.
3
Use Our Framework
With an understanding of our principles and data scales, you can now utilize our Framework to select the ideal chart and color palette that effectively conveys your story.
Ripple Charts
Here is a breakdown of our current Ripple charts and their intended purpose.
| Chart | Goal | Function |
|---|---|---|
| To compare values across a category. | Comparison | |
| Show the makeup of a whole and how parts relate to it. | Composition | |
| Line Chart | Visualize data over time to identify patterns and trends. | Trend |
Principles
Let's start by reviewing a few core principles of Ripple's data visualizations. To transform data into meaningful stories, our visualizations should be Accurate, Accessible, and Actionable.
Accurate
Ensure the data accurately reflects reality and that the visualization represents this data correctly. Accuracy fosters trust, which is essential since our clients depend on our visualizations for important decisions.
Creating Accurate Data Visualizations
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Begin by understanding the user's primary goal and problem
Use personas to narrow the problem statement and define the visualization's scope, ensuring the solution directly addresses their needs.
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Only include data that is necessary and complete for the user's problem
Eliminate extraneous information to help users focus on key insights.
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Anticipate common data challenges such as small sample sizes, missing data points, and statistical outliers
Design for these scenarios early to identify where the data visualization might fail or require an alternative solution.
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Avoid distorting results that can cause misinterpretations
Using inaccurate scale measurements or uneven data increments can obscure significant differences.
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Avoid using biased terminology in labels
Stay impartial to avoid influencing the outcomes.
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Avoid misrepresenting the data
Ensure that the visual design clearly and truthfully reflects the quantitative values being presented.
Ask yourself the following questions:
- What questions does this visualization answer?
- Would this data be interpreted accurately without explanation?
- Is the level of detail appropriate for the user?
Accessible
Data visualizations should be accessible to all, considering varying technical skills, cognitive abilities, and device types. Designing for accessibility enhances clarity and impact.
Creating Accessible Data Visualizations
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Include detailed labels and descriptions to summarize key information, especially for visually impaired users
Detailed descriptions may be necessary for complex visualizations to explain trends and insights.
- Ensure that all colors used in charts, such as bars, lines, or slices, meet the required contrast ratio 3:1 against their backgrounds
- Ensure a legend is close in proximity to data being visualized when possible
- Ensure that interactive chart elements have targets that are sufficiently large (44x44px)
- Consider offering data in a tabular format to simplify screen reader parsing
- Never rely on color alone to convey meaning
Ask yourself the following questions:
- How would a screen reader announce this?
- Can we simplify this to enhance clarity for all users?
Actionable
The goal of data visualizations is to empower users with actionable data to prompt informed decision making. Whether it's exploring the data visualization (curate, transform, or extract the data), or using it to take action in context of their workflow and greater institutional practice (monitor assessment progress, assess an outcomes), actionable visualizations drive user engagement.
Creating Actionable Data Visualizations
- Understand the specific goals of your users and tailor the visualization to support their objectives
- Ensure the key insight from your data visualization is prominent and easy to understand
- Incorporate visual cues, annotations, or callouts that suggest actions or additional insights for users to guide their next steps
- Use visual hierarchy to direct attention to the most important aspects of their data
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Ensure the visualization fits naturally into the user's existing process
Provide them options to export and share data in formats that enable their next steps outside of the product, including further analysis and collaboration.
Ask yourself the following questions:
- What actions should the visualization prompt?
- Is the key takeaway or insight from the data immediately clear?
Data Scales
Designing a visualization begins with understanding the type of data you're depicting. Data Scales (or levels of measurement) describe the nature of information, categorizing variables into nominal, ordinal, interval, and ratio. Each combination of data scale will determine which chart type and color palette best communicates the story.
| Data Scale | Definition | Example |
|---|---|---|
| Nominal | Categories representing distinct groups with no inherent order, ranking, or quantitative difference between them. | Scholarships, Publications, Presentations, Scholarly Articles, Student Demographics (majors, departments, course types) |
| Ordinal | Categories that have a clear implicit order, with no quantifiable difference. | Student Satisfaction Levels (Very Dissatisfied to Very Satisfied), Project Progress (Not Started, In-Progress, Completed) |
| Interval | Numerical data that is ordered with equal intervals and no true zero. | Time Scales (Academic Years, Semesters), GPA ranges, Test Score Ranges |
| Ratio | Numerical, continuous data that is ordered with equal intervals. Has a true zero to allow for meaningful comparisons of magnitude. | Student Enrollment, Graduation Rates, Test Scores, Ages |
Examples of determining Data Scales
Goal
My users are Faculty Members that need to determine which of their courses scored the lowest on student satisfaction in the previous academic year, so that they can focus on improving that course in the next year.
| Data | Data Type | Data Scale |
|---|---|---|
Student satisfaction levels (very dissatisfied, dissatisfied, neutral, satisfied, to very satisfied) |
Ordered categories | Ordinal |
Courses (ENGL 101, HIST 202, BIO 101) |
Categories that are distinct from one another and cannot be ordered | Nominal |
Goal
My users are academic leaders that need to see the distribution of enrolled students per department and compare those rates across academic years, so that they can see how enrollment has fluctuated over time and take the necessary actions to help departments that have struggled the most.
| Data | Data Type | Data Scale |
|---|---|---|
|
Student enrollment numbers (0–5,000+) |
Continuous, numerical data starting from zero | Ratio |
Department (English, History, Psychology) |
Categories that are distinct from one another and cannot be ordered | Nominal |
Academic Years (2020–2025) |
Numerical, ordered data with no zero | Interval |
Functions
Data visualizations fall into four primary functions: comparison, composition, distribution, and trend. Understanding these functions will help you grasp their purpose and choose the right visualization to convey information effectively.
Comparison
Best Practices
- Use to compare quantities across different, nominal categories
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Use to show precise comparisons of categories
These charts can display exact values, including decimal places.
- Begin the axis at zero to avoid exaggerating differences in the data.
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Sort the data based on the axis to communicate what is useful for the user to find what they need.
For instance, if the user is looking to identify the least effective category, sort the values in ascending order.
Examples
- Compare enrollment rates of all departments.
- Compare faculty awards by department.
Composition
Best Practices
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Use to show percentages or share-of-total
The total should always add up to 100%.
- Limit to up to 6 category values to ensure understanding
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Don't use for precise comparisons of categories
Composition charts emphasize the whole and its parts rather than comparing individual segments to each other.
Examples
- Show breakdown of total admissions by activity type.
- Show breakdown of response rate by engagement type.
Distribution
Best Practices
-
Use to visualize the frequency of numerical data across interval ranges (bins) to identify patterns
This visualizes how often values cluster and occur within ordered numeric intervals (bins). To visualize how values change over time, use a trend chart.
- Ideal for identifying variations, clusters, and statistical outliers by visualizing the spread or shape of data.
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Group data into meaningful ranges called bins.
Too many bins can hide the data pattern, while too few bins fail to represent it effectively. Whenever possible, use consistent bin sizes so users can accurately interpret frequency.
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Do not use for small datasets
There should be enough data to reveal a meaningful shape or curve.
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Do not use for nominal categories
Distribution charts are designed for continuous, numerical ranges. Bins represent ranges of a continuous variable (e.g., score ranges, time ranges, value ranges), not named categories.
Examples
- Show the distribution of student satisfaction scores grouped into score ranges.
- Display the distribution of course enrollment rates across predefined ranges.
Charts
- Column Chart
- Grouped Column Chart
Trend
Best Practices
-
Use to show how data changes over time.
This visualizes how a metric evolves over a continuous, chronological sequence.
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Ensure time increments (days, months, years) are equal.
Uneven gaps can misleadingly exaggerate or hide the rate of change.
- Only use when data is time-dependent or has a logical, chronological order.
- Don't use when data is categorical.
Do not use for non-sequential categorical (nominal data). Use a comparison chart instead.
Examples
- Track performance of departments over time.
- Compare enrollment rates of all departments over time.
