Data Visualization Toolkit

Creating Your Visualization

Data visualizations play a crucial role in providing context and effectively communicating diverse data sets to various audiences. A well-designed data visualization should bring clarity, engage the audience, and enhance their understanding of the data's insights.

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Data Visualization Toolkit Contents

Questions to Consider

Once you have identified the purpose and the audience, it's time to choose the appropriate visualization. Start by considering the audiences next steps after they gain the insights from your data. Ask yourself:

Is it sufficient to provide the results in a PDF or a screenshot for inclusion in a report or presentation?
Does a graph best highlight the key insights, or is a visualization even necessary?
Would a structured summary table be more effective for the stakerholders needs?

By answering these questions, you can determine the most suitable format for presenting your data. Tailoring your approach to the user's requirements ensures that the insights are both accessible and actionable. Depending on the stakeholder, different data visualizations may be needed. Consider the table below when making these decisions:

Avoiding Common Pitfalls in Data Visualization

Creating effective data visualizations is crucial for accurately conveying information and insights. However, there are common pitfalls that can undermine the effectiveness of your visualizations. Understanding these pitfalls and how to avoid them can significantly enhance the clarity and impact of your data presentations. Below, are some suggestions on ensuring you create effective data visualizations. The visualization graphic examples are taken from this blog post by csg Solutions.

Have a Clear Purpose and Audience

Ensure your data visualization is created with a specific purpose and audience in mind. This helps in making the insights more apparent and relevant. Understanding the audience's needs and the primary question your visualization aims to answer is crucial for effective communication.

Choose the Right Type of Visualization

Select the appropriate chart or graph type that best represents your data. Different types of visualizations are suited for different kinds of data and insights. For example, bar charts are great for comparisons, while line charts are ideal for showing trends over time.

Use Text and Labels to Clarify, Not Clutter

Text and labels should be used to enhance understanding, not to clutter the visualization. Clear and concise labels help the audience quickly grasp the key points without being overwhelmed by unnecessary details.

Use Color to Highlight Important Information

Color can be a powerful tool to draw attention to key data points or to differentiate between categories. Use color strategically to emphasize important information and to make comparisons easier.

Avoid Misleading Visualizations

Ensure that your visualizations accurately represent the data. Avoid common pitfalls such as distorted scales, truncated axes, and inappropriate chart types that can mislead the audience. The goal is to present the data accurately and clearly.

Keep Your Visualizations Simple

Simplicity is key to effective data visualization. Avoid adding too many elements that can make the visualization confusing. Focus on the essential data points and use a clean, minimalistic design to convey your message clearly.

Best Practices for the Design Process

Creating effective data visualizations is essential for transforming complex data into clear, actionable insights. By following best practices, you can ensure that your visualizations are not only visually appealing but also informative and easy to understand. The following tips will guide you through the process of designing impactful data visualizations that effectively communicate your message and engage your audience.

Capitalize on Consistency: Use consistent design elements throughout your visualizations as they help to create a cohesive and professional look.
Avoid Unnecessary Comparisons: Only present data side by side if it makes sense to compare them.
Ensure Intuitive Layouts: Make your visualizations easy to understand at a glance.
Don't Limit Design Choices: Explore beyond default graphing programs. For a deeper dive on this topic, check out the Data Visualization Tools section of the toolkit.
Focus on the Take-Home Message: Highlight the key insights for your target audience.
Minimize Jargon: Use clear and simple language.
Choose Readable Fonts: Ensure your text is easy to read and contrasts well with the background.
Provide Context: Include labels, legends, and annotations to help users understand the data and the interactions available.
Tell a Story: Use interactive visualizations to tell a compelling story with your data. Highlight key insights and guide users through the data exploration process.
Make Interactivity Intuitive: Ensure that interactive elements are easy to use and understand. Provide clear instructions or tooltips to guide users.

Interactive Visualizations

Interactive visualizations are powerful tools that allow users to engage with data dynamically, enabling them to explore different aspects of the data and gain deeper insights. Unlike static charts or graphs, interactive visualizations respond to user input, making data exploration more intuitive and engaging.

Benefits of Interactive Visualizations

Enhanced User Engagement: Interactive elements such as hover effects, clickable areas, and zoom functions make the data exploration process more engaging. Users can interact with the data to uncover insights that are most relevant to them.

Deeper Insights: By allowing users to drill down into specific data points, interactive visualizations can reveal hidden patterns and trends that might not be apparent in static visualizations. This deeper level of analysis can lead to more informed decision-making.

Personalized Experience: Interactive visualizations can be tailored to the needs of different users. For example, a dashboard can provide different views and filters based on the user's role or preferences, making the data more relevant and useful.

Real-Time Data Exploration: Users can explore data in real-time, adjusting parameters and filters to see how different variables interact. This capability is particularly useful for monitoring live data streams or conducting what-if analyses.

Examples of Interactive Visualizations

Dynamic Dashboards: Dashboards that update in real-time and allow users to apply filters, sort data, and view different metrics. For example, a Provost Office dashboard might let users filter by date range, view different KPIs, and compare performance across different departments.

Drill-Down Charts: These charts enable users to click on a data point to see more detailed information. For instance, a faculty evaluation dashboard might allow users to click on a graph or table metric to see a breakdown of data by college or sub-department.

Interactive Maps: These maps allow users to zoom in and out, click on regions to get more detailed information, and toggle between different data layers. For example, an interactive map of admissions data for a national university can show enrollment by region, attrition rates, and admissions capacities.

Tools for Creating Interactive Visualizations

Excel/Google Docs: Is an excellent tool for data visualization because it offers a wide range of chart types, easy-to-use formatting options, and powerful data manipulation capabilities, making it accessible for users to create clear and insightful visual representations of their data.

Tableau: A powerful tool for creating interactive and shareable dashboards. Tableau allows users to connect to various data sources, create complex visualizations, and share them with others.

PowerBI: Microsoft's business analytics tool that provides interactive visualizations and business intelligence capabilities. PowerBI integrates with various Microsoft products and services, making it a versatile tool for organizations.

D3.js: A JavaScript library for producing dynamic, interactive data visualizations in web browsers. D3.js allows for highly customized visualizations and is widely used for creating complex and interactive charts.

For a deeper dive on this topic, check out the Data Visualization Tools section of the toolkit.

Storytelling With Data

Data storytelling is a powerful technique that combines data visualization with narrative elements to convey a compelling message. This approach helps to contextualize data, making it more relatable and understandable for the audience. By weaving data into a narrative, you can highlight key insights, drive engagement, and facilitate better decision-making. The key elements of data storytelling are as follows:

Context: Provide background information to set the stage

- Importance: Context helps the audience understand the relevance and significance of the data. It sets the scene and provides the necessary background to make sense of the information being presented.
- How to Provide Context: Explain the source of the data, the time period it covers, and any relevant events or conditions that might have influenced the data.
- Example: If you're presenting accreditation data, mention any curricular modifications, leadership changes, cohort/academic year challenges, or other external factors that could have impacted the metrics during the period.

Narrative: Use a clear and engaging storyline to guide the audience through the data

- Importance: A narrative helps to structure the data in a way that is logical and easy to follow. It keeps the audience engaged and makes the data more memorable.
- How to Create a Narrative: Identify the main message or insight you want to convey and build your story around it. Use a beginning, middle, and end structure:
  - Beginning: Introduce the topic and provide context.
  - Middle: Present the data and highlight key insights.
  - End: Summarize the findings and suggest next steps or actions.
- Example: If you're telling a story about student course evaluations, start by introducing the importance of student feedback, then present data on student satisfaction scores, and conclude with recommendations for improving the student classroom experience.

Visuals: Support the narrative with relevant and impactful visualizations

- Importance: Visuals make the data more accessible and easier to understand. They can highlight trends, comparisons, and outliers that might be missed in a text-heavy presentation.
- How to Use Visuals Effectively: Choose the right type of visualization for your data (e.g., bar charts for comparisons, line charts for trends, pie charts for proportions). Ensure that your visuals are clear, well-labeled, and free of unnecessary clutter. Use colors and highlights to draw attention to key points.
- Example: Use a line chart to show the trend of student graduation trends over time, with different colors representing different campuses or colleges.

For a deeper dive on this topic, check out the Tell Your Data Story section of the toolkit.

Readings and Resources

6 tips for creating effective data visualizations (with Examples). (csg Solutions)

Chen, H. 2017. Information visualization principles, techniques, and software. (Library Technology Reports)

Cohen-Tanugi, J. 2024. Best practices for data visualization. (Harvard Library)

Gatto, M. 2015. Making research useful: Current challenges and good practices in data visualization. (Reuters Institute for the Study of Journalism)

Knaflic, C. 2015. Storytelling with data: A data visualization guide for business professionals. (Wiley)

Kokoski, C. 2024. Data storytelling: Ultimate guide + 30 examples. (Writing Beginner)

Mahler, C. 2023. 7 best practices for data visualization. (influxdata)

Matthingly, M. 2024. Interactive data visualization: Definition, examples, benefits. (ceros)

Mattison, R. 2023. Data storytelling: How to tell a great story with data. (ThoughtSpot)

Shah, K. 2020. Designing dashboards for multiple target audiences with SAS® visual analytics. (SAS Global Forum)

Sheikh, M. 2024. Interactive data visualization: Examples, techniques & tools. (Visme)

The secret to getting people to use dashboards. (Tableau)

Why data visualization matters. 2024. (kanerika)

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