Last updated on Feb 28, 2024

What are the key principles of data visualization for evaluation?

Data visualization is the process of presenting data in graphical or pictorial forms, such as charts, graphs, maps, or dashboards. Data visualization can help evaluators communicate their findings, insights, and recommendations more effectively and persuasively to different audiences, such as stakeholders, funders, policymakers, or beneficiaries. However, not all data visualizations are created equal. Some can be misleading, confusing, or irrelevant, while others can be clear, informative, and engaging. How can evaluators create data visualizations that serve their evaluation purposes and meet the needs and expectations of their audiences? In this article, we will discuss some of the key principles of data visualization for evaluation, based on the best practices and guidelines from experts and practitioners in the field.

1 Know your data

Before you start designing your data visualization, you need to understand your data: what type of data do you have, what are the main variables and relationships, what are the patterns and trends, what are the outliers and anomalies, what are the limitations and uncertainties? Knowing your data will help you choose the most appropriate and accurate visualization method, such as a bar chart, a line chart, a pie chart, a scatter plot, a map, or a dashboard. It will also help you avoid common pitfalls, such as using inappropriate scales, axes, or colors, or displaying too much or too little data.

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2 Know your audience

Another important principle of data visualization for evaluation is to know your audience: who are they, what are their backgrounds and interests, what are their questions and expectations, what are their preferences and biases, how will they access and use your data visualization? Knowing your audience will help you tailor your data visualization to their needs and goals, such as informing, educating, persuading, or inspiring them. It will also help you choose the most suitable and appealing format, style, and tone for your data visualization, such as a static or interactive, simple or complex, formal or informal, or colorful or monochrome.

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Tiffany Rosenzweig

Researcher | Policy Analyst | Ph.D.
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In considering the background of audience members, it's important to think about their experience with data. The language used to talk to data analysts compared to consumers of data is often very different. Think about this for both the visualization and any presentations.

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3 Know your purpose

A third key principle of data visualization for evaluation is to know your purpose: what are you trying to achieve with your data visualization, what are the main messages and stories you want to convey, what are the actions and outcomes you want to influence or support? Knowing your purpose will help you focus your data visualization on the most relevant and important data, and highlight the key findings, insights, and recommendations. It will also help you use the most effective and persuasive techniques and elements for your data visualization, such as titles, labels, legends, annotations, captions, icons, or images.

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Gunjika Vishwanath Misra (She/ Her) 🚀

On a mission to help every woman & girl become financially literate, secure & independentI National President-Home & Security Council, WICCI| Board Member, 7Even Consultancy| 20X LI TOP VOICE| TOP Person Ambassador
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Understanding your purpose is crucial in data visualization for evaluation as it guides what to present and how. It ensures clarity and relevance, helping users grasp insights efficiently. Without a clear purpose, visualizations risk being cluttered or misleading. Knowing your purpose ensures focused, impactful visualizations that effectively convey insights to diverse audiences.

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4 Follow the best practices

A fourth key principle of data visualization for evaluation is to follow the best practices: what are the standards and guidelines for creating high-quality and ethical data visualizations, what are the common mistakes and errors to avoid, what are the tips and tricks to improve your data visualization skills and tools? Following the best practices will help you ensure that your data visualization is accurate, clear, consistent, honest, and accessible. It will also help you avoid some of the common pitfalls and challenges of data visualization, such as misleading or distorted data, cluttered or confusing charts, inappropriate or offensive colors, or technical or compatibility issues.

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5 Seek feedback and improvement

A fifth and final key principle of data visualization for evaluation is to seek feedback and improvement: how can you test and evaluate your data visualization, how can you collect and incorporate feedback from your audience and peers, how can you revise and refine your data visualization based on the feedback and results? Seeking feedback and improvement will help you assess the effectiveness and impact of your data visualization, and identify the strengths and weaknesses of your data visualization. It will also help you improve your data visualization skills and tools, and learn from your experiences and best practices.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Carolina Castro

Business Administration, Marketing, Sales, Fundraising, Project Management, Customer Service, Public Relations, Advertising,Media Management and training.
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Yo sugiero incluir las distintas inteligencias al momento de usar gráficos porque no todos los seres humanos aprenden de forma visual de manera qué hay que agregar movimiento y sonidos preferiblemente al igual de importante son los títulos y leyendas de los gráficos para atraer la atención.

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Program Evaluation

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What are the key principles of data visualization for evaluation?

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1 Know your data

2 Know your audience

3 Know your purpose

4 Follow the best practices

5 Seek feedback and improvement

6 Here’s what else to consider

Program Evaluation

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