Data Visualization for the Non-Designer: Getting Started
Data Visualization for the Non-Designer: Getting Started
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Data Visualization for the Non-Designer: Getting Started


Data visualization empowers spotting patterns and communicating insights from data. However, many avoid exploring data visually thinking it requires design skills they lack. Luckily, numerous tools exist today enabling anyone to create basic charts and graphs without artistic expertise.

This guide aims to empower non-designers to unlock the benefits of data visualization. We will cover:

  • Core principles for effective, intuitive data visualization
  • Step-by-step guidance on basic viz types like charts, plots, maps and more
  • Tips for selecting optimal visual formats for different data goals
  • An overview of essential design considerations for non-designers
  • Recommended easy-to-use data visualization tools
  • Strategies for elevating basic visuals through simple design improvements
  • Examples demonstrating quality data visualization without artistic skills

By the end, you will have the confidence to start creating basic data visualizations that provide impactful insights – even with no formal design training. Let’s showcase the data storytelling power accessible to anyone!

Why Visualize Data as a Non-Designer?

Some key reasons to explore data visualization without extensive design skills:

Spot Trends and Patterns

Visual formats reveal trends and relationships hidden in tables of numbers.

Simplify Complexity

Charts and graphs distill large multifaceted datasets down to keytakeaways.

Boost Memorability

Visuals stick in minds better than dry tables and metrics alone.

Enhance Reports and Presentations

Data visuals make presentations, research reports and articles more engaging and readable.

Improve Comprehension

Humans understand concepts more intuitively through pictures versus just numbers and words.

Enable Quick Insight Sharing

Data visualization provides an efficient channel for communicating findings and analysis.

Uncover New Questions

Seeing data graphically prompts new avenues of questioning and exploration.

Democratize Access to Insights

Tools today lower barriers to hands-on data analysis for non-experts.

Let’s examine core principles to guide your data visualization process…

Core Design Principles for Effective Data Viz

Some key principles to follow when visualizing data as a beginner:

Clarify Your Purpose – Start by defining the specific story or insight you wish to communicate with the visualization.

Pick Appropriate Viz Types – Use the simplest format that will communicate your insight effectively given data types.

Eliminate Clutter – Remove all nonessential visual elements that don’t directly contribute to the main story.

Draw Attention to Key Areas – Use basic formatting like color, size and boldness to establish visual hierarchy.

Label Responsibly – Include concise descriptive titles and axis labels so context remains clear.

Check Assumptions – Review that visual encodings like color and position intuitively match viewer expectations.

Review for Integrity – Ensure data is represented proportionally without distortions or exaggerations.

Iterate for Improvement – Solicit informal feedback on draft visuals to identify areas for refinement.

Applying these core principles will help craft intuitive visuals – even with no formal training.

Step-By-Step Guide to Basic Data Visualization Types

Here is an overview of constructing basic chart and graph types to visualize data insights:

Bar Charts

  1. Place categories or groups across X axis.
  2. Set measured metric values on Y axis.
  3. Plot bars for each item with height proportional to its value.
  4. Order bars intuitively (e.g. largest to smallest).
  5. Label axes and add concise title.

Bar charts work well for comparing metrics across groups.

Line Charts

  1. Place continuous variable like time across X axis.
  2. Set measured metric values on Y axis.
  3. Plot data points connected by lines to show trends over time.
  4. Annotate specific events using reference lines or points.
  5. Label axes clearly and add descriptive title.

Line charts visualize data evolving continuously over time.

Scatter Plots

  1. Place two continuous variables on X and Y axes.
  2. Plot data points at x/y coordinates dictated by data values.
  3. Segment or color code groups of points if needed.
  4. Draw regression trendline if relevant to observe correlations.
  5. Clarify axes scales and labels.

Scatter plots reveal associations and patterns between two variables.


  1. List categories, groups or individual metrics as column headers.
  2. Populate rows with corresponding data values.
  3. Sort columns to place highest-impact metrics first.
  4. Format fonts and cell widths to optimize readability and alignment.
  5. Apply color sparingly to direct attention.

Tables provide an efficient way to look up or compare specific values.


  1. Create base map matching geographic scope of data.
  2. Encode data variables like values, categories, or densities using color hues, saturation, choropleth shading etc.
  3. Set color legend clearly explaining data variable encodings.
  4. Add labels, markers and highlights to draw attention to notable locations.

Maps inherently provide contextual insights around location, proximity, regions and more.

Following these basics allows constructing clear basic data visualizations to unlock insights.

Tips for Picking Optimal Data Visualization Types

When starting out, follow these tips for picking appropriate visual formats:

Consider data types and relationships – Quantitative, categorical, temporal, geospatial etc.

Focus on your key insight or finding – Emphasize this in the visual design.

Use the simplest format possible – Start with basic charts before more elaborate options.

Avoid overcomplicating – For example, don’t use a 3D chart if 2D would communicate just as well.

Avoid chart junk and gimmicks – Unnecessary visual adornments usually just distract.

Review for clarity and accuracy – Ensure formats intuitively represent the actual data.

Try different options – Experiment to determine what makes relationships clearest.

Matching visual formats to goals maximizes communication effectiveness.

Essential Design Considerations for Non-Designers

Some key quick design improvements to consider:

Order clearly – Sort bars, lines, regions etc in intuitive sequence, often largest to smallest.

Group related elements – Use alignment, proximity, containment and connection to tie related items visually.

Establish hierarchy – Make key elements and labels bolder, larger or darker.

Prioritize whitespace – Use ample whitespace to reduce visual density.

Limit color variety – Restrict to 2-3 colors with deliberate meanings attached.

Clarify through labeling – Don’t make viewers guess – add concise explanatory text.

Refine formatting – Adjust sizing, layout and font styles to enhance readability.

Check for distortions – Ensure charts don’t exaggerate trends through axis scales or aspect ratios.

Even simple tweaks significantly boost clarity, focus and aesthetics.

Recommended Data Viz Tools for Non-Designers

Some excellent, easy-to-use tools include:

Tableau Public – Straightforward drag and drop data visualization with publicly shareable dashboards. Great for beginners.

Google Sheets Charts – Instant chart creation from spreadsheet data. Handles many quick analysis needs.

Datawrapper – Browser-based charting focused on simplicity and ease-of-use. Great defaults require little customization.

Microsoft Excel Charts – Excel still offers speedy options for basic data charts and graphs for ad hoc analysis.

Venngage – Intuitive infographic template options make elevating basic charts easy.

Visme – Simple drag and drop infographic creator with icons, fonts and color palettes to start customizing basic charts.

Flourish – Templates and handy presets available for tweaking basic bar, line and pie charts.

Infogram – Easy infographic creation with templates for embellishing charts with explanatory visual context.

Many options now enable creating straightforward, informative charts and graphs without design expertise.

Simple Tweaks to Elevate Basic Data Viz

Some easy upgrades to try:

Meaningful annotations – Add concise text callouts explaining key data points.

Axis and legend labels – Ensure metrics and color encodings are clearly defined.

Y-axis start at zero – Avoid exaggerating small changes by carefully aligning axes scales.

Hover tooltips – Implement mouse hover tooltips revealing underlying data values.

Lines and arrows – Illustrate relationships and flows using lines judiciously.

Participant photography – Add photos of people involved to make stories more relatable.

Infographic context – Surround bare charts with explanatory illustrations, icons and captions.

Color coding – Apply color minimally but effectively to categorize or highlight insights.

Even fundamental design improvements significantly boost clarity and engagement.

Examples of Quality Data Viz without Artistic Skills

Let’s look at a few examples of insightful data visualization created without extensive design expertise:

Excel Dashboard – This Excel dashboard offers key business metrics in simple, well-formatted sparklines and bar charts.

Datawrapper Summary – A clear HTML table summarizes key survey findings with bold highlighting calling out insights.

Tableau Public Health Map – This public health dashboard maps state health metrics using intuitive shading and color encodings.

Google Sheets Timeline – Effective use of chart types, color and labeling provides trends overview in Google Sheets.

Venngage Infographic – This easy bar chart infographic uses icons, text and color effectively to explain a process.

Visme Flowchart – Annotated flowchart diagrams use color, alignment and arrows artfully to illuminate a system.

These samples demonstrate the power of even simple, clean data visualization done right.

Key Takeaways for Getting Started

Some core tips for unlocking the benefits of data visualization:

  • Know your communication goal before visualizing data
  • Default to basic chart types like bars, lines and scatter plots first
  • Eliminate nonessential items and clutter obstructing focus
  • Use color, size and boldness minimally but intentionally
  • Add concise text labels and axis definitions for clarity
  • Experiment and iterate to determine optimal formats
  • Take advantage of handy visualization tools enabling intuitive designs
  • Seek informal feedback from colleagues to refine and improve

By following fundamental design best practices, anyone can create basic, functional, insightful data visualizations using today’s technologies.


In summary, data visualization provides significant benefits for spotting trends and communicating findings which should not be obstructed by lack of artistic expertise. Focus first on identifying your core story. Then use the simplest visual formats possible, along with easy-to-use data visualization tools now available, to construct basic charts and graphs that illuminate insights effectively. Apply purposeful tweaks like annotations and color encoding to take clarity to the next level without formal design skills. Solicit suggestions for improvement as you build proficiency. But have confidence unlocking data visualization, in all its empowering benefits, as an absolute beginner. The data storytelling power accessible through basic, well-designed charts and graphs is immense. Visualize your data story!

Frequently Asked Questions about Data Visualization for the Non-Designer: Getting Started

1. Why should I visualize data if I’m not a designer?

  • Visualizing data helps in spotting trends, simplifying complexity, enhancing memorability, improving comprehension, and enabling quick insight sharing. You don’t need design skills to create basic charts and graphs that effectively communicate insights.

2. What are some core design principles for effective data visualization?

  • Core design principles include clarifying your purpose, picking appropriate visualization types, eliminating clutter, drawing attention to key areas, responsible labeling, checking assumptions, reviewing for integrity, and iterating for improvement.

3. Can you provide a step-by-step guide to basic data visualization types?

  • Basic data visualization types include bar charts, line charts, scatter plots, tables, and maps. Each type has specific steps for construction and visualization goals, such as comparing metrics across groups or visualizing data evolving over time.

4. How do I pick the optimal data visualization types for my data?

  • Consider the data types and relationships, focus on your key insights, use the simplest format possible, avoid overcomplicating, review for clarity and accuracy, and try different options to determine what makes relationships clearest.

5. What are some essential design considerations for non-designers?

  • Essential design considerations include ordering clearly, grouping related elements, establishing hierarchy, prioritizing whitespace, limiting color variety, clarifying through labeling, refining formatting, and checking for distortions.

6. Can you recommend some easy-to-use data visualization tools for non-designers?

  • Recommended tools include Tableau Public, Google Sheets Charts, Datawrapper, Microsoft Excel Charts, Venngage, Visme, Flourish, and Infogram. These tools simplify the process of creating informative charts and graphs without requiring design expertise.

7. What are some simple tweaks to elevate basic data visualization?

  • Simple tweaks include adding meaningful annotations, ensuring clear axis and legend labels, aligning Y-axis to zero, implementing hover tooltips, using lines and arrows to illustrate relationships, incorporating participant photography, adding infographic context, and applying effective color coding.

8. Can you provide examples of quality data visualization without artistic skills?

  • Examples include Excel dashboards, Datawrapper summaries, Tableau Public health maps, Google Sheets timelines, Venngage infographics, and Visme flowcharts. These examples demonstrate effective data visualization done right without extensive design expertise.

9. What are some key takeaways for getting started with data visualization as a non-designer?

  • Key takeaways include knowing your communication goal, defaulting to basic chart types, eliminating clutter, using color and labeling intentionally, experimenting and iterating, taking advantage of visualization tools, and seeking feedback for improvement.

10. How can I conclude the FAQ?

  • In conclusion, data visualization offers significant benefits for communicating findings, even without design skills. By following fundamental design principles, leveraging easy-to-use tools, and applying purposeful tweaks, anyone can create basic, insightful data visualizations. Start visualizing your data story today!

By Dani Davis

A true visionary in the realms of tech writing, digital storytelling, and e-commerce, Daniel Davis (known as Dani) has carved out an exceptional career spanning over 15 years. Born and raised in San Francisco, Dani's innate affinity for technology and creative expression propelled them to explore the intricacies of computer science while honing their storytelling abilities. Their unique blend of technical expertise and narrative prowess laid the foundation for their multifaceted success. Dani's journey has been marked by groundbreaking achievements, including authoring bestselling books that demystify complex technological concepts through captivating narratives. As the founder of the influential online platform "TechTales," Dani has created a hub for educational content, podcasts, and video essays that cater to tech enthusiasts worldwide. Moreover, as the head writer of, a leading resource for e-commerce and digital marketing, Dani has established themselves as a preeminent authority in the field of online business and entrepreneurship. Their consulting work, speaking engagements, and advocacy efforts have inspired countless individuals, solidifying their legacy as a true pioneer in the digital age.

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