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Data Visualization Principles With Good Data Examples: Guide

Data visualization principles banner

The ability to transform numbers into clear and insightful stories is more important than ever. Data visualization is the key to unlocking these stories, but with so many charts and graphs, how do you know which best represents your information?

This guide dives into the core principles of data visualization, providing real-world examples to illustrate how to choose the right chart type, use color effectively, and design dashboards that resonate with your audience. By the end, you’ll be equipped to create data visualizations that look great but effectively communicate your message.

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Why Data Visualization Matters?

Why data visualization matter in a modern world - infographic - created by ajelix

Infographic representing why and how data visualization matters

In an era inundated with information, visualization provides a lifeline. Our brains process visual information faster and more intuitively than text. It bridges the gap between raw numbers and human comprehension, empowering individuals and organizations to harness the power of data effectively.

The Power of Visualizing

Cognitive Benefits of Visual Information Processing

Human brains process visuals faster and more efficiently. Illustrated information taps into our pattern recognition capabilities. That enables us to grasp intricate relationships, spot anomalies, and conclude more easily.

How Visualization Enhances Data Understanding and Decision-Making

Visualizations provide a roadmap through the numbers complex landscape. It simplifies the information, allowing us to extract insights swiftly. Here’s an example of how representing the same data within the table vs chart you can make better decisions.

Example of data visualization importance - graph helps you make better decisions - picture

Infographic emphasizing the importance of visualization, image by author

This example enlarges the importance of visualization as you can’t make decisions from a table. You simply can’t see the whole picture behind the numbers. Contrary to the graph it’s clear that there’s a stock shortage and you need to order more items.

When making decisions, visual representations provide clarity, reducing the risk of misinterpretation and promoting well-informed choices.

Related Article: How To Make A Bar Graph in Excel?

How To Prepare Your Data?

Preparing and Cleaning Data for Visualization

Before you create visualizations, you should refine the information. This involves removing inconsistencies, filling gaps, and ensuring accuracy. The adage “garbage in, garbage out” rings true – without clean data, even the most compelling visuals can lead to incorrect conclusions.

Garbage in, garbage out - data specialist quote - data visualization quote

Quote from analytics specialists “Garbage in, garbage out”.

Identifying the Right Data Sources for Different Types of Visualizations

You should sort all the information to understand if it’s suited for the specific visualization type. Selecting the appropriate series attributes is crucial. Line charts are great for depicting trends, while scatter plots excel at showing correlations. Understanding these nuances is vital for crafting effective visuals.

Connect data sources so you could create dashboards with all your KPIs infographic

Dashboard design for data analysis. Image created by data visualization tool by Ajelix

Choosing the Right Visualization Types

Exploring Different Types of Charts and Graphs

The world of visualization is diverse, offering a plethora of chart types: bar charts, pie charts, histograms, and more. Visualizing graph data from the amount of data with radar chart, line graph, and time series analysis.

Visualization TypeBest AtLess Suitable For
Pie Chart– Comparing parts of a whole (percentages) – Showcasing categorical data with limited categories (usually 4 or less)– Complex data with many categories
– Highlighting trends or relationships over time
Bar Chart– Comparing categories of data
– Showing trends or changes over time (horizontal bar charts)
– Large datasets with many categories
– Visualizing proportions within a category
Line Chart– Showing trends or changes over time
– Comparing multiple trends
– Highlighting specific data points
– Visualizing complex relationships between variables
Column Chart– Comparing categories of data
– Highlighting magnitudes or differences between categories
– Showing trends over time (less effective than line charts)
Scatter Plot– Identifying relationships between two variables
– Visualizing outliers or clusters in data
– Comparing more than two variables
– Communicating trends to a broad audience
Heatmap– Identifying patterns or trends across multiple categories
– Visualizing data with two or more dimensions
– Highlighting specific data points
– Datasets with very few categories
Map Chart– Displaying geographical data
– Visualizing trends or patterns across geographic locations
– Data without a geographic component
– Complex data with many variables
Box Plot– Comparing distributions of data across categories
– Identifying outliers in data
– Large datasets with many categories
– Visualizing trends over time
Stacked Bar Chart– Showing the composition of a whole across multiple categories
– Comparing trends for multiple categories over time
– Datasets with many categories
– Highlighting individual data points
Area Chart– Showing the magnitude or change of a value over time (similar to a line chart)
– Emphasizing the volume or total quantity under the curve
– Highlighting specific data points
– Comparing trends for many datasets simultaneously
Treemap– Visualizing hierarchical data structures
– Identifying relationships between parts of a whole
– Data with very deep hierarchies
– Datasets with many categories that are not hierarchical
Time Series Chart– Showing trends or changes over time
– Highlighting specific periods or events within the time series
– Comparing many trends simultaneously
– Data with high frequency
Donut Chart– Similar to pie charts, for comparing parts of a whole (percentages)
– Can accommodate slightly more categories than the pie chart due to the open center
– Useful for highlighting a specific metric in relation to the whole
– Data with many categories (can become cluttered)
– Not ideal for comparing trends over time
Different chart types table

Each has its strengths and weaknesses, and understanding these nuances helps in selecting the right tool for the job. Learn how to create an effective data dashboard that tells a story.

Matching Visualization Types to Information and Objectives

Selecting the right visualization isn’t just about aesthetics. It’s about aligning the visualization type with the information’s inherent characteristics and the goals you aim to achieve. A misaligned choice can lead to confusion rather than clarity. You should aim to have to understand effective dashboard data visualization flow chart rather than bad data examples.

Related Article: How To Delete A Chart in Excel?

Clarity and Simplicity

Importance of Clutter-Free and Intuitive Visuals

Simplicity is the ultimate sophistication in visualization. An overload of elements can obscure the message. Clean visuals with minimal distractions ensure that the audience’s focus remains on the data story. Keeping in mind basic principles design will be easier for you to make storytelling with data.

Clean chart vs cluttered chart - good and bad data visualization examples

Misleading data visualization examples and good visualization, image by author

An example presents the difference between a cluttered chart and a clean chart. You should aim for the one on the right. The first chart contains too many labels and information that it’s hard to understand. Find out how to visualize financial data.

It’s unclear what is the story behind this chart. However, a clean chart highlights important information therefore it’s easy to make a decision.

Simplifying Complex Numbers through Visual Representation

Complex information can be daunting, but visualizations simplify the journey. They distill intricate concepts into digestible forms. Making it easier for non-experts to grasp and appreciate the insights within the set.

Related Article: How To Change Series Name in Excel?

Color Theory and Visual Appeal

Using Colors Strategically to Convey Information

Colors are more than aesthetics – they convey meaning. Proper color selection can highlight key points, differentiate categories, and evoke emotions, all of which enhance the effectiveness of visualization.

Bad chart color example vs good color chart example

Good vs bad coloring chart example, image by author

Creating Harmonious Color Palettes for Clear Visualization

Harmony in color palettes isn’t just about aesthetics; it’s about aiding perception. Poorly chosen color combinations can lead to confusion or misinterpretation. Harmonious palettes ensure that the message remains crystal clear. You can use AI that will match and pick the colors for your charts.

Visualizing Relationships and Patterns

Revealing Correlations and Patterns in Data

Visualizations excel at unearthing hidden relationships. Scatter plots reveal correlations, while line charts expose trends. These patterns might remain invisible in raw info but become evident through the power of visuals. Explore the world of business intelligence and analytics.

Chart with two lines examples with correlation for data

See the example above to understand best practices for simple data correlation visualization.

Avoiding Misleading Visuals

Recognizing and Rectifying Common Visualization Pitfalls

Misleading visuals can lead to misguided conclusions. It’s crucial to be aware of pitfalls like distorted scales, truncated axes, and inappropriate comparisons. Ir will ensure that your visualizations remain accurate.

bad dashboard example
Bad dashboard visualization example, image by author

Ensuring Accurate and Ethical Information Representation

Integrity is paramount in visualization. Accurate representation involves transparently presenting data points, acknowledging uncertainty, and avoiding manipulative techniques that can distort the truth.

Related Article: How To Explode A Pie Chart in Excel?

How To Visualize Data With Ajelix BI?

Time needed: 1 hour

Here’s a step-by-step guide on how easy it’s to create professional-looking dashboards using Ajelix BI report builder.

  1. Data Upload or Connection

    Upload: Prepare your data as a CSV, Excel, or Google Sheets. Drag and drop it onto the Ajelix BI platform or use the upload option.
    SQL Connection: If your data resides in an SQL server, Ajelix BI offers a direct connection. Provide your server credentials and choose the relevant database/table.
    Connect data sources so you could create dashboards with all your KPIs infographic

  2. Use AI Dashboard Generator for faster result

    For quick and efficient visualization you can use an AI-powered dashboard generator. Select the dataset and AI will analyze your data and suggest a dashboard layout with relevant charts. You can use this as a starting point to customize it further.
    Use ai dashboard generator for quick visualization screenshot from ajelix bi

  3. Customization

    1. From the available chart types (bar, line, pie, etc.), drag and drop the desired ones onto your workspace.
    2. Assign specific data fields from your uploaded data to each chart for visualization.
    3. Customize chart appearance (colors, fonts, labels) and data formatting (number displays, percentages) using the provided options.
    Steps for customizing charts changing colors, data screenshot from excel

  4. Advanced KPI Creation With SQL Functions (Optional):

    For complex KPI creation, Ajelix BI allows the writing of custom SQL queries within the platform. This enables calculations, aggregations, and data manipulation beyond the basic options. To get these features go to “Data modeling”
    Note: Experience with SQL is recommended for this step but not mandatory if the user understands Excel formula syntax.
    Data preparation step in ajelix bi screenshot

  5. Sharing Your Dashboard

    Once satisfied with your visualization, click the “Share” option. Ajelix BI will generate a unique link for your dashboard. Share this link with anyone you want to grant access to view your data visualization.
    Sharing settings on ajelix bi dashboard editor

Additional Tips:

By following these steps, you can leverage Ajelix BI to transform your data into clear and insightful visualizations. Remember, start with the basic data upload or connection, then explore customization and advanced options based on your needs.

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Choosing the Right Tools for the Job

Now that you understand the core principles of data visualization, it’s time to explore the tools that can help you bring your data stories to life. There’s a vast array of data visualization software available, each with its own strengths and weaknesses. Selecting the right tool depends on several factors, including:

  • Data Complexity: Simple spreadsheets might be manageable with basic tools like Microsoft Excel, while complex datasets might require more sophisticated software like Tableau or Power BI.
  • Technical Expertise: Some tools offer drag-and-drop interfaces for beginners, while others require programming knowledge for advanced customization.
  • Budget: Free and open-source options exist, but premium software often unlocks additional features and functionalities.
  • Collaboration Needs: If teamwork is crucial, consider cloud-based tools that facilitate shared dashboards and real-time updates.

Tools For Easy Data Visualization

  • Ajelix BI: user-friendly drag-and-drop self-service platform with AI functionality. Great option for small business owners who need basic analytics features.
  • Tableau: Industry leader with powerful features for data manipulation, analysis, and visualization. Offers a wide variety of chart types and customization options.
  • Power BI: Microsoft’s business intelligence platform known for its integration with Excel and other Office products. Well-suited for creating interactive dashboards and reports.
  • Qlik Sense: User-friendly tool with a focus on self-service analytics and data exploration. Known for its intuitive interface and associative data exploration capabilities.

Did you know..?
Businesses already use AI to create data dashboards with 1-click. Check our latest feature on Ajelix BI platform and uncover AI insights from your data.

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Free and Open-Source Tools:

  • Google Data Studio: A user-friendly platform for creating interactive dashboards and reports. Integrates seamlessly with other Google products like Sheets and Analytics.
  • Tableau Public: Free version of Tableau with limited publishing capabilities. Great for exploring the software and creating basic visualizations.
  • Apache Spark: Open-source framework for large-scale data processing and visualization. Ideal for data scientists and developers working with Big Data.

Other Notable Options:

  • Infogram: Cloud-based platform ideal for creating social media-friendly infographics and reports.
  • Looker: Data exploration and business intelligence platform geared towards developers and data analysts.
  • Zoho Analytics: Comprehensive business intelligence suite with data visualization capabilities. Integrates with other Zoho products and services.

Remember, the “best” tool is subjective. Experiment with different options to find one that aligns with your specific needs, skillset, and budget. Most data visualization tools offer free trials, allowing you to test-drive the features before committing.

Conclusion

Key Data Visualization Principles On How To Make Data Visualization

Good data visualization examples isn’t just about aesthetics – it’s a science that marries data and design to communicate insights. Clean visuals, meaningful narratives, and accurate representation are the cornerstones of effective data visualization.

Empowering Better Decision-Making Through Thoughtful Visualization

In an era of information overload, advanced data visualization serves as a guiding light. By embracing design principles for data visualization and leveraging its potential. We equip ourselves to make informed decisions that drive progress.

Learn more about Excel and Google Sheets hacks in other articles. Stay connected with us on social media and receive more daily tips and updates.

FAQ

When should I use a bar chart vs. a line chart?

Bar charts are for comparing categories (e.g., sales by region). However, line charts show trends over time (e.g., stock price over months).

What are some common mistakes people make in data visualization?

Here are some common data visualization mistakes to avoid, all concise:
Clutter: Too many elements or data points in one chart.
Unclear Labels: Missing or confusing axis labels and chart titles.
Misleading Colors: Colors not intuitive or not accessible to colorblind viewers.
Wrong Chart Type: Using a bar chart for trends (use line chart) or vice versa.
3D Effects: Distracting and can distort data perception.

What tools can I use to create data visualizations?

Here are a few options: Spreadsheets (Excel, Google Sheets), Presentation Software (PowerPoint, Keynote), Standalone Tools (Ajelix BI, Power BI), and Online Tools (Canva, Infogram).

How can I make my data visualizations more visually appealing?

Spruce up your data viz with these quick tips:
Focus on Clarity: Keep it simple and avoid clutter.
Color Counts: Use color strategically to highlight key data points.
Consistent Design: Maintain consistent fonts, colors, and styles throughout.
Label It Right: Clear and concise labels for axes, titles, and data points.
Whitespace is Key: Don’t cram elements – allow breathing room for visual impact.

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Ad hoc financial analysis report example from ajelix bi portal

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