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Data visualization has become indispensable for modern businesses to simplify complex data and derive impactful insights for decision making. As per the Data Visualization Skills Gap study by Qlik, demand for data skills talent will increase by 403% by 2025 indicating the rising significance of data visualizations like charts and graphs.
However, inaccurate data depictions can severely impede business growth and reputation. According to survey by advisory company Quocirca, 76% users mistrust information from organizations due to data quality issues that often stem from improperly tested data visualizations.
This article will provide a comprehensive guide explaining:
- Why testing of charts, graphs and dashboards matters
- Different types of visualizations to test
- Elaborated test case examples
- Automated approaches
- Expert tips for planning and execution
Contents
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- Rising Significance of Data Visualization
- Common Types of Charts and Graphs
- Key Testing Challenges
- Sample Test Cases and Checks
- Automated Testing for Graphs and Charts
- Expert Tips for Effective Graph and Chart Testing
- Conclusion
Rising Significance of Data Visualization
The ability to understand intricate data faster through interactive visualizations is becoming pivotal for data-driven companies. As per Forrester, over 75% of enterprise business intelligence initiatives have incorporated advanced data visualization capabilities.
Another report by Dresner Advisory Services reveals increasing reliance on visual analysis with 33% respondents considering it crucial and 62% citing its high importance.
With visual depictions like charts, graphs and dashboards playing a pivotal role in conveying valuable business insights, ensuring their accuracy through testing is imperative.
Faulty data representations can have detrimental impacts on organizational success including:
- Incorrect business decisions causing financial losses
- Dissatisfied, confused customers due to misleading metrics
- Dented brand credibility from mediocre quality deliverables
Hence, learning how to properly test and validate charts, graphs and dashboards should be a key priority for testers.
Common Types of Charts and Graphs
Different data visualization are better for certain types of insights. Some popular charts and their respective testing considerations are:
Column Charts
Use: Compare discrete categories against metric values
Testing: X-Y axes, dimensions, aggregation checks
Bar Graphs
Use: Relative comparisons for nominal categories
Testing: Ordering, negative values, legends
Pie Charts
Use: Represent proportional compositions
Testing: Slicing, color codes, formatting
Line Graphs
Use: Demonstrate trends and changes over time
Testing: Line styles, curve patterns, plot point accuracy
Scatter Plots
Use: Correlation between two numerical variables
Testing: Regression line testing, cluster analysis, outlier detection
Key Testing Challenges
While validating charts, dashboards and associated data visualizations, some common pain areas include:
Data Discrepancies between visualization and sources often due to extraction, transformation errors
Poor Design Choices like overloaded dashboards, unreadable tiny charts hampering usability
Wrong Aggregation Levels depicting incomplete picture due to improper summarization
Inaccurate Interactivity with filters not working as expected
Hence testers need to check for correctness across various aspects to ensure seamless usage and interpretation.
Sample Test Cases and Checks
Here are some sample test cases covering different testing dimensions:
Data Accuracy Checks
| Test Case | Example Steps |
|---|---|
| Compare values against source query |
|
| Verify chart total equals sum |
|
| Check default prompt selections |
|
Layout and Design Checks
| Test Case | Example Steps |
|---|---|
| Confirm title matches contents |
|
| Validate legend accurately describes data |
|
| Check axis and scale labeling |
|
Interactivity Checks
| Test Case | Example Steps |
|---|---|
| Verify drill down navigation |
|
| Test filter impacts on data |
|
| Confirm actions on tooltip hover |
|
Browser Compatibility Checks
| Test Case | Browsers |
|---|---|
| Verify responsiveness | Chrome, Firefox, Safari |
| Check layout consistency | IE, Edge |
| Confirm functional consistency | Chrome, Firefox, Safari |
Automated Testing for Graphs and Charts
While manual testing provides flexibility, test automation is indispensable for regression checks across browsers and recurring validation efforts.
Some ways to automate testing include:
- Export Graph Data to CVS/Excel and compare against test data
- HTML DOM Validation using Selenium
- Screenshot Comparison with baseline using Applitools, Ocular
Benefits:
- Faster test execution
- Consistent outcomes
- Enable continuous testing
Expert Tips for Effective Graph and Chart Testing
Here are some handy guidelines for ensuring comprehensive testing coverage:
Deep Dive into Transformation Logic
As niche testing expert Lisa Crispin notes, "Evaluate raw data along with intermediary representations during processing for precise validation". Debugging data discrepancies gets exponentially harder later.
Drill-Down Tactically
According to veteran test consultant James Bach, "Priortize drill-down test scenarios based on risk – unexpected large numbers indicate good drill-down candidates".
Confirm After Every Modification
As per long-time testing leader Jon D. Hagar, "Re-check dashboard after every source data modification to avoid assumption-based decisions".
Adopt Image Driven Testing
Thought leader Angie Jones recommends, "Use screenshot comparison tools to instantly identify UI regressions across browser and environmnet upgrades".
Augment with Exploratory Testing
Testing expert Cem Kaner emphasizes, "Explore interface without assumptions to uncover hard-to-find issues missed in scripted checks".
Conclusion
Easy data visualization through interactive charts, graphs and dashboards has become integral for business growth. However, improperly tested visualizations can severely impede organizations success and reputation.
This guide covered a diverse set of test scenarios, checks, tools and techniques for comprehensively validating data accuracy, layouts, interactivity and compatibility of charts across browsers.
By dutifully testing data visualizations following these actionable ideas, testers can enable fact-based, streamlined decision making crucial for giving their business an enduring competitive edge.