Data Visualization
Data visualization is the graphical representation of data to make patterns, trends, and insights visible.
Classification
- ComplexityMedium
- Impact areaBusiness
- Decision typeDesign
- Organizational maturityIntermediate
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Incorrect data leads to misleading visualizations.
- Overly complex graphics can cause confusion.
- Misuse due to incorrect interpretations.
- Use clear and engaging designs.
- Test visualizations with the target audience.
- Pay attention to accessibility.
I/O & resources
- Identify Data Sources
- Select Tools for Visualization
- Gather User Requirements
- Visualized Data
- Reports
- Dashboards
Description
Data visualization is a key aspect of data analysis that helps translate complex data into understandable formats. Through visual representations such as charts and graphs, users can efficiently interpret data and make informed decisions.
✔Benefits
- Improves data interpretation.
- Enhances decision-making.
- Promotes data communication.
✖Limitations
- Can appear overloaded with large datasets.
- Limited underlying data can lead to incorrect conclusions.
- Additional training may be required.
Trade-offs
Metrics
- User Engagement
Measurement of how actively users interact with the visualizations.
- Error Rate
Number of errors in the data visualization.
- Loading Times
Time required to load visualizations.
Examples & implementations
Real-time Sales Analytics
Creating dashboards to monitor sales figures in real-time.
Customer Satisfaction Reporting
Using data visualization for reporting customer satisfaction surveys.
Market Research Results
Visualizing market research results for better decision-making.
Implementation steps
Identify the target audience for the visualization.
Collect and analyze data sources.
Create and test visualization prototypes.
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated Visualization Tools.
- Technical debt in infrastructure.
- Insufficient documentation.
Known bottlenecks
Misuse examples
- Using a single visualization for all data.
- Lack of user feedback in design.
- Neglecting data sources.
Typical traps
- Excessive Complexity.
- Lack of maintenance of visualizations.
- Failure to adhere to best practices.
Required skills
Architectural drivers
Constraints
- • Technological Limitations
- • Budget Constraints
- • Data Usage Guidelines