Disadvantages of Data Visualization
- Misinterpretation: Incorrect or misleading visualizations can lead to misinterpretation of data, resulting in erroneous conclusions.
- Complexity: Creating effective visualizations requires expertise in both data analysis and visualization techniques, which can be challenging for beginners.
- Subjectivity: Interpretation of visualizations can be subjective, leading to different conclusions based on individual perceptions.
- Data Overload: Visualizations can sometimes overwhelm users with too much information, making it difficult to focus on key insights.
- Technical Limitations: Certain types of data may not lend themselves well to visualization, or the tools available may have limitations in handling certain data formats or sizes.
Difference between Data Visualization and Business Intelligence
Data visualization is one of the analytical tools used in data science and it simply refers to the process of representing data graphically to allow data communications and expression of patterns. The tool aims to make the charts, graphs, and maps simple to understand as well as to discover the facts that fill the blanks. Unlike business intelligence, though, the latter “extends to various processes, methodologies and technologies that get used in the course of data retrieval and interpretation with a view of returning more relevant decisions inside companies.
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