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1. Incomplete or inaccurate data: Incomplete or inaccurate data can lead to poor results.
2. Unclear goals: To successfully analyze data, one must have clear goals and expectations.
3. Insufficient data collection: if the data is not collected properly, the analysis may be biased.
4. Insufficient resources: successful data analysis sometimes requires a lot of resources, such as hardware and software.
5. Missing or inconsistent metadata: Metadata is important to assess the quality of data.
6. Insufficient knowledge: A lack of expertise and experience can lead to poor results.
7. Data integrity issues: If data is not properly validated, it can lead to erroneous results.