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Multiple copies of same data that mismatch are known as
  • a)
    Data redundancy
  • b)
    Data repentance
  • c)
    Data inconsistency
  • d)
    None of these
Correct answer is option 'C'. Can you explain this answer?
Verified Answer
Multiple copies of same data that mismatch are known asa)Data redundan...
Data Inconsistency:
- Data inconsistency refers to the presence of multiple copies of the same data with different values.
- It occurs when updates or modifications to the data are not properly synchronized across all copies or instances.
- Data inconsistency can lead to confusion and errors in data analysis and decision-making processes.
Data Redundancy:
- Data redundancy refers to the duplication of data in multiple locations or databases.
- It occurs when the same data is stored multiple times unnecessarily.
- Data redundancy can waste storage space and increase the risk of data inconsistency.
Data Repentance:
- "Data Repentance" is not a term used in the context of data management. It is likely a typographical error or a term that does not exist.
None of these:
- This option suggests that none of the given choices are correct, which is not accurate.
Conclusion:
- The correct answer to the question is Data inconsistency (Option C).
- Data redundancy (Option A) is a related concept but refers to the duplication of data, not the mismatch of data values.
- Data repentance (Option B) is not a valid term.
- None of these (Option D) is also incorrect, as data inconsistency is the correct answer.
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Most Upvoted Answer
Multiple copies of same data that mismatch are known asa)Data redundan...

Data Inconsistency:

Data inconsistency refers to having multiple copies of the same data that do not match or are conflicting with each other. This can lead to confusion, errors, and inefficiencies in data management.

Causes of Data Inconsistency:
- Data entry errors
- Lack of standardized data formats
- Poor data integration processes
- System failures or malfunctions

Impact of Data Inconsistency:
- Inaccurate decision-making
- Reduced data quality
- Increased risk of errors
- Wasted time and resources in resolving discrepancies

Preventing Data Inconsistency:
- Implement data validation rules
- Use data integration tools
- Ensure data is entered consistently across systems
- Regularly audit and clean up data

Conclusion:

In conclusion, data inconsistency can have detrimental effects on an organization's operations and decision-making processes. It is essential to address this issue through proper data management practices to ensure data accuracy and reliability.
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