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A "no-go" in data analysis refers to a practice or approach that is generally considered inappropriate, unethical, or unreliable. Here are some examples of no-go's in data analytics:
Lack of data security: When data analysts do not take sufficient measures to ensure the security of sensitive data, it can lead to data breaches and loss of trust.
Manipulation of data: Deliberately manipulating data to achieve certain results or conclusions is a serious breach of the integrity of data analysis.
Ignoring bias: If systematic biases or prejudices are ignored in data analysis, the results may be biased and unreliable.
Lack of transparency: if the methods, algorithms, or assumptions used in data analysis are not transparently disclosed, this can affect confidence in the results.
Exceeding competencies: When data analysts act outside their area of expertise and perform complex analyses for which they are not adequately qualified, this can lead to erroneous results.
Inappropriate interpretation: inaccurate or disproportionate interpretation of data can lead to incorrect conclusions and distort the meaning of the results.
Lack of validation: if data analysts do not adequately check or validate their results, errors or inaccuracies may go undetected.
It is important that data analysts adhere to ethical standards, ensure data integrity, and promote responsible practices.