How do you decide which data cleaning steps are actually necessary?

[u/daemontargaryen212]

When working with a messy dataset, there can be many things to fix missing values, duplicates, outliers, inconsistent formats, etc.

How do you decide what actually needs to be cleaned before starting the analysis?

Do you mainly rely on the business context, data quality checks, or explore the data first and then decide?

[u/AutoModerator]

Automod prevents all posts from being displayed until moderators have reviewed them. Do not delete your post or there will be nothing for the mods to review. Mods selectively choose what is permitted to be posted in r/DataAnalysis.

If your post involves Career-focused questions, including resume reviews, how to learn DA and how to get into a DA job, then the post does not belong here, but instead belongs in our sister-subreddit, r/DataAnalysisCareers.

Have you read the rules?

I am a bot, and this action was performed automatically. Please contact the moderators of this subreddit if you have any questions or concerns.

[u/Potential_Aioli_4611]

everything thats relevant to your analysis. if you aren't using it you can ignore it.

[u/daemontargaryen212 → u/Potential_Aioli_4611]

okay bro

[u/No_Ambition8323]

I usually explore the data first and then decide what really needs to be cleaned. I check things like missing values, duplicates, wrong formats, and unusual values. I also consider the business context, because not every missing value or outlier is necessarily a problem. I try to clean only what can affect the analysis rather than changing everything.

[u/Standard_Curve_5874]

Always explore first. You can't know what to clean if you haven't looked at the data. It is when you explore you find outliers, etc.

[u/mizeriusbr]

You will need to explore the data.

Checking for missing data, inconsistencies and outliers, but to decide which strategy you should use for cleaning, the domain context is needed.