How much time should I spend on data cleaning?

Plan for significant effort - often 60-80% of project time. Under-investing in cleaning leads to poor model results. It's not glamorous but determines success.

Should I automate data cleaning?

Where possible, yes. Automated pipelines catch issues early, apply consistent rules, and scale. But some cleaning needs human judgment - especially for domain-specific issues.

How do I handle missing values?

Options: delete rows (if few), fill with mean/median/mode, use ML imputation, create "missing" category, or leave null if your model handles it. Choice depends on: why missing, how much, and impact.

What is data quality vs data cleaning?

Data quality is the overall state (is data good?). Data cleaning is the action (making data better). You assess quality, then clean to improve it, then reassess to verify improvement.

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Data Cleaning

The process of detecting and correcting errors, inconsistencies, and inaccuracies in datasets to improve data quality.

In-Depth Explanation

Data cleaning (or data cleansing) is the process of identifying and correcting errors in data. It's a critical but often underestimated part of any data or AI project.

Common data quality issues:

Missing values: Null or empty fields
Duplicates: Same record multiple times
Inconsistent formats: Dates, phone numbers, addresses
Invalid values: Out of range, wrong type
Outdated information: No longer accurate
Typos and errors: Human entry mistakes

Cleaning techniques:

Deduplication
Standardisation (format consistency)
Validation (check against rules)
Imputation (fill missing values)
Outlier detection and handling
Cross-reference verification

Tools for data cleaning:

Python (pandas, great_expectations)
OpenRefine
Data quality platforms
Database constraints
ETL tools with cleaning capabilities

The 80/20 rule:

Data scientists spend ~80% of time on data preparation
Only ~20% on actual modelling
Clean data dramatically improves model quality

Business Context

"Garbage in, garbage out" - AI model quality depends on data quality. Investing in data cleaning pays dividends in model performance.

How Clever Ops Uses This

We emphasise data cleaning for Australian business AI projects, knowing that quality data is the foundation of reliable AI systems.

Example Use Case

"Cleaning customer database: deduplicating merged accounts, standardising addresses to postal format, filling missing postcodes from suburb lookup."

Frequently Asked Questions

Related Terms

Data Quality ETL

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