Data Cleansing
Data Cleansing is the process of making data accurate and ready to use, like fixing spelling mistakes, removing duplicate entries, and filling in missing information. Think of it as tidying up messy spreadsheets or databases to make them reliable for business decisions. It's a crucial first step in any data project, similar to how you would organize and sort through paperwork before starting a big task. Other common names for this include data cleaning, data scrubbing, or data wrangling - they all mean the same thing: making sure the information is correct and usable.
Examples in Resumes
Led Data Cleansing projects that improved customer database accuracy by 95%
Developed automated Data Cleaning processes for monthly sales reports
Performed Data Scrubbing on legacy systems containing 10+ years of customer records
Implemented Data Wrangling procedures that reduced processing time by 50%
Typical job title: "Data Analysts"
Also try searching for:
Where to Find Data Analysts
Online Communities
Professional Networks
Example Interview Questions
Senior Level Questions
Q: How would you design a data quality framework for a large organization?
Expected Answer: A senior analyst should discuss creating company-wide standards, implementing automated checking systems, training staff on best practices, and establishing processes to maintain data quality over time.
Q: How do you handle conflicting data from multiple sources?
Expected Answer: They should explain approaches to reconciling differences, establishing a single source of truth, and creating rules for which data sources to trust in different situations.
Mid Level Questions
Q: What methods do you use to identify duplicate records?
Expected Answer: Should explain different matching techniques, like exact matching versus fuzzy matching, and how to handle similar but not identical entries.
Q: How do you deal with missing data?
Expected Answer: Should discuss different approaches like removing incomplete records, filling in missing values with averages, or using more advanced techniques depending on the situation.
Junior Level Questions
Q: What basic steps do you take when starting a data cleaning project?
Expected Answer: Should mention checking for obvious errors, removing duplicates, fixing spelling mistakes, and ensuring consistent formatting.
Q: How do you maintain data quality while cleaning?
Expected Answer: Should discuss keeping records of changes made, creating backups, and double-checking work to ensure accuracy.
Experience Level Indicators
Junior (0-2 years)
- Basic data validation and formatting
- Spreadsheet cleaning techniques
- Simple duplicate removal
- Basic data quality checks
Mid (2-5 years)
- Automated cleaning processes
- Complex data validation rules
- Multiple source data reconciliation
- Data quality reporting
Senior (5+ years)
- Data quality framework design
- Team leadership in data projects
- Advanced automation solutions
- Data governance implementation
Red Flags to Watch For
- No experience with basic data validation techniques
- Lack of attention to detail in own work
- Unable to explain their cleaning process
- No knowledge of data quality best practices
Related Terms
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