Catalog
method#Data#Analytics#Data Governance#Integration

Data Cleaning

A systematic method for cleaning data: identifying, correcting, and removing inaccurate, incomplete, or inconsistent records.

Data cleaning is a structured method for identifying, correcting, and removing inaccurate, incomplete, or inconsistent records in datasets.
Established
Medium

Classification

  • Medium
  • Technical
  • Design
  • Intermediate

Technical context

Relational databases (e.g., PostgreSQL)Data warehouse / lake (e.g., Snowflake, S3)ETL/ELT tools and workflow orchestrators

Principles & goals

Transparency: document all transformations and rulesIterate: take small safe steps with validationOwnership: involve domain knowledge for rule definition
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Over-cleaning can remove relevant information
  • Lack of traceability with insufficient documentation
  • Incorrect rules lead to inconsistent results
  • Manage versioned rules and transformations
  • Maintain test data and regression suites for cleaning
  • Document results and decisions for auditability

I/O & resources

  • Raw data from multiple sources
  • Schema definitions and field metadata
  • Domain rules and quality requirements
  • Cleaned and standardized datasets
  • Data quality reports and metrics
  • Audit logs and transformation mappings

Description

Data cleaning is a structured method for identifying, correcting, and removing inaccurate, incomplete, or inconsistent records in datasets. It includes validation, standardization, deduplication, data profiling and missing-value treatment as well as rule-based transformations. The goal is a reliable, documented data foundation for analytics and operational use; it reduces risk and improves decisions.

✔

  • Increased reliability of analytics and reports
  • Reduction of operational errors through clean data
  • Better decision basis and automatable processes

✖

  • Not all errors are automatable or permanently fixable
  • Requires maintenance of rules and mappings
  • Scaling for large, heterogeneous datasets is effortful

  • Error rate before/after cleaning

    Percentage of detected and corrected errors in the dataset.

  • Number of handled duplicates

    Number of merged or removed duplicates.

  • Cleaning throughput time

    Time required for cleaning processes per record or batch.

OpenRefine for ad-hoc cleaning

Interactive tool for quick normalization and transformation of small to medium datasets.

ETL pipeline with validation layers

Automated cleaning as part of an ETL process with validation and correction steps.

Master data matching via rules and ML scoring

Combination of rule-based preprocessing and ML-based matching for complex duplicate cases.

1

Perform data profiling to identify main issues

2

Define quality rules and transformations

3

Implement automated pipeline and set up monitoring

⚠️ Technical debt & bottlenecks

  • Incomplete documentation of transformations
  • Hard-coded rules in legacy scripts
  • Missing automated tests for cleaning logic
Lack of format standardizationInsufficient metadataManual review as a bottleneck
  • Removing all nulls without contextual checks
  • Generic deduplication without domain logic
  • Overwriting historical data without audit logs
  • Underestimating effort for rule maintenance
  • Overstrict validation rules excluding legitimate values
  • Ignoring performance implications for large volumes
Knowledge of data modeling and SQLUnderstanding of domain rules and business logicExperience with data profiling and cleaning tools
Data quality and trustworthinessScalability of cleaning processesTraceability and auditability
  • • Privacy and compliance requirements
  • • Limitations from source systems and interfaces
  • • Compute and storage limits for large volumes