The Intensive Effort of Manual Data Munging
While munging tools add efficiency, manual data cleaning still remains crucial for many scenarios. Steps in hands-on munging include:
- Exporting data from source systems into spreadsheet software. This facilitates direct inspection and manipulation.
- Scanning for data inconsistencies like varying date formats, spelling errors, outliers etc.
- Correcting invalid records and formatting issues to maintain consistency.
- Sorting and filtering records by various criteria to surface anomalies.
- Checking values against expected value ranges to catch illogical or extreme outliers.
- Applying find-and-replace across records to standardize language.
- Concatenating and splitting columns to restructure information.
- Adding annotations and documentation for context on changes made.
This meticulous manual process demands sharp attention to detail. But it enables nuanced data remediation. Complimented by munging tools, it creates high-fidelity data products.
What is Data Munging in Analysis?
Data is the lifeblood of the digital age, but raw data in its natural state is often messy, inconsistent, and laden with defects. Before analysis can commence, rigorous data munging is required to transform the raw material of data into a strategic asset that fuels impactful insights.
In this article, we’ll delve into the process of transformation of raw data.
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