Challenges of Data Munging
- Extensive time and effort needed for meticulous data cleaning.
- Requires a fusion of statistical, computational and domain expertise.
- Difficult to scale manual techniques across exponentially growing data.
- Hard to develop rule-based logic covering all corner cases of dirty data.
- Need for ongoing maintenance as new data is collected and systems evolve.
- Lack of flexibility using rigid scripts when new use cases emerges.
- Prone to unintended consequences like stripping out useful outliers.
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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