Why is Data Munging Important?
Real-world data is often riddled with defects from myriad sources. Human errors in recording information, gaps in data collection, biases in sampling methodology, inconsistencies across data sources and technical glitches can all introduce various problematic anomalies. Using such data “as is” for modeling and analysis generates faulty assumptions and misleading insights that can misguide critical business decisions.
Proper data munging is like quality assurance – it enhances data integrity and enables analytical models to operate as expected for reliable results. For data-driven organizations, low quality data has a high cost. Munging is a strategic investment that pays long-term dividends.
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.