Did you know, data professionals spend almost 73% of their time just wrangling the data? This means it’s an indispensable aspect of data processing. It helps business users make concrete, timely decisions by cleaning and structuring raw data into the required format. As the data is becoming more unstructured and diverse, data wrangling is becoming a common practice among top organizations.
Accurately wrangled data ensures that quality data is entered into analytics or downstream processes for consolidation and collaboration. Data wrangling is essential to optimize the data-to-insight journey and support accurate decision-making.
Data wrangling can be arranged into a consistent and repeatable procedure using data integration tools with automation capabilities that clean and convert data sources into a reused format as per the end requirements. You can perform crucial cross-data set analytics after reverting data to a standard format. Moreover, data wrangling with Python is typical as Python employs different methods to wrangle the data stored in different data sets.
Data munging is used for diverse use-cases two of the most commonly used ones include:
- Fraud Detection
- Customer Behavior Analysis
