Data wrangling can be defined as the process of cleaning, organizing, and transforming raw data into the desired format for analysts to use for prompt decision-making. Also known as data cleaning or data munging, data wrangling enables businesses to tackle more complex data in less time, produce more accurate results, and make better decisions. 

Importance of Data Wrangling :

Data wrangling software has become such an indispensable part of data processing. The primary importance of using data wrangling tools can be described as:

• Making raw data usable. Accurately wrangled data guarantees that quality data is entered into the downstream analysis. 
• Getting all data from various sources into a centralized location so it can be used.
• Piecing together raw data according to the required format and understanding the business context of data
• Automated data integration tools are used as data wrangling techniques that clean and convert source data into a standard format that can be used repeatedly according to end requirements. Businesses use this standardized data to perform crucial, cross-data set analytics. 
• Cleansing the data from the noise or flawed, missing elements
• Data wrangling acts as a preparation stage for the data mining process, which involves gathering data and making sense of it. 
• Helping business users make concrete, timely decisions

Data wrangling software typically performs six iterative steps of Discovering, Structuring, Cleaning, Enriching, Validating, and Publishing data before it is ready for analytics.

Data Wrangling Examples :

Data wrangling techniques are used for various use-cases. The most commonly used examples of data wrangling are for:

• Merging several data sources into one data-set for analysis
• Identifying gaps or empty cells in data and either filling or removing them
• Deleting irrelevant or unnecessary data 
• Identifying severe outliers in data and either explaining the inconsistencies or deleting them to facilitate analysis   

Businesses also use data wrangling tools to 

• Detect corporate fraud
• Support data security
• Ensure accurate and recurring data modeling results
• Ensure business compliance with industry standards
• Perform Customer Behavior Analysis
• Reduce time spent on preparing data for analysis
• Promptly recognize the business value of your data
• Find out data trends