Six Core Data Wrangling Activities - Download White Paper (PDF)


Six Core Data Wrangling Activities

Six Core Data Wrangling Activities

According to DataWatch.com, “Data wrangling is the process of cleaning and unifying messy and complex data sets for easy access and analysis. This process typically includes manually converting/mapping data from one raw form into another format to allow for more convenient consumption and organization of the data.”

With the ongoing adoption of big data, many companies have come to take positive advantage of data wrangling, or data munging as it is sometimes referred to, as a step in the right direction to prepare data for a wider range of analysis.

Whether it is horses, cattle, or data, wrangling by itself is hard and unglamorous work. It is isn’t typically done all in a day’s work. Step by step, it offers new ways in which to allow for better question asking and drive toward a goal of more effective data analysis.

There are 6 core Data Wrangling activities which include:

#1 Discovering

This activity identifies features of data and determines the value of the dataset. Data handlers use data type inference and interactive quality bars to gain immediate visibility about trends and data issues, guiding the transformation process.

#2 Structuring

These types of actions change the form or schema of data. Splitting columns, pivoting rows, and deleting fields are all forms of structuring.

#3 Cleaning

Users identify data quality issues, such as missing values, and apply the correct transformation to fix or delete these values from the dataset.

#4 Enriching

To gather all necessary insights into one file, the existing dataset must be enriched by joining and aggregating multiple data sources.

#5 Validating

A final check is done for any missing or mismatched data that wasn’t corrected during the transformation process. After data has been wrangled, you must validate that the output dataset has the intended structure and content before publishing.

#6 Publishing

When the data is successfully structured, cleaned, enriched, and validated, it’s time to publish the wrangled output for use in downstream analytics processes.

The data wrangling process allows for users to see a wide range of statistics and analytics that aim to add value to the organization and help to make better business decisions.

If you want to know more about the proper tools and techniques to use for effective data wrangling, click the link below for more information.


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