Data Wrangling

- Overview

Businesses have long relied on professionals with data science and analytical skills to understand and leverage information at their disposal. With the proliferation of data, due to the development of smart devices and other technological advancements, this need has accelerated. 

It’s impossible to choose a single data science skill that’s most important for professionals. One thing that's certain, however, is that insights are only as good as the data that informs them. This means it’s vital for organizations to employ individuals who understand what clean data looks like and how to shape raw data into usable forms to gain valuable insights. This is where data wrangling comes into play. 

Data wrangling is the process of transforming raw data into a more usable format for analysis or machine learning (ML). It's also known as data munging, scrubbing, or remediation.

Data wrangling involves: cleaning and structuring data, handling missing or inconsistent data, formatting data types, and merging different datasets.

The goal of data wrangling is to improve data quality and make it more accurate and meaningful. This leads to better solutions, decisions, and outcomes.

- Examples of Data Wrangling

Data wrangling is important for ensuring data is processed and managed according to legal and ethical standards. It helps organizations manage their data governance policies and reduce the risk of compliance issues.

Data wrangling can be conducted manually or automatically. In businesses with a data team, data scientists and other team members usually lead the data wrangling process. In smaller organizations, non-data professionals may be responsible for cleaning data.

Some examples of data wrangling include:

• Merging multiple data sources into a single dataset
• Identifying gaps in data and filling or deleting them
• Deleting data that's unnecessary or irrelevant
• Identifying extreme outliers in data and either explaining the discrepancies or removing them

[More to come ...]