Data Wrangling

What is data wrangling?  

Data wrangling refers to the process of cleaning, structuring, and enriching raw data into a desired format for better usability and analysis. It transforms messy, unstructured data into high quality, accurate data sets ready for applications like analytics, machine learning, and visualization. 

Why is data wrangling important? 

Data wrangling is a critical step in data science projects. It enables: 

• Higher quality data input for analytical models by removing errors, inconsistencies, and duplicate data. 
• More accurate data visualizations and reporting by ensuring complete, consistent data.  
• Better training data for machine learning algorithms through data normalization and formatting.  
• Reduced time spent on data preparation through automation. 

Major steps in data wrangling 

The main steps in data wrangling include: 

• Structuring unstructured data like text or images into organized, machine-readable formats.  
• Cleaning data by fixing missing values, errors, outliers, and duplicates. 
• Validating and filtering unwanted, irrelevant data based on rules. 
• Enriching data by merging with other complementary data sources. 
• Transforming data into appropriate formats like tables for analysis. 
• Aggregating data for summarization, reporting, and analytics. 

Data wrangling process 

The data wrangling process typically involves: 

• Data collection and delivery from diverse sources and formats like databases, files, web APIs. 
• Exploring and profiling data to assess quality issues and define requirements. 
• Defining data wrangling workflow activities based on data assessment.  
• Selecting appropriate wrangling tools and techniques for each activity. 
• Validating and refining wrangled data sets to catch issues. 
• Making cleaned, normalized data available for downstream analytics use. 

Challenges in data wrangling 

Some key data wrangling challenges include: 

• Dealing with variety of complex unstructured data types like text, audio, video. 
• Managing large volumes of data using scalable methods. 
• Handling messy, inconsistent real-world data from multiple sources.  
• Lack of standards and governance over different data sources. 
• Iterative nature of data wrangling work requires frequent adjustments. 

Automating data wrangling  

Many data wrangling and data aggregation tasks can be automated using ETL tools, scripting languages like Python and R, machine learning, and specialized wrangling software tools to improve efficiency and data quality. 

How LexisNexis supports data wrangling 

LexisNexis provides robust solutions to facilitate data lakes through an unrivaled API with credible data, delivered exactly how you need it. With Nexis® Data+ Solutions, users gain access to an extensive repository of over 36,000 licensed sources and 45,000 total resources in more than 37 languages. This wealth of data ensures that organizations can integrate, analyze, interpret, and derive meaningful insights from large data sets to inform their strategies and decision-making processes. 

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