Data Cleaning 

What is data cleaning? 

Data cleaning, also known as data cleansing or scrubbing, refers to the process of detecting and removing corrupt, inaccurate, incomplete, duplicated, or irrelevant parts of data sets to improve quality. It transforms raw data into a more accurate and consistent format for analysis and reporting.  

Why is data cleaning important? 

High quality data is critical for meaningful analysis. Data cleaning is important because: 

• It improves data accuracy and consistency by fixing errors and standardizing formats in the data. This provides a more reliable basis for analysis. 
• It increases the reliability of analytical models and business decisions by ensuring the input data is correct.  
• It avoids costs associated with bad data like operational inefficiencies, erroneous analytics results and misleading data visualizations. 
• It meets regulatory compliance needs regarding data quality for reporting accuracy. 

Key steps in data cleaning 

The main steps involved in data cleaning are: 

• Identifying incomplete, incorrect, or irrelevant data by profiling and auditing data. 
• Removing duplicate records to avoid double counting and inconsistencies. 
• Fixing formatting inconsistencies like date formats to ensure data integrity. 
• Filtering unwanted noise in data like invalid entries to improve data relevancy.  
• Resolving data integrity issues like broken links between databases to maintain consistency. 
• Standardizing data formats for consolidation to enable unified analytics. 

Data cleaning methods 

Some popular data cleaning techniques are: 

• Data profiling using statistics to assess overall quality and detect anomalies. 
• Parsing and standardization to ensure consistent formatting and values. 
• Merge/purge for deduplication by identifying duplicate entries based on rules. 
• Data validation using integrity checks and constraints to identify issues. 
• Outlier analysis using statistical techniques to detect anomalies and errors. 
• Data enrichment from external sources to fill in missing values. 

Data cleaning challenges 

Some key data cleaning challenges include: 

• Variety in data types, formats, and sources makes standardization difficult. 
• Scale of data requiring efficient and automated cleaning methods.  
• Complex workflows and dependencies require coordination. 
• Lack of standards and governance leads to inconsistencies.  
• Manual errors can be introduced in cleansing processes. 

Automating data cleaning 

Data cleaning can be automated using ETL tools, machine learning algorithms, and specialized software that can standardize, match, validate and transform data sets with minimal manual work. This improves efficiency and data quality. 

How LexisNexis supports data cleaning 

LexisNexis provides robust solutions to facilitate data cleaning 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 analyze, interpret, and derive meaningful insights from large datasets to inform their strategies and decision-making processes. 

Learn about Nexis^® Data+