Data Cleansing

What is Data Cleansing?

Data cleansing, also known as data cleaning or data scrubbing, is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant data within your dataset. It aims to improve the quality of the data so it can be used more effectively for various purposes.

Why does data cleansing matter?

Put simply, data cleansing means better quality data, and that yields a lot of impactful benefits for any business. Here are some of the top ones:

• Better data for better analysis: Clean data yields more accurate and reliable results, allowing you to make better business decisions based on analytics.
• Machine learning models: Training AI and machine-learning models with clean data leads to better predictions and performance.
• Customer relationship management: Accurate customer data ensures targeted marketing and personalized customer experiences, allowing for better results from your CRM system.
• Fraud prevention: Identifying and removing invalid or suspicious data using proper quality control, helps combat fraudulent activities.

Learn more: What is data cleansing, and why is it so important?

What does data cleansing involve?

Here are the elements that typically comprise your standard data cleansing process:

• Finding errors: Finding inconsistencies, typos, missing values, outliers, and other issues in the data.
• Data validation: This means to check data against predefined rules or external reference sources to ensure it's accurate and consistent.
• Correction and filling: Fixing errors, imputing missing values based on valid data points, or removing completely erroneous records.
• Standardization: Formatting data consistently according to predefined rules or industry standards - for example, address formatting requirements based on geographical standards.
• Deduplication: Eliminating duplicate records to avoid skewed results and wasted storage space. Learn more: What is data deduplication?
  
  

What are the types of data cleansing?

Data cleansing comprises a wide-ranging set of techniques to ensure your data is of the accuracy required. These may include (but are not limited to):

• Data profiling: Analyzing the data to understand its characteristics and identify potential issues.
• Parsing: Parsing means breaking down data into smaller components for easier analysis and manipulation.
• Pattern matching: Identifying and correcting data based on predefined patterns or rules.
• Fuzzy matching: Identifying potential duplicates or similar records even with minor variations. Learn more: What is fuzzy matching?
• Clustering: Grouping similar data points to identify outliers or anomalies.
  
  

Overall, data cleansing is a crucial step in any data-driven process. By ensuring your data is clean and accurate, you can unlock its full potential and extract valuable insights for better decision-making and improved outcomes. There's an easy way to do so - use Loqate's Data Cleanse!

Our easy-to-install solution takes care of both data cleansing and maintenance, at the push of a button. Get started today by booking a demo with our friendly experts, or find out more on our Data Maintenance page.