Data Cleansing Explained: Steps, Strategies, and Best Practices

Some recent studies show that data analysts spend around 70–90% of their time just cleaning up data. And that shows us how important data cleansing is, but still there are many companies that deal with low-quality data and end up costing them millions in bad decisions. 

In this article, you will learn everything you need to know about data cleansing, with basic concepts and some advanced strategies that you can use. You will learn some practical steps to clean your data and help you build systems that keep your data clean over time.

What is Data Cleansing?

Data cleansing is the process of finding and fixing errors, inconsistencies, and gaps in your datasets. Think of it as spring cleaning for your database – you’re getting rid of the junk, organizing what’s useful, and making sure everything is in its proper place.

The process involves several key activities:

Activity	Purpose	Example
Error Detection	Find mistakes in your data	Spotting “Califronia” instead of “California”
Duplicate Removal	Remove repeated records	Eliminating multiple entries for the same customer
Data Standardization	Make formats consistent	Converting all dates to MM/DD/YYYY format

Data cleansing differs from data transformation in important ways. While data transformation changes the structure or format of data for specific uses, data cleansing focuses on improving the actual quality and accuracy of the information itself.

• What is data quality?

Data quality is how useful your data is for what you need. Good data is accurate, complete, consistent, and up to date. Bad data, on the other hand, can cause wrong business decisions, failed marketing campaigns, and even compliance problems.

If you want to learn more about data quality, read the fullest details in our article: What Is Data Quality

Why Data Cleansing Is Very Important

Bad data costs businesses far more than just time and frustration. The financial impact can be staggering:

There are many organizations which lose some significant revenue just because of the fact of poor data quality. When you have customer records which are incomplete or incorrect, you might send marketing materials to the wrong addresses, miss sales opportunities, or make strategic decisions based on flawed information.

Here’s what happens when you negligent data cleansing:

1. Your Customers Get Frustrated: 

Nothing annoys customers more than getting the wrong order delivered to the wrong place. Or when you call them by the wrong name because your records are messed up. These small mistakes add up to big trust issues.

2. You Could Face Legal Problems: 

If you’re in healthcare or banking, wrong data isn’t just embarrassing – it can get you in serious trouble with regulators. We’re talking about fines that can put smaller companies out of business.

3. Your Leaders Make Bad Choices: 

Companies have lost entire market opportunities, launched failed products, and made costly acquisitions based on incorrect information. Bad data turns strategic planning into expensive guesswork.

Common Data Quality Issues

1. Missing Data Problems

Missing data is probably the most obvious quality issue. It shows up as blank cells, null values, or incomplete records. Sometimes fields are accidentally left empty during data entry. Other times, systems fail to capture certain information.

The challenge with missing data isn’t just that it’s absent – it’s deciding what to do about it. Should you fill in estimated values? Delete incomplete records? Or leave the gaps and work around them?

2. Duplicate Records

Duplicates happen more often than you’d think. They occur when:

• The same information gets entered multiple times
• Different systems create separate records for the same entity
• Data gets imported multiple times
• Slight variations in data entry create near-duplicates

A customer might appear as “John Smith,” “J. Smith,” and “Johnny Smith” in the same database, creating three separate records for one person.

3. Format Inconsistencies

Inconsistent data formats create significant operational challenges. Contact information may be stored with varying formats – some with parentheses and dashes, others as continuous strings. Date fields frequently appear in multiple formats such as MM/DD/YYYY, Month DD, YYYY, or DD-MM-YY variations.

4. Outdated Information

Data gets stale quickly. People change jobs, move to new addresses, and update their contact preferences. Companies merge, rebrand, or go out of business. What was accurate last year might be completely wrong today.

Data enrichment becomes crucial here – the process of enhancing your existing data with fresh, accurate information from reliable sources.

The Data Cleansing Process: A Step-by-Step Guide

Effective data cleansing follows a structured approach. While every dataset is unique, this process provides a solid foundation for most cleansing projects.

Step 1: Data Assessment and Profiling

Before you can clean your data, you need to understand what you’re working with. This involves:

Assessment Type	What to Look For	Tools to Use
Volume Analysis	How much data do you have?	Database queries, file size checks
Structure Review	What fields and formats exist?	Data profiling software, sample queries
Quality Audit	Where are the main problems?	Data quality metrics, automated scans

Start by taking a representative sample of your data and examining it closely. Look for patterns in errors, common missing fields, and areas where inconsistencies cluster.

Step 2: Data Standardization

Standardization means establishing consistent formats across your dataset. This includes:

Format Standardization: Choose standard formats for dates, phone numbers, addresses, and other common fields. Stick to these formats consistently.

Naming Conventions: Decide how to handle company names, personal names, and product names. Should it be “Microsoft Corporation” or just “Microsoft”?

Code Standardization: If you use codes or abbreviations, make sure they’re applied consistently throughout your data.

Step 3: Duplicate Detection and Removal

Finding duplicates requires both automated tools and human judgment. Exact duplicates are easy to spot, but fuzzy duplicates need more sophisticated approaches.

Modern data automation tools can identify potential duplicates by comparing multiple fields and calculating similarity scores. However, you’ll still need human review for borderline cases.

Step 4: Data Validation and Verification

Validation checks ensure your data meets specific criteria. This might include:

• Verifying email addresses have proper format
• Checking that dates fall within reasonable ranges
• Confirming postal codes match their cities
• Ensuring numerical values fall within expected ranges

Delivery point validation is particularly important for businesses that rely on direct mail marketing. This process confirms that addresses actually exist and can receive mail.

Step 5: Missing Data Handling

Decide how to handle missing information based on your specific needs:

Approach	Description	When to Use
Deletion	Remove records with too much missing data	When records are largely incomplete
Imputation	Fill in missing values with estimated data	When patterns allow reliable estimation
Flagging	Mark is missing data, but leaves it in place	When transparency is required

The right approach depends on how you plan to use the data and how much missing information you’re dealing with.

Data Cleansing Best Practices

Following proven data cleansing best practices will save you time and improve your results:

1. Document Everything

Keep detailed records of what you change and why. This documentation helps with:

• Understanding what was done if issues arise later
• Training new team members
• Auditing your cleansing process
• Replicating successful approaches on other datasets

2. Start Small and Scale Up

Begin with a small subset of your data to test your cleansing approach. Work out the kinks on a manageable sample before applying the process to your entire database.

3. Validate Your Results

After cleansing, test your data quality improvements:

Metric	Before Cleansing	After Cleansing	Improvement
Duplicate Records	15%	2%	87% reduction
Missing Critical Fields	25%	5%	80% reduction
Format Inconsistencies	40%	8%	80% reduction

4. Build Quality Checks Into Your Workflow

The best data cleansing strategy includes ongoing quality monitoring. Set up automated checks that flag potential problems as new data comes in.

Cloud-Based vs. On-Premises Solutions

Cloud data migration has made cloud-based cleansing tools increasingly popular. They offer scalability and reduced infrastructure costs but may raise concerns about data security and compliance.

On-premises solutions provide more control over sensitive data but require significant IT resources to maintain and scale.

Industry-Specific Data Cleansing

Different industries face unique data cleansing challenges that require specialized approaches.

1. Healthcare Data Management

Healthcare data management involves particularly complex cleansing challenges. Patient records must be accurate for safety reasons and complete for proper care coordination.

Key healthcare cleansing challenges include:

Patient Matching: Ensuring the same patient doesn’t have multiple records in the system. This requires sophisticated matching algorithms that can handle name variations, typos, and demographic changes.

Coding Accuracy: Medical codes must be precise for billing and treatment purposes. A coding accuracy support system helps ensure diagnoses and procedures are coded correctly.

Compliance Requirements: Healthcare data must meet strict privacy and accuracy standards. Data security in healthcare regulations requires careful handling of cleansed data.

The benefits of data analytics in healthcare become much more significant when the underlying data is clean and reliable. Clean data enables better patient outcomes, more accurate research, and improved operational efficiency.

2. Marketing and Customer Data

Direct mail marketing requires extremely clean address and demographic data. Even small errors can result in wasted marketing spend and poor campaign performance.

Customer data cleansing focuses on:

• Accurate contact information
• Consistent customer preferences
• Unified customer views across channels
• Proper segmentation data

Direct mail marketing strategy success depends heavily on data quality. Clean data ensures marketing messages reach the right people at the right addresses.

3. Financial Services

Financial institutions face strict requirements for data accuracy and data interoperability in healthcare and other sectors they serve. Insurance data migration projects often reveal significant data quality issues that must be addressed before systems can be successfully migrated.

Advanced Data Cleansing Techniques

As your data cleansing maturity grows, you can employ more sophisticated techniques to handle complex scenarios.

1. Machine Learning in Data Cleansing

AI and machine learning are transforming how organizations approach data cleansing. AI-based solutions predict and model data behaviors, automatically identifying and rectifying complex patterns of anomalies, which enhances data accuracy and efficiency.

Machine learning excels at:

• Detecting subtle patterns in data quality issues
• Predicting likely values for missing data
• Identifying fuzzy duplicates that rule-based systems miss
• Learning from user corrections to improve over time
  

2. Statistical Methods for Data Quality

Advanced statistical techniques can help identify outliers, validate data distributions, and assess the overall quality of cleansed data.

These methods are particularly useful for large datasets where manual review isn’t practical.

3. Real-Time Data Cleansing

Modern businesses increasingly need clean data in real-time. This requires building cleansing rules and validation checks directly into data collection and processing systems.

Real-time cleansing prevents poor quality data from entering your systems in the first place, reducing the need for extensive cleanup projects later.

Data Migration and Cleansing

Data migration projects provide excellent opportunities to clean up data quality issues, but they also present unique challenges.

• Migration Planning Considerations

Data migration strategy should always include cleansing as a key component. It’s much easier to clean data during migration than to deal with quality issues in the new system.

Data migration best practices include:

• Profiling source data early to understand quality issues
• Building cleansing rules into the migration process
• Testing cleansed data thoroughly before final migration
• Planning for data migration risks related to quality problems

• Common Migration Challenges

Data migration phases often reveal quality issues that weren’t apparent in the source system. Cloud data migration challenges can be particularly complex when dealing with large volumes of poor-quality data.

• Healthcare Migration Considerations

Healthcare data migration requires special attention to patient safety and regulatory compliance. Data migration audit processes must verify that critical patient information remains accurate throughout the migration.

The Best AI Data Operations Software Purpose-Built To Automate And Scale Your Data Processing

If you want to clean up your messy data and make it work better for your business, with tools that fit your budget and needs, BettrData has exactly what you need.

Every data process gets set up just for you – we take in your files, fix the problems, add missing pieces, and deliver clean data that’s ready to use. Our smart platform and focus on quality mean your data stays clean and helps you make better choices for years.

Try BettrData today and turn your data problems into business wins!

For More:

1. Data Security in Healthcare: Complete Protection Guide
2. Data Automation Without Manual Intervention | BettrData
3. Decoding Compliance Data: A Straightforward Guide for Everyone