2505 Data Quality And Validation
Data Quality and Validation
Introduction to Data Quality
Data quality is the foundation of any successful application. Poor quality data leads to incorrect reports, bad decisions, frustrated users, and failed business processes. High-quality data is accurate, complete, consistent, and trustworthy.
Preventing data quality issues is far easier and cheaper than fixing them later. In this article, you'll learn strategies to ensure your data remains clean, accurate, and reliable from the start.
Why Data Quality Matters
Poor data quality has serious consequences:
Business Impact
- Incorrect Reports – Decisions based on bad data lead to poor outcomes
- Lost Revenue – Duplicate customers, missed opportunities, pricing errors
- Customer Dissatisfaction – Wrong contact information, incorrect orders
- Compliance Issues – Regulatory violations due to inaccurate records
- Wasted Time – Staff spending hours cleaning data instead of productive work
Technical Impact
- Performance Issues – Duplicate records slow searches and queries
- Integration Failures – External systems reject invalid data
- Broken Relationships – Connection fields fail with inconsistent data
- System Errors – Invalid data causes crashes and failures
Real-World Example
A sales team has three duplicate customer records for "John Smith" with different emails and phone numbers. Marketing sends three promotional emails to the same person. The customer complains about spam. Sales doesn't know which record is correct. The CRM shows three separate purchase histories instead of one. Customer lifetime value calculations are wrong.
All of this could have been prevented with duplicate detection.
Dimensions of Data Quality
Data quality has multiple dimensions to consider.
Accuracy
Data correctly represents reality:
- Email addresses are valid and deliverable
- Phone numbers have correct format
- Addresses exist and are properly formatted
- Numbers are within expected ranges
- Dates are valid and logical
Completeness
All required data is present:
- No missing required fields
- Records have all necessary information
- Related records properly linked
- No partial or incomplete entries
Consistency
Data is uniform across system:
- Same format throughout (capitalization, spacing)
- Consistent naming conventions
- Standardized abbreviations
- Matching data in related tables
Uniqueness
No duplicate records:
- Each entity represented once
- Unique identifiers properly enforced
- No redundant information
Timeliness
Data is up-to-date:
- Current contact information
- Recent status updates
- Valid as of today
- Regular refreshes from sources
Validity
Data conforms to rules:
- Follows defined formats
- Meets business rules
- Within allowed value ranges
- Matches expected data types
Data Validation Strategies
Implement validation at multiple levels to prevent bad data entry.
Field-Level Validation
Built into field settings:
Required Fields
Mark critical fields as required:
- Users cannot save record without filling them
- Prevents incomplete records
- Use sparingly—too many required fields frustrate users
Field Types
Choose appropriate field types:
- Email – Validates email format automatically
- Phone – Validates phone number format
- Number – Accepts only numeric values
- Date – Ensures valid dates
- Dropdown – Limits to predefined options
Min/Max Values
Set acceptable ranges for numbers:
- Price must be greater than 0
- Age must be between 0 and 120
- Quantity must be positive
- Prevents obviously wrong values
Max Length
Limit text field length:
- Phone: 20 characters
- Zip code: 10 characters
- Prevents excessive data entry
- Ensures consistent formatting
Unique Constraints
Mark fields that must be unique:
- Email addresses (for user accounts)
- Product SKUs
- Order numbers
- System prevents duplicate values
Form-Level Validation
Validate across multiple fields using form rules:
Conditional Required Fields
Make fields required based on other values:
- If Customer Type = "Business", Company Name is required
- If Shipping Method = "Express", Express Options is required
- If Age < 18, Parent Guardian is required
Cross-Field Validation
Validate relationships between fields:
- End Date must be after Start Date
- Discount cannot exceed Price
- Quantity Shipped cannot exceed Quantity Ordered
Complex Business Rules
Implement business logic validation:
- Total must equal sum of line items
- At least one product must be selected
- Credit limit not exceeded
Record Rule Validation
Use record rules for validation before save:
Trigger: Before Record Saved
Validate and prevent save if invalid:
- Check Conditions – Evaluate data quality rules
- If Invalid – Show error message and prevent save
- If Valid – Allow save to proceed
Example: Credit Limit Validation
On Orders table:
- Trigger: Before record saved
- Condition: Order Total > Customer Credit Limit
- Action: Show error "Order exceeds customer credit limit"
- Prevent Save: Yes
Example: Duplicate Detection
On Customers table:
- Trigger: Before record saved
- Condition: Record with same email already exists
- Action: Show error "Customer with this email already exists"
- Prevent Save: Yes
Import Validation
Validate data during import process:
- Tadabase automatically validates against field types
- Check import preview for errors before importing
- Fix errors in source file
- Re-import corrected data
- Download error report for rows that failed
Duplicate Prevention
Duplicates are one of the most common data quality issues.
Causes of Duplicates
Duplicates occur when:
- Multiple users create same record simultaneously
- User doesn't search before creating new record
- Import contains duplicate entries
- Data entry variations (John Smith vs. J. Smith)
- Case sensitivity issues (john@email.com vs. John@Email.com)
- Whitespace differences ("Company" vs. "Company ")
Prevention Strategy 1: Unique Fields
Mark identifying fields as unique:
Setting Unique Constraint
- On field settings, enable "Unique" option
- Field will not allow duplicate values
- System shows error if duplicate attempted
- Works for email, SKU, order number, etc.
Best Fields for Unique Constraint
- Email – For user accounts and contacts
- SKU/Product Code – For inventory items
- Order Number – For transactions
- Employee ID – For personnel records
- License Number – For certifications
Prevention Strategy 2: Search Before Create
Encourage users to search before creating:
Implement Search-First Workflow
- Design forms that prompt search first
- Show potential matches based on partial input
- Require user to confirm "not found" before creating
- Provide clear instructions to check existing records
Example: Customer Creation Form
- When user types email in form
- Show message: "Checking for existing customer..."
- If match found: "Customer exists! Would you like to view their record?"
- If no match: Allow creation to continue
Prevention Strategy 3: Duplicate Detection Rules
Use record rules to detect potential duplicates:
Simple Email Match
On Customers table:
- Trigger: Before record saved
- Condition: Check if any existing record has same email
- Action: Show warning "A customer with this email already exists"
- Prevent save or allow override (depending on requirements)
Fuzzy Matching
Detect similar (not identical) duplicates:
- Check for name similarity
- Same phone with different format
- Similar company names
- Warning: "Possible duplicate found: John Smith (john.smith@email.com)"
Prevention Strategy 4: Data Normalization
Standardize data on entry:
Case Normalization
Use record rules to standardize case:
- Convert emails to lowercase
- Proper case for names (capitalize first letter)
- Uppercase for codes and SKUs
- Prevents "john@email.com" and "John@email.com" being treated as different
Whitespace Removal
Trim leading/trailing spaces:
- Use TRIM() function in record rules
- Prevents " Company" and "Company" being different
- Apply on save to all text fields
Format Standardization
Standardize formats:
- Phone: Convert (555) 123-4567 to 5551234567
- Remove special characters for comparison
- Store in consistent format
Duplicate Detection Reports
Find existing duplicates:
Create Duplicate Detection Page
- Build report showing potential duplicates
- Group by email or phone
- Show records with matching values
- Allow merge or delete actions
Scheduled Duplicate Check
Run automated duplicate detection:
- Create scheduled task to run weekly
- Query for duplicate emails/phones
- Generate report of findings
- Email to data steward for review
Data Cleaning Techniques
Fix existing data quality issues.
Identifying Data Issues
First, find problems:
Missing Data
Find records with blank required fields:
- Filter: Email is blank
- Filter: Phone is blank
- Filter: Status is blank
- Generate list for cleanup
Invalid Formats
Identify format issues:
- Emails without @ symbol
- Phone numbers with letters
- Dates in wrong format
- Numbers with text
Inconsistent Data
Find inconsistencies:
- Mixed capitalization
- Varying abbreviations
- Different date formats
- Inconsistent naming
Cleaning Strategy 1: Manual Correction
For small numbers of records:
- Export records with issues
- Correct in Excel/Sheets
- Import with update mode
- Verify corrections
Cleaning Strategy 2: Batch Updates
For systematic issues:
- Identify pattern (e.g., all states abbreviated)
- Create update batch
- Apply transformation
- Verify results
Example: Standardize State Names
Convert abbreviations to full names:
- Export customers with State field
- Use Excel VLOOKUP or find/replace
- CA → California
- NY → New York
- Import update
Cleaning Strategy 3: Automated Rules
Use record rules to clean on save:
Auto-Capitalize Names
Record rule:
- Trigger: Before save
- Action: Update First Name = PROPER({first_name})
- Action: Update Last Name = PROPER({last_name})
- Automatically fixes capitalization
Auto-Format Phone
Record rule:
- Trigger: Before save
- Action: Remove all non-numeric from phone
- Action: Format as (XXX) XXX-XXXX
- Standardizes all phone numbers
Auto-Lowercase Email
Record rule:
- Trigger: Before save
- Action: Update Email = LOWER({email})
- All emails stored in lowercase
Cleaning Strategy 4: Scheduled Maintenance
Run regular cleaning tasks:
Weekly Data Cleanup
Scheduled task:
- Find records with blank emails
- Mark as "Incomplete"
- Notify data steward
- Generate cleanup report
Monthly Standardization
Scheduled task:
- Find non-standard formats
- Apply standardization rules
- Log changes made
- Report on improvements
Validation Rules Library
Common validation patterns to implement.
Email Validation
Email field type automatically validates basic format. For additional validation:
Check for @ symbol:
- Condition: Email does not contain "@"
- Error: "Please enter a valid email address"
Check for domain:
- Condition: Email does not contain "."
- Error: "Email must include domain (e.g., @company.com)"
Prevent temporary emails:
- Condition: Email contains "tempmail" or "throwaway"
- Error: "Temporary email addresses not allowed"
Phone Validation
Minimum length:
- Condition: Length of Phone < 10
- Error: "Phone number must be at least 10 digits"
Numeric only:
- Condition: Phone contains letters
- Error: "Phone number must contain only numbers"
Date Range Validation
Future dates:
- Condition: Start Date > Today
- Use for: Scheduled events, future-dated transactions
Past dates:
- Condition: Birth Date > Today
- Error: "Birth date cannot be in the future"
Date sequence:
- Condition: End Date < Start Date
- Error: "End date must be after start date"
Numeric Range Validation
Positive numbers:
- Condition: Price < 0
- Error: "Price must be positive"
Percentage range:
- Condition: Discount < 0 OR Discount > 100
- Error: "Discount must be between 0 and 100"
Reasonable age:
- Condition: Age < 0 OR Age > 120
- Error: "Please enter a valid age"
Text Pattern Validation
Zip code:
- Condition: Length not equal to 5 or 10
- Error: "Zip code must be 5 or 10 digits"
SKU format:
- Condition: Does not match pattern "ABC-1234"
- Error: "SKU must follow format: ABC-1234"
Data Quality Monitoring
Track and report on data quality metrics.
Quality Metrics to Track
Completeness Rate:
- Percentage of records with all required fields filled
- Target: 95%+ completeness
Accuracy Rate:
- Percentage of records with valid data
- Based on validation rules
- Target: 99%+ accuracy
Duplicate Rate:
- Percentage of duplicate records
- Target: <1% duplicates
Timeliness:
- Percentage of records updated in last 90 days
- Depends on use case
Creating Quality Dashboard
Build dashboard page showing:
- Completeness Chart – Records with/without required fields
- Validation Errors – Count of records failing validation
- Duplicate Report – Number of potential duplicates
- Data Age – Records by last modified date
- Quality Score – Overall data quality percentage
Scheduled Quality Reports
Automate weekly reports:
- Scheduled task runs Sunday night
- Calculate quality metrics
- Generate quality report
- Email to data steward
- Highlight areas needing attention
Best Practices
Follow these principles for maintaining data quality.
Prevention Over Correction
Preventing bad data is easier than fixing it:
- Implement validation at data entry
- Design forms that guide users
- Use dropdowns instead of free text when possible
- Provide clear instructions and examples
- Make it hard to enter bad data
User Education
Train users on:
- Why data quality matters
- How to search before creating
- Proper data entry formats
- Consequences of poor data
- Their role in maintaining quality
Regular Maintenance
Schedule recurring cleanup:
- Weekly duplicate detection
- Monthly data standardization
- Quarterly comprehensive review
- Don't let issues accumulate
Data Stewardship
Assign responsibility:
- Designate data steward for each table
- Give them authority to enforce standards
- Provide tools and dashboards
- Hold them accountable for quality
Continuous Improvement
Regularly review and improve:
- Analyze quality metrics trends
- Identify recurring issues
- Update validation rules
- Refine cleanup processes
- Learn from mistakes
Practical Exercise
Implement comprehensive data quality system.
Exercise: Customer Data Quality System
Scenario: Implement a complete data quality system for a customer database.
Step 1: Add Validation Rules
- On Customers table, mark Email as required and unique
- Set Phone minimum length to 10
- Create record rule: Before save, convert email to lowercase
- Create record rule: Before save, TRIM all text fields
- Test by entering customer with various formats
Step 2: Implement Duplicate Detection
- Create record rule on Customers: - Trigger: Before save - Condition: Any record exists with same email - Action: Show error "Customer with this email already exists" - Prevent Save: Yes
- Test by trying to create duplicate customer
Step 3: Create Data Quality Dashboard
- Create new page "Data Quality Dashboard"
- Add Summary component showing: - Total Customers - Customers with complete data (all fields filled) - Customers missing phone - Customers missing email
- Add chart showing completeness over time
- Add table showing incomplete records
Step 4: Build Duplicate Detection Report
- Create page "Potential Duplicates"
- Add table showing customers grouped by email
- Filter to show only emails with multiple records
- Add action buttons to merge or delete
Step 5: Implement Auto-Cleanup
- Create scheduled task "Weekly Data Cleanup"
- Schedule for Sunday 2 AM
- Action: Find customers missing email
- Action: Mark as "Incomplete"
- Action: Generate report
- Action: Email report to data steward
- Test with manual execution
Step 6: Test Entire System
- Test all components: - Try creating duplicate customer (should fail) - Enter customer with missing data (should warn) - Enter customer with wrong format (should correct) - View quality dashboard - Check duplicate report
- Verify all validation working
- Document for users
Next Steps
You now understand data quality and validation in Tadabase. You can implement validation rules, prevent duplicates, clean existing data, and monitor data quality metrics.
In the next article, you'll learn about backups and restore—protecting your data from loss and implementing recovery procedures.
Next: Continue to Backups and Restore to learn data protection strategies.
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