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Data Management - Clearpoint Systems Docs

Learn about Clearpoint Systems Technology's data handling capabilities including validation, normalization, synchronization, and storage for enterprise data requirements.

Clearpoint Systems Technology provides comprehensive data management capabilities for handling business data across integrated systems. This guide covers data validation, normalization, synchronization, and storage options.

Data Architecture

Clearpoint manages data through a layered architecture:

┌─────────────────────────────────────┐
│           Data Sources               │
│  ┌─────────┐  ┌─────────┐           │
│  │   ERP   │  │   CRM   │  ...      │
│  └─────────┘  └─────────┘           │
└─────────────────────────────────────┘
           ↓ Data Extraction
┌─────────────────────────────────────┐
│        Data Processing               │
│  ┌─────────┐  ┌─────────┐           │
│  │Validation│  │Normalization│           │
│  └─────────┘  └─────────┘           │
└─────────────────────────────────────┘
           ↓ Data Transformation
┌─────────────────────────────────────┐
│        Data Storage                  │
│  ┌─────────┐  ┌─────────┐           │
│  │Operational│  │Historical  │           │
│  │   Store   │  │   Archive   │           │
│  └─────────┘  └─────────┘           │
└─────────────────────────────────────┘

Data Validation

Validation Rules

Configure validation rules to ensure data quality:

validation_rules:
  customer_data:
    required_fields: ["name", "email", "address"]
    field_validations:
      email:
        format: "email"
        max_length: 255
      phone:
        format: "phone"
        country_code: "US"
      postal_code:
        format: "postal_code"
        country: "US"
    cross_field_validation:
      - name: "state_zip_match"
        description: "State must match postal code"
        fields: ["state", "postal_code"]
        
  financial_data:
    required_fields: ["amount", "currency", "date"]
    field_validations:
      amount:
        type: "decimal"
        min_value: 0
        max_value: 999999999.99
        precision: 2
      currency:
        allowed_values: ["USD", "EUR", "GBP", "CAD"]
      date:
        format: "iso_date"
        range: "past_10_years_to_future"

Custom Validation Functions

Create custom validation logic:

custom_validations:
  business_email:
    function: "validate_business_email"
    description: "Ensure email domain matches company domain"
    parameters:
      allowed_domains: ["company.com", "partner.com"]
      
  duplicate_check:
    function: "check_duplicate_record"
    description: "Prevent duplicate customer records"
    parameters:
      fields: ["email", "phone"]
      tolerance: "fuzzy"

Data Normalization

Field Mapping

Standardize data fields across systems:

field_normalization:
  address_standardization:
    input_fields: ["street", "city", "state", "zip", "country"]
    output_fields: ["address_line1", "address_line2", "city", "state", "postal_code", "country"]
    transformations:
      - name: "standardize_street"
        type: "regex"
        pattern: "^\\s+|\\s+$"
        replacement: ""
      - name: "validate_state"
        type: "lookup"
        source: "us_state_codes"
      - name: "format_postal_code"
        type: "format"
        format: "zip5_plus4"
        
  phone_normalization:
    input_fields: ["phone", "mobile", "fax"]
    output_fields: ["phone_e164", "phone_formatted"]
    transformations:
      - name: "extract_digits"
        type: "regex"
        pattern: "[^0-9]"
        replacement: ""
      - name: "format_e164"
        type: "format"
        country_code: "+1"

Data Type Conversion

Handle data type differences between systems:

type_conversions:
  date_time:
    source_formats: ["mm/dd/yyyy", "dd-mm-yyyy", "iso8601"]
    target_format: "iso8601"
    timezone_handling: "preserve_original"
    
  currency:
    source_currencies: ["USD", "EUR", "GBP"]
    target_currency: "USD"
    exchange_rate_source: "daily_fed"
    
  boolean:
    source_values: ["Y/N", "1/0", "Yes/No", "true/false"]
    target_values: ["true", "false"]
    case_sensitive: false

Data Synchronization

Sync Strategies

Choose the right synchronization strategy for your data:

StrategyDescriptionBest For
Real-timeImmediate data sync on changesCritical operational data
BatchScheduled sync at intervalsLarge data sets, non-critical
Event-drivenSync triggered by business eventsWorkflow-based processes
HybridCombination of strategiesComplex requirements

Real-time Synchronization

Configure real-time data sync:

real_time_sync:
  customer_updates:
    trigger: "database_change"
    source: "sap.customers"
    destinations: ["salesforce.accounts", "netsuite.customers"]
    latency_target: "<5s"
    conflict_resolution: "last_update_wins"
    
  order_status:
    trigger: "webhook"
    source: "ecommerce.orders"
    destinations: ["erp.orders", "crm.opportunities"]
    webhook_endpoint: "/webhooks/order-updated"
    authentication: "hmac_sha256"

Batch Synchronization

Configure batch processing for large data sets:

batch_sync:
  daily_reconciliation:
    schedule: "0 2 * * *"  # 2 AM daily
    source: "financial_system.transactions"
    destinations: ["accounting_system.ledger"]
    batch_size: 1000
    processing_window: "4h"
    error_handling: "continue_on_error"
    
  weekly_master_data:
    schedule: "0 3 * * 0"  # 3 AM Sunday
    source: "hr_system.employees"
    destinations: ["all_systems.employees"]
    full_refresh: true
    notification_on_completion: true

Data Storage

Operational Data Store

Store frequently accessed operational data:

operational_store:
  customer_data:
    retention: "7_years"
    indexing: ["customer_id", "email", "phone"]
    compression: "enabled"
    encryption: "at_rest"
    backup_frequency: "hourly"
    
  transaction_data:
    retention: "10_years"
    indexing: ["transaction_id", "date", "amount", "customer_id"]
    compression: "enabled"
    encryption: "at_rest"
    backup_frequency: "continuous"

Historical Archive

Archive historical data for compliance and analytics:

historical_archive:
  cold_storage:
    retention: "permanent"
    storage_class: "glacier"
    compression: "maximum"
    encryption: "at_rest"
    access_time: "<12h"
    
  compliance_archive:
    retention: "7_years"
    storage_class: "standard"
    compression: "enabled"
    encryption: "at_rest"
    immutable: true
    access_logging: "detailed"

Data Lineage

Lineage Tracking

Track data movement and transformations:

data_lineage:
  customer_journey:
    source: "web_form.submission"
    transformations:
      - step: "validation"
        system: "clearpoint"
        timestamp: "auto"
      - step: "normalization"
        system: "clearpoint"
        rules: ["phone_format", "address_standardize"]
      - step: "distribution"
        system: "clearpoint"
        destinations: ["crm", "erp"]
    audit_fields: ["user_id", "timestamp", "system", "action"]

Compliance Reporting

Generate compliance reports for data handling:

compliance_reports:
  gdpr_data_processing:
    schedule: "monthly"
    scope: "eu_customer_data"
    include:
      - "data_sources"
      - "processing_activities"
      - "data_retention"
      - "access_logs"
    format: "pdf"
    recipients: ["compliance@company.com"]
    
  sox_controls:
    schedule: "quarterly"
    scope: "financial_data"
    controls: ["access_control", "change_management", "data_integrity"]
    format: "excel"
    recipients: ["audit@company.com", "cfo@company.com"]

Data Quality Monitoring

Quality Metrics

Monitor data quality metrics:

quality_metrics:
  completeness:
    threshold: 95%
    fields: ["email", "phone", "address"]
    alert_below: 90%
    
  accuracy:
    threshold: 99%
    validation_rules: ["email_format", "phone_format"]
    alert_below: 95%
    
  consistency:
    threshold: 98%
    cross_system_comparison: true
    alert_below: 95%
    
  timeliness:
    threshold: "<5m"
    critical_data: ["orders", "payments"]
    alert_above: "10m"

Data Quality Alerts

Configure alerts for data quality issues:

quality_alerts:
  validation_failures:
    threshold: "10_per_hour"
    notification: ["email", "slack"]
    recipients: ["data_steward@company.com"]
    
  sync_delays:
    threshold: "15m"
    critical_systems: ["erp", "crm"]
    notification: ["email", "pager"]
    
  data_anomalies:
    detection: "statistical"
    sensitivity: "medium"
    notification: ["email"]

Best Practices

  1. Define data standards — Establish clear data quality standards
  2. Implement validation early — Validate data at point of entry
  3. Monitor continuously — Set up automated quality monitoring
  4. Document transformations — Maintain clear documentation of data changes
  5. Plan for retention — Define appropriate retention policies
  6. Test thoroughly - Validate data flows before production deployment

Troubleshooting

Data Sync Failures

Check sync status and error details:

GET /api/v1/sync/status?integration=sap_erp&last=24h

Validation Errors

Review validation failures and patterns:

GET /api/v1/validation/errors?date=2024-01-15&field=email

Performance Issues

Monitor data processing performance:

GET /api/v1/performance/metrics?component=data_processing&last=1h