How can Intuilize help improve our data quality for better business intelligence?

Discover how Intuilize's data quality assessment and governance framework creates a foundation for reliable analytics and decision-making

Poor data quality costs distributors millions through incorrect pricing, inventory mismanagement, and missed opportunities. 

Intuilize's data quality and governance services help you transform unreliable data into a trusted foundation for analytics and decision-making.

The Data Quality Challenge for Distributors

Distributors face unique data challenges due to:

• Large product catalogs with frequent changes
• Complex customer pricing arrangements
• Multiple data sources including ERP, WMS, and CRM systems
• Historical data inconsistencies from mergers and system changes
• Decentralized data entry across multiple locations
• Varying levels of data standards enforcement

These challenges often result in:

• Inconsistent product descriptions and classifications
• Inaccurate customer segmentation
• Unreliable cost and margin calculations
• Incomplete transaction history
• Duplicate or conflicting records

Our Data Quality Improvement Process

1. Comprehensive Data Assessment
   
   • Evaluation of data completeness, accuracy, consistency, and timeliness
   • Identification of critical data elements impacting business performance
   • Gap analysis comparing current state to industry best practices
   • Quantification of business impact from data quality issues
2. Data Cleansing and Enhancement
   
   • Standardization of product descriptions and attributes
   • Customer record deduplication and enrichment
   • Normalized pricing and discount structures
   • Historical transaction cleansing and validation
   • Taxonomy and hierarchy development for products and customers
3. Governance Framework Development
   
   • Data ownership and stewardship definition
   • Data quality standards and policies
   • Data entry procedures and validation rules
   • Ongoing monitoring and measurement processes
   • Continuous improvement mechanisms
4. Implementation Support
   
   • Process redesign to support data quality
   • User training on data governance procedures
   • System configuration for data validation
   • Monitoring tools implementation
   • Change management support

Specific Data Areas We Address

• Product Data
  
  • Standardized naming conventions
  • Complete and consistent attributes
  • Proper categorization and hierarchies
  • Accurate cross-references and alternates
• Customer Data
  
  • Segmentation alignment
  • Consolidated hierarchies and families

• Transactional Data
  
  • Consistent coding and categorization
  • Accurate cost and margin calculations
  • Proper allocation of discounts and rebates
  • Linkage between related transactions
• Vendor Data
  
  • Accurate lead times and order parameters
  • Consistent item cost structure
  • Relationship mapping

Business Benefits

• 15-25% improvement in pricing accuracy
• 20-30% reduction in inventory discrepancies
• More reliable business intelligence and reporting
• Enhanced ability to leverage advanced analytics
• Improved employee trust in systems and data
• Foundation for successful digital initiatives