- Incomplete directories, hindering fast, well-informed operational and managerial decision-making
- Poor data quality, including duplicates, incorrect entries and non-unique records
- Ineffective or missing search tools, making it harder to find the right information
- Inconsistencies in master data across systems, resulting in informational discrepancies
- Difficulty or inability to consolidate information from multiple systems
- Regulatory risks, such as fines for incorrect classification under national Commodity Nomenclature codes
- Product duplication due to incorrect labeling
- Excessive labor costs associated with manually correcting data issues
- Stock overages when system data does not reflect actual inventory levels
To address these challenges, data normalization is crucial. It involves streamlining diverse business data into well-organized, stable structures. This approach reduces data redundancy and makes it easier to quickly search and access relevant information.