Data Quality Management

The introduction and use of information systems for corporate decisions requires the provision and processing of high-quality and therefore correct information and data in the system. For companies the costs of poor-quality data and information amount to approximately 20% of their respective turnover and thus up to several billion euros, according to estimates in various studies.

Yet hardly any companies put a figure on the costs of poor data quality. But even without quantifying it, it is clear that the consequential costs make it necessary to introduce a system of quality management for data and information. The following dimensions of data quality are distinguished: 

• Representative: Interpretability, proper presentation and storage
• Access-based: Security, ownership and privacy
• Intrinsic: Existence, correctness, exactness, objectivity, credibility
• Contextual: Relevance, granularity, integrity and completeness

A quality management system for data and information should therefore include measures and processes which allow the safeguarding of the aforementioned quality characteristics. Here too, the Siron technology offers, for example, the following functionalities:

• Complex file synchronisation procedures,
• Hashing,
• Virtual files and compression procedures,
• Unicode support, and
• Functions for comparing similar word patterns and word groups based on fuzzy logic, e.g. for checking for duplicates.

Data management is a key function within enterprise-wide information management. The quality of the information management ultimately depends on the data management. Non-existent or inadequate data management can result in exploding costs, loss of confidence in the correctness, accuracy and completeness of data and information, non-compliance with laws and regulations, as well as inefficient and unsystematic data validation (data reconciliation).