Data and information are among most valuable assets of today's companies. In the case of insufficient quality at the data level, it is not possible to obtain the necessary, or rather the information we obtain will be incomplete and incorrect. Incorrect data can lead to bad decisions and damage the organisation's image.

Common Problems In The Area Of Data Quality

• precision of data - for example, verbally specified parameters of product instances
• completeness of data - for example, incomplete address information of the customer
• singularity of data - for example, duplication of the customer
• consistency of data - for example, various addresses of the customer in various systems

Data Quality Versus Information Quality

Projects focused on data quality solve problems at the data level itself, for example records in database tables or data processed in the course of corporate technical processes. A data-quality solution is usually implemented by integrating a standard software product (e.g. from Informatica, Trillium Software or other firms) or through custom development of such a solution. Parts of projects focused on data quality are often new elements in the overall system architecture and their integration with existing information systems.

Though the solution of problems with information quality must include a data-quality solution, it rather focuses on processes. A large part of such a project involves meetings and conversations with the customer's employees, analysis of the problem and design of the solution. The result of the project mainly consists in documentation with a proposal of changes in company processes and design of a supporting technical solution that will enable such changes.

How To Get Started With Profinit

We recommend beginning with an audit of data quality in your company, on the basis of which the following process and solution design will be determined. Profinit specialises in both data and information quality. We offer standard software products and custom development of solutions.