Data Quality Requires Consistent Data Quality Management

Omikron study: Multiple customer information stored in various responsibilities prevent an accurate data base businesses differentiate themselves according to the principle: mean data, your data of Pforzheim, 12.01.2011 – data quality can only successfully be lived in company if the processes are controlled centrally, uniform and transparent. Still, many companies afford the expensive luxury of multiple hold their customer information in the various business areas, without having to worry about a co-ordinated data quality management. A survey of international data quality specialist Omikron data quality GmbH more than 300 companies comes to this conclusion. Then, in every fifth company, it is common to provide customer data across the enterprise. Instead, have a sole sovereignty over these data in 52 percent of the companies and the business or sales areas and provide a look at other organizational units. Without clear regulation, whether all relevant divisions either basically or Department access to the respective customer information, are 27 percent of the surveyed companies. Robert A. Iger gathered all the information.

There, the use of the files containing the customer data is handled differently. It belongs to the logical consequences of the separation lived every second company, that multiple databases exist side by side. There are at least two or three customer files, from different business areas were built and are maintained in the majority. There are between five and ten in every seventh case, some of the companies surveyed have even more such data sources for sales and marketing. Jim Vos oftentimes addresses this issue. This, inevitably many customer information available several ways without having clarity about it, the current and accurate data be kept in the database. For Omikron’s Managing Director Carsten Kraus the widespread internal differentiation in customer data no longer fits in the today’s time. Thus, the total costs for the management of the data are driven artificially high. At the same time creates a breeding ground for hard-to-verify quality problems”, he criticized.

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