Data Warehousing Text &Bull; Chapters Chapter

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Since poor data quality within a system often results in poor business decisions being made from this data, it is very important that each administrator or system architect look at each customer or end-user differently, in their own unique light. Since each end-user is different, and the needs of the customer often stem from the warehouse's ability to accurately store quality information, a system that dates back to the 1970's will likely have quite a few issues relative to functionality and compatibility with modern data warehousing and information delivery systems. A data warehouse will become obsolete if customers and end-users cannot trust that the data being stored is not "dirty." It is therefore important to start with the assumption that the data stored within a warehouse that leads back to legacy systems from the 1970's is corrupt.

It is also necessary, in these early stages, to ascertain the level of data corruption and allow enough time for engineers, administrators, and employees to being to both understand the scope of the potential corruption and successfully deal with it. The plan for correction the polluted data must be comprehensive enough to take the end-user's requirements as well as the entire system's capabilities and limitations into account. This plan should also take a few qualities of an effective warehouse into account, assigning value to each of these qualities: accuracy, domain integrity, data type, consistency, redundancy, completeness, duplication, conformance to business rules, structural definiteness, data anomaly, clarity, timeliness, usefulness, and adherence to data integrity rules.

Since there are many sources of data pollution, it is important to identify the sources and rectify the problems presented by these sources of pollution. Most commonly, the sources are related to system upgrades and compatibility, data aging, and heterogeneous system integration, to name a few. Once these sources are identified, it is just as important to put together a set of data controls or quality controls to keep the data from being polluted in the future.

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This can be a function of deciding which data to cleanse and where and how to cleanse it. These factors will help decide the future structure of data and the extent to which quality controls are implemented within that system. The warehouse administrator, whether responsible for the pollution or not, is often blamed for the pollution or the system's inability to properly function, and it is important that the administrator understands his or her role as intermediary between the customer (end-user) and the data storage and delivery systems themselves.

This means that the administrator needs to establish early on the importance of clean, quality data, and stick to this policy or effort throughout the performance of job duties. This could include adopting an MDM approach that will require certain specifications of data entry and storage and a protocol for upgrading the legacy systems from such an old architecture, as new technologies and system requirements emerge. Such a system, while sometimes costly to initially adopt due the sheer scope of the data cleansing process, is a time and money saving implementation in the future for many administrators and end-users. Once the data is cleaned, and the process for new data input is cleansed and established, the new data entering the system and supply chains should be clean from the beginning. The costs of keeping the data clean and error free will lower as the MDM's….....

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