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The Seven Deadly Sins Of Enterprise Data Quality

The Seven Deadly Sins Of Enterprise Data Quality

Atchison Frazer looks at the issues that can lead to problems with data quality

Enterprise data quality problems can be categorised into three main areas:

All these complex processes are an important part of data processing and therefore cannot be cut off in an effort to avoid problems. The only way to maintain the integrity of data is to make certain that all these processes work as intended and avoiding the seven major causes of data quality problems.

Most of the time, databases begin with the conversion of data from a pre-existing source. Data conversion never goes as seamlessly as intended. Some parts of the original datasets fail to convert to the new database, while other datasets just mutate during the process. The source itself could also not be all that perfect to begin with. To avoid problems, more time must be spent on profiling the data, as compared to time spent on code transformation algorithms.

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When combining old systems with new ones or phasing out systems, data consolidation is crucial.  Problems may arise, especially when unplanned – resulting in hastened system consolidations.

Batch feeds are large data exchanges that happen between systems on a regular basis. Each batch feed carries large volumes of data and if bottlenecks occur, this can cause problems for consequent feeds. This can be avoided by using a tool to detect process errors and stop them from causing performance problems.

Real-time interfaces are used to exchange data between systems.;

 



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