Consequences of poor data quality in an organization


In this article we explain how a poor data quality can affect institutions. If you are interested in this topic, we also suggest the post “Data quality: the relevance of quality in data management”.

Poor data quality: what does it mean?

Poor data quality means Significant risks in decision-making and in the operation and administration of companies.. Due, those who make decisions in a company tend to leave out some things that they should know and even seem to be informed but with incorrect information.

Poor data quality is one of the main indicators of failed projects and, often, identified as the main cause of failures. procedural flaws, being also the main cause of wrong decisions in an organization.

data quality

Cost reduction and revenue maximization strategies, especially those that rely on automated tools and solutions, suffer significant deviations and delays (in some cases they cannot be achieved) due to poor data quality.

Another consequence of data errors is the costly effect on customers since they produce high levels of dissatisfaction, by making mistakes such as incorrect personal data, invoices with wrong amounts or wrong addresses, among others. To which must be added the cost generated by the client for the time spent fixing the problem created by the error..

In addition there is a cost in time and resources for the company dedicating itself to the detection and correction of errors, leading to huge productivity issues and delays in managing other important tasks, among other unpleasant consequences.

The effect of data errors on the success of new computer applications is also considerable, since they must provide a unique and precise vision, at the same time to be correctly associated and interrelated with all the sources to use.

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