In this post you will discover why it is important to ensure the consistency of business data, one of the dimensions of data quality.
Asking ourselves why avoiding inconsistency in business data means addressing the need to have quality information to get the most out of it. That is, in order to obtain competitive advantages thanks to reliable and timely data that meets the main dimensions of data quality .
As is well known, the objective of data should be to support and drive the business strategy . To this end, data quality projects are initiated, ideally within a data governance that facilitates its practical use. From making critical decisions to its use in any initiative or process.
Data quality as an essential part of MDM
Consistency, a dimension of quality
From a broader perspective, data governance , and in particular its quality, is one of the fundamental pillars of data-driven companies, whose essential characteristic is none other than their orientation dentist database data in order to gain in effectiveness, vision and competitiveness.
Regardless of the type of company or whether it wishes to move towards this model, there is no doubt that data is a valuable asset of the first order. Only quality data, understood as data that meets business needs, enables advantageous use both in operational processes and in analytical uses that support strategic decision-making.
In this sense, consistency is one of the main dimensions of data quality , along with completeness, conformity, precision or, among others, integrity. Avoiding incomplete, imprecise or fragmented data, duplicates or inconsistent data, for example, is essential to achieving quality data.
Specifically, consistency is one of the dimensions of quality that are considered necessary for data to have consistency, especially in the era of big data in which we find ourselves immersed. Even more so now, therefore, given how essential it is to work with more and more varied sources of information, it is essential to ensure this consistency , among other dimensions of quality.
Regardless of whether a piece of data has errors or not, it is essential to go further to satisfy the concept of quality that new times demand. Since inconsistent data translates into inconsistent information , this is a risk we cannot take if we aspire to have quality data.
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The challenge of implementing data quality initiatives
The risk of creating inconsistent data is very common, either due to the concurrence of updates in different applications that contain the data or as a result of incorrect data entry. Thus, once the data inconsistency has occurred, we will have several copies of the same data that will not match each other.
Why avoid business data inconsistency
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