Why avoid inconsistency of business data

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inconsistencia de datos comerciales
Asking ourselves why avoiding inconsistency in business data means addressing the need for quality information to get the most out of it. In other words, in order to obtain competitive advantages thanks to reliable and timely data, that comply with the main data quality dimensions.

As it is well known, the objective of the data should be support and promote business strategy. To this end, data quality projects are started, ideally within a data governance that facilitates its practical use. From critical decision making to its use in any initiative or procedure.

Consistency, a dimension of quality

From a broader perspective, the governance data Y, fundamentally, its quality, constitutes one of the fundamental pillars of data-driven companies, whose essential characteristic is none other than its orientation to data to gain efficiency, vision and competitiveness.

Regardless of the type of company in question or if you want to move towards this model, data is clearly a valuable asset of the highest order. Only quality data, understood as those that adjust to the needs of the business, enable advantageous use in both operational processes and analytical uses that support strategic decision-making.

In this aspect, the consistency constitutes one of the main dimensions of data quality, along with the completeness, accordance, precision or, among other, integrity. Avoid incomplete data, inaccurate or fragmented, duplicates or inconsistent, Let's say, is essential to achieve quality data.

Specifically, Consistency is one of the dimensions of quality that are considered essential for a data to have it. Fundamentally, in the era of big data in which we are immersed. With more reason now, therefore, given the relevance of working with increasingly varied sources of information, it is essential to ensure that consistency, among other dimensions of quality.

Regardless of whether a data has errors or not, it is essential to go further to satisfy the concept of quality requested by the new times. Since inconsistent data translates to inconsistent information, this is a risk that we cannot take if we aspire to have quality data.

The risk of creating inconsistent data is very common, either for the concurrency of updates in different applications that contain them or as a consequence of an incorrect introduction. A) Yes, once data inconsistency arises, we will have multiple copies of the same data that will not match each other.

Returning to the case of updates as a feasible generator of inconsistencies, we would find different addresses for the same client, as a result of an out-of-sync update. In files that have not been updated, decidedly, the previous address will remain, so in these it will not be updated.

An obstacle that, Besides, is related to data fragmentation across application silos, again another compelling reason justifying the need for master data management and, in general, quality data governance.

In today's digital environment, in summary, companies must demanding when facing the data quality challenge, one of the essential requirements to convert them into value. The ultimate goal is to have authentic and valid data for our purpose, what it means to delve into the dimensions of quality, among which inconsistent data represents a key aspect.

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