The management of the data that a corporation has has been gaining weight in recent times, becoming today a key operation to ensure the future of any organization. Data is, without a doubt, one of the greatest assets that a company has and one of the fundamental pillars on which any self-respecting corporate strategy is based. This has led, in many business sectors, to a growing interest in providing tools that allow the management of ever-increasing volumes of data , often paying more attention to the quantity than to the quality of the data compiled. However, and in a proportion practically parallel to the emergence of new data sources and systems, their quality and accuracy has been declining to the point of reaching truly significant levels: currently, it is estimated that around 25% of the new data that is generated are imprecise , incomplete, fragmentary or simply plain wrong.
As expected, in the face of this new reality, priorities in data management have begun a significant shift in favor of quality , without undermining the importance of quantity but demanding, at the same time, the standardization of indicators and processes. that guarantee the highest quality of the available data and the systems for its processing ( the data quality standards ) . pe0066459 Common mistakes in data quality management Success or failure in data quality management is closely linked to the risks assumed in this type of management operations, and especially with the system and measures adopted to address them; However, and despite the obvious importance of having a correct quality management system, in many cases Phone Number List the attention is focused more on the management processes themselves than on the quality of the object of treatment , in this case , data. This error, as frequent as it is critical, consists mainly of relying practically exclusively on the supervision and maintenance of quality standards in data analysis processes, presuming that partial, incomplete, duplicate, erroneous data...
They will not pass the quality filters established in the management system itself, leaving in the background the consideration of implementing supervision systems and early detection of poor quality data . Among other drawbacks, this can lead to significant dysfunctions and saturations when integrating new data into previously structured databases, which can lead to a complete failure of the data analysis strategy adopted, and the consequent loss of corporate competitiveness. , among other serious damages (the free guide 10 keys to defining your corporate data management strategy provides extensive information on the issue, among other matters of interest). Providing a system for continuous supervision of newly incorporated data (as well as existing data) therefore becomes a first-order necessity. A system that must integrate early detection tools that allow data to be filtered and categorized according to its quality, before being required by the management system (which will first transform it into relevant information and then into sensitive knowledge for decision-making). ), its degree of coherence, timeliness and reliability, among other determining factors.