Toda organización debe tomarse en serio la calidad de sus datos, no solo para evitar consecuencias negativas de varios tipos (decisiones equivocadas, riesgos legales, tener una mala Data SourceA "Data Source" refers to any place or medium where information can be obtained. These sources can be both primary and, such as surveys and experiments, as secondary, as databases, academic articles or statistical reports. The right choice of a data source is crucial to ensure the validity and reliability of information in research and analysis....) pero para obtener Profits de relevancia capital, among others, los relacionados con la competitividad de la compañía, la mejora de la imagen, la disminución de costes y carga de trabajo, así como la toma de mejores decisiones o la fidelización de los clientes.
En el entorno corporativo, thus, lograr la calidad de los datos es un objetivo general, entendiendo la calidad de la información como práctica ausencia de datos erróneos, duplicates, caducados, de difícil acceso o, as an example, presentados en formatos inapropiados.
Las compañías grandes, medianas, Small and even micro-enterprises face information management problems in their daily operations:
- There is an increasing amount of data from various sources, Both internal and external
- Processes are computerized and automated
- The volume is increasing
- Systems communicate with each other
Y All these processes are a new reality in which companies are immersed. Thus, They will face Similar problems, Even when the amount of data will vary depending on the size, The activity, and a series of variables.
effectively, The problems repeat themselves and, in reality, They are not minor, since Data quality is one of the most serious issues at the corporate level, Requiring urgent and rigorous action in all types of companies.
A departmental or global data quality project?
The ultimate goal of a data quality project It is not about focusing on a departmental or partial action, but on achieving an implementation that covers the entire company. But nevertheless, in practice Approaching it comprehensively is complex and also costly, so on repeated occasions it is decided to carry out a gradual implementation.
In that progressive action we would create small niches and appropriate quality rules because, once these foundations are generated, developing it gradually later. Therefore, would be about Putting the data quality project into practice in small steps., from less to more. A project can start wonderfully from a small niche and add improvements over time.
Es esencial comenzar el proyecto, inclusive de forma paulatina, puesto que es un trabajo que va sumando, hasta englobar a toda la organización, y ahí es cuando se consigue. la federación de los datos. Desde ese momento en adelante, Se ha mejorado la infraestructura para corregir los problemas, con lo que se ha completado la tarea, aún cuando hay que mantener los resultados, por lo que de ninguna manera ha terminado. Será necesaria su supervisión y adaptación constante para garantizar los mejores resultados posibles.
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