Abstract
Data governance is a subject that is becoming increasingly important in business and government. In fact, good governance data allows improved interactions between employees of one or more organizations. Data quality represents a great challenge because the cost of non-quality can be very high. Therefore the use of data quality becomes an absolute necessity within an organization. To improve the data quality in a Big-Data source, our purpose, in this paper, is to add semantics to data and help user to recognize the Big-Data schema. The originality of this approach lies in the semantic aspect it offers. It detects issues in data and proposes a data schema by applying a semantic data profiling.
Highlights
The general management and business managers must have a unified vision and usable information to make the right decisions at the right time
Several tables (Tk, k = 1,7) are used to store the different artefacts corresponding to the results of the semantic data profiling process
If we have two categories with the same percentage, we choose another sample from the data source and apply the semantic data profiling
Summary
The general management and business managers must have a unified vision and usable information to make the right decisions at the right time. The data quality governance has become an important topic in companies. Its purpose is to provide accurate, comprehensive, timely and consistent data by implementing understandable indicators, easy to communicate, inexpensive and simple to calculate. In the big-data era, the quality of the information contained in a variety of data sources, is becoming a real challenge. Data quality and semantics aspects are rarely joined in the literature [1]-[3]. Our challenge is to use semantics to improve the data quality. Misunderstanding of the data schema is an obstacle to define a good strategy to correct any anomalies in the data. Very often metadata are not enough for understanding the meaning of data
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