The Data Quality Life Cycle

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It is important to consider that data quality projects are continuous improvement projects. Therefore, to obtain the best results it is essential to follow a good procedure. We tell you which are the 6 key processes to take into account.
It is important to consider that data quality projects are continuous improvement projects. Therefore, to obtain the best results in the implementation of these projects, the use of clear and well-established procedures is imperative.

Six key processes or tasks are identified that must be reflected and addressed in each data quality project.

What are the six key processes in data quality projects?
1.Discovery. The first step towards data quality.

It is used to explore undocumented data models and/or sources , thereby amazon database rapid identification and measurement of these. Discovery is an iterative process that does not require extensive initial modeling, but does require the skills necessary to understand the relationships between the information. Analytical capabilities are another requirement at this stage of any data quality initiative, as it is generally divided into three categories:

Data preparation
Data analysis
Advanced analytics

2. Profiling. A stage that should not be forgotten in data quality initiatives

Data matching or profiling is a Data Quality audit with the delivery of a dashboard that identifies, classifies and quantifies quality problems within all sources.

The objective of the audit is to generate a tangible measure of data quality at the beginning, which will clarify the current conditions of the information, providing visibility on aspects as relevant as the existence of duplications or redundancies in the data.



Data quality as an essential part of MDM



3. Cleaning. A key process to ensure data quality

Data cleansing is the process of detecting and rectifying inaccurate or corrupted data in a database. The process is primarily used in databases where incorrect, incomplete, inaccurate or irrelevant data is modified, replaced or deleted.