Data management series data quality an end-to-end view

The importance of implementing data quality monitoring processes has already been widely discussed in this blog, through articles such as Data Engineering for Martech — Data Quality — Série Engenharia de Dados. Part . Data Lineage: Ensuring data quality , Data quality and its importance for marketing strategies and 5 Reasons to implement Data Quality . However, this article intends to expand the discussion even further, providing a complete view on the implementation of Data Quality solutions and highlighting that these solutions are not just technical improvements in engineering processes, but benefits for all areas that generate, work or consume data in some way.

It is important for the team

Technical Scope Within the technical scope, delimited here as the scope of action of the team of data engineers, the quality of the data collected is essential to avoid errors and future maintenance work. Typically, the data engineering team is Australia Mobile Number List responsible for creating pipelines and processes for extracting, processing. And ingesting data. It is important for the team to pay attention to data quality during all stages to ensure they deliver high quality data to their consumers. Through a data quality process, the engineering team can monitor, identify, and proactively act on data-related issues.


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The consequences of generating

Without the data quality monitoring and validation process, the engineering team would continue to store incorrect or inconsistent data indefinitely. Until one of the data consumers alerted them to the error. The consequences of generating incorrect EG Lists or inconsistent data are the delayed perception of the error. If the error is noticed late by the analyst team. They will notify the engineering team about what happened. Which can lead to loss of reliability of the consumed data and greater correction work. For example, imagine that an analyst consumes data from the last 5 months and realizes that the data is incorrect.

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