Lars Albertsson – Engineering data quality

Garbage in, garbage out - we have all heard about the importance of data quality. Having high quality data is essential for all types of use cases, whether it is reporting.

Garbage in, garbage out - we have all heard about the importance of data quality. Having high quality data is essential for all types of use cases, whether it is reporting, anomaly detection, or for avoiding bias in machine learning applications. But where does high quality data come from? How can one assess data quality, improve quality if necessary, and prevent bad quality from slipping in? Obtaining good data quality involves several engineering challenges. In this presentation, we will go through tools and strategies that help us measure, monitor, and improve data quality. We will enumerate factors that can cause data collection and data processing to cause data quality issues, and we will show how to use engineering to detect and mitigate data quality problems.

big data

An extensively researched list of top microsoft big data analytics and solution with ratings & reviews to help find the best Microsoft big data solutions development companies around the world.

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