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In this post, let’s discuss the importance of Data Quality in Big Data for analytics.
Completeness in data relates to eliminating any sort of gaps from the collected data to ensure that it has all the essential insights which we are looking for. Simply, the data which analysts are going to work on for extracting the insights needs to be complete as incomplete data leads to skewed or misleading results.
The term consistency relates to maintaining the data sets that are collected from various sources to be kept in the same format whenever we need to compare them. Maintaining all the data sets of a single data item in the same format is referred to as consistency.
Maintaining accuracy in the collected data has become a big challenge. The term accuracy in the data relates to how accurate data describes the real-world conditions it aims to describe. If the collected data is inaccurate, it leads to incorrect results.
The term Timeliness relates to whether the collected data is relatable to the recent event or it isn’t. To obtain accurate results, data needs to be recorded as soon as the occurrence of the event in the real-world.
If the collected data meets all these criteria, it simply signifies that data is of high-quality.