Data cleanup, or data rubbing, is the process of detecting and correcting or removing data or records that are incorrect from a database. It also includes correcting or removing unformatted or duplicate data or records. The data removed in this process is often referred to as “dirty data”. Data cleaning is a necessary process to protect data quality. Large businesses with extensive datasets or assets typically use automated tools and algorithms to detect such records and correct common errors (such as missing zip codes in customer records).
The most powerful big data circles have rigorous data cleanup tools and processes to ensure that data quality is protected and trust in datasets is high for all types of users.
Cluster analysis or clustering is a statistical classification technique or activity that involves grouping a set of objects or data in such a way that those contained in the same group (cluster) are similar to each other but different from those in the other group.
Product lifecycle management refers to the examination of a product as it goes through certain stages of its lifecycle.
The main tasks of data analysts are to collect, process and analyze data, as well as prepare reports that can consist of graphs, diagrams, tables and other visuals.
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