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.
Predictive analysis is the analysis of big data to make predictions and determine the likelihood of future outcomes, trends, or events occurring.
Predictive analysis, a type or extension of predictive analysis, is used to recommend or predict certain actions when certain information states are reached or conditions are met.
Comparative analysis means the comparison of two or more processes, document, dataset, or other objects. Pattern analysis, filtering, and decision tree analytics are types of comparative analysis.
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