Descriptive analysis is the analysis of historical data to determine what is, what has changed, and what patterns can be identified.
Considered the most basic type of analysis, descriptive analysis involves breaking down big data into small pieces of usable information, so that companies can understand what is happening in a given business, process or set of operations. Descriptive analytics can provide insight into current customer behaviors and business trends to support decisions about resource allocations, process improvements, and overall performance management. Most industry observers believe that descriptive analysis represents a large part of the logical analysis used by companies today.
Predictive analysis is the analysis of big data to make predictions and determine the likelihood of future outcomes, trends, or events occurring.
Data Latency is the ability to load and update data in near-real time, while supporting query workloads at the same time.
Pattern recognition occurs when an algorithm places iterations or regularities within large datasets or into different datasets.
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