A data pattern is simply what it sounds like......a pattern that emerges within a data set. A number of useful techniques have emerged in recent years to assist people in deciphering data patterns in volumes of data. It has become popular to refer to the methodologies that have developed in this endeavor as data mining.
One of the first steps in understanding data patterns is to ensure the data set in question contains entries related to the correct variables of interest. If you are working for a cell phone company, and the varible of interest you are looking for is "attrition rate for male customers aged 30-35", your data set needs to include information such as customer age, and customer gender.
A secondary step is to clear up the data by removing erroneous values and missing values. Most data
mining software packages have easy options for filling in null or missing data, but some do not. Once you are confident that the data set is reliable, a number of simple statistical and graphical approaches can be applied. Time series plots and simple two-variable scatter plots can be effective visualization techniques in the need for understanding data patterns. Simple control charts can also be effective in displaying patterns of variation over time.
If you would rather do classification, prediction, or determine association among variables, a host of data mining tools such as neutral networks, classification and regression trees, multiple linear regression and association approaches can be used.
If you would like to see what Data Mining can do for your company, send us an email at Info@Launsby.com or call us at 1-800-788-4363 or 719-282-1143 for a free company overview.
We also have an always growing dictionary of quality control terms. We have the following data mining terms currently, and are always adding more;