Data can be described as the backbone of any six sigma project. This is because the whole idea of six sigma and operations is to use statistics to manage operations in the factory workshop. Hence, for a six sigma team to understand the types of data and when and how to use them is of vital importance. Here are the types of data that are used for statistical analysis:

Continuous Data: Continuous data is of the type that must be measured as against the type that we can count. Consider for example length of an object as a data type. Length of an object can be between 1 feet and 2 feet, it can be 1.5 feet, it can even be 1.54 or 1.546 feet depending upon the number of decimals and the degree of precision that have been decided in the data collection plan.

Discrete Data: Discrete data is the data that needs to be counted as opposed to being measured.

Here the values fall in one of the categories.

Binary: The data in such cases needs to be entered in one of the two categories like true or false, applicable to six sigma analyses or not applicable to six sigma analysis. Many times the outcomes of success and failure determine the operations of the shop floor. It is in such cases that the binary discrete category comes in handy.

Ordered Categories: The data in these cases needs to be entered in one of the multiple categories that are ranked. Here there may be more than two categories. In fact there usually are more than two categories involved. The categories may be based on the relative importance or on some type of number scale.

Unordered Categories: The data in these cases is entered in one of the multiple categories that need not be ranked. There are usually more than 2 categories. Data lying in one category is usually no different from data lying in any other category.

Count: This is simple counting of data without any categorization involved. This represents the discrete variables in its truest form but is rarely used in the six sigma process because it does not provide much analytical insight into the variables being studied.

Why is the Type of Data Important ?

The type of data is important because it has material impact on the analysis. Where continuous data is involved, the probability of an exact event becomes zero, ranges need to be used. For instance the probability of the length of an object being exactly 2 feet is zero in a continuous distribution. However, if the measurement is discrete the results can be found out.